In this episode of TBR Talks, we explore why AI infrastructure demand is creating both unprecedented opportunity and strategic divergence, and what these trends could mean for the future winners in the AI era. Principal Analyst Angela Lambert and Senior Analyst Ben Carbonneau, both of TBR’s IT Infrastructure team, join host Patrick Heffernan for a discussion on how TBR is approaching AI research in the infrastructure sector, including how AI is reshaping the business models of leading infrastructure vendors, including Dell, Hewlett Packard Enterprise, Lenovo and Supermicro.
Profitability and business strategy impact of AI disruption
How TBR developed a metric to measure impact of increasingly diverging strategies
Predictions for the rate of strategy divergence over the next year
“What we’re seeking to understand here is how well are vendors managing to A, capture the opportunity that exists, but also are they doing so at a sacrifice to other parts of their business or profitability? And what’s the long-term viability of these different business strategies? So, I think that’s really what we want to better understand and bring in a metric that compares the growth potential as well as profit strategy as kind of one of the facets of how we evaluate the OEMs,” said Lambert.
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Infrastructure Strategies: Measuring Profitability in the Age of AI
TBR Talks Host Patrick Heffernan: Welcome to TBR Talks: Decoding Strategies and Ecosystems of the Globe’s Top Tech Firms. Where we talk business model disruption in the broad technology ecosystem from management consultancies to systems integrators, hyperscalers to independent software vendors, telecom operators to network and infrastructure vendors, and chip manufacturers to value-added resellers. We’ll be answering some of the key intelligence questions we’ve heard from executives and business unit leaders among the leading professional IT services and telecom vendors.
I’m Patrick Heffernan, Principal Analyst, and today with Angela Lambert, Principal Analyst for TBR’s IT Infrastructure Practice, and Ben Carbonneau, Senior Analyst for TBR’s IT Infrastructure Practice, about how TBR is approaching our new AI research in the infrastructure sector and our new RAMP metric.
Profitability and business strategy impact of AI disruption
Angela and Ben, welcome back to TBR Talks. Very exciting to have you back here. And we’re talking about all of the research that we’re doing now at TBR around the disruption that artificial intelligence is causing to the companies that we cover. So, forget about the whole broad disruption that AI is doing to all of society and all of technology in the whole world. Let’s just talk about the companies that we look at, the companies you’re looking at in your practice, Angela, and companies you look at, Ben, and what their adoption of AI is doing to them. So, I know in the services space, we had a certain kind of way of looking at it, but tell us a little bit about what’s been happening in the hardware, the infrastructure space over the last couple of years that has led us to where we are now. Angela?
Angela Lambert, TBR Principal Analyst: Sure, yeah, I’ll kick off with that. So, I think when we broadly look at the IT infrastructure market and what has changed over the last couple of years, there’s just been such an explosion of opportunity to sell AI infrastructure, which on the surface sounds wonderful. But there are some implications to kind of diving in and serving a new hyperscaler and neocloud market that’s a little bit different than the enterprise market that the infrastructure OEMs are very ingrained in. And I think one of the major ways you see the impact from a business model point of view is profitability. So, while there is- vast opportunity for revenue growth, in short, what we see is that there’s also some real concerns about what chasing that opportunity could do to the business as a whole in terms of profit, in terms of the business model overall. So really, we’re looking at today, how are the business strategies changing within the individual vendors and what decisions are they making based on those dynamics?
Patrick: Just to set the stage a little bit, some of the specific vendors that we’re talking about, like when you say all that, are?
Angela: Some of the major ones in here would be Dell Technologies, Supermicro, HPE, as well as Lenovo.
Patrick: Lenovo, okay. Excellent. And it’s interesting, so the opportunity is massive in terms of the sort of the market that they have in front of them, but it may not be as profitable as what they’re used to, and that could be a huge challenge for them, short-term and long-term.
Angela: Exactly, right. So that’s where we’re seeing some diverging OEM strategies come in, right? So, on one end of the spectrum, you have, dive headfirst into this opportunity of serving hyperscalers and neoclouds. On the other end of the spectrum, you have a strategy of doubling down in enterprise or also in the sovereign opportunities as well, where the customer needs are obviously very different, right? There’s the enterprise who needs more of an end-to-end solution. They need more services, and they prefer kind of a more all-in-one solution versus what we see in terms of serving the large, huge, billion-dollar orders that are on a massive scale, but really a very different service profile.
Introducing the RAMP metric
Patrick: Right. And then Ben, what are some of the trends you’re seeing across the companies you cover?
Ben Carbonneau, TBR Senior Analyst: Sure. So, covering Dell, HPE, Lenovo, Supermicro, I think exactly what Angela’s saying is that divergence and strategy has become really apparent, especially between maybe Dell Technologies, who I see as the OEM that we cover that’s most willing to take on those really large scale, lower margin deals with neoclouds, versus HPE, who originally did take on more service provider AI systems deals, but has increasingly shifted to being more selective, targeting enterprises and sovereigns. I think a lot of that has to do with the company’s acquisition of Juniper and their networking focus, which really aligns well with the sovereign opportunity and more distributed AI inference that the enterprises will be doing. But what we’re seeing there is while Dell’s taking a lot of gross margin dilution in those really large-scale deals, HPE has maintained a more durable gross margin profile. However, something that we predicted happening and has actually happened more quickly than we had anticipated is the effects of scaling on operating margin.
Patrick: Hmm.
Ben: IT was always our idea at TBR that if Dell could grow these low margin deals at a rate fast enough, it would wind up offsetting gross margin dilution from the perspective of their operating expenses not growing as quickly as revenue growth. And we’re starting to see that now. And that’s really what our metric here, Revenue-adjusted Margin Productivity, or RAMP, is looking at. It’s not just looking at whether an OEM is capturing that AI driven infrastructure revenue, but also how effectively the OEMs are taking in that increased demand and that opportunity and then either turning it into improved operational leverage and a stronger margin profile or whether they’re kind of in the middle, whereas where I would see maybe more of a Supermicro where they’re almost at a tipping point of growth and having that offset the gross margin dilution or whether they’re kind of going down the road that HPE is going for and kind of staying true to the more traditional customer set, those enterprises that they’re used to serving.
Patrick: And that metric, the, say it again, I know it’s RAMP, but what’s the?
Ben: Revenue-adjusted Margin Productivity.
Patrick: So, Revenue-adjusted Margin Productivity is a metric that is useful in an AI age because why? Like sort of why would you not have been looking at that five years ago, what is it that’s making that metric useful to look at now as a way of reflecting on or looking at the disruption that AI is causing these companies in particular?
Ben: I think the big change has really been neoclouds coming out of the scene. So, an Infrastructure as a Service provider, you could think of them as a kind of a Tier 2 cloud service provider, smaller than the hyperscalers. Typically, a lot of cloud service providers would have gone to ODMs, not this OEM industry set, but ODMs to serve them and deliver those low-cost, high-volume servers. But now we’re seeing this group of neoclouds go to OEMs to serve them, which has just completely kind of upended what was the traditional OEM infrastructure market.
Patrick: Right. And so, Angela, the RAMP is a metric, but what sort of what are we looking for? What’s a good RAMP and what’s a bad RAMP? I mean, I’m guessing up and down is the simple thing, but it’s gotta be more than that, right?
Angela: I think for me, the key thing is revenue adjusted, right? So, if we take the most recent quarterly results, for example, Dell’s infrastructure business grew by almost 200% compared to a year ago, which is incredible, but does that alone, you know, does that say enough about kind of what’s going on within the market and the business model? So, what we’re seeking to understand here is how well are vendors managing to A, capture the opportunity that exists, but also are they doing so at a sacrifice to other parts of their business or profitability? And what’s the long-term viability of these different business strategies? So, I think that’s really what we want to better understand and bring in a metric that compares the growth potential as well as profit strategy as kind of one of the facets of how we evaluate the OEMs.
Developing a metric to measure impact of increasingly diverging strategies
Patrick: And then you have to pick a starting point. So how did you come up with a starting point for where you would start looking at the data and devising this metric RAMP? And, sort of, what was your starting point and why?
Angela: I think our starting point, one of our starting points was really sitting together and saying, wow, these companies are harder to compare than they’ve ever been before.
Patrick: Huh.
Angela: Their personalities are diverging more so than ever because of the AI infrastructure shift in the market. And we would, you know, sometimes when you put some of the different metrics side by side I’ve looked at for 15 years and like, well, it’s just, the compares are a little different and you have to add a lot of nuance when you’re describing each of these things. So, I think a lot of what Ben and I have been talking about is how can we, I don’t know if it’s really making a level playing field, but how can we kind of assess the strategic choices that each of these companies are making and who’s succeeding within these different customer segments such as the enterprise space, sovereigns, the neoclouds, the hyperscaler space? That’s really what we want to look at is kind of of how are they targeting those customer segments and how well are they executing on their relatively unique strategies?
Patrick: Right. And I love that you use the word personality because I think there are times when at TBR, we actually do kind of assign personalities to companies, whether we like it or not. I mean, we try and be very analytical and thoughtful about it, but you also can’t help but sort of personalize the companies that you’re covering and the strategic choices that they’re making.
Discussing Dell and Supermicro’s RAMP scores
So, when you think about the four companies you mentioned and you think about where their current RAMP is, do you see- you’re saying the strategies are diverging. Do you see a real strong divergence coming in their performance with respect to RAMP in the next few years?
Ben: Definitely, for sure. So, I did some back of the envelope calculations where we’ll be putting out some RAMP reports on the companies we’ve mentioned pretty soon here. But just looking at the calculations, they kind of aligned with exactly what I would have expected, which is Dell’s RAMP performing very well. Obviously, revenue growing very quickly, as Angela said earlier, and then also having reached that tipping point where that scaling is completely offsetting gross margin dilution and resulting in operating margin expansion. So, we’re seeing Dell performing really well.
And then on the other, I guess maybe on the other end of that spectrum, you see a company like Supermicro who’s also growing very quickly, but who has a much different kind of operating expense profile. I think that’s due to the way that we’re seeing these OEMs shift in their target customer base, where Supermicro might have been more into low margin high volume box moving, if you want to call it that. What we’re seeing is maybe Dell shift a little more in that direction right now, just because that’s where the opportunity is. And as a result, Supermicro hasn’t realized those same kind of scaling benefits just because of where they were and where their OpEx structure was previously.
I think going further down the line, something that I’m really interested to see, and I think it’ll be something that we see maybe in five or ten years when we’re looking at these RAMP scores is how well does Dell’s strategy age and how durable is that strategy? Obviously right now with revenue growing so quickly, the effects of scaling on operating margin are really noticeable. I think we get to a point where the initial AI infrastructure build out is done, and maybe those really large-scale neocloud deals start to slow down. And I think that’s when it’ll be really interesting to see how their kind of a more aggressive strategy targeting scale balances against a company’s like HPE’s strategy of maintaining margin.
I think maybe the last thing I would say on that is it’s important to note that while Dell’s going after these deals with neoclouds right now, that’s really where there’s a lot of infrastructure demand and build out, that at the end of the day, Dell wants the enterprise opportunity just as much as HPE does. And I think it’s a situation where HPE is more in a position to leverage their networking business and kind of stay in this limbo waiting for enterprise AI adoption to really accelerate and take advantage of that, whereas Dell is capturing the immediate opportunity while also positioning itself for the enterprise AI opportunity. But that’s where the higher margins are. All OEMs want that. So, I think it’ll be really interesting to see if Dell’s strategy of scale and close alignment with NVIDIA translates to them winning in enterprise AI, or if HPE’s strategy of really leaning into networking and a more integrated solution is what is the winning strategy in the end.
Patrick: That’s fascinating because I think everyone is expecting enterprise adoption to be growth for everyone, that it’s going to happen. It’s going to happen on a scale that’s so massive that everybody’s going to benefit from it. But you’re right, that which way you’re leaning in terms of where you are now, and especially with HPE leaning more into the networking, pretty fascinating.
Peer set for RAMP analysis now and possible future peer sets
One other question for you guys. It’s four companies you’re talking about, that’s the peer set. Are there other companies within your broader coverage that RAMP would apply to? Or is it more because this particular set is diverging in such interesting ways on their strategy that you wanted to focus on these four?
Angela: I think the peer set will be expanded over time. The main reason we’re starting here is because in the infrastructure side, AI server has really been where the most I guess aggressive growth has been over the last couple of years. But storage, for example, is another area where things are starting to pick up. It probably will never reach the hundreds of percent year to year growth.
Patrick: Right.
Angela: But it is, that’s another example of area where companies like Everpure or NetApp, for example, will also come into the fold in the analysis. And as Ben alluded to, on the enterprise side, there’s also a lot of emphasis on network modernization as well. So, I think that there’s opportunity for that peer set to expand in a couple of different directions.
Patrick: So, in the services side, we have the Human Intensity Reduction Index, which is helpful because services is a people business. So, reducing the intensity of the human element in it is a great way to measure what’s happening. But we were- we had to have peer sets based on similarity in their business model. So, you can’t compare a McKinsey to a Wipro, it just doesn’t make sense. With an Everpure and a NetApp, would you still be using RAMP and comparing them to Dell and HPE? Or will you have a new metric that looks at the disruption that is inherent in those businesses that’s caused by AI and what that means going forward?
Ben: I think we’ll have to look at them in those groups of peer sets, kind of similar to what you’re saying you do with the kind of different peer sets in services. And that’s just because of the different impact on margins and AI and AI hardware in particular across the different segments of the infrastructure stack. So particularly in server, that’s where we see gross margins really being diluted quite a bit with accelerated systems. I think less so is the margin dilution in storage and in networking. So, we’ll have to look at those companies in groups of storage vendors, compute server vendors, and then also networking vendors. I think another thing to note is that while HPE is included in this initial peer set, we’ll be looking specifically at the company’s AI server and systems business. We have those numbers modeled and are available in some of the products that we’ve put out more recently, like the AI Infrastructure Market Landscape and the Infrastructure Benchmarks and Market Forecasts. We’ll be looking at that segment in particular so we can score HPE, a company with a more diverse portfolio in the same way that we score a company like a Supermicro who is much more server centric.
Patrick: And RAMP will be included in, sort of, all of the company specific reports going forward as well? Is that the idea?
Angela: We will be looking at it from, obviously from a benchmarking aggregate perspective, but the plan is to also have individual vendor breakdowns as well as part of that.
Predictions for rate of strategy divergence over the next year
Patrick: So last question, because you brought up there’s such a divergence in these strategies, in a year from now, are we going to be talking about the great success of one of them, or are we going to talk about the absolute collapse of another one? Or do you think, I guess I don’t want to put you on the spot of picking winners and losers a year from now. What I’m really asking is, do you think the divergence that you’re seeing now will accelerate over the next year, or will it continue at a pace where you see the differences, but they’re not so dramatic that you’re picking winners and losers?
Ben: I think the divergence remains. I don’t know if it accelerates materially. I think it remains there through at least the end of 2027.
Patrick: Okay.
Ben: And I think it really has to do with AI infrastructure build out driven by the neoclouds. And then I think if we roll it back another layer, if I think about AI infrastructure spend and the replacement cycle of refresh and bringing in new systems and making those investments. I think that really ties back to token economics. So, I think it really matters what a company like NVIDIA does, how fast they can bring increased tokens per watt rates with new platforms. Because I think the faster they do that, the more incentive is there for a company like a CoreWeave or a Nebius to invest in the newest systems. So, I think there’s a few dynamics at play, obviously kind of an NVIDIA driven market right now.
Patrick: Yup.
Ben: But we’ll see kind of what some of these other companies have on the horizon. Of course, there’s a lot of AI chip startups right now going to market with some integrated systems that look at different ways of reducing token costs. And then AMD still kind of there.
Patrick: Still there, yeah, yep.
Ben: So, we’ll be looking.
Patrick: Angela, do you agree, disagree.
Angela: I agree and I think where we’re at today, when demand still outpaces supply, it’s only winners, right?
Patrick: Yeah, good point.
Angela: There’s maybe a hierarchy within that, but everyone gets a trophy for today. But I think what Ben’s alluding to on when the AI build-outs potentially level off, and that’s when we’ll kind of see who has the best enterprise and sovereign strategies that carry them for an even longer period of time.
Patrick: Excellent. Well, you guys mentioned tokenization, token economics, sovereignty, and everybody gets a trophy. So we’ll come back to this in six months or so, nine months or so, and check in on where RAMP is and who still gets a trophy and how much token economics and sovereignty have changed all that. So excellent. Ben, Angela, thank you so much.
Angela: Thanks, Patrick.
Ben: Thank you.
Patrick: We’ll be taking a break over the summer and we’ll be back with season 6 later in 2026.
Don’t forget to send us your key intelligence questions on business strategy, ecosystems, and management consulting through the form in the show notes below. Visit tbri.com to learn how we help tech companies, large and small, answer these questions with the research, data, and analysis that my guests bring to this conversation every week.
Once again, I’m your host, Patrick Heffernan, Principal Analyst at TBR. Thanks for joining us and see you next week.
TBR Talks: Decoding Strategies and Ecosystems of the Globe’s Top Tech Firms
Join TBR Principal Analyst Patrick Heffernan weekly for conversations on disruptions in the broader technology ecosystem and answers to key intelligence questions TBR analysts hear from executives and business unit leaders among top IT professional services firms, IT vendors, and telecom vendors and operators.
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https://tbri.com/wp-content/uploads/2026/06/TBR-Talks-S5E16-Website.png13501080Jen Gasperhttps://tbri.com/wp-content/uploads/2021/09/TBR-Insight-Center-Logo.pngJen Gasper2026-06-19 11:39:482026-06-19 11:40:59Infrastructure Strategies: Measuring Profitability in the Age of AI
In this episode of “TBR Talks,” TBR Principal Analyst Allan Krans and TBR Senior Analyst Stephanie Long join host Patrick Heffernan for a discussion on how TBR is measuring the impact of AI in the cloud & software and telecom markets. The discussion examines real-world examples of AI adoption, from automating network infrastructure monitoring and improving software development productivity to redefining workforce efficiency and operational decision making. They pair highlight often-overlooked benefits of AI, including improved employee productivity, enhanced workplace safety, and greater work-life balance through automation and agentic technologies.
TBR Talks is produced by Technology Business Research, Inc.
Edited by Haley Demers
Music by Burty Sounds via Pixabay
Art by Amanda Hamilton Sy
AI Disruption Index: Measuring AI’s Real Impact on Cloud, Software and Telecom Business Models
TBR Talks Host Patrick Heffernan: Welcome to TBR Talks: Decoding Strategies and Ecosystems of the Globe’s Top Tech Firms. Where we talk business model disruption in the broad technology ecosystem from management consultancies to systems integrators, hyperscalers to independent software vendors, telecom operators to network and infrastructure vendors, and chip manufacturers to value added resellers. We’ll be answering some of the key intelligence questions we’ve heard from executives and business unit leaders among the leading professional IT services and telecom vendors.
I’m Patrick Heffernan, Principal Analyst, and today we’ll be talking about how TBR’s new HIRI metric is applied in the cloud and telecom sectors with Allan Krans, Principal Analyst for TBR’s Cloud Practice, and Stephanie Long, Senior Analyst for TBR’s Telecom Practice.
Background on Allan and Stephanie
Stephanie and Allan, welcome back to TBR Talks. I’m excited for today because we’re going to talk about stuff I don’t know anything about. So, that doesn’t always happen on TBR Talks. So, this is going to be great for me. I’m going to have a ton of questions. But before we dive in, maybe you could each just give us a little bit of your background, a little bit of the practice that you’re running here at TBR. Allan, I’ll let you go first.
Allan Krans, TBR Principal Analyst: Okay, sounds great. So, I head up the Cloud and Software Practice. Both are still around. I’ve focused on software for a little bit longer than cloud, but really watched the evolution from one industry to the other. Tracked it pretty much since the beginning. I’ve looked a lot at the business model change between those two practices as it went both from the provider and the vendor side, which a lot of new providers came on the scene. And so, we’ve covered them as well as the rise of AI and all that new IP that the cloud providers that have been around now for over a decade are adjusting to and integrating into their overall position in the market. So, it’s been a continuing evolution, most recently with the onset of AI.
Patrick: Yeah, and how many years at TBR?
Allan: 20.
Patrick: Wow.
Allan: And a little bit more. I was, fun fact, I was a TBR intern when I was a junior in college and then joined, left for a little bit, and I’ve been back for 20 years now.
Patrick: That’s fantastic. Wow, excellent. All right, Stephanie, slightly different story, but let’s hear the practice first and then the background.
Stephanie Long, TBR Senior Analyst: Sure. So, I’m in the Telecom practice, have been in the industry for about 11 years. And one of the things that’s interesting about the AI and it’s the evolution of AI and how it’s impacting tech and life, is that having been in the industry for a while, you see sort of these trends come in, you see the hype come with it, and then you see where the chess pieces fall as that hype sort of fizzles out and leaves the visibility into what the reality is going to be. And being able to see that unfold in real time, sort of living the history is something that I have always found interesting.
Patrick: Yeah. And so, 11 years in the industry, some time with TBR before, back to TBR now, and in between with Hitachi, right?
Stephanie: Yep.
How TBR is measuring AI impact: Comparing services to cloud
Patrick: Excellent. So, where we’re coming out right now with AI, so you mentioned the hype. And within the Services practice, the struggle that we ran into was there was no clear way to measure or evaluate what AI was doing to the companies that we covered. So, it was easy to say, what are they offering their clients? It was easy to say, these are the different capabilities they’ve developed, but they invested a billion, 2 billion, 3 billion in what? We didn’t know. Everyone was talking about what they were doing internally with AI, but there was no really good way to measure it.
So given that uncertainty, the way we decided to look at it was to take the hypothesis of a theory that increasing adoption of AI within a services company would allow them to continue to make profitable revenues, increasingly profitable revenues with fewer people. That’s the whole eliminate the jobs part of AI that everybody talks about. So given that services is a people business, that seemed like a really important way to look at what was AI going to do with respect to disrupting the actual companies that we cover.
So, we developed the Human Intensity Reduction Index, which is intentionally a scary sounding thing because it’s exactly what it is. You’re reducing the intensity of the humans in terms of their influence of how they’re driving increasing revenue growth. So that’s what we started with. We also developed a way to look at the business models within services to say, these are the different components of the services business model, commercial, delivery, operations, and partner model, and how are those models being disrupted as these companies adopt AI internally. Again, not what are they selling or what are they enabling their clients to do, but what is it doing internally to them? Because at the end of the day, we’re TBR, we look at these companies individually and we say, what’s their business model? What’s their strategy? What’s their performance?
So that’s the approach we took in services, a very services, talent, people-centric look at the world. And that’s why we have the Human Intensity Reduction Index. But Allan, can you tell us how, when you think about AI disruption within the software and cloud space, what’s it going to look like for you? How are you guys going to roll out a measurement of that, a metric around it?
Allan: Sure. Yeah, I think overall, starting with HIRI, is it still a good framework? Because although the efficiencies and the revenue per employee and the types of operations that they’re involved in are different, people are still at the core of running the business of the cloud, and software, and software as a service providers. So, we’re starting with HIRI. And already when we look at how some of the early metrics are playing out, you can see the force multiplier effect of being in a cloud and software business. They’re able to get tremendous efficiencies by very little change in headcount and the efficiency of that headcount. So instead of single digit HIRI results, we’re seeing 10% to 20% HIRI results. So double digit force multipliers for the efficiency within those organizations.
But we also wanted to add metrics that look at the unique nature of the cloud and software. So, it’s much more sales and marketing driven, and IP and development driven. So, we already have a SMIRI, so sales and marketing efficiency index.
Patrick: Right.
Allan: And when you look at that, that’s a big area of cost. It’s also a big driver of the overall revenue performance for those vendors. It’s not the consultive sale, it’s programs and territories and a lot of expense going into travel and entertainment and marketing, all those things. Again, areas that a little change in the efficiency, leveraging AI and the capabilities that some of the vendors are promoting themselves to their clients, and you can get a pretty dramatic impact in terms of disconnecting headcount growth from revenue growth. And so really overall, HIRI is actually a bigger multiplier impact for the vendor results so far. Some of that goes to the hyperscalers have bigger levels of efficiency. So bigger operations, more automation. And so, they’re able to automate across the overall business and get some pretty dramatic results so far. Whereas the SaaS providers are leveraging more of the sales and marketing efficiencies and seeing bigger SMIRI results in terms of leveraging AI for their go-to-market activities and getting a lot of efficiency and again, disconnecting, seeing revenue growth disconnect from continued headcount growth. Head count is fairly stable overall on average actually across vendors, but the growth has continued. So, I think that’s part of the overall story is it’s not always replacing employees, but it is making the business more effective with stable or delayed or slowed growth in terms of the hiring.
Patrick: And are sales and marketing, is that typically one of the largest operational spends, the largest budget line items for the cloud and software companies?
Allan: It is absolutely. Sometimes it can be on par with R&D because obviously the innovation is very important. But yeah, anywhere from 15% to 30% of revenue for these firms can go to sales and marketing. So it does make a big impact on the bottom line margin and being able to reinvest back in infrastructure and continued innovation which is a big focus really for the hyperscalers in particular, as they look to build out the AI data center footprint and capitalize on the- there’s just not enough capability to meet the demand that’s there.
Patrick: Right.
Allan: So, every dollar they can save to invest to grow that more quickly is something that they’re trying to achieve.
Patrick: Right. And that’s, to me, that’s really fascinating because you could apply that SMIRI, you could apply that approach to any company and say, what is the largest, your largest expense that’s related to people and how is adopting AI going to actually change that and what’s it going to do for your bottom line? So yeah, that’s pretty fascinating. I got to keep thinking about what the implications are for that. And especially because it ties then back to, you know, we’ve been talking for a while about how IT directors and CTOs and all that have been challenged by the increasing cost of cloud for years now, but now you’re going to add on AI on top of that. And we’re seeing- in the same time that we’re seeing less reduction in total headcount than we probably expected, we’re also seeing a greater anxiety around what the cloud and combined with the AI bills are going to be. So, if the cloud and software providers are able to continue to drive profitable growth with fewer people, then there might be a way that this starts to balance out for the CTOs and the ITOs.
Allan: Yeah, I mean, it’s all taking, you know, all these investments, whether it’s on the customer side or the vendor side, there is a theoretical fixed pool of money that they need to allocate in the best way that they can. So, it’s not always straight to the bottom line. It could be investing to capture growth.
Patrick: Right.
How TBR is measuring AI impact in the telecom space
Excellent. Stephanie, in the telecom space, very different, but the same kind of factors coming into play where adoption of AI is disrupting the business model. So how within the Telecom practice are you looking at what’s coming next?
Stephanie: So, I want to tie it into the cloud and hyperscale story initially, because something that’s interesting and is happening in telecom space is that AI is the catalyst for the network modernization, which is impacting how hyperscale customers are looking at and investing in their capabilities. The increase in data workflows coming from AI causing a fundamental shift in how the network traffic is behaving and flowing, and also the need for substantial uptick in low latency, high-capacity connectivity, especially for the hyperscale customers to meet the demands of their customers, so there’s this sort of flowing of needs through the whole technology space as a result of AI and how that cascades through the stack.
From a telecom perspective, there’s sort of broader terms that are being used. AI is one of them, but we’re also seeing more broadly, automation is something that’s been happening since before GenAI came to the scene. And it’s sort of like shifting it into high gear now that we have AI layered on top of that. But it’s a trend that’s been happening for years in the telecom space as companies look to optimize things they’ve already been doing with maybe less manpower or having the same manpower do more things. They’re changing how the companies are presenting in this sort of modern era. AI being sort of the buzzword du jour that’s layered on top of a practice that’s been happening for years.
Patrick: You mentioned automation. I’m glad you brought that up because within the services space for us, HIRI represents not just AI, but all of the changes, all the analytics, automation, everything that’s gone into- really since the advent of ChatGPT in the fourth quarter of ‘22. So we’re looking at understanding that some of it is organizational change, some of it is greater adoption of automation, and some of it is AI with the idea that increasingly it will be AI-enabled solutions that are creating that ability to do. But you also said the same manpower to do more things. And that’s where I think it’s fascinating because Allan, you mentioned that you’re already seeing a HIRI in the double digits in the cloud and software space. In services, we haven’t seen anybody hit double digits yet. A lot of it is in the low single digits. And that’s because it’s being most frequently deployed to do more with the same people, not simply get rid of people and be able to serve those clients and generate that same kind of revenue. So, what’s the metric in the telecom space that you’re developing that’s a counterpart to or similar to HIRI?
Stephanie: So, we’re still aligning to a similar definition of HIRI as the Services practice is, but the story behind that number is coming at it from a different angle, talking about the things we just mentioned, like it’s more than just AI, there’s the automation component, there’s the modernization and transformation that’s been going on for years, and you’ve made those investments and then you see the fruits of that labor. It’s not an instant gratification, so to speak, on those investments. And also to sort of Allan’s point earlier about sales and marketing, the next level of that, the customer experience, there’s automation going into, operators serving their end customers, things like leveraging agentic AI to support customers, so you need fewer humans supporting those customers, many things they can do themselves, which customers appreciate the faster outcomes for their desires. It could be, they’re having a support struggle they need help with, or maybe they’re able to use some of these self-serve skills to tweak their subscriptions without having to involve humans in the process.
Measuring qualitative business model changes
Patrick: Right. So, to move from, so the HIRI is a very, it’s the math is the math. And for most of the practices, I think, the math is the math because we have those numbers. A lot of the numbers are reported. We have a number of companies that don’t report any of their numbers, so we have to go find them ourselves. But still, we’re looking at reported figures and trying to put it into our own, both taxonomy, but our own approach and our own metric. But then when it comes to the business model disruption, are you seeing in the telecom space the same kind of disruption to the way that they are they’re doing pricing, so the commercial model is changing. Are you seeing the way they’re delivering connectivity? Is that changing because of AI? How much disruption are you seeing in the business model for the telcos that is the companies themselves being disrupted and how they do what they do, how they make money?
Stephanie: So, I’ll give an example of a use case. So, you have these towers all over the place. They could be in pretty remote locations. They could be in city centers. You could have them attached to sides of buildings. You could have the big towers out in the woods or the wilderness or wherever they end up being. And you’re responsible for maintaining those and ensuring they’re staying up. And people now more than ever want things as instantly as possible. So in order to do that, you could leverage some automation tools, something like a drone, provided you’re complying with all of the legality that goes with that to scan your physical infrastructure, look at it to see if anything looks off, and then you can use automation or AI capabilities to analyze those images and determine if you need to send a human being out there to fix something or if something is going to break and you can use those images to determine we should do X, Y, or Z before we have a problem. If there was a natural disaster, for example, you may be able to analyze the impact more quickly and therefore mitigate the problem faster by using some of these AI-centric and automatable technologies to arrive at that outcome.
Patrick: Allan, just to pivot from telco and what’s happening there to the cloud and software space, but specifically around the business model disruption, like how much are you seeing, how much is Microsoft or AWS’s business model been disrupted because of their own adoption of AI?
Allan: I think it’s been disrupted tremendously. If you look at starting with what they do at the core, it’s, you know, do big development projects and continuous innovation and maintenance. So coding was really one of the early use cases that stuck with AI and made sense and had a business model and an ROI that justified its continued use. And so, you do see not only an option, but encouragement to use as much AI as possible for these development projects to increase the pace of innovation, to improve the coding and the bug fix activities. So, it started there and then flowed into just like their customers, probably a little bit earlier than their customers, looking at customer service and sales and marketing and all of the different aspects of running cloud environments and developing software as a service solutions. So, it’s really flowed through very rapidly most elements of what they’re doing internally for these large companies. And that’s, as you know, it’s a challenge to harness and manage and control and measure. So that’s still ongoing. But certainly the experimentation phase is well over, and it’s been rolled out in production. And now it’s about optimizing and finding the right mix and levels. And it’s different for every company.
Salesforce is a good example of a company that actually, if you look at the numbers, isn’t performing great on HIRI, isn’t performing great on SMIRI. And they even, Marc Benioff is talking about how they’ve tried a lot of, tried it everywhere, found out that for sales and marketing in particular, they’re not seeing a lot of benefit. They still need humans face to face to be able to close deals, build relationships. And so, if you look at their overall sales and marketing headcount, it’s actually trending up a bit. And as a result, their SMIRI score is actually in the negative, which is the only company for all that we cover, which showed that effect. But again, it was a balance of, roll it out broad, see what works, and then pull back in areas where you’re not seeing the benefit. And sales and marketing, at least for now, for Salesforce, seems to be one of those areas where they’re pulling back a little bit.
Where companies are seeing the benefits of AI adoption
Patrick: And I think that we just said about, so rolling out, see where the benefits are, pull it back if you have to. I think we’re definitely in that sort of pull back phase right now where there’s a lot of companies and a lot of, in the same way that there’s a lot of hype about how great AI is, there’s now a lot of hype and angst about the negatives of AI. So maybe we can end with that. Just some thoughts on what are some really positive stories around AI? I’ve talked about Human Intensity Reduction Index. You’re talking about SMIRI. We’re talking about sort of all the downsides on people. But do you see, and Allan, go first, then Stephanie, come to you. Like, do you see, is there like a feel-good story around the way AI is actually helping the people who are in these companies that are getting disrupted?
Allan: Yeah, I’m not sure if I have one particular story, but I think for a lot of the repetitive administrative tasks, which really exist in most of the roles within large organizations, there’s definitely, and I think it’s been over the past six months where more times you go for some of these tasks or to do- to analyze a long document that would have taken, you know, a half day, maybe a full day in some cases, being able to get a result and an analysis back that helps employees move forward with confidence now after they’ve gone through and checked a couple times. I think the confidence for the quality of the models that have been released over the past six months has increased tremendously and is making people more effective. So that may not show up in the HIRI and the SMIRI for a couple quarters, but I think we’re seeing it ourselves as we use AI internally.
Patrick: Yeah, and we’re using it as well. So, Stephanie, a feel good story, a humans are not the problem story for AI.
Stephanie: Yeah, I have another perspective on AI and sort of a benefit to sort of life. You can use, in theory, an agent, agentic AI, to monitor things that need to be monitored 24/7 that you would have previously monitored with a human. You could have an agent do some of that monitoring while you’re, you know, living life. And it shifts the burden of that 24/7 away from the humans and enables the technology to sort of take over in some of the less intense moments of the monitoring. You could enable it to alert you when certain things are detected. You can pop back on and address problems as they come up, but it does enable you that flexibility to step away that you may not have had previously when everything had to be manual. And enabling that work-life balance for people improves life quality, provided you make it through the headcount cuts that are also related to AI.
Patrick: Right. And are you seeing, and in the telco space in particular, are you seeing any other applications of AI that are sort of benefiting, that are just a pure net positive? Because again, I feel like we get a lot of the disruption negative stories, but there’s some net positive stories in AI, in telco.
Stephanie: One of the things that I feel like gets lost in the loop because we talk a lot about AI taking people’s jobs and making things more difficult for people is with the example of drones assessing these towers and the physical infrastructure, you’re actually making things safer for some people because those people would otherwise have to physically go out there and climb a tower and assess something. That’s dangerous work. If you can do that with technology instead, it is a human benefit from a safety perspective to be able to do some of that stuff without needing to send a human into, you know, a place that was just ravaged by a natural disaster. If you can use some automated technologies to do that instead, you can make things a little bit safer for people in some ways. I know a lot of times when we talk about AI and we talk about people, it’s often a very negative perspective of AI taking the jobs, but there are these human benefits as well.
And something that I think is a little bit more unique to telco than to cloud and hyperscale where there’s edge deployments and things like that in other technology areas, but telecom has a lot of those. And being able to monitor things that are in maybe less accessible environments using tools and AI enables people to not have to go to those places unless it’s absolutely necessary. And also if you had to do physical monitoring of physical sites, even if they’re not in a remote location, that’s still a person leaving their home base to go somewhere else, which is time away from the parts of your life that are the reason why you do the things you do for work, right?
Patrick: Right.
Stephanie: Whether that’s your pet or your family or you just love hiking the mountains in your local area, being able to have more control over sort of the where and when in your life can be enabled using AI technologies.
Patrick: Yeah, that’s a really good point. And when you mentioned the agents too, it made me think like we’re, I don’t know how many years away from having an agent intensity reduction index. Like how soon do we see like the agents are great, but we need to retire them as well.
Company callouts: Who will be talked about the most
Just want to wrap up with one thing, because so TBR, we’re always focused on individual companies. So not like winners and losers, but like if you think in a year from now, what’s the company over the course of this coming year that you think is going to be the most- I hate the word interesting. It’s the one that you know you’re going to spend a lot of time on because it’s the one that’s going to change the most, or it’s the one that’s going to force others to change the most. What’s the sort of the most dynamic, news making, change making company among the company sets that you cover? Stephanie, I’ll let you go first because you look like you’re completely unsure what you’re going to say.
Stephanie: Well, you assessed my body language correctly.
Both: *laughs*
Stephanie: I’m thinking of it from this perspective. So, okay, now I’m going to come at this from left field because we have, so we’re talking about operators in the telecom space and there’s this concept of the AI phone that’s coming to the market.
Patrick: Oh, yeah.
Stephanie: And so, I’m not super privy to the nitty gritties on that, but it’s a whole other level of AI that’s being brought to the average person. And I think that this whole fundamental shift of the shiny, complicated technology, but in the hand of just your average ordinary person and the multiplier of impact you can get from doing something like that. Like if you think back to the flip phone and then the iPhone comes to the stage, and it fundamentally changes how you exist as a person. And now if you, God forbid, misplace your phone for 5 minutes, you can like watch your heart rate rise on your Fitbit or other device that’s monitoring your heart rate because it’s now sort of an extension of who we are as people. And I think that now if we’re going to layer AI on top of that, it’s really going to fundamentally shift how we as people are going about life.
Headcount reductions and how much are they actually impacted by AI
I have a question for you to spin it around.
Patrick: Uh-oh, this rarely happens, so let’s go.
Stephanie: Switch it up. So one of the things that I think about when I’m looking at the headcount reduction in AI is, in my opinion, many times, AI is the scapegoat and the easy thing to blame for a headcount reduction that was going to happen regardless. So, which is part of the value of HIRI, in my opinion, is that we are peeling back, sort of, the fluff that’s being layered on top of, oh, that was an AI reduction to was it really? So, I want your perspective on sort of that element of these headcount reductions.
Patrick: Yeah, three things. One, to make it worse, I think some of those headcount reductions are done not because they were necessary or because it was a business case for it, was more it was a marketing or a Wall Street kind of making the numbers case and sort of a following the leaders, so when one tech company is able to lay off 10% of its workforce. Every other tech company says, well, then we can lay off 10% of our workforce. And it’s not being thought through in terms of what is the long-term benefit of keeping the people we have and training them and all that. It’s just, well, we got to follow the crowd and do that. So, it’s worse than just blaming AI.
The second thing we’re already seeing where companies that had talked about needing fewer people to serve the clients they already have and then increase their clients are actually adding people. Headcount, Allan, to your point earlier, headcount in some of these companies is actually going up. And it’s going up because they’re realizing that AI is more expensive, and agents are still more expensive than people. So as long as agents are more expensive than people, it’s better to hire people to do some of the tasks that need to get done in order to continue to drive revenue. So, it’s going up for that reason.
The other reason it’s going up, which is fascinating, is this graduating class right now out of university, they were, what, freshmen, their freshman year, midway through their freshman year is when ChatGPT came out? So, for them- or maybe sophomore year. Anyway, they’re AI native in a way that nobody else has been before. So, this new set of fresh university graduates have a fundamental understanding and a comfortableness with AI, as well as a fear of where it’s going. And you hear that a lot from a lot of these recent graduates as well, but they need less time training. So, it’s easier to hire people now that have fluency with AI that will be immediately applicable. You don’t need to spend as much time training them as maybe you have the people that you already had in your company. So, I think we’re starting to see that trend in what I would say is the right direction, where the companies are realizing that it’s not a headcount reduction that’s actually going to improve their bottom line. It’s actually going to be taking the savings that they’re getting out of applying AI and being able to do, as you said earlier, do more with the people that you have and even add people to do an increasing amount of work. Does that answer the question?
Stephanie: It does.
Patrick: Okay. Is it a little scary too that these things are happening this fast? Yes.
Stephanie: I like the silver lining you spun though, because I know there’s a lot of talk in the news about, oh, I wouldn’t want to be graduating college this year. There’s going to be no jobs for the entry level person. They’re all being replaced with AI, and this really doom and gloom story, but you’ve put sort of the feel-good spin on it. And this is the first generation that is coming at AI with some organic knowledge, and it’s not being shoehorned into their life, they’re growing up with it.
Patrick: I think organic knowledge and, you know, sort of eyes wide open to the positives and negatives, because these are the same university students who got caught up in the early applications of AI when it was clunky, when it hallucinated a lot more than it does now, when students and professors both got caught up in, did you write this test using AI? Did you answer this test using AI? They’re so immersed in it that they’re both fluent with it, organic with it, but also fully understanding the dangers that are inherent in AI.
Company callouts: Oracle
So, thank you for taking us off on that tangent. Really enjoyed that and made me completely forget the question that I had that I was supposed to ask Allan. Oh, the company, right? The company.
Allan: Yeah, for me, I think it’s Oracle for a couple reasons. I think they have more to gain from AI than the other hyperscalers. So, they were a distant kind of fourth in the market. AI has really given them an opportunity to grow and it shows in their backlog, secure a lot of big deals that could really change their position in the market. And then also they have just been ruthless throughout their entire history with operating expenses, very efficient, but still looking to drive further. And so, I think they’ll be the most ruthless with how AI is used in terms of increasing efficiency in an already efficient organization.
And so, they could be some of those examples of at the far end of the extreme, how far can you go in terms of reducing expense, reducing headcount, and leveraging some of these tools. Not everybody’s going to go to that degree. That’s kind of their position in the market, but it’ll be interesting to watch.
Patrick: Right. It’d be fascinating to see too who, as we see in the services side, companies becoming more tightly wedded to a specific set of preferred technology partners. So not exclusive, but preferred. Which of the companies that we cover make that kind of get married to and do that preferred relationship with Oracle and who picks somebody else and how much does Oracle’s, as you described, sort of ruthless ability to reduce costs and the way that they price, how much does that factor into what those alliances look like?
TBR’s new coverage; AI Disruption Indexes
So, before we go, we are launching a new research stream, an entirely new research practice at Technology Business Research, AI Disruption Index, or the AI Disruption Research Area. So, I know in Services we’re going to have by peer set AIDI reports for every one of the companies that we cover. Putting in peer sets because you can’t really compare the HIRI and the business model disruption at McKinsey to what’s happening at Wipro. They’re wildly different business models and different companies. And so that will start rolling out very, very soon and be able to find it on the site. But Allan, for the Cloud and Software practice, what are the reports? Is there a name for the reports or what are they going to look like?
Allan: Yeah, it’s really two groups. So, the first is the Cloud Infrastructure AIDI and then the Cloud Software and Applications AIDI. And then within that, we’re going to have 9 vendor profiles across them, looking at the largest providers and going into more detail around their specific use of AI as well as monetization of AI.
Patrick: Yeah, that’s a good point. We’ll have vendor profiles as well. It’s a lot more than nine off the top of my head. I don’t know how many it is, but too many to manage. And then in the telco space, what’s the thinking? What’s coming next?
Stephanie: So, in the telco space, we’re going to have two sub-streams of the AIDI on the horizon coming out later in the summer. It’s going to be the enterprise networking set of companies and then the US telecom operator set.
Patrick: Okay, excellent. And all of that will be easy to find on Insight Center. It’s going to be its whole separate practice area.
Final thoughts
So excellent. We’re going to have to come back in six months, revisit all of this, see where the SMIRI is, see what metrics that get developed in the telco space. Thank you both.
Allan: Thanks.
Stephanie: Thanks
Patrick: Tune in next week for the season 5 finale of TBR Talks.
Don’t forget to send us your key intelligence questions on business strategy, ecosystems, and management consulting through the form in the show notes below. Visit tbri.com to learn how we help tech companies, large and small, answer these questions with the research, data, and analysis that my guests bring to this conversation every week.
Once again, I’m your host, Patrick Heffernan, Principal Analyst at TBR. Thanks for joining us and see you next week.
TBR Talks: Decoding Strategies and Ecosystems of the Globe’s Top Tech Firms
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Measuring the evolution of business models and productivity improvements
Analyzing the operating model, commercial model, partner model and delivery model
“The way we’re seeing — again, it’s a broader trend, the way we are seeing from that Human Intensity Reduction Index is that we’re anticipating now, as part of our forecast, is probably a HIRI of about 20 to 25% is where we think is going to be that kind of ‘sweet spot’ for the services organizations, and then margin, profitability, operating profit expanding another 5% to 7% over the next three to five years. That’s going to be a strong indicator of, you know, really a business model change, and the real transformation of how those companies go to market,” said Hristov.
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The AI Impact: Measuring Productivity, Profitability & Business Model Change
TBR Talks Host Patrick Heffernan: Welcome to TBR Talks: Decoding Strategies and Ecosystems of the Globe’s Top Tech Firms. Where we talk business model disruption in the broad technology ecosystem from management consultancies to systems integrators, hyperscalers to independent software vendors, telecom operators to network and infrastructure vendors, and chip manufacturers to value-added resellers. We’ll be answering some of the key intelligence questions we’ve heard from executives and business unit leaders among the leading professional IT services and telecom vendors.
I’m Patrick Heffernan, Principal Analyst, and today we’ll be talking about TBR’s new Human Intensity Reduction Index metric, or HIRI, with Boz Hristov, Principal Analyst for TBR’s Digital Transformation Practice, and Chris Foster, TBR’s President.
AI maturity and measuring AI in the ecosystem
All right, Boz Hristov, welcome back to TBR Talks, and Chris Foster, welcome for the first time to TBR Talks.
Chris Foster, TBR President: Thank you.
Patrick: Super happy to have you guys here. We’re going to talk about, of course, what we talk about all the time now in the spring of 2026, and that’s artificial intelligence. AI in everything, but it’s also kind of impossible to measure. We’ve been talking about for a while how the companies we cover make announcements about how much they’re investing in AI, or they announce partnerships around AI, but no one has a set definition of what exactly an AI investment is. And it’s really hard to track how much of an impact AI is having on the companies we cover. And because we focus on those companies specifically, we’re trying to figure out a way to measure and evaluate the impact of AI, not just that they’re bringing to their clients, but what they’re actually doing to themselves internally. But to sort of set the stage for that, Chris, what have you seen and what have you heard about AI maturity and measuring AI within the broad technology ecosystem?
Chris: Yeah, I think there are three dimensions that I hear quite a bit about. And I think the dimensions also apply. A lot of things I think about are in the context of our business and being a services business ourselves and serving the IT community. I think we have some of the same opportunities as the broader marketplace does, but three of the things that I hear are one is, how do you consume AI yourself? What tools do you use? How do you use them? What do you use it for? That’s the most tactical thing, which to me is analogous to the tools someone in a trade uses. So, everybody has access to the same tools. So, first and foremost, figuring out which tools you’re going to use and what you’re going to use them for, which lends itself to productivity. So, everybody wants to get more productive, do more with less. The second element or thing that I hear a lot about is how are you helping customers? So, are you providing tools that enable your customers to access, in our case, our research more easily, or in the case of a lot of our customers, how to use their technology more easily? And the third dimension I hear about, and this really applies to our business as well as our customers’ business, but how do you continue to add value in the world of AI? We keep seeing more and more examples of how advanced AI is and the ability to do more and more. And when we’re in a services business where the humans add the value to what we’re doing, how are we going to be able to continue adding value? So, as we look at the maturity curve on AI, two things that I think I see folks struggling with are, are you getting efficiency returns for implementing it? And what value are you as a business, as a technology firm or services firm, going to be able to continue to add as AI moves further and further up the value stack.
Patrick: I want to jump right to the first thing you said about the tactical deployment of AI within our firm, but really within any firm to go after productivity improvements. Have you, so in the last couple of years is where we’ve seen ourselves adopting more AI internally. What are some of the things you think have held us back? And what are some of the things you think we’re going to be doing a year from now, much better than we’re doing right now.
Chris: Yeah, I think it’s the classic things around change and change management. No matter how much you anticipate it, no matter how much you prepare for it, being able to manage change and doing things in a different way is generally harder than actually figuring out the technology or figuring out how to use a new tool. And probably another dimension of that is getting consistency. So having everybody using the tool and using it the same way. And some of the ways around that, I think, just as a small firm, some of the things we’re implementing just around knowledge sharing and the ability to ask your peers how they’re using the tool, what’s working well for them, I think is critical in success.
But I think a year from now, I think like anything, what we’re doing today and what we were doing two years ago is going to look very rudimentary compared to what we’re doing today. And I think the tools will get easier to use, the expertise will continue to build. We’re seeing obviously more and more in the higher education space around how to use the tools and as we move forward, and I think as everybody in our industry moves forward, you’ll start to see people come into the business that are born with AI tools. Basically, so much of what we saw 15 or 20 years ago where people were being born digital and not having to figure out things were just native in cloud. Things are native in applications. So, I think AI will just become more native, both in terms of on technology as well as in the human brain.
Patrick: And I’m glad you mentioned the knowledge management part of it too, because I think that’s, there’s the change, the consistency, the knowledge management. I think that knowledge management too, we’ve already seen it where people who are new to the firm or just amongst each other, being able to build up our skills and share something that- Boz and I were talking today, and he had just used a particular prompt within ChatGPT to drive a result that was really kind of amazing. And again, it’s something you learn- we’re learning about it every day through that shared thing.
Introducing HIRI: Measuring the evolution of business models and productivity improvements
So, Boz, turn to you now. So, we- looking at the services companies that we cover, the sort of the idealized model of what AI could do is eliminate the need for any people. You have a billion-dollar company that has one employee or zero employees. Within the services space, services is a people-based business. So, when you started to think about that challenge, what happens to the people within a services space as a services company adopts AI-enabled solutions, you came up with HIRI. So, can you walk us through what that is?
Boz Hristov, TBR Principal Analyst: Sure. I mean, HIRI, just to kind of expand on that, I mean, it’s kind of a working version of our, what we call Human Intensity Reduction Index. And really what it does is we’re trying to better understand how companies are really trying to scale the opportunity around driving a non-linear revenue growth model. Fewer people, more revenue, right? Historically, the outsourcing business has always been labor arbitrage based. The linearity between expanding headcount and revenue has been well documented, right? Ten years ago, when RPA came around, there was a first aha moment and everybody was like, maybe that’s going to crack the code. It really didn’t, right? And now with AI, going beyond just a task-oriented automation, workflow automation, and really providing the opportunity to drive the generation of new content, and really trying to go beyond that kind of just mundane task automation, I think this is where we’re starting to see and hear how the vendors themselves are applying both internally to their own operations, but also how they deliver services.
So, for us, from a research perspective, we’re trying to look into the evolution of their business models and trying to understand what’s the best way to measure that productivity improvement, right? And is there something that is a unique set of metrics and data that companies think about and use to measure their own success, but also for us from a research perspective that can help us to build a model that can track that evolution and say, okay, how much is that really based on AI versus how much is based on offshore leverage? How much is pricing? How much is the culture of that organization and so forth? So here in the Human Intensity Reduction Index or AI influenced productivity proxy, however you want to call it, I think it’s in a way of like allows us to track that improvement in productivity. Helps us to better understand what the economics looks like for the vendors. How does that translate into profitability, right? Not just the revenue expansion from a non-linear fashion, but profitability. Maybe look into what’s the impact on the day sales outstanding cycle, or maybe their bookings. I mean, there’s other ways to measure. So, for us, it’s about understanding, as companies use more of AI to deliver services, how does that translate into the profitability impact, in the margin profile, to the value that they create to the shareholders, to the stakeholders as a whole. So, it is a way to measure that improvement, it’s a way to measure that evolution of those business models. Again, AI is one element of it. It’s kind of the corner factor of what those companies are aiming to do. We certainly recognize there’s a lot of other variables to be considered, and it’s a moving target.
Patrick: Right. And so, when you talk about HIRI, it’s more than just the revenue per employee. You’re not just looking at how much the revenue per employee has changed and headcount has changed. You do that part, and that’s sort of, in my mind, I see that line. But then you always have to have it paired with or with, so operating margin change or whatever it might be, right?
Boz: Yeah, so you have to see it in the context of the broader economic profile of the organization, right? And operating margin is certainly a common factor, common denominator for all organizations that you can really see the health of the organizations. I mean, you can argue that companies, you know, some companies look at gross margins versus operating margin and so forth. But we, from a services perspective, at least we are looking at the operating profit as a way of the common denominator that’s helping us to understand how those variables are evolving and how they’re changing and what’s the impact.
The headcount movement, you know, as it changes, as we look into it, we saw probably in the last 18 to 24 months a decline in headcount, which was for the first time since we started tracking the kind of outsourcing business 20 plus years ago in the services industry. So that was the early indicator. The big question was like how much of that is AI automation, how much is a slower demand, right? So that was kind of the big- and then I think while the initial 12 to 18 months maybe have been a lot more of that slower demand and discretionary slowing down. I think we kind of have hit that inflection point. We’re starting to see more of the influence of AI impacting the Human Intensity Reduction Index. So, we saw those indexes for some of the larger systems integrators and consulting services hovering in the –5% to -10% about three years ago when GenAI came around. We’re now seeing it hovering at like a +5%, +7%, you know, essentially. While their margins are either staying flat, some of them are improving, in some cases, margins are declining, and there’s a reason for that because some of those companies are a little bit more innovative in terms of they want to reinvest back some of the savings that they generate as a result of fewer employees. So, they’re putting back into different tools and how they’re actually applying those tools in your service delivery and trying to monetize that.
So, the way we’re seeing, again, it’s a broader trend, the way we are seeing from that Human Intensity Reduction Index is that we’re anticipating now, as part of our forecast, is probably a HIRI of about 20 to 25% is where we think is going to be that kind of “sweet spot” for the services organizations, and then margin, profitability, operating profit, expanding another 5 to 7% over the next three to five years. That’s going to be a strong indicator of, you know, really a business model change, and the real transformation of how those companies go to market, how much are they using AI internally but also how they’re using for service delivery with their clients.
Patrick: Yeah, and two aspects of that too is you started looking at HIRI or you started taking the metrics from right when GenAI launched.
Boz: Yes.
Patrick: And then because we can do it quarterly, we’re going to be able to see, not- it’s benchmarking, but it’s sort of real-time benchmarking how that change is happening.
Analyzing the operating model, commercial model, partner model and delivery model
So, the other piece to AI maturity, so that’s the data side, the sort of qualitative side, is around the business models.
Boz: Yes.
Patrick: Is how are the companies changing their business models because of adopting AI at scale? And so out of the- so we identify the four business models of the operating model, commercial model, partner model and delivery model. So out of those four, just thinking ahead to the next, this year, so through the rest of 2026, which of those four you think are going to be, is going to be the most disrupted, the ones where companies are going to focus the most attention on making the changes that Chris was talking about are so essential when you’re trying to adopt AI at scale? Is it commercial? Is it delivery? Is it operations? Is it partner?
Boz: I would say the commercial is the hardest, but the most important out of all four, because essentially that’s the transaction. That’s where those companies get paid, because they’re not operating in a vacuum on their own. They’re operating in an ecosystem, right? So, they have to think about the partners, think about the stakeholders on the client side. So, we’re starting to see some evolution in the commercial models, but we also understand that there’s a lot of moving pieces and no one has really cracked the code on the right commercial model moving forward. There’s some early indications of what the model could look like in the next three to five years, more the stable model and the way it’s shifting from more of a times and materials away from the traditional model.
I think what we’re going to see is the operations model is where we’re going to start seeing a little bit more, you know, companies trying to set the foundation because the operation model is culture, it’s leadership, it’s governance. It’s trying to think about who is actually involved and how is an organization going to be able to drive the business internally with our own stakeholders because there’s a lot of partners and salespeople and delivery people that have been doing business one way, and now they have to collapse silos and they have to do all these things differently and think about how much is that tool going to impact my job and think beyond that, essentially, right? So, there’s a generational shift in some of the larger organizations, some reorganizational efforts and programs that have been going on even prior to AI. So, I think this is operational model, I think it’s a critical kind of a building block as those companies while they’re working on the other three.
The partner model, as I said, everyone’s operating an ecosystem. I think it is an opportunity between the partners to get closer together. I think the commercial model, as I mentioned a moment ago, I think will be that kind of a sticking glue because services companies continue to talk about outcomes and think about delivering outcomes to clients, milestone-based or project-based or scope, whatever the case might be. There’s kind of the broader outcome discussion. One technology partners continue to talk about subscription and consumption, right? So there’s a little bit of a gap between the messaging and the execution and what’s actually how you measure value because services companies really focus on client retention and they’ll do anything and everything versus a technology company typically is like the next sale, close the quarter and that’s it and move forward, right?
Patrick: Right.
Boz: So, if that can start, the partner mode starts to be a little bit more innovative of thinking and going to market, and that’s why we’re seeing some of the larger services providers tapping really heavily on the AI native companies and expand the ecosystem, right? You know that driving revenue from the established partner ecosystem is important for the ongoing business, but now expanding into like the Anthropic and the OpenAI or the Mistral AI of the world, you know, those are the companies in the cursor, you know, that will actually provide that next wave of opportunities for services companies because I think those AI native companies, AI native partners, I think will be likely more open and easier to see the value to that outcome focus versus the maybe more traditional partners that a little bit more still have a bigger, large businesses to run.
Patrick: Yeah, and I think from our perspective, to get back to one of Chris’s points about the value that we provide, because the ecosystem is changing in the way that you just described, applying the way that we look at the world and the way that we provide value to our clients to include these AI native companies and how they’re partnering with the services companies is something we’re going to have to do an increasing amount over the next couple of years.
Boz: 100%, yeah. As we’ve discussed in the past, in other episodes here on the podcast, I mean, we have built an ecosystem intelligence research coverage over the last three+ years, and we continue to grow that coverage both from a vendor-specific coverage and the relationship, but also the depth of the existing one, as we’ve discussed. You know, looking at the services companies across nine technology partners, we started expanding that on a geo level, growing now the ecosystem with adding those AI native companies, because there is going to be a discussion around those companies being on a two-dimensional discussion where is it just an Accenture plus Anthropic or do we need to include them part of the Accenture plus Microsoft plus Anthropic, whatever the three or four-way partnership is.
Patrick: Right.
Boz: And we’re starting to do right now as we’re building out the next iteration of those research reports around the data management companies, the Snowflake and the Databricks of the world, and looking into that three-way of relationship between the SI and the hyperscaler and data management companies. So, there’s a- we are kind of building the pathway for those AI-native companies, because naturally they’re just going to come up and be as important, if not even more than some of those legacy partners in the past.
Patrick: Yeah, I can predict now there’s a season 6 podcast episode on exactly that topic.
Adding the value layer of the human element
So, Chris, so everything Boz has been saying from the TBR research agenda, thinking, the way that we’re approaching this challenge around AI and maturity and measuring it. How much does that resonate and reflect what you’re hearing from our clients as they’re asking about questions around AI?
Chris: Yeah, I think living in the world in which we do, we hear things from clients. The question is really about the value we’re contributing. And as I’m listening to Boz speak, I start to think about this, the value proposition and the layer of value that any firm adds to an engagement gets skinnier and skinnier or thinner and thinner. So, it’s really for us to be aware of that value that we’re adding on top. And I mean, the question when we have two types of research, we have off the shelf kind of research, which to me, it’s really important that that be forward-looking with predictions and insights built by humans that can’t be generated easily through AI. And then the custom engagements that we do that get down to answering very precise questions.
But I do think that we have to be very cognizant of that human layer that we’re adding on top that adds particularly the forward-looking elements into the engagements that we do. And I think it’s true for, you know, Boz talks a lot about efficiency and we look at the maturity of these AI models, but those are really value proper rotors that things that were done that customers are willing to pay for that added value to engagements are able to be done through technology. So, I just, my under arching or overarching concern, or I think opportunity, is for firms to add value on top of what the technology can do. So, I think going forward, it’s about being very cognizant while you’re using AI to make yourself more efficient that the real priority is the value layer that you’re still adding on top of all the technology that’s being implemented.
Patrick: Right. There’s still the knowledge, the experience, the ability to talk about trends that couldn’t be picked up by AI because they’re in your head, and you know them because you’ve been looking at this field for so long.
Chris: Right. Or in my experience, and tell me if you see this differently, but AI is somewhat linear in the way it operates, and it’s very good at coming up with the next thing in a sequence, but what’s beyond that next thing? That’s where I think the human elements are still super valuable.
Extrapolating beyond the services industry
Patrick: Yeah, I want to come back to that. And I want to actually ask Boz a question about what you just said. But before that, so we’ve talked almost exclusively so far about services. And I guess being honest, TBR Talks, we tend to talk about services a lot. But from your perspective, Chris, looking at the firm as a whole, these AI issues and the challenges around metrics that Boz brought up, you’re seeing those across cloud and telco and infrastructure and devices as well?
Chris: I think when I look at our business and coverage of the IT market, whether it’s hardware, software, services, telco being the areas that we go deep into, they’re all a little bit different. So, I think you guys know very well what happens in the services business and it’s human intensive. But if I look at the hardware space as an example, the hardware space is more technology intensive than it’s ever been. I look at a company like Dell that’s had massive headcount reductions over the last couple of years and really become a very close partner of companies like NVIDIA where it’s really building your business off of a demand model. So, I think AI in the race to build out the infrastructure, I think the challenges are a little bit different there. I think if I look at the cloud companies and the hyperscalers, in some ways it’s all around the power question and how efficient they can be in build outs of data centers. So, I think it’s the question or the challenges are a little different in all the spaces. And telecom, for instance, is you’re looking at a business where the service providers, the Verizon’s AT&Ts, are very mature, and it’s about how do you get super efficient in a highly scaled business that is super competitive? So, the challenges are all a little different, but it still comes down to the question of how do you maintain differentiation in models that become highly scaled and highly efficient.
Patrick: Right. And that’s, I guess, part of the beauty of- is you brought up so many topics that we touch on in the services space, like the power question when you talk about the hyperscalers. We touch on them, but we touch on them primarily from how are the services companies helping the companies that are challenged with that. We haven’t been thinking it through like all the way through what’s going to happen to the services companies themselves because of those exact challenges. One last question for you, Boz. Chris mentioned sort of the AI tends to be linear. Is that your experience too?
Boz: I mean, I think from a technology perspective, yes, in terms of the first degree of response. I think he was alluding to the second or third degree of implications where the humans step in and can actually provide much more value. I think from that perspective, yes. I think the opportunity for AI is to drive non-linear revenue growth, the scalar of that. But I think I agree with him in terms of when it comes to, you know, it’s good as based on what you ask him, you know, and you know, the thinking that, you know, the ingenuity of the human mind, you know, and just kind of trying to be a little bit more creative about asking a question a different way. Again, second, third, fourth degree, you know, derivative implications of a particular question. Unless you ask that question, it’s going to be more- it’s not going to be as creative as a human mind might be, at least for the time being.
Patrick: Yeah, and I’m hearing-
Boz: For the time being, I put that disclaimer.
Chris: Yeah, right. And the one thing, we’ll see- but the one thing that AI probably isn’t very good at is, as long as there’s an element of human behavior involved, AI is never going to account for all the possible human behaviors.
Boz: Yeah.
Patrick: Right, it’s like economics would be a perfect pure science if humans didn’t get in the way.
Chris: Right.
Patrick: And we talk about technology is never the problem. It’s always humans. You can flip that around with AI and say, technology, AI, is never the problem. It’s that humans have the opportunity to be ingenuous and creative and all that kind of thing.
Reflecting back on being a 22-year-old about to enter the workforce
All right, this has been excellent. Chris, I have one last question for you because this is a question I’m asking everybody in season five. And this whole season for TBR Talks, we’ve talked a lot about longevity in technology. So not getting too caught up in the hype around everything that’s new with AI, but talking more about the long view. And part of that long view comes back to thinking about yourself when you were 22 years old. And I’m specifically picking out 22 years old because our daughter is about to graduate from college and the whole world is in front of her. And she can imagine what she wants to do and where she wants to be in 20, 30 years. So, thinking that through, I’ve had a lot of conversations with people about what were they thinking at 22? What did they think they were going to be doing when they were 22 years old? And I’ll give you an example of that. One woman told me she got a degree in mechanical engineering. She’s now in IT, but she wishes she had just joined a Formula One racing team and been a mechanic in the labs with them. So that was her like dream job at 22. That’s where she thought she might end up. So, 22-year-old Chris Foster, what was the dream job that you thought you were going to be doing?
Chris: Being right here with you.
Patrick: *laughs*
Patrick: *laughs*
Chris: That’s what I imagined. I’m not sure if you’re asking the question about providing advice to a 22-year-old. At 22, like a lot of people, I didn’t have that passion to be a fireman or want to be an astronaut, but I always liked business and I always wanted to be around business. So, I did, I feel like I did end up in a place over the years where always around business and how business works and business operates. But my advice to that 22-year-old is follow your passion. If it is F1, go to F1, you’ll find your way. And if you go to a profession, this is cliche, but if your occupation is your passion, you’ll never work a day in your life.
Patrick: And I will say, people have answered that question with advice. And a lot of it follows in the same track of like, sort of, if you think you want to go do it, go do it and execute, you know, be good at it, show up every day, be responsive, be responsible, all that kind of stuff. So, and I mean, if you wanted to be in business when you were 22, you’re running the firm now, that’s pretty great.
Chris: Yeah, and I think it’s about following that. And I think the other thing is, don’t do stuff just because you’re trying to please somebody else.
Patrick: Amen to that. Yeah, absolutely.
Final thoughts
Chris, thank you very much for finally coming on the podcast. I appreciate it, Boz. You’re welcome every time, of course. And we’ll talk again soon.
Chris: Thank you.
Patrick: Tune in next week for another episode of TBR Talks.
Don’t forget to send us your key intelligence questions on business strategy, ecosystems, and management consulting through the form in the show notes below. Visit tbri.com to learn how we help tech companies, large and small, answer these questions with the research, data, and analysis that my guests bring to this conversation every week.
Once again, I’m your host, Patrick Heffernan, Principal Analyst at TBR. Thanks for joining us, and see you next week.
TBR Talks: Decoding Strategies and Ecosystems of the Globe’s Top Tech Firms
Join TBR Principal Analyst Patrick Heffernan weekly for conversations on disruptions in the broader technology ecosystem and answers to key intelligence questions TBR analysts hear from executives and business unit leaders among top IT professional services firms, IT vendors, and telecom vendors and operators.
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https://tbri.com/wp-content/uploads/2021/09/TBR-Insight-Center-Logo.png00TBRhttps://tbri.com/wp-content/uploads/2021/09/TBR-Insight-Center-Logo.pngTBR2026-06-17 08:07:112026-06-17 08:07:58The AI Impact: Measuring Productivity, Profitability & Business Model Change
In this episode of “TBR Talks,” TBR Senior Analyst Kelly Lesiczka joins host Patrick Heffernan to discuss TBR’s latest research on the consulting and systems integration market. Kelly shares insights into the shift being seen around outcome-driven engagements, how the personas and buyers of these consultancies will evolve over the next five years, and the divergence of SI-focused players and consulting-focused players.
Episode highlights:
Engagements: Outcome-driven versus fixed-price
Consulting model evolution
HCLTech’s alliance and acquisition strategy
“Some of the key elements I would say, I think the key trends in the market and the market influencers were definitely heavy on how we look at it. You can look at some of the disruptors like AI; fixed pricing is really changing how these companies are going to have to structure their contracts going forward; the talent composition, how that’s changing; and then the geopolitics situation, which is always changing, plays a role. The evolution of the consulting space, though, is definitely a key thing, and that makes it harder because you don’t know what it’s actually going to look like in five years, let alone two years from now. And so, what is encompassed in that evolution? We’re going to see these new businesses emerging, look at managed services, how much that’s grown over the past five years, not necessarily in terms of revenue but the prevalence of it and how the companies are coming out,” said Lesiczka.
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The Future of Consulting Services: AI, Fixed Pricing and Managed Services
TBR Talks Host Patrick Heffernan: Welcome to TBR Talks: Decoding Strategies and Ecosystems of the Globe’s Top Tech Firms. Where we talk business model disruption in the broad technology ecosystem, from management consultancies to systems integrators, hyperscalers to independent software vendors, telecom operators to network and infrastructure vendors, and chip manufacturers to value-added resellers. We’ll be answering some of the key intelligence questions we’ve heard from executives and business unit leaders among the leading professional IT services and telecom vendors.
I’m Patrick Heffernan, Principal Analyst, and today we’ll be talking about TBR’s new Consulting and Systems Integration Market Forecast with Kelly Lesiczka, Senior Analyst in TBR’s Professional Services Practice.
How TBR approached creating the Consulting & Systems Integration Market Forecast
Kelly, welcome back to TBR Talks. Very excited to have you here.
Kelly Lesiczka, TBR Senior Analyst: Yeah, thank you. I feel like it’s been so long.
Patrick: It probably has been, but what we’re going to dive into today is a market forecast. And speaking about so long, I resisted for a long time doing market forecasts just because I was kind of afraid that it was the equivalent of throwing darts at a dartboard and saying what was the growth rate going to be? But as a team, we came up with an approach to doing market forecasts across professional, IT services, consulting and systems integration, and then the federal IT services space. So, you had the honor, the privilege of doing consulting and systems integration. So, tell us about what that was like, and then what does the forecast end up looking like coming out of all the research?
Kelly: Yeah, sure. So, thinking about the market forecast, I think it’s kind of tricky, as you said, just kind of throwing a dart. It’s hard to get a real grasp on what the entire market looks like. And I think it kind of goes back to the essence of what TBR is and thinking about who the existing players are and what they’re doing. So, looking at what we closely track and then seeing directionally where they’re going, where they’re headed, what they’re trying to do. And then some of the key elements I would say, I think the key trends in the market and the market influencers were definitely heavy on how we look at it. You can look at some of the key elements I would say, I think the key trends in the market and the market influencers were definitely heavy on how we look at it. You can look at some of the disruptors like AI, fixed pricing is really changing how these companies are going to have to structure their contracts going forward, the talent composition, how that’s changing, and then the geopolitics situation, which is always changing, plays a role. The evolution of the consulting space, though, is definitely a key thing, and that makes it harder because you don’t know what it’s actually going to look like in five years, let alone two years from now. And so, what is encompassed in that evolution? We’re going to see these new businesses emerging, look at managed services, how much that’s grown over the past five years, not necessarily in terms of revenue, but the prevalence of it and how the companies are coming out.
Patrick: Yeah. And so, then when you looked out, I want to come back to a couple things on that, but when you looked out five years, what did it look like in terms of the growth of the market for consulting and systems integration?
Kelly: Yeah, I think it is pretty consistent growth going forward. I think we’ll see a lot more of the specialized services. We were talking about earlier the technology partners and the alignment coming out of these consulting vendors. I think aligning more closely with them and bringing on more of those resources in-house, the need for a better blend of the technology as well as the business and consulting knowledge will definitely become more essential. A lot of the vendors too, there is a lot of ongoing restructuring and reorganization. And I think how those companies balance that with the direction they’re going, it could make or break them really in terms of figuring out who they are and what opportunities they actually want to pursue and go after.
Patrick: Yeah, there’s always been a lot of reorganization and leadership change and structural change within the companies in the consulting and systems integration space. But it feels like over the last few years, there’s been more of that. You just-
Kelly: Yeah, I think it’s just an ongoing evolution. I think it was about two years ago, they said transformation needs to be ongoing. And I think it’s something maybe these vendors took to heart themselves in terms of just continually updating who they are and making sure they’re going where they should be.
Patrick: Right, and reinvention has become such a buzzword.
Kelly: Yeah.
Patrick: Such a buzzword that Accenture has an entire business practice named after it.
Kelly: Yeah. *laughs*
Scenario discussion: Outcome-driven versus fixed-price
Patrick: So then one thing we tried to do in the forecast is have what I’ve, sort of, in my mind, I’ve been calling kind of the gray forecast, which is like, this is the baseline of what we think is going to happen, but then have scenarios that would explain what could go differently. Like where could things spill out different than what this sort of gray baseline forecast is. So, what were a couple of the scenarios within C&SI Market Forecast?
Kelly: Yeah, I think the first big one was around the shift on outcome-driven engagements, and looking back at that fixed plus piece. A lot of the vendors had traditionally focused on solving a business problem. So that’s just how they went, the nature of consulting, they wanted to start with the business problem and go out from there. And I don’t know that outcome driven is that much different than that. I think it’s just the value of the contract and the engagement is just more going to be directly tied to it. So, in a sense, it’s just more checks and balances for the vendors to make sure they’re actually achieving what they set out to achieve. And kind of a risk on the vendor too, taking more of the risk from the client. They’re just getting a lot more pressure, I think, in terms of what they’re actually doing. So, are the clients actually getting the benefit from them? And I think the questions are falling back down. It’s, oh, you adopt every new technology, but then it’s where do you kind of figure out what’s actually doing something for you?
Patrick: And part of that is the adoption of the, by the, of and by and for, the consultancies and the IT services companies of AI, and AI equals transparency. So as you gain additional transparency, the pressure from clients to say, it’s not just fix my problem, it’s get to this outcome, because now we can more transparently see what that outcome is, and looking backwards, more transparently see what it was that the consultancy actually did that led to that outcome.
Kelly: Yeah, I think that’s a good way to look at it.
Scenario discussion: Consulting model evolution
Patrick: Was there another scenario?
Kelly: Yeah, we had two. This one is similar, kind of follows along, but it’s around the consulting evolution. Consulting is based around people and permission, and so we wanted to dive a little more deeply into that kind of thought process. And I think it’ll still be the essence of what consulting is around people and permission, but it’s the way that the companies build up that talent and gain that permission and who they’re actually working with now as opposed to who their personas and their buyers were five years ago, what they’ll look like five years from now moving forward.
Patrick: So I want to dive into some specific companies, but so, I was going to hold this to the end, and I was going to hold this until the end but I’m going to ask you now because you’ve talked about the evolution in consulting and you listed a bunch of things that went into how you made the forecast. So, AI, obviously, changes in pricing, changes in talent management, the macroeconomics and the geopolitical, the ecosystem and how alliances are different now. The consulting, and you just mentioned people and permission, which is so fundamental. So, the consulting business model hasn’t ever changed. I mean, in 100+, 200+ years, since the first time somebody paid for, tell me what to do or do it for me, or tell me what to do and help me do it, the real core parts of consulting. It’s really not changed. So, in your thinking looking out to 2030, are all those factors, AI, pricing, talent, macro, geo, ecosystem, are they all going to force change in the consulting business model? Or are we just going to have a slightly different model, not a really dramatically changed consulting model?
Kelly: Yeah, that’s a good question. There are so many different ways to look at it, but I don’t think it’ll be too radically different. I think the essence will still be there. I think it’s just what they’re doing. I think in a podcast, it was the McKinsey lead, he was talking about how they’re looking for a mixture of skills now. So not just MBA hires, but now they’re looking at liberal arts hires. So, I think they’re just trying to, I think the benefit for consulting would be around bringing in a better background and more experience from different areas, because that’s one of the pushes around specialized skills. So, it’s like now you need to bring in all of these different pieces, all the different experiences, and it gives you a different result, especially where you’re looking at specific outcomes, you’re tying this more directly. It’s not just a framework that you’re following, I wouldn’t say. I think it’s going to require more value, more specialized skills, so more knowledge around either industry or these technologies. So, it’s not just the business knowledge you need and the finance knowledge, it’s more- a little bit of everything, I feel like.
Patrick: Right. And an increased emphasis on the people skills and the ability to, which is, so we heard from both a PwC partner and a BCG partner talking about how as they’re hiring into their consulting practices, it’s less about those MBA honed skills around finance, around accounting, around operations, around supply chain, whatever it might be. And it’s more about can this person actually connect with another human being? Because at the end of the day, that’s what AI doesn’t displace, and that’s what people actually pay money for in the consulting space.
Kelly: Yeah.
Patrick: So, in that way, probably the consulting business model isn’t going to change.
Fujitsu’s consulting build out is one to watch
But set that aside now, and let’s talk about specific companies, because to you looking out to 2030, what are the companies in the consulting and systems integration space that you think are going to be the most interesting, the most, and that’s a terrible word to use. What companies do you think will be the most dynamic, the most disruptive, be the most fun to talk about between now and 2030.
Kelly: I think Fujitsu is a very fun company to watch. They’re often forgotten about because they’re so heavily based in Japan and that’s where a lot of their business is, but there’s so much more to them than just Japan. And their build out of consulting, while it’s not a disruptor for any of the other vendors. It’s definitely something to watch because of the way that they’re doing it. It’s more or less organic to who they are. Obviously, it’s not something they had historically, but the way that they’re building it up and really integrating it across everything they do, it makes a big difference. And tying it back to their Fujitsu Uvance business as well, it’s just, it’s more holistic, I guess you could say. I just think it’s a very interesting company to watch from that perspective. It’s something they didn’t have. They’re building it from the ground up, something they didn’t have at all. But they’re actually doing well in terms of scaling that, even coming from nothing.
Patrick: Yeah, and at the very beginning of season five of TBR Talks, we interviewed the CEO of Fujitsu Americas.
Kelly: Oh, yeah.
Patrick: That whole discussion was so illuminating in terms of how much is changing in Fujitsu. And I think it’s a good company to look at for two, three very specific reasons. One, the alliances that their consulting team is forming with technology partners is super important. Two, yeah, they’re not an existential threat to the largest of the consultancies, but they certainly are coming in and taking away some market share. And then three, they’re changing. And anytime anyone within an ecosystem can evaluate and understand and look at the factors behind one of their peers or competitors changing, you can learn a lot. Because you can see what mistakes they’ve made. Not that they’ve made any, of course, they’re perfect, but you can see what choices they’re making and then decide how does that reflect on the choices you have. So yeah, totally agree. Fujitsu is a good company to keep an eye on. So, who else?
HCLTech: Alliance and acquisition strategy
Kelly: The other one that I like- well I like them all. But HCL is always cool to watch because I think they have a good alliance and acquisition strategy. It’s all very strategic and focused. So, they’re looking for a specific capability, a specific region, whatever the goal is. But they’re very strategic about how they use them. And so, they will occasionally purchase consultancies that have either digital technology skills or digital transformation skills, software related consulting or even just engineering or semiconductor experience. But I think they’re very good at using their partners in acquisitions. And I think that’s something- it could be advice for others, a good guidebook for others to watch and just see how they’re plugging all this in because they’re getting the return on those investments. So, it makes a big difference. For them at least.
Patrick: You’re like the ideal guest because you’re bringing up another TBR Talks podcast episode from earlier this season where we talked to Alan Flower at HCLTech.
Kelly: Oh, yeah.
Patrick: Right, who had a lot to say about exactly those points. And with HCLTech, I think it’s interesting too because the company itself has always been among the smallest of the large India-centric IT services providers. But probably the one that has changed the most and has introduced the most kind of different services, different lines of services, acquired IBM software at one point. So really a more dynamic company compared to some of those other companies.
Kelly: Yeah.
The divergence between SI-focused players and consulting-focused players
Patrick: So, I just want to ask about when we look at consulting and systems integration, from TBR’s research perspective, we sort of have two different paths that we go down. One is C&SI as part of the broader IT services package. And then the other is focusing more on the consulting slice and looking at management consulting. So when you, in the forecast, I don’t know if, honestly, I don’t know if you did this, did we sort of split out like the management consultancies and their trajectory in terms of growth and the SIs and their trajectory in terms of growth? Or did we kind of pull it all together and look at the demand side of, you know, this is what the demand is going to do for consulting and systems integration altogether?
Kelly: I think it’s difficult to look at them from the same lens because they have very different clients in the projects that they’re consulting on. I think one of the things we discussed with the Management Consulting Benchmark, not the C&SI Market Forecast, was around the emergence of a lot of these smaller boutique consultancies that are using technology to kind of replicate what they’re doing. Because a lot of times they approach it with just a framework that they’re using, and that’s so repeatable. You can’t necessarily show your different or really show value through doing something like that.
Patrick: True.
Kelly: It has to be more organic. But we didn’t break it out that way, but I think it’s hard to look at them from the same lens. Especially with the things that they’re going after, I think are very different, and the projects that they’re kind of approaching are a little different. I would venture to say, the more traditional IT services would grow more quickly around it just because of what they’re doing, the scale that they’re coming from, as opposed to the more heavily management consulting ones and the areas that the management consulting ones are going to shift their focuses to.
Patrick: Right. It’ll be curious to see this. So, the demand right now that we’re hearing about for consulting is there’s a lot of it around AI and sort of strategy for AI, deployment of AI within an enterprise, getting the best, you know, the ROI on an AI deployment. There’s a lot of consulting around that right now, which is great for the pure consultancies. But over time, once companies begin to adjust, it’s sort of like cloud. I mean, once you’ve gotten over the hump of how do we leverage cloud, then you’re just, you’re no longer looking to a McKinsey or a Bain or a BCG to say, explain cloud to us. And right now, a lot of companies are having to say, explain AI to us, but that’s only going to last so long.
Kelly: Yeah.
Patrick: And then curiously, we’ll see also what happens as these companies that we’re talking about also deploy AI internally and how much that changes. I mean, the talent pyramid that you mentioned earlier, the structure of talent within these companies could change dramatically over the next five years. as AI begins to displace some of the, probably more in the middle, less so the entry level, because you still need to bring people in and use the apprenticeship model. But the folks that are kind of in the middle that missed out on learning about AI when they were in college and have not yet sort of reached that more senior ranks.
Kelly: Yeah, that’s true. Yeah, we’ll see how it goes, but I think they’ll be- I think they’ll definitely separate in a sense on what they’re actually trying to do, where these consultants will focus their energies and what they’ll try to do.
Patrick: Right. So, I think maybe for the next market forecast, one thing we can try and do is look at this, the divergence between the real SI focused players and then the real consulting focused players. And then of course, we’re going to have those companies right in the middle, the Deloittes of the world and the Accentures that do a whole lot of both. And how do they decide which of those two trajectories they want to go down?
Kelly: Right.
Patrick: So yeah, so I guess we’re already setting up our next conversation about this.
Kelly: Yeah, I still stick true to strategy consulting. I know a lot of people forget. They think it’s a thing of the past or it’s not as big as it was. But I think with the changing of business models of what AI is doing to these companies in terms of their own strategy is what they want to do. I think it might be labeled differently moving forward, but I think it’s still going to be a viable area to go.
Patrick: Yeah, I think fundamentally, the question, “what should we do,” is a question that companies are always going to look for advice for. And they’re always going to look to someone who’s trusted, they’re always going to look to someone who has experience, and they’re always going to look to someone who has that outside in perspective on a company. So, the demand for strategy consulting may kind of ebb and flow, but it’s never going to go away forever.
Kelly: No, definitely not.
Final thoughts
Patrick: Yeah. Excellent. Well, that’s a positive note to end on, so we’ll end it right there. Thank you, Kelly, so much.
Kelly: Thank you. It was great to be on.
Patrick: Awesome.
Tune in next week for another episode of TBR Talks.
Don’t forget to send us your key intelligence questions on business strategy, ecosystems, and management consulting through the form in the show notes below. Visit tbri.com to learn how we help tech companies, large and small, answer these questions with the research, data, and analysis that my guests bring to this conversation every week.
Once again, I’m your host, Patrick Heffernan, Principal Analyst at TBR. Thanks for joining us, and see you next week.
TBR Talks: Decoding Strategies and Ecosystems of the Globe’s Top Tech Firms
Join TBR Principal Analyst Patrick Heffernan weekly for conversations on disruptions in the broader technology ecosystem and answers to key intelligence questions TBR analysts hear from executives and business unit leaders among top IT professional services firms, IT vendors, and telecom vendors and operators.
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https://tbri.com/wp-content/uploads/2026/06/TBR-Talks-S5E13-Promo.png13501080TBRhttps://tbri.com/wp-content/uploads/2021/09/TBR-Insight-Center-Logo.pngTBR2026-06-01 11:07:522026-06-01 11:09:18The Future of Consulting Services: AI, Fixed Pricing and Managed Services
In this episode of “TBR Talks,” Senior Analyst Elitsa Bakalova and Research Analyst Jill Cookinham, both part of TBR’s Professional Services team, join host Patrick Heffernan for an in-depth discussion on the IT services market through 2030. The conversation touches on the major push for sovereignty in Europe; the emerging AI-related opportunities in IT services and manufacturing, automotive, supply chain and through automation; and the four levers companies can leverage to accelerate growth over the next two to three years.
Episode highlights:
European sovereignty push
AI adoption
IBM Consulting’s greatest strengths and weaknesses
“So, we started hearing a lot about the physical AI, specifically being adopted in industrial sectors, driving new opportunities around IT services and manufacturing, automotive, supply chain, through automation, through IoT and data-driven operations. So, this is really something that’s emerging, and it will be beneficial for vendors, or specifically IT services providers, that have developed their digital engineering capabilities. So, for example, robotics capabilities, digital twins, real-time monitoring, and vendors with such capabilities are becoming more open to working with clients and capturing growth in heavy industries. And we know that heavy industries right now are under pressure, especially in Europe. Manufacturing and automotive are big contributors for multiple IT services providers, especially in Europe, and supply chain disruptions are causing challenges for growth, especially in the automotive sector, which has been struggling globally. So the vendors that have developed those digital engineering capabilities, they’re not a whole lot, but there are companies like Accenture and Capgemini and some of the India-centric providers that have established expertise around engineering services. So that’s something that’s going to drive more opportunities, specifically in physical AI use case adoptions for the industrial sector,” said Bakalova.
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Edited by Haley Demers
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Art by Amanda Hamilton Sy
IT Services Market Forecast: The Levers to Pull to Accelerate Growth
TBR Talks Host Patrick Heffernan: Welcome to TBR Talks: Decoding Strategies and Ecosystems of the Globe’s Top Tech Firms. Where we talk business model disruption in the broad technology ecosystem from management consultancies to systems integrators, hyperscalers to independent software vendors, telecom operators to network and infrastructure vendors, and chip manufacturers to value-added resellers. We’ll be answering some of the key intelligence questions we’ve heard from executives and business unit leaders among the leading professional IT services and telecom vendors.
I’m Patrick Heffernan, Principal Analyst, and today we’ll be talking about TBR’s new IT Services Market Forecast with Elitsa Bakalova, Senior Analyst, and Jill Cookinham, Research Analyst, both part of TBR’s Professional Services practice.
How TBR approached creating the IT Services Market Forecast
All right, Jill and Elitsa, welcome to TBR Talks, season five, and we’ve got something new this year, some research that we’ve just released, a market forecast for IT services. We have never done market forecasts before in the IT services and professional services space for a whole host of reasons, but those don’t matter anymore. We did it. So, we got one rolled out there. And so, what I’d love to talk about today is the thinking that went into coming up with the forecast, because forecasting is, it’s not just throwing a dart at a dartboard and taking whatever percentage shows up. There was a lot of thinking and a lot of research that went into it. I wanted to talk about some of the scenarios and why we did scenarios. And then talk about, of course, specific companies, because we’re TBR, that’s what we do. So, Elitsa, maybe if you could lead us off and just talk through your thinking, your research, your approach to doing a market forecast for IT services?
Elitsa Bakalova, TBR Senior Analyst: Yeah, so this is our first IT Services Market Forecast. So, you know, we initiated the process from scratch. And our initial lever for designing and creating this market forecast was to gather all the knowledge and understanding of IT services companies within TBR. And TBR, we research companies; we have a big knowledge base and analysts that are experts on each vendor that we include in our research. So that was the, you know, that library and data and information within everyone’s heads and understanding of the companies, that was really the foundation for designing the market forecast. So, TBR, we publish reports on 20 companies on a quarterly basis. So, each one of the analysts is an expert in several of those companies. We also publish an IT Services Vendor Benchmark on a quarterly basis that includes 30 IT services providers. We track multiple metrics and data points, and we do analysis across those 30 IT service providers. So, we collected that knowledge that we have, and we started building out the benchmark. And those 30 vendors that we include in the IT Services Benchmark, they comprise approximately 30% of the IT services market. So, we have a solid foundation for starting to model the data and create our expectation for the market forecast, which goes through 2030.
So, the next item that we added was our understanding about the consolidation in the IT services industry and what’s the effect of inorganic growth on some of the vendors. We track vendors on a quarterly basis. We see multiple acquisitions, large and small, that happened every quarter. So- and they affect the growth profiles and just the competitive landscape in the IT services market. So, we factored this type of modeling within our data sets to come up with what our expectation is for IT services revenue size in 2030. And we also looked at the macroeconomic and political environment and the effects those factors have on IT spending, on discretionary spending, on customer behavior, on overall trends to help us understand how each vendor that we track and just overall IT services revenues, how it would trend through 2030.
Patrick: So, Elitsa, it would be fair to say that when it came to forecasting, you started with that foundation of all the data and all the analysis we have on these companies. But then you looked at trends both from the macroeconomic view, but also the company specific or IT services specific view, right?
Elitsa: That’s correct, yes. We use multiple points of view. We use the vendor view and our knowledge and what we hear from each vendor saying about their growth outlook projections in the next few years, and also external factors, such as the macroeconomic and political environment, which is a very significant factor. And we’ve seen that over the past few years, negatively affecting discretionary spending and just buyer behavior. And we also looked at the consolidation of IT services for organic contributions.
Patrick: Yeah. And then, so, and sorry to interrupt, and then we also have, by going backwards, by benchmarking, we’re able to say how much these companies projected growth and how accurate they were over the years prior to now. So, we had that information, that analysis, that data coming into it as well.
Scenario discussion: European sovereignty push
And I think what- I know one of the ideas we had here was to sort of set a baseline. I kept calling it the gray forecast, meaning sort of this is the most basic forecast of where we think IT services is going in the next five years. But then we wanted to say, let’s do some what ifs, some scenarios. And I think part of the reason why we kind of have to do that is because we all went through the pandemic six years ago. We all know that there were things that happened that just so dramatically disrupted the market, but you can’t necessarily predict the next pandemic, but you certainly can say, well, what would happen if. If there was a massive acceleration of AI adoption, if there was another market, you know, global financial meltdown. So maybe Jill, you could talk through one or two of the scenarios that are in this market forecast.
Jill Cookinham, TBR Research Analyst: Yeah, sure. I think the one that I personally find most interesting is the major push for sovereignty in Europe. So, a lot of people think of the need for sovereign solutions, which companies like Capgemini and Atos Group are really doubling down on, especially since they have such a big presence in EMEA. But I think that something else that’s going to be on the horizon is that France and Germany are doing a major push right now for using companies that are no longer US-based. So as an example, by 2027, France wants to no longer use US-based companies for video conferencing for the French government. So, for example, like Zoom or Google Meet and Microsoft Teams, which I think could be pretty huge since those are things that everyone uses. So, I think that a lot of the IT services companies are going to need to be more willing to partner with smaller and more like emerging companies in this way, which should be pretty different and pretty interesting. So, I think that it goes beyond just having data centers and things of that nature be sovereign. I think it’s going to need a little bit more depth. And I also think that some of these governments in the EU may start labeling certain companies as higher risk.
Patrick: Yeah.
Jill: Which would be really interesting in my opinion, because I think that you have companies like Accenture that sometimes win deals in Europe, but really the big ones are like Capgemini and Atos Group and things of that nature. So, I think that they’re going to have to really kind of sharpen their teeth on that a little bit. Otherwise, I think that the companies that are already winning big in that space might just win even bigger because government agencies might be less willing to work with foreign-based companies. And I think that it’ll go beyond just in the government sector as well. I think that, you know, things in financial services and other sectors that are highly regulated may kind of want to play it safe and do, you know, what governments say, because they don’t want to have any controversies and that might be better for them in the long term.
Patrick: Right. Their risk would be the companies themselves would see their, when they think about their risk profile, if they’re following the example of the government, particularly around an area like sovereignty and data sovereignty, you could end up with a real balkanization of the companies that are providing IT services. And most of the time, while that’s better for the clients because they’re getting a better price or they’re getting more options, overall it would sort of depress the market for- take market share away from some of the big companies we cover.
Jill: Totally.
Patrick: So, it would certainly alter what the forecast looks like.
Scenario discussion: AI adoption
Elitsa, how about you? What’s a scenario that could make a real difference in the next five years?
Elitsa: I think just the overall adoption of AI and the form of adopting AI. So, we started hearing a lot about the physical AI, specifically being adopted in industrial sectors, driving new opportunities around IT services and manufacturing, automotive, supply chain, through automation, through IoT and data-driven operations. So, this is really something that’s emerging and it will be beneficial for vendors, or specifically IT services providers, that have developed their digital engineering capabilities. So, for example, robotics capabilities, digital twins, real-time monitoring, and vendors with such capabilities are becoming more open to working with clients and capturing growth in heavy industries. And we know that heavy industries right now are under pressure, especially in Europe. Manufacturing and automotive are big contributors for multiple IT services providers, especially in Europe. And supply chain disruptions are causing challenges for growth, especially in the automotive sector, which has been struggling globally. So, the vendors that have developed those digital engineering capabilities, they’re not a whole lot, but there are companies like Accenture and Capgemini and some of the India-centric providers that have established expertise around engineering services. So that’s something that’s going to drive more opportunities, specifically in physical AI use case adoptions for the industrial sector.
Patrick: Yeah, and I think if you look back to sort of 2017 to 2019, there was a lot of hype and a lot of excitement around IoT, around digital twin, around blockchain, but there was sort of this idea of smart cities, of smart ports, of actually bringing so much more IT to the manufacturing space, to the sort of hard physical environment. And the challenge at that time was, of course, connectivity and, you know, connectivity plus data. So, we fast forward to now, and what we realize now is that AI was actually the other missing piece. And so when you add greater connectivity, greater data management and greater AI to- there is this huge opportunity for IT and IT services to really explode in the physical AI space, as they call it, or just in the physical space of actually making IT more important to what’s happening in a factory, in a port, in a smart city, in all those different physical environments. And of course, that would then accelerate growth as well across IT services.
Capgemini will grow in line with the market
So, Elitsa, you mentioned Accenture and Capgemini, and Jill, you mentioned Atos. So, Elitsa, you go first. What’s a company you think in the current market forecast is most likely to- I’ll let you go either exceed or lag behind the overall trend?
Elitsa: So, I’m going to go with Capgemini because it’s a company that’s going to grow in line with our expectation of the overall market, IT services market growth. So, we expect the IT services market to expand by approximately 6.9% CAGR over the next five years to $2.1 trillion in 2030 from $1.6 trillion in 2025. And Capgemini is going to grow, in our estimations, about 6.3% CAGR through 2030. So, it’s pretty much close to the overall IT services growth. And it’s because this company has been investing in developing its portfolio in line with demand trends and making some strategically important acquisitions historically. For example, I I talked about digital engineering. Capgemini acquired Altran several years ago to increase its product engineering capabilities. And now recently, it acquired WNS to add AI-enabled intelligent operations capabilities and to strengthen the business process services portfolio. So, you know, adding more AI and specifically agentic AI in business process services, which is a heavily labor-intensive capability. So, it’s been making very concerted and logical additions to its portfolio to drive growth in line with market and our expectations.
There’s been a strong push into AI-enabled intelligent operations, sovereign cloud specifically as well. And Jill talked about sovereign and Capgemini is really strong European provider with European coverage. So that really makes sense. Expanding in sovereignty space and then industry specific transformation capabilities. You know, Capgemini is really well known for its financial services expertise, public sector, manufacturing. So really positioning to capture transformational opportunities in regulated and Europe-centric markets. It’s also benefiting from growth in large deals in managed services lately in the past year, capturing opportunities again with demand, increasing the AI activity and AI service delivery capability internally. So, you know, moving along multiple lines and areas that we constantly hear are in demand in the market is something that will help Capgemini keep in line with market expansion.
Patrick: In line with, but slightly ahead of.
Elitsa: I would say slightly- a bit slightly lower, 6.9 overall market, 6.3 Capgemini.
Patrick: Okay, and so then just, you mentioned managed services, and I know we also have a Consulting and Systems Integration Forecast coming out. Just curious if you think that Capgemini is going to be adept at using their managed services capabilities to grow their consulting practice as well?
Elitsa: Absolutely, yes. And Capgemini has an established consulting practice, branded Capgemini Invent, and it’s been there for- that business has been there for many years. But since managed services is something that’s in demand, and the company keeps saying that recently, activities are very much driven by driving cost optimization, operational efficiencies. So that itself is driving managed services opportunities and deals. But then, you know, AI is kicking in, regulatory changes within, you know, specific countries are kicking in. So, this is definitely driving a lot of opportunities for consulting to come together with managed services opportunities.
Patrick: Yeah, it’s definitely a change in the way that the sort of the traditional consulting tip of the spear.
IBM Consulting: its greatest strength is its greatest weakness
So, Jill, how about you? What’s the company that stands out for you as either going to accelerate above the forecast or perhaps lag?
Jill: So, I think I want to talk about IBM Consulting. We specifically contrast these two in the market forecast, and I think that was something that was really interesting for the both of us to write on. So, the most interesting thing to me is I think that IBM’s like greatest strength is often its greatest weakness in terms that it’s always really doubling down on its own solutions. So specifically with everything with watsonx and really having a platform-led approach, which I think really provides the company with stability and how much they focused on hybrid cloud in the last like 5 or 10 years, which I think is really huge. But I think that almost that holds it back in a way, because it’s really trying to go to this next step that right now, they’re kind of losing visibility in digital transformation, which, that’s going to kind of provide them the cash short term. So, I think that that’s going to be something that’s pretty interesting, especially as you have other companies like OpenAI, and everything changing so quickly that they’re constantly trying to find new integrations and things that they can do. But I think that how much they lean on their own solutions, which can allow it to be a lot more sticky with clients.
Patrick: Right.
Jill: But I think that in a sense, it almost seems like they’re playing it kind of safe, which is I think kind of reflective of we’re projecting that they’re going to grow at a 4% CAGR, which is- I think like 6.9% is the overall.
Patrick: So slower.
Jill: So slightly lower. Yeah, and I kind of think that that may be why.
Patrick: Yeah, that’s a good point. And I mean, IBM has been around for 100+ years. They have transformed in a lot of different ways over the last five years, shedding Kyndryl being a good example of that. And changing the way that they partner more broadly, and they’re not just focused on only their own solutions, but they’re still focused on their own solutions in a lot of ways. So, it’s fascinating.
Jill: Yeah, totally. And I think that like they’ve gotten a lot better at partnering with people, but they’re not as good as Accenture and some of the ones that have been doing it for a while, which I think that, you know, partners are everything now. So, I think that they’re really going to have to work on being very close with their partners. But as you’re saying, like a lot of times they’re competing directly with them. I’d argue more than a lot of others because they’re so platform-led.
Patrick: Right, reminds me a little bit of HCLTech in their sort of, because HCLTech has a software practice as well. They’re also focused on the- and maybe that’s something I’ll have to look into the forecast where we see HCLTech going.
IBM Consulting: its greatest strength is its greatest weakness
So, Jill, how about you? What’s the company that stands out for you as either going to accelerate above the forecast or perhaps lag?
Jill: So, I think I want to talk about IBM Consulting. We specifically contrast these two in the market forecast, and I think that was something that was really interesting for the both of us to write on. So, the most interesting thing to me is I think that IBM’s like greatest strength is often its greatest weakness in terms that it’s always really doubling down on its own solutions. So specifically with everything with watsonx and really having a platform-led approach, which I think really provides the company with stability and how much they focused on hybrid cloud in the last like 5 or 10 years, which I think is really huge. But I think that almost that holds it back in a way, because it’s really trying to go to this next step that right now, they’re kind of losing visibility in digital transformation, which, that’s going to kind of provide them the cash short term. So, I think that that’s going to be something that’s pretty interesting, especially as you have other companies like OpenAI, and everything changing so quickly that they’re constantly trying to find new integrations and things that they can do. But I think that how much they lean on their own solutions, which can allow it to be a lot more sticky with clients.
Patrick: Right.
Jill: But I think that in a sense, it almost seems like they’re playing it kind of safe, which is I think kind of reflective of we’re projecting that they’re going to grow at a 4% CAGR, which is- I think like 6.9% is the overall.
Patrick: So slower.
Jill: So slightly lower. Yeah, and I kind of think that that may be why.
Patrick: Yeah, that’s a good point. And I mean, IBM has been around for 100+ years. They have transformed in a lot of different ways over the last five years, shedding Kyndryl being a good example of that. And changing the way that they partner more broadly, and they’re not just focused on only their own solutions, but they’re still focused on their own solutions in a lot of ways. So, it’s fascinating.
Jill: Yeah, totally. And I think that like they’ve gotten a lot better at partnering with people, but they’re not as good as Accenture and some of the ones that have been doing it for a while, which I think that, you know, partners are everything now. So, I think that they’re really going to have to work on being very close with their partners. But as you’re saying, like a lot of times they’re competing directly with them. I’d argue more than a lot of others because they’re so platform-led.
Patrick: Right, reminds me a little bit of HCLTech in their sort of, because HCLTech has a software practice as well. They’re also focused on the- and maybe that’s something I’ll have to look into the forecast where we see HCLTech going.
The levers to pull to accelerate growth
So, I want to wrap up with a question that I’m going to sort of not allow you to give the consulting answer to. I’m going to force you, both of you, to give a direct answer to a direct question. So, thinking about the next couple of years in IT services and the companies we’ve talked about and the market trends, the macro trends, we’ve talked about the specific trends within IT services, you’ve highlighted a couple companies where they’re going. There’s only a limited number of levers that companies can actually pull that are going to either accelerate their growth or, well I guess accelerate their growth is what they want, but you can make missteps, no doubt. There’s only a few things that they can do. And I’m going to take AI off the table because the disruption that comes through adopting AI internally and the disruption to the business models is something we have a whole separate line of research about. We’re going to have a whole podcast episode about exactly what’s happening with the adoption of AI in these companies. But from the very traditional running the company perspective and when you think about the next two to three years with these companies, there’s only, I’m going to say there’s four different levers they can pull. I know there’s more than that, but I’m going to limit you to four.
There’s acquisitions, and by acquisitions I don’t mean continuing at the same pace they always have. I mean, changing their strategy around acquisitions with the exception of Accenture. But maybe that’s it, actually become more Accenture-like in an acquisition pace. There’s alliances. Everybody partners with everybody, I’m not saying there’s not a lot of alliances out there. What I mean is can you actually change the way that you ally with technology companies? Can you go all in on NVIDIA in a meaningful way? Can you go all in on Palantir? Can you go all in on Salesforce? So, changing your acquisition pace, changing the way that you do alliances. There’s the leadership, organizational structure, the reinvention. You can restructure yourself. And we saw that from Accenture recently with reinvention services. We’ve seen it with Deloitte rolling out Operate, which is their version of managed services. And then we’ve seen leadership changes in a lot of companies. So that’s another lever that you can pull. And then the last one would be the opposite of acquisitions, divestiture, which again, I mentioned Kyndryl. There was an example of how IBM changed their growth trajectory by dividing up their business, not by acquisitions. So, Elitsa, I’ll let you go first. Out of those four levers, when you think about Capgemini, which is the lever that they’re most likely to pull and change course on that? Or which is the one, maybe not most likely, which one should they use in order to help them accelerate beyond the predicted growth curve for IT services?
Elitsa: Of the four, I think leadership is the most important lever, not just for Capgemini, but for any IT services company specifically, or just IT company in general. If you have stable leadership that has a vision about the direction of the company and has consistent strategy and execution and implementation of the strategy, it’s going to be a successful IT services provider. We’ve seen companies transform constantly and change leadership multiple times. And I’m talking about senior leadership and CEO level.
Patrick: Right.
Elitsa: And it’s something that destabilizes the company. So, every time you change a CEO or a CFO or any C-suite executive, comes a new person with a new vision, with a new direction, and it drags the company from executing on its strategy. It first changes the strategy, and then it takes time to implement the new strategy. It drives costs with reorganization, just general changes within the structure and the direction. It affects everything that a company does. It affects mergers and acquisitions. It affects the way the company partners with technology providers or startups, you know, that direction as well. So, I think the leadership position is really the important part of the equation.
For Capgemini specifically, we’ve seen solid leadership team for multiple years now that has the direction, that has been investing logically in specific portfolio areas and specific acquisitions to expand the portfolio, client reach, resources, even in low-cost locations, or add AI to the mix. So really, the leadership is my number one priority for any vendor.
Patrick: Yeah, that’s a great answer. I absolutely love that. So that kind of sets you up, Jill. So, what’s your answer going to be for IBM?
Jill: I think Elitsa’s answer was really great. I think I’m going to focus a little bit on alliance structure for IBM because I think that it’ll become more important. I think that lots of IT services companies are starting to realize that partnering is a must, not only to win revenue short-term, but also for innovation. And so, you can innovate so much more and kind of be at the forefront when you have much closer partnerships. And I think that something that may be in the pipeline is if you look at something like Accenture and they’re having their own dedicated business groups for those things, I think that really digging deep and having those meaningful connections with your partners and really understanding the depth of all their services. And when you work that closely, I imagine that you’ll be able to be more at the forefront of innovation, which I think will be super important, especially in this next phase. So, I think that’s what I would say. I think that IBM needs to lean more into it. And I think that sometimes it might be a little bit uncomfortable.
Patrick: Yes.
Jill: And it might take some figuring out, especially since like, as I’ve been saying, like they have so many software products and so much IP, but I think that they can also offer a lot to their alliances because of that reason. So, I think it’s just going to take some more learning. I think that that’ll be also really important for them.
Patrick: It’s a really good point. I think of Accenture and SAP, which for decades have actually shared code, meaning SAP, a software company, that’s all they actually have is code and a really good sales force. They have shared code with Accenture at that level of innovation, at that level of partnership. And you’re right, I think for IBM, they’ve got to get more comfortable with having that kind of, that deep kind of relationship with their alliance partners so that they can then do that kind of innovation. And that comes back to Elitsa’s point, because you don’t get that kind of alliance partnership without leadership that is sustained, committed, all in on it.
Jill: Exactly.
Final thoughts
Patrick: So excellent. Elitsa and Jill, thank you so much for coming in. This has been really good. I know we’re going to get a lot of feedback on this market forecast. We already have. To indicate whether people think we’re right or wrong. And in a year from now, we’re going to revise this market forecast and we’ll be back to have the same conversation. Thank you both very, very much.
Jill: Absolutely.
Patrick: Tune in next week for another episode of TBR Talks.
Don’t forget to send us your key intelligence questions on business strategy, ecosystems, and management consulting through the form in the show notes below. Visit tbri.com to learn how we help tech companies, large and small, answer these questions with the research, data, and analysis that my guests bring to this conversation every week.
Once again, I’m your host, Patrick Heffernan, Principal Analyst at TBR. Thanks for joining us and see you next week.
TBR Talks: Decoding Strategies and Ecosystems of the Globe’s Top Tech Firms
Join TBR Principal Analyst Patrick Heffernan weekly for conversations on disruptions in the broader technology ecosystem and answers to key intelligence questions TBR analysts hear from executives and business unit leaders among top IT professional services firms, IT vendors, and telecom vendors and operators.
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In this episode of “TBR Talks,” Patrick Corcoran, Vice President, Global Head of Marketing and External Relations at Hitachi Digital Services, joins host Patrick Heffernan and TBR Senior Analyst Stephanie Long for a discussion on partner marketing, including the benefits of a 360-degree relationship with partners and the biggest challenges in navigating partner relationships.
Additionally, Patrick [Corcoran] shares insights into the merging of GlobalLogic and Hitachi Digital Services.
Episode highlights:
The importance of partner marketing for end-to-end organizations
The biggest challenges when navigating partner relationships
Hitachi’s pillar of sustainability
“So, they’ve introduced Lumada 3.0 now, and, you know, I think Lumada is – the way that I always think about it is that it’s a virtue or an ethos of who Hitachi is, right? It’s one of those things that connects all of the people together when they think about technology, and they think about offerings, and they think about what they’re doing at a company, right? It’s not to be thought of as a sort of single piece of technology or a platform or a piece of infrastructure, but I look at it more of an ethos of who we are now,” said Corcoran.
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Edited by Haley Demers
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Art by Amanda Hamilton Sy
Hitachi Digital & GlobalLogic: Combining Ecosystems and Marketing
TBR Talks Host Patrick Heffernan: Welcome to TBR Talks: Decoding Strategies and Ecosystems of the Globe’s Top Tech Firms. Where we talk business model disruption in the broad technology ecosystem from management consultancies to systems integrators, hyperscalers to independent software vendors, telecom operators to network and infrastructure vendors, and chip manufacturers to value-added resellers. We’ll be answering some of the key intelligence questions we’ve heard from executives and business unit leaders among the leading professional IT services and telecom vendors.
I’m Patrick Heffernan, Principal Analyst, and today we’ll be talking about partner marketing, IT/OT, and the merging of GlobalLogic with Hitachi Digital Services, with Patrick Corcoran, Vice President, Global Head of Marketing and External Relations at Hitachi Digital Services, and Stephanie Long, Senior Analyst for TBR’s Telecom Practice.
Full circle career point
Patrick, welcome to TBR Talks. We’re in season five. I’m really happy you’re here and we’re in person, which is great. As we have experienced over these five seasons, having these conversations in person opens up so much more. So, thank you for coming to the TBR studios and recording this. And maybe if you could just start off with giving us a little bit of your background and then we’ll jump into, we’re here, Stephanie’s with us too, I should say, and we’ll jump into all the questions we have for you.
Patrick Corcoran, Vice President, Global Head of Marketing and External Relations at Hitachi Digital Services: Well, it’s been great for you guys to host me here. I’m super impressed with the way that TBR has been evolving over the years, and not only from an awesome office space, but just the way that you’re so focused and grounded in the research that you do. It’s been great to finally get here in person and see everybody, which is always good. I appreciate the opportunity for the discussion today. I’ve been in Hitachi now for 4.7 years, according to my LinkedIn. So close to five, but we’ve been through a couple different rounds of what Hitachi actually means in the market. So, we started with Hitachi Vantara, which was a large SI that had both the storage business and the IT services business. And then in October of ‘23, we spun out a company called Hitachi Digital Services, which was the leading SI arm for all things Hitachi. And now as we approach this new fiscal year, April 1st, we are now merging with a five-year acquisition, GlobalLogic. And that name is still being decided, of course. But we are now making sure that the world is seeing all of the IT services capabilities of Hitachi under one umbrella, which is important. And personally, I’ve been in this industry now for 13 years. Prior, I was at DXC, which we all know, large old school provider. And then a company called Luxoft, which was an early engineering nearshore organization, which was larger than GL at that time when we were growing. And we ended up getting acquired by DXC because they wanted to add more strength to their application side of the business. So, it’s kind of a full circle for me when you think about everything that’s happening. End to end is coming back now, and I think that’s why things are starting to merge on our end as well.
Patrick: Excellent. And your role at Hitachi now, is it different than what you were doing with DXC?
Patrick C: Yeah, so right now, for the last two and a half quarters, I have taken on the role of head of marketing and comms. And at DXC, I looked after our advisor relations channel along with our partnership with PwC, which went back to the HPE CSC part of that business.
Patrick: Right.
Patrick C: So, another long-standing relationship there. And then at Luxoft, very similar trail, starting on the AR front and ending up in the head of marketing at the end of that. So very fortunate to have sort of two different sets of the American Dream career at two organizations.
The importance of partner marketing for end-to-end organizations
Patrick: Right. That’s fascinating, and I definitely want to talk about marketing as we go through this. One thing we can start off with, though, is I know you mentioned the big announcement with GlobalLogic. The other thing we talked about is your partnership, your growing partnership with ServiceNow. And so definitely want to hear a little bit about that, but I’d love to hear, because you’ve been around with companies like Luxoft and DXC, and you’ve had these roles in AR and marketing, you’ve seen the change in the way that companies partner across the ecosystem. I’d love to hear what you think about the way that Hitachi partners, ServiceNow specifically, sure, but also the broader picture of how you operate within the ecosystem and how much you’ve seen that change in the last 4.7 years.
Patrick C: So, I’ve been a big advocate of partner marketing for the longest time, because when you’re competing against, we’ll call it the top 10 providers that essentially own the advertising space, you need to leverage other brands to get your story out there. And most of the time when providers are claiming end-to-end, there’s always areas of depth that are anchored by a partner, whether it be in the hyperscaler world, the platform world, or the niche world like AI. So, for me, partner engagement, partner marketing, partner strategy, partner go-to-market, partner sell with, and so on and so forth, is incredibly important for organizations that are claiming end-to-end experience because they need depth, not just breadth. And that’s where the partners kick in.
Now, from a branding perspective, anything that’s always co-branded, co-built, IP-related, go-to-market related, it makes an enterprise reader much more interested to see because it’s not a sole voice telling you what’s right, what’s wrong.
Patrick: Right.
Patrick C: And it makes it a lot easier for the partners to really shore up their investments with the providers because they see real commitment. So, over the last 10 years, I would say that the partner ecosystem has become so important that any provider not doing partner marketing or partner engagement correctly is going to be way behind. And now with the world of AI, I’m sure you guys see it too, the number of boutiques out there or the number of firms that are branding themselves as AI, is really taking a front seat in how does your AI portfolio evolve. So, we have an MoU, Hitachi does, with OpenAI, which was signed with their CEO and our CEO, Tokunaga-san, back in, I think, a few months ago. So, we’re taking that- the next level with AI as well. But it’s the two in the box has never been more important than it is today.
The biggest challenges when navigating partner relationships
Patrick: And what are some of the challenges that you still run into with a successful partnership and a successful alliance. And I’ll tell you, our own research shows that where you sit within the ecosystem determines what you’re not getting, what you’re looking for still from your alliance partners. That makes sense. But almost universally, alliance leaders tell us what they’re not getting from their partners in the ecosystem is more knowledge sharing, more knowledge management, more understanding of where they’re going. Like what’s coming in the next year or two from you, my partner, so that I can plan my own business around what you’re intending to do. Is that the biggest challenge you see or is there something else that’s even bigger?
Patrick C: So, the business planning part is always difficult because the partner has to manage multiple relationships and the business of each of the providers is more dictated by their own client engagements, whether it’s partner related or not. So, if somebody’s having a good year with bookings, it makes it always easier to manage and predict some of the investments’ future for that partner. If you’re having a bit of a tough year with bookings, then that always impacts where your OpEx is going to go with a partner all the way through to what are you replacing laptops internally.
Patrick: Right.
Patrick C: So, it’s really important to have a successful partner engagement when your internal business is on the at least neutral to upright, or else it’s gonna impact your investments there. And that’s gonna create a problem. And what happens is, you know, everything is moving so fast now. If two companies have a great year and they do a huge amount of investment in partner X, and then three don’t have such a good year, and then they try to bounce back, there’s a whole year that the other companies are ahead of it now. So that’s where you guys come in, because you’re able to fill that gap of where are they? Because last year we missed out. Are we six months behind? Are we 18 months behind? So, it’s really important that the business is in tip-top shape in order to leverage the partners, because they’re not going to sit back and wait.
Patrick: Yeah. Why should they?
Patrick C: You know, I mean, regardless if there’s a quid pro quo or otherwise, you know, you have to be able to hold up your share of the bargain to it.
Patrick: Yeah. Excellent. Stephanie.
The 360-degree approach to partnership
Stephanie Long, TBR Senior Analyst: Yeah so, you mentioned the merging of GlobalLogic with Hitachi Digital Services, and you mentioned Tokunaga-san being at the helm and sort of his vision for the broader Hitachi. And I’m sort of curious to get your thoughts on how that unique position of Hitachi services within this broader Hitachi ecosystem from a partner perspective, what sort of advantages or competitive angles or edges does that provide to the business?
Patrick C: So, one of the cool things about Hitachi is IT and OT. So, there are businesses within the Hitachi Limited that build things. There are businesses that produce applications. There are businesses that do both. And so, you’re always having to touch a different partner in a different context. So, if you’re building something at a factory, you’re still going to have an ERP system up and running in order to do that. If you’re deploying at a client request an ERP system, you have to go through all the software licensing and all that other stuff. So obviously the connections between an internal use of a partner and the external implementation of a partner’s software need to be connected to maximize the value. So, there’s really been this push to have a 360-degree relationship across all these partners because that gives you the ability to get to the highest level that you want to be. It helps you reach certain certifications of X number of people engaged. It helps with having cross-company investments into what are we going to do with this type of partner.
So, I think Tokunaga-san’s vision of a 360-degree approach to partnership at Hitachi is really going to give us a competitive edge because of the size of the organization, right? It’s all 60, 65 billion in terms of revenue across all these different areas. So that’s really important. And it also helps us because there’s a new concept that we’ve introduced called Customer Zero, which is we have factories that require systems like MES and so on. There’s also factories that our clients have that they want to make smart. Well, why not leverage our smart factories to show them how to make their factories smart? So, to bring that practitioner angle from shop floor to top floor, which means there’s several partners around that ability to make a factory smart, as an example, is truly a differentiator in the market. There’s no other provider that has this type of ecosystem. So, it’s a really cool story to tell now.
Hitachi’s pillar of sustainability
Stephanie: I’m also curious to gain your perspective on one of the things that I find interesting about broader Hitachi’s perspective is its ongoing commitment to social innovation and sustainability. How would that play into this partner ecosystem and how maybe you may choose to partner or work together with these other companies?
Patrick C: So, sustainability for sure is a, you know, one of the key pillars of who Hitachi is. And that means not only selling sustainability services, but also being a sustainable company itself. So, we have a very rigorous sustainability mandate across all the organizations to ensure that everyone’s meeting certain criteria depending on the business that you’re in. So, we, you know, Hitachi Digital Services recently released our sustainability report, which we had a silver ranking from EcoVadis, which is one of the key, I would say, committees responsible for evaluating that type of sustainability. I think we’re in the top 6%. So that’s pretty good for a service provider, especially when everybody travels all the time, right? So, but having a mandate at a larger level, especially from an OT perspective, has really put a different spin on what we mean by being sustainable. It’s not just LED lights and not traveling as much and ensuring that the laptops are efficient on the electrical grid and so on. This gives you, like, factories have to be Amica work level type of organizations. So, learning from that and then helping use that in the market is, again, something that makes us real disciplined in everything we do about social motivation.
Patrick: So, a couple of things on sustainability. I’m really glad you brought that up. We have consistently been asked about sustainability and how companies are actually bringing sustainability services. But to your point, this sort of back to the customer zero, like actually doing it themselves. And while it may have faded off a little bit, it’s still there. And it’s still a high priority for a lot of companies that we work with. And we want to talk about IT-OT though for a second, because you mentioned your ability to show clients your, like, so Hitachi Rail, you guys build physical, actual rail cars, right? And so, you can take clients and say, here’s what it looks like to build this actual physical thing in a smart factory and here’s all that. But at the end of the day, there’s not so much IT-OT convergence because the people who are buying and using IT and the people who are buying and using OT are very different people, even if they’re in the same organization. So how does Hitachi make sure that they’re not bringing the wrong persona to the table from Hitachi to talk to IT, to talk to OT, because there isn’t actually that convergence.
Patrick C: So, I’ll give you an example from a partner perspective. One of the consultancies we work with is Eraneos, primarily EMEA-based. They have good footprint in the DACH region and the Nordics, and they’re also used by Hitachi Energy as a consultant. So, we have built a partnership with them, which we use to primarily push asset-heavy transformation projects, industries that is. So, one of the things that we do is we have a semiconductor factory in Lenzburg, Switzerland, which is top of the end in terms of smart factory, right? 4.75 out of five. And we host there enterprise leaders from manufacturing organizations and give them a tour of the factory and explain to them, hey, this was a X amount dollar investment five years ago, and here’s the transformation, and this is what you need to do to become a smart factory. So, Eraneos comes in and talks about the business side of how to make these investments, the OpEx, the CapEx, the governance, the program management. And then we come in and talk about the IT front and connecting that to the OT practitioners within there. So, the last time we did this, we had 15 different enterprises from, I would say, Fortune 500s within Switzerland come to the factory. And we have an awesome MD who is basically the product lead but he has a marketing background too. So, it gives him the ability to story tell around what this means, but answer every technical question under the sun. And so, we polled the audience and said, raise your hand if you think your factory, after you saw this tour, is at a five out of five, four out of five, three out of five? The highest we got was two out of five in terms of level of smartness, right?
Patrick: Yeah.
Patrick C: And so, you know, that shows us that there is still a lot of shop floor OT maturation that needs to happen. So partnering with Eraneos has given us this opportunity to really help connect the disjointedness between the IT and the OT side, because we come in as a provider, they come in as the consultant, and then we obviously have the OT factory to show the connection between the two. So, it takes some time to get your head around that model, but we’ve done this now. I think we’ve done four different factory tours with names that you would all know in the market and have shown them what it’s like to become and how to run a shop floor and how to get to the board floor about how to make these investments. So that’s one example.
Now, you mentioned rail. I was just in Hagerstown, Maryland, where we put together a Hitachi Rail factory. It’s more of a distribution center because the factory, the trains are built in Italy and shipped there.
Patrick: Right.
Patrick C: And we are using that as a tour because it has the robotic dogs that do the assessment around the trains. It has an entire customer experience center that talks about the different technologies, things like sustainability platforms and the work around HMAX, which is a new platform that we’ve launched. And so being able to leverage that on site to bring clients to it, to bring partners to, the art of the possible is there.
Patrick: Yeah.
Patrick C: And when you get IT and OT in the same room and you start to show them the connection between the two, it makes it a lot easier to deal with the disjointedness that certainly exists between the majority of those companies.
How the marketing job is different because of AI
Patrick: Right, absolutely fascinating. I do want to ask you about, well, two more things. Because of your marketing background and your marketing position now, the work you’re doing now, two years ago, three years ago, when GenAI launched it was like coders, software coders, procurement, and marketing. Those were the three jobs that were going to be gone, you know, right away. Obviously, it hasn’t happened. But how much are you seeing your job as different because of AI now? Your marketing job as different.
Patrick C: So, I use it every day.
Patrick: Yeah.
Patrick C: Now, I’m primarily using it, you know, a GenAI perspective. But we do have agents deployed in marketing that are doing everything from finding contact information and media contacts all the way through to writing white papers. So, we’ve embraced the AI environment for all of go-to-market as much as we can. So that’s been good.
Now, our CTO, who was on stage once, and I’ll never forget what he said, he was, you know, a question came up very similar. Jobs, what’s going to happen? And his response was, we have to stop thinking about AI’s impact on losing jobs and killing jobs. We have to think about how do we leverage AI to restructure what the jobs actually do. And if you remember, I think The Economist came out with the article in the early days saying the biggest threat to a job is not AI, but it’s someone who uses AI.
Patrick: Right.
Patrick C: And so, I think that’s the mindset now that everyone, whether it’s marketing, HR, legal, any back office, front office, middle office function, needs to adopt. And I’ll be, I’m enrolled now in one of the Kellogg courses for sort of those accelerated certificate marketing courses. So, I’m very curious to see what my competitors or peers are doing with AI. And I know there’s a certain pillar that’s focused on that. So, and I know that discussion on AI has changed every, it’s almost like every six months now. So, we’re fully embraced in it, you know, and I will share with you when I get the information of what they’re talking about at Kellogg on AI. Our teams want to get certified in it, both internally and externally, whether it’s a LinkedIn cert or some of the training that we have internally mandated for us around AI. Everybody should be using it. They should be familiar with it, whether it’s helping design or helping it to write or helping it be creative and just be more efficient. I can only imagine the impact on an analyst role too. I mean, how much- this gives you time to actually think.
Patrick: Exactly.
Patrick C: Not just write and anyways.
Patrick: Not just cut and paste graphs into a slide deck.
Patrick C: *laughs*
Where will we be in 15 years with AI?
Patrick: So, let’s take that and I want to look out, I want to talk about the longitudinal view of technology. We’ve been talking about that a lot in this season of the podcast. In a little bit of reaction to how much we talked about the hype around AI, but I kind of want to look both forwards and backwards. Like when you think about where we were with emerging technologies. And let’s call AI an emerging technology for the moment. Blockchain.
Patrick C: Mobile app dev.
Patrick: The Metaverse.
Patrick C: Metaverse, IoT.
Patrick: IoT, all that stuff. So smart cities, all that kind of stuff. So that’s where we were however many years ago. And now AI seems to be different. It seems to be more revolutionary than just an evolution of, you know, 5G plus blockchain plus IoT equals analytics. That’s much faster, much better. It’s more than that, or maybe it isn’t. And then, so start there and then go out another, let’s go out another 15 years with the other end of it, where AI is no longer emerging. What do you see as how much will have changed in the next 15 years?
Patrick C: So, I wonder if, and I’d be curious for both of your feedback on this, I wonder if because AI nowadays has a B2C impact. Like, so when IoT came out, the consumers weren’t- nothing was changed from a workforce perspective when IoT came out. When cloud came out, right, okay, nothing was really changed from a consumer perspective or a workplace perspective in terms of impact. I mean, so I’ve been, the way that I’ve been thinking about this is because there’s a- there’s such an end user element to AI now that the impact is different than any of those other trends that happened before. I mean, does that make sense?
Patrick: It does, and it reminds me that we have an Amazon Alexa in our kitchen that we use for the same things everybody else uses it for, lists and-
Patrick C: Turn the light on.
Patrick: Trivia of the day, question of the day. At times, when you ask Alexa a question, the answer is so bad that it sort of reinforces like, okay, AI is not going to take over the world because this thing that’s had its software updated just recently still can’t answer that very basic question. And then there are other times when it’s incredibly helpful and incredibly useful. And I just think that’s the sort of, you’re right, we’re getting exposed to AI very differently than we ever got exposed to the other technologies.
Stephanie: Well, to that point too, you also have sort of this grassroots adoption of a technology. And the users are deciding what they’re going to want to use it for, whereas when it rolls out in the other perspective, you’re sort of being informed what you’re about to use this new technology for.
Patrick C: Well said, and I think the democratization of it is going to have a much bigger impact, bottom up than top down, for sure.
So, I’ll give you an example. We were running a workshop on one of our clients, and it was a, I don’t know, Gen. Z all the way through to baby boomer room of about 150 people. So, the keynote speaker was somebody from one of the well-known brands. And they talked about your, what you asked about the 15-year vision. So, everything all the way to physical AI and Metaverse and how all this stuff is going to change. And the feedback we got before we, as we were planning our contribution to the internal workshop, don’t scare the workforce with the future of AI. So that was, I don’t, so we let that presentation take care of that. So, I got on stage and, you know, I could look around and I could get this sense of people, there are users of AI, and then there were those who were still trying to figure it out. So, I started by saying, if I’m right, there’s still people in this room who don’t know which television remote to use when to turn on an app or lower the volume. And you see certain people laughing. Because if that’s still a problem, then the threat of AI coming in and ripping apart everything is not going to happen at the pace that some of these thought leaders are projecting. Because we still have basic issues around general technology. You know, it’s happening fast, there’s no doubt. It’s something that’s definitely in the workforce, like we talked about. But It’s not fundamentally there where things are very, very different.
Patrick: And to tie it back to what you said earlier about the factories in Switzerland, I mean, there’s so much that can happen on the IT digital AI front, but until it’s physically actually making a difference in how things are made and moved and constructed and all that, you’re still- we’re all still physically in this space, so we still have to move around in it.
Patrick C: And there’s still humans in that factory. It’s a hybrid environment.
Patrick: Yeah.
Patrick C: The shop floor is still managed by human beings, you know, with technology as playing a bigger role in it. But again, I look at that as an efficiency piece. So, it allows the workers to focus on some of the bigger issues.
Patrick: Yeah. Stephanie, I have one last question. Do you have anything else you wanted to raise?
Hitachi Lumada
Stephanie: I did want to sort of layer on to the conversation we were having earlier about the merge of GlobalLogic with Hitachi Digital Services and understanding how from a broader Hitachi perspective, the Lumada overlay is going to play in to this one Hitachi vision with this sort of modernized services arm.
Patrick C: So, they’ve introduced Lumada 3.0 now. And, you know, I think Lumada is, the way that I always think about it is that it’s a virtue or an ethos of who Hitachi is, right? It’s one of those things that connects all of the people together when they think about technology and they think about offerings, and they think about what they’re doing at a company, right? It’s not to be thought of as a sort of single piece of technology or a platform or a piece of infrastructure, but I look at it more of an ethos of who we are now. I mean, would you agree from your experience at Hitachi?
Stephanie: Yeah, Lumada seems to be sort of overarching through everything that you’re doing. And as the business units of the broader Hitachi evolve, so does Lumada along with it.
Patrick C: Well said. Well said. Better answer than mine, in fact.
Patrick: You’re not taking her back. She’s staying here.
Patrick C: *laughs*
Reflecting on career goals from age 22
Patrick: All right, last question. So, this is something I’ve been asking everybody this season, and it stems from a conversation I had in Toronto earlier this year. We were talking about careers and life and where life takes you. And our daughter is 22. She’s going to be graduating from university in a month. She’ll be happy I stopped talking about her on this podcast. But so, at 22, you know, you think back to when you were 22, the whole world’s in front of you, right? And you have in your mind, this is what I would love to do, your 22-year-old self. I haven’t asked you this question yet on the podcast, have I? No, okay. So that’s the question. What did your 22-year-old self want to do. And the first time I had this conversation, the woman up in Canada said she got a degree in mechanical engineering and she wished she had just found a way to join a Formula One team. And just, it would have been, for her 20 years ago, 25 years ago, she would have joined it. And that’s her passion. That’s which she wished she could have done. And she wished she had done instead of being where she is now in a technology company. But anyway, so Patrick, when you were 22, what was the 22-year-old Patrick thinking, this is what I want to be in life?
Patrick C: Well, I went to undergrad for three of the four years studying to be a history teacher. And so, I was going to a local college, St. Joseph’s University, and I was also coaching football at high school. So, I thought these are, I’ll just get a job where I- right. Now I graduated in 2008, so we all know what happened then. But that aside, when I started to get into a lot more of the political science courses at St. Joe’s and learning about political economy, grand strategy, sort of the different theories around why states are doing what they’re doing, I got very much interested in international affairs. So, I stopped the teaching piece, and I went to get my MA in global affairs. And I think what I wanted to do was work at a think tank, be an analyst, sort of get involved in decision-making, advising on foreign policy, whether it’s an internship at the Council of Foreign Relations or trying to get a job at the State Department. That’s where my sort of head was. So, I moved to D.C. and I started my doctorate at Catholic U to further on this global affairs piece because I was noticing a lot of think tank leaderships had at a minimum a master’s degree or doctorate. And it was still hard to find an entry-level gig at that stage, 2009, 2010. And I needed to find a job while I was in DC. So, I was applying to everything. Now I think back and I go, I don’t even know how I applied to jobs that long ago, given the way LinkedIn’s evolved.
Patrick: Right.
Patrick C: But I think through Indeed.com, I get a call from the head of research at the Public Affairs Council. The Public Affairs Council was set up by Eisenhower to essentially be one of the associations for government relations professionals, i.e. lobbyists.
Patrick: Right.
Patrick C: So, this guy calls me and says, it sounds like you have a very interesting background. You know how to do research. You’re interested in global affairs. I’m looking for a contractor maybe 20 hours a week or a little bit more to help me do all the research we do. So, a lot of benchmarking, like very similar to what an analyst would do, right? So, I said, this sounds great. I’d love to do it. And I’ll never forget, it was probably my first real job interview, even though it was only a contracting role. And he says, do you have a dollar amount in mind? I’m like, well, you know, what, $12 an hour? He goes, how about 30? I said I’m in.
Patrick: Sold.
Patrick C: Yeah. So fortunately, Adam, great guy, he brought me on and I stayed on there for about a year and a half and learned a whole lot about what it was like to have a real job. You know, like this was a fully structured association. And then I decided, all right, it was time to move back to New York. And you know, at that point I said, I don’t think the PhD route is something I want to do because I really loved working. And when you think about long-term of that, getting five years of writing and who knows where you’ll be. The last thing I want to do is have to move to the Midwest and teach at a local school. So anyway, and when I tell this story, I’m still amazed how I got into IT. So, I get back to New York and I’m going through sort of interview after interview. And I did some time with a mortgage broker. He had a private shop. And then I got a call from a company called the International Institute for Learning. Which, their target audience is chief learning officers.
Patrick: Okay.
Patrick C: So, they sold project management training, search, Lean Six Sigma, and they have a gap. They said, oh, the new guy’s here. He can be the ITIL practice lead. So now I’m how to educate myself on all this stuff and what’s the go to market. And ultimately, at the end of the day, it was a sales job. I had to help the sales of ITIL training and V3, I think, had come out at that time. So, I was doing that for a year. And we went to a Gartner event because Gartner, they may or might still have this, but they had a project management section within the symposium. So, we had gotten a booth there to meet with the CLOs and to some extent IT leaders that wanted to train their teams. And I meet someone there who is in investor relations, but she’s looking for somebody to run analyst relations. And we just started to talk, and I said, analyst relations, I could do that. It sounds like something relating to what I’ve been trying to do for the last couple of years, but in the IT sector. And I never, ever thought I would be in this space.
Patrick: Right.
Patrick C: So, as I’m trying to make this long story shorter, we basically, her and I had stayed in touch for about six months, and I wake up one day to a video, this company called Luxoft just went public. And she was the one who managed with the leadership team the whole IPO.
Patrick: Right, wow.
Patrick C: Because they were developing this role of AR, but it wasn’t ready yet because they wanted to get listed first. So come around, now we’re in August of 2013, and everyone says, oh, it sounds like, you know, we have a candidate here to run the Gartner relationship at that time and the Forrester relationship, right, back in 2013. And I took on the job. And I didn’t know at that point the difference between a briefing and an inquiry.
Patrick: *laughs*
Patrick C: And it wasn’t until one of the sales reps at Forrester actually educated me on how I needed to do this job. So, I had to really BS my way through it, but then I eventually got a handle on it. And so, the rest was in history. I mean, I learned-
Patrick: Fascinating. Yeah. Wow.
Patrick C: I got really lucky. I mean, so my advice to a lot of graduates is, that it’s hard to know what you want to do until you actually start doing something. And I mean, I look at wanting, trying to teach students in 7th grade how to pass a Regents to where I am now, figuring out what’s our strategy going to be as a new organization.
Patrick: Right.
Patrick C: Two totally different pieces, but that all evolved because of where I had worked and who I had met. So, value the network and the people that you meet as much as you can, because everybody, anybody could have a potential opportunity that you want to get across. And I mean, just when you think about the steps, meet this person here and go to this event and this guy hires you and this happens, you just got to sell yourself and people will give you a chance. We just brought on somebody on our marketing team who just is out of school and has expressed interest in partner marketing. So, I don’t, I’m not going to, I look at the way I got into a company. So, if somebody doesn’t know the hard-core difference between what to do differently with Google versus Microsoft with AWS at 21 years old, I’m not going to hold that against them.
Patrick: Of course not.
Patrick C: So, you find the people that show the earnestness that they want to learn and the excitement that they want to join a company and figure out their career, and you can be a teacher and a leader.
Patrick: And it sounds like, you mentioned an Adam, and then it sounds like the woman that was at Luxoft, like good people too is part of it, too. People you want to work with.
Patrick C: 100%. Yeah.
Patrick: All right, Stephanie, you’re on the hook. What did 22-year-old Stephanie think she was going to do with her life?
Stephanie: So coincidentally, I went to school to be a history teacher as well. But when you peel the onion back of what my actual fundamental goal was, it was to make a difference. And I believed at the time, my 21, 22-year-old self believed that to do that, I had to work with children as a teacher to help make a difference at that age. But as I’ve aged, I realized that you get to make that difference at any age in life. You just have to change the lens that you’re looking at the world from to find the opportunity. But it exists in many different ways, in many different places, at many different ages to have that influence on making a difference.
Patrick: And a lot of us here at TBR are children, so you’re able to influence us. You’re still working with children, whether you think so or not.
All: *laugh*
Patrick C: That’s a good point, and I think I filled that gap by joining the Knights of Columbus.
Patrick: Oh, yeah, all right.
Patrick C: That’s given me the ability to make, you know, donations. Everywhere from Sloan Kettering to St. Jude’s to food banks and clothing drives. And I think to your point, everyone that’s interested in being a teacher wants to make a difference, right? That’s sort of one of the driving factors. But sometimes it’s not the career path you end up on and you still have this want to make a difference. And that’s been the way that we’ve been, you know, I’ve personally been able to do that.
Final thoughts
Patrick: Right. Excellent. Well, thank you, Stephanie. Thank you, Patrick. Enormous fun. This conversation went in a lot of different directions than I didn’t expect. So really appreciate it. Thanks for coming in. Now that I know you’re only down in New York, you’re close by, we’ll expect you to come back soon.
Patrick C: 100%. Thanks for having me.
Patrick: Thanks.
Tune in next week for another episode of TBR Talks.
Don’t forget to send us your key intelligence questions on business strategy, ecosystems, and management consulting through the form in the show notes below. Visit tbri.com to learn how we help tech companies, large and small, answer these questions with the research, data, and analysis that my guests bring to this conversation every week.
Once again, I’m your host, Patrick Heffernan, Principal Analyst at TBR. Thanks for joining us, and see you next week.
TBR Talks: Decoding Strategies and Ecosystems of the Globe’s Top Tech Firms
Join TBR Principal Analyst Patrick Heffernan weekly for conversations on disruptions in the broader technology ecosystem and answers to key intelligence questions TBR analysts hear from executives and business unit leaders among top IT professional services firms, IT vendors, and telecom vendors and operators.
“TBR Talks” is available on all major podcast platforms. Subscribe today!
https://tbri.com/wp-content/uploads/2026/05/TBR-Talks-S5E11-Cover.png13501080TBRhttps://tbri.com/wp-content/uploads/2021/09/TBR-Insight-Center-Logo.pngTBR2026-05-19 16:46:082026-05-19 16:46:53Hitachi Digital & GlobalLogic: Combining Ecosystems and Marketing
In this episode of “TBR Talks,” Rob Kopel, Partner, AI Leader at PwC Australia, joins host Patrick Heffernan as well as TBR Principal Analyst Bozhidar Hristov and TBR Senior Analyst Alex Demeule to discuss practical delivery of AI solutions to clients. Rob shares his insights into how clients with more mature AI strategies are managing risks associated with adoption as well as where augmentation, rather than automation, makes sense in terms of cost efficiency
Episode highlights:
Managing risk in mature AI strategies
Working with an AI lab
Augmentation versus automation and workforce rationalization
“They really enjoy the augmentation benefits that they get from AI, and they see that augmentation is what they want out of this technology. And I think the reasoning that they often give behind that is really logical, which goes something like: Full automation is not a competitive advantage. It’s a competitive disadvantage because none of these companies are really training their own multibillion-dollar model. They’re not training the next GPT-7. They are just giving the same text to the same model that their competitors also have access to. And so, if they can fully automate with something with AI that’s off the shelf, so can everyone else, right? Including startups, including competitors that they’ve never even heard of yet. It becomes table stakes; whatever that task is, it’s not an edge for them in the market,” said Kopel.
Listen and learn with TBR Talks!
Submit your Key Intelligence Questions for Patrick and his guests: https://bit.ly/3T9VZek
TBR Talks is produced by Technology Business Research, Inc.
Edited by Haley Demers
Music by Burty Sounds via Pixabay
Art by Amanda Hamilton Sy
AI and the Workforce: Why Augmentation Is Winning Over Automation
TBR Talks Host Patrick Heffernan: Welcome to TBR Talks: Decoding Strategies and Ecosystems of the Globe’s Top Tech Firms. Where we talk business model disruption in the broad technology ecosystem from management consultancies to systems integrators, hyperscalers to independent software vendors, telecom operators to network and infrastructure vendors, and chip manufacturers to value-added resellers. We’ll be answering some of the key intelligence questions we’ve heard from executives and business unit leaders among the leading professional IT services and telecom vendors.
I’m Patrick Heffernan, Principal Analyst, and today we’ll be talking about practically delivering AI solutions to clients with Rob Kopel, Partner, AI Leader at PwC Australia.
Context for Rob’s AI expertise
Rob, welcome to TBR Talks. We’re in season five, and we’re very excited to have you. I’m joined today by Principal Analyst, Boz Hristov, and Senior Analyst, Alex Demeule. None of us are in the office together, but we’re very excited to be talking to you. And I know we met in Australia at lunch in Sydney, which for everybody else is just on your side is just simply lunch in Sydney. For us, it was very exciting because I got to go to Sydney and meet with you and your colleagues at PwC. It was a fantastic event, and we had such a great lunch I wanted to have you come on and have a chat with Boz and Alex and talk about AI because you’ve been in this field for so long, despite professionally not being that far into your career. So, I’d love for you to give us a little bit of your own background and then we’ll dive into some of the questions we wanted to ask you about AI.
Rob Kopel, Partner, AI Leader at PwC Australia: Yeah, I’d love to. So as a bit of background, I originally started out in the insurance AI space back before AI was as well built out as it was today. We actually spent a lot of time taking old servers that had been shut down from the decommissioned from the IT department and racking them in our own server rack in the mail room, which frustrated everyone in the office to no extent. And we challenged some actuarial guys to build better AI models than they could build standard actuarial models. And we managed to smash them. So, I think ever since that, probably almost 12 or so years ago now, I’ve been in the AI space. And I think I came to PwC Australia with a bit of a mission to build up our capability and almost startup style, build up the AI opportunities both within the firm and then take those lessons to our clients. And so, for the last six or so years, that’s what I’ve been focused on is building our Australian AI factory and then taking those lessons of how we reinvent ourselves to support how our clients are able to do it too. And I think with the explosion of intelligence we’ve seen, it’s been a really exciting time.
Patrick: That’s amazing that you started your AI journey with perhaps one of the most boring and traditional businesses, insurance and actuarial tables.
Rob: Yeah, I would, I would not disagree with that, but I would say, math nerds can be fun occasionally, so don’t hate too much.
Patrick: Okay, fair enough, fair enough. Well, I mean, it actually has done a lot of things for you and brought you to PwC where you have that sort of startup opportunity.
Client sentiments around AI and adoption
One thing I want to kind of start off with, I’m curious about this, because again, it was a great, great meeting you in person and chatting over lunch in Sydney. And one of the things that sort of struck me about our lunch and then the presentation that you made during the day, during the Analyst Day, was there’s sort of a sense we have as analysts looking at it from the outside, that clients are sort of, they’re bought into AI. They’re absolutely sort of on board with the disruption that’s coming. They’re certainly fearful of it. They’re not sure they’re making the right investments. They’re not sure they’re going to see that return on those investments. But what I’m really curious is, because you’re working with clients day-to-day, are you seeing any kind of real pushback where there’s sort of a, we actually don’t need to adopt AI, or we don’t believe there’s going to be this great disruption that comes from it, or we don’t believe there’s going to be any value that comes? Do you still see any kind of naysayers, like real sort of, you know, I don’t want to say head in the sand, but you know what I mean?
Rob: Yeah, I don’t think we even, I haven’t encountered it for, maybe I’ll say, 14 or so months at this point. I think it used to be far more common of a trend. I think especially when you’d spend time really deep in with different professions and you ask them, is this fundamentally changing how you’re working or is this just saving you 30 minutes a day? You would hear a lot of maybe it’s saving me 30 minutes a day, I don’t see how this will change. Over the last four months, we’ve seen a step change in that though. There was a point where everyone had summer break, maybe December, where people really could spend the time on their own, playing with the latest and greatest AI tools, where when they really dedicated the time to it, they were able to experience kind of what the potential of these tools actually is. And every single conversation I’ve had since that point, even at the board levels, there’s always one person in the room who’s spent the time with advanced AI tools, and they will be able to communicate how meaningful that was to them and just how different, how big that difference is. And I think that speaks to a bigger trend in the market.
When I’m talking to clients, we often discuss how fast people are going. You know, what does going fast mean? Is that adoption, you’re giving your staff maybe one, two, three different AI tools, and they’re saving 45 minutes a day. Is that startup style business reinvention? You are potentially pivoting into whole new markets. You’re actually reinventing the way you do processes. But I really try and force myself to zoom out a little bit and try and see the entire playing field. Where would Usain Bolt be if he was moving fast in the AI adoption market, right? What would so fast that it’s actually dangerous look like? Where should I see myself on that scale? And I think when you take that approach, you end up saying almost, if I honestly look at the market, I don’t see that many catastrophic failures of people going too fast. And when I talk to clients, I don’t hear about building things burning. I don’t hear about companies going out of business. And almost to me, that seems a little bit odd. When you naively think about it, if there is such a massive advantage in betting the house on AI that competitive pressures would force you into an all or nothing strategy today, you would expect to see that. And you would expect to see some companies who managed to pull off that strategy, right, wipe the floor with their competitors. But we really don’t see that today.
So, I think the one thing I do want to raise, and I always raise, is that there is this massive reliability problem that AI has today. And I think that is why we see some people say it’s massively effective, and some people say it’s massively not effective, even though that second category has declined so much. And I think the easiest way I have to illustrate this is that if we went out, if I took you out to a finance firm, any one in Australia, we could go in the building, we go up to the 12th floor, we go and find someone who’s just working randomly at a desk, and we asked them, what task are you actually working on right now? What are the inputs? What is the job? What are the outputs? And we gave that task to AI, I think you would find that over 90% of the time, AI could do that task immediately. And in fact, you would prefer the work that the AI did the majority of the time. But that one third of the time, it’s significantly worse than what the human did. And you simply can’t trust it to always do that. And so, this eventuates, and it comes up through the data in what you see, which is that AI can do 90% of the tasks, but it only gets used for less than a quarter of them at the end of the day. And so, the best companies and the best people I see who are able to see the value and grab the value, they’re able to see this opportunity, this difference here. And they’re able to actually take it.
To give you one example, as well, there’s a really standout hospitality client I’ve been working with. And they really get this. So, they’ve done two things. They’ve done the table stakes first. They’ve enabled their staff carte blanche access to secure AI tools. And then they’ve also set up a small team, not a massive transformation project, not a huge steering committee. They’re really a hit squad who’s been in their business for decades, who understands it implicitly. And They also understand the reliability problem. They’ve got a direct line to execs and a mandate to essentially go find where AI can be genuinely helpful to them and actually focus on how they can change how the business operates. And that team has been massively successful for them. I think they’ve done more than one project per month. They’ve done, with AI at their backing, with a small team. And that’s going from customer service into marketing and so on. And those aren’t just time-saving measures, but they’re really kind of reshaping the customer’s experience for their clients and reshaping how their business functions. So, I think it’s really important to recognize that there’s a reliability issue here. But if you are able to actually understand and master that, which we see clients do, and I think everyone’s slowly picking up over time, it really sets what I see as clients being able to do that can set them apart. And I think today, most organizations, although they’re slowly getting there, they’re only really doing that give staff AI tools and give them a little bit of training today.
Managing the risk in mature AI strategies
Boz Hristov, TBR Principal Analyst: Rob, this is Boz. This is a great overview of where you see the clients. It just made me think about some of the risks that may be associated with the various speeds of adoption or, you know, going too fast or not moving fast enough. In your experience, I mean, how would you describe clients with the more mature AI strategies manage that risk?
Rob: Yeah, I think, so that risk comes down to this understanding of how well AI can be relied upon in different cases. And generally speaking, when we talk about different ways of applying AI, we are talking about either we are looking at it in an automation sense, or we’re looking at it in an augmentation sense. And the automation sense, you often hear about, we really need to reinvent entire parts of our business, maybe whole processes, more standard business model reinvention. Because in order to get automation gains, you can’t just automate step three in an eight-step process because your bottleneck is now going to be step two, and it’s going to be step four, and five, and step one. You really need to redesign your entire process to support automation and make sure the throughput matches on the input and output sides. That requires standard project management governance. It requires standard product management governance. You’re building a tech solution, essentially, at the end of the day. And that’s where we see the best governance today, is where people have this understanding of reliability and standard product management. So that’s automation.
On the augmentation side of the world, this requires your staff to have really deep understanding of where they can and can’t rely on AI. And currently, by and large, the best way to build the skill set that we see is firstly through training, but secondly, through putting the impetus on your staff that they have to own their own AI journey, and they have to show that they’re owning that. Because a lot of the time, one of the failure modes we see is that when I go and I chat with a client, they’ll say, hey, well, we’ve got this AI team in the center who’s incredible. But everyone at the coalface is going and saying, okay, we’ve got this incredible AI team for me. They’re going to handle it. I don’t need to do anything. And taking that impetus away from every staff member across the board means that they feel like they can rely on someone else to deal with AI change for them, and they aren’t actually going to get the experience on the tools. They won’t understand the reliability challenges. And then when they do have to use them, it causes issues because they can’t actually interpret whether a response from AI is good or bad. They don’t have that intuition yet. And so, we really strongly believe, obviously, in end-to-end AI governance, proper setup of your frameworks, policies, trainings, how to’s. But intuitionally, I really encourage people to more focus on how well, when they actually go and talk to staff members, do they understand the pros and cons or risks and benefits of AI when they use it? Because that’s really critical on the augmentation side. So, it’s a bit of a combination of those two worlds and the best people are getting both right at the moment, there’s no doubt about it. And if you get only one, you will get less than half the benefit. You really need both combined.
The talent reskilling conversation
Boz: Yeah, that’s a fair way to put it, augmentation and automation, especially as it pertains to the skills. And that’s one thing that we hear in the market. I mean, everyone is talking about reskilling talent to ensure they have the AI-enabled bench. But us as analysts, we struggle sometimes to understand, you know, how much of that effort is centered on the professionals that are more on the engineering side of the house versus the client engagement? And do those two different, in terms of AI skills, are they different from what the knowledge base has to be? And most importantly, I mean, more importantly, I guess, how do you measure impact from any AI reskilling either on the engineering or on the client engagement side? So, there’s a two-part question that I have, but just maybe think about what we’re hearing in the market as well.
Rob: I’ll do my best to hit both parts. But I think generally speaking, there’s a real trap in the reskilling conversation today, which is this idea that we take people, we give them a reskilling that they need to do, and we treat it as this fixed target of what they actually need to achieve. You know, what skills do they need, great, let’s go train them. But the interface for AI and the interaction mechanism, it keeps changing so rapidly. By the time we’ve actually rolled out something, the tools, the programs have generally moved on. And to give maybe just a bit of insight into that, and maybe the best case study that I’ve seen is this one of software engineering, which we’re talking about, right? And really, I want everyone, I always encourage everyone to look at this because engineers were really, and still are, the first cohort to really go through this existential crisis at pace, right? And I think it tells you a lot about what’s actually going to come for other careers and professions.
And if you go and you roll time back a little bit, this all started a few years ago with GitHub Copilot. You know, an awesome piece of software that got released that was essentially a smarter autocomplete. It could finish your sentence for you when you’re writing code. And then we improved it a little bit. A few months later, we got this new Codex model, and it could write a few lines of code. It could maybe even write a whole small chunk, like half a paragraph for you. And a few months after that, we get a new tool, right? This one’s called Cursor. It’s incredible. You have this little agent who sits beside you as you work. And it can kind of suggest whole chunks of code. And you can ask it, go and write this little user icon for me and add it to my software. And you can approve one by one, as the chunks crop up, you can say, tick, I’m happy with this chunk and include it.
And then Cursor improved. The agents improved, right? And they evolved to the point where it was too tedious to go and approve each individual little chunk, and all of a sudden it would go and you’d say, hey, go and implement this whole new page in my application, and it would go out and do that, and I would just review the code at the end of the day. And that was a real game changer in how our software engineers started doing work. Additionally, since then, we’ve had awesome tools like Codex and Claude Code, where you’ll write the entire pull request for our software engineers. They’ll go do a whole ticket for you, and you just review that at the end of the day. And very finally, last month, now we’re getting to this point where I tell the agent what to do. It sends me a video showing the new feature it built. I just go and I highlight with a red marker, on the bit I don’t like in the video, and I send it back to it, and then it goes and continues working. And that entire progression has happened in one, maybe two years.
And when you notice what’s actually changed is that the human has gone from writing code manually by hand, to reviewing code manually by hand, to reviewing the outcomes that coding actually leads to. And the skill of how they used AI at each one of these stages was really different from each other, right? But the thing that stayed constant throughout was their software engineering judgment. Can they look at the output and know if that output is right? Do they understand the problem well enough to actually direct the AI to do the right bits of work and catch mistakes as they happen? And I think that brings kind of the really important framing to the surface. which is that we are seeing AI create a bit of this kind of K-shaped economy at a job level. And I know econs often talk a lot about kind of this K-shape, but I don’t think enough people talk about it, where essentially when AI is coming to a role, whether it’s software engineering or other types of roles we often encounter, it can go in one of two directions. It either is removing high-skill work and leaving the drudgery, or it’s removing low-skill work, and it’s leaving the judgment.
The classic example of this is if you take a London cab driver, when GPS first arrived. These drivers have spent years, years earning the knowledge, memorizing every street, every route, every shortcut. And that was their expert skill. When GPS arrived, it destroyed the intrinsic value of that knowledge overnight. The work that they were doing didn’t go away, but it went downhill a lot. There was lower wages, there was less pay, there was less expertise required, it was more commoditized. Anyone with a phone, you know, with Uber could go and do that job. The opposite of that one is accountants, for example, when digital spreadsheets arrived, right? Reduction in manual work, no more manual ledgers, no more manual reconciliation, the drudgery vanished. And the bit that was left, which we’re seeing for software engineers right now, is advisory, right? It’s judgment, it’s interpretation. And that profession got more interesting, more valued, right? And pay actually increased.
So when I’m talking to clients about reskilling, I often am talking to them not so much in the lens of the question of, here is the exact reskilling about picking up AI software your people need, but it’s firstly, for each role in your business, which arm of the K-shape is it on? Is AI potentially stripping away some of the judgment and leaving the drudgery? Then we would encourage, we want you to go and reskill those people for other roles, right? For other areas they can build their judgment and expertise in, we don’t want to train them to use AI itself because AI is going to be able to use AI.
There was a great case study recently, I’m sure you saw, of a global retailer who was able to transition all of their customer service staff and start a new line of business, which has been massively profitable with those same customer service staff because they cross-trained them. But in the case that AI is stripping out the drudgery of the job and it’s just leaving the judgment, which I would actually say is a majority case right now, like with software engineers. That’s the best-case scenario. And that’s where we want your staff to actually leverage AI to massively increase their productivity. But we still want to focus really the skilling and the training on their judgment and abilities, because that is the multiplicative effect on AI. And you can see, these are completely different responses from each other.
And lastly, I guess, on your question of engineering versus client engagement question, I think we’re seeing teams shift a lot, not only in that you can be smaller and you can move a lot faster, but I’m seeing a lot that when our engineers are able to move at these ridiculous paces because they’ve stripped out of the drudgery, their blocker changes a little bit from not only purely on the engineering, can I see what is wrong with the code, but also the blocker becomes the people around them who are feeding them, this is what we actually need to do. These are the business requirements. And so, these staff who are almost cross-functional, who maybe you call them T-shaped staff, they are going massively up in demand and their ability to deliver an end-to-end project or product rapidly is increasing. And so I think what we’re focusing on today at I’ll say PwC and many other businesses as well, is really increasing the breadth of our experts, not just the depth, but we do need them to have that depth, otherwise they won’t have the right amount of judgment to actually review what the AI is producing.
Patrick: Yeah, Rob, I think that’s fascinating, and to me it’s frightening because you- it’s difficult to teach judgment. I mean, I’m not saying you either have it, or you don’t, but frequently it comes through experience and pain. It doesn’t just come, you know, out of a textbook or watching a YouTube video.
I do want to challenge you on one thing. I want to bring Alex into this conversation, but before we do, you said the first group of people to sort of feel that existential threat from AI was engineers. I actually, I have, and Boz can back me up on this, a very vivid memory of a PwC event in Boston, it’s got to be three years ago now at least, where we saw a marketing application of AI that it was just, it looked like it basically reduced the human component by about, let’s say, 90%. Basically, the AI tool could do so much of the work that went into traditional marketing. And this was like to develop collateral for sales pitches and engagements. And it was just sort of, that to me was an eye-opener in terms of the existential threat to a certain profession. But I’ll take your point, engineering and marketing probably went hand in hand in that existential threat moment, right?
Rob: Yeah, I think so. And I think if you look at the stats and figures, there’s two really massively exposed industries ahead of everything else, which is customer service reps as number one, if you go and you sort by number of jobs that we estimate to see some level of impact. And number two is software engineering at the moment. I think for years, there were all these expectations and market of self-driving cars, what impacts is that going to have on the trucking and moving industry? And that really hasn’t eventuated because of that same kind of risk reliability point we were talking about earlier, right? Where to have something that is moving at 60 miles an hour on the road that weighs tens of thousands of pounds is so incredibly dangerous. You need 99.9999% certainty that it’s going to operate safely, right? And you need to be 100 times safer than the average human driver. But when we’re talking about interactions that often when you are phoning up a business today, it already isn’t a good experience. AI can actually improve that experience on average, and the risk is so so far much lower, right? And so, it’s really these cases of, well, where can you get the productivity benefit within the reliability threshold that AI has there today. And I think that is where we’re seeing big impacts in customer service, where we’re seeing big impacts in software engineering. And I think next off the rank, which we’re already starting to see a little bit, is in financial analysts and services, where AI has been massively improving in its capabilities recently.
Augmentation vs automation and workforce rationalization
Alex Demeule, TBR Senior Analyst: That’s awesome. Great. And hi, Rob, this is Alex Demeule jumping in. And this is- the next question is something that I feel like we have been really kind of talking about this entire conversation so far. We’re talking about automation versus augmentation, and I love the K-shaped economy call out and looking at sort of drudgery versus judgment and sort of the role of AI. But I think all this kind of comes back to sort of the corporate goal for AI. And so obviously one of the things that’s been in the headlines and has been a part of this conversation for so long is just is this a cost efficiency goal or is this a way for me to increase productivity and be able to do more with what I have? And when we talk about augmentation versus automation, I think a lot of the story that I’ve heard from you is just about sort of identifying where augmentation makes sense versus automating for cost efficiency. But is there sort of a through line for how enterprises in the economy today with the sort of geopolitical risks and the cost considerations, is when we net this out and look at software developers, marketing, and going into financial analysts, is there a through line towards wanting to sort of decrease headcount? Or is this a story of reinvestment and job growth, or maybe not job growth, but unlocking new job opportunities over the long term.
Rob: There’s something that is a good question that I may throw back to you on this, Alex, which is that it’s really around- when I talk to business leaders and I talk to staff, at least in Australia today, they’re surprisingly aligned on this when you get down to the facts. I think there’s a story at the moment of, you know, business leaders want to automate everything in existence, and staff are terrified of being replaced in their role. And I don’t think that really holds up today. I’m not saying it won’t hold up in the future, but I don’t think it holds up today. Because when I talk to clients who are really interested in this and when I talk to staff as well, they say the same thing, which is that they really enjoy the augmentation benefits that they get from AI, and they see that augmentation is what they want out of this technology. And I think the reasoning that they often give behind that is really logical, which goes something like full automation is not a competitive advantage. It’s a competitive disadvantage because none of these companies are really training their own multi-billion dollar model. They’re not training the next GPT-7. They are just giving the same text to the same model that their competitors also have access to. And so, if they can fully automate with something with AI that’s off the shelf, so can everyone else, right? Including startups, including competitors that they’ve never even heard of yet. It becomes table stakes, whatever that task is, it’s not an edge for them in the market.
And I think it was a good example of this early in the year when there was an AI lab, they published just a plugin, a legal plugin. And when you went and you read what was in that legal plugin, it was literally a, I don’t know, it was a 12-page Word document, right? It was just a text file. There was no new model, there was no proprietary technology in that plugin. But when they did that, it wiped, I think, almost 300 billion US dollars off legal stocks in a single day. Not because it’s doing something different, right, but because the market had suddenly, I guess, started pricing that in that anyone could actually do that. And so, I think you get a lot of this demand for, I want augmentation because my competitive advantage isn’t in AI itself. It’s in what my people do with it that my competitors can’t do, right? It’s in my people’s heads. It’s in their institutional knowledge. It’s in their judgment, right, that we’re talking about before. It’s in the relationships that they have. And that’s my competitive moat, essentially. AI is just amplifying whatever is already there. And I think when you talk to staff in the business, they want the same thing. They want augmentation. They don’t want to do the drudgery, right? I haven’t met anyone yet who is really missing the drudgery work, if that makes sense.
Alex: Yeah, that’s a fantastic answer. And I can completely see the case for, you know, access to the capabilities being sort of commoditized. And so that judgment layer and kind of whose hands are on the tools is where it really makes a difference. And that keeps the human in the loop narrative really strong.
Rob: 100%. And I think it’s always interesting to also try and argue the steel man. And I think there is also a lot of discussion going on in the market that is a bit obscure. I’m sure you’ve heard about this phrase of AI washing, which is important to call out, which essentially goes something like if you go and you actually interview executives and you say, hey, how many of you have made headcount cuts in anticipation of AI versus how many of you have made headcount cuts based on the results that you’ve gotten from AI so far? Over two thirds will actually tell you we’re making headcount cuts in anticipation of AI benefits. And if you go and ask all of them after the fact and you say, hey, how many of you were able to actually replace those roles with AI at the moment today, it’s less than 10% will actually say we’ve been able to do that, right? So, I think a lot of what gets written about today as AI-driven workforce rationalization is unfortunately cost cutting in a trench coat, right? It’s AI giving you a nice story that you can tell your board. That sounds innovative rather than we need austerity measures or similar to that.
Alex: Yeah, and do you think that that’s true even in sort of a software development capacity, you know like, obviously the block announcement was one of the big headlines that we saw. And I’ve been in complete agreement with you. I think that a lot of the rationalization that we’ve seen has been sort of AI washed. And we saw a massive hiring spree sort of on that back of COVID. And so, tech companies, especially, are probably in a position of being sort of workforce rich, and so the ability to use AI- but when we were talking about the software development, when I think about one slice of maybe truth in sort of being able to get more for less out of your workforce, it’s got to be in that software development field.
Rob: Yeah, I agree with you that there is a lot of potential there. I think it’s still very hard to quantify at the moment. Maybe to call out a few key pieces that really rattle around my head on this. One is that software engineering is very interesting work in that it’s essentially limitless work. There’s no product, no solution that doesn’t need more adjustments. It doesn’t need some bug fixes. It doesn’t need some feature enhancements. It doesn’t need some updates to the latest iOS version to run on your phone. And because of this, you would naively expect that as people become more productive, there’s still a marginal value to having them on board because there’s just infinite work that comes with software. And you would expect that in markets where there isn’t infinite work, you know, I think the classic example is something like a tax return where even if you can do 1,000 tax returns a year because you’re so productive, you’re still only going to do one tax return a year unless you’re crazy.
So, I think in software, you would naively expect that you still want to keep your software engineers because there’s just so much more value that you can add with them on board. And I think that is roughly what we actually see in the market, right? Which is that experienced roles don’t necessarily disappear at the moment, but it is these new entry level roles that are kind of disappearing. And it’s those people who don’t have the judgment to actually review the AI outcomes that start disappearing. But firms are hanging on to the software engineers that they have, and they are hiring, but they’re hiring at that kind of expert level end where they can get a 10x increase in productivity from AI, but they aren’t hiring at that kind of entry level end where they can’t get any benefit from AI because they don’t have the experience to review what AI has actually generated. And so, I think there’s a few very interesting effects that are adding up together. But I think this is aligning to what you see when you look at kind of US data, when you look at kind of Australian data. We’re seeing kids out of uni, four to six months out of uni, in software engineering, particularly, and in STEM slightly more broadly, their hiring rates are lower than the darkest days of COVID now, essentially. And so obviously, there’s a massive impact there. And we don’t see that across medical roles. We don’t see that across other roles in Australia. But we see that across software engineering and a little bit more broadly STEM. But for experienced devs, the market is as hot as it’s ever been in Australia, essentially. So, there’s a real concerning adjustment there.
Alex: That’s super helpful and definitely something to keep in mind. I want to hand it off to Boz, who I think is going to follow up with something on AI companies. Go ahead, Boz.
TBR’s new HIRI metric
Boz: Yeah, I think the topic of like, you know, what you’re saying, how much the workforce reduction is a result of AI today versus anticipation, I think it’s an ongoing discussion. We’ll be starting to look into some metrics of some of the professional services companies as well and trying to better understand, you know, the productivity improvement. What does that mean? Can companies do more revenue, more profitable revenue with fewer people? And how do you measure that impact? And how much do you put that weight on AI versus utilization versus offshore leverage versus pricing, you know, all these other ways to think about it, right? So, I mean, we’re starting to kind of put some pulse on the market a little bit and trying to gauge of that direction, but it’s so, there’s so much happening in the meantime. So, it’s so much, it’s hard to, like you said, to really make it concrete, this is happening because of AI, like a complete, like, you know, an agent or AI specific number to pin it down.
Patrick: Yeah, actually, Boz, let me interrupt you for a quick second. I know where you’re going, but- and I want to interrupt you because Rob asked me this question over lunch in Sydney. What did I think about the idea of the sort of the zero human or the one human company where, sort of everything could be done by agents and you just have the one person. And I absolutely laughed it off and said it wasn’t possible. But now we’ve been working on this metric that Boz came up with to measure exactly what does that productivity gain look like. And I can tell you right now, it does not look like we’re heading towards the one human or the, Rob, what was the term you used for it?
Rob: A zero human company.
Patrick: Yeah. All right. I guess that was it. Yeah. So just want to say, just want to put it out there that we have been doing some further thinking on that and throwing some metrics at it, but definitely it certainly resonates. Boz, I know you had another question on a slightly different topic. You want to go ahead down that road.
Boz: Yeah.
Rob: Maybe before we go to that, sorry, and maybe a question to you, Boz: I’d just love to hear, how have you thought about building that metric? And what do the figures or initial findings kind of look like? I’m just very interested.
Boz: So, I think, so the metric, the way we’re looking at it, and it’s still kind of a little bit of like a preview for you and some of the listeners on the podcast, but it’s what we call human intensity reduction index, essentially. And it’s exactly kind of looking at the change, of the headcount understanding for the output for revenue, right? And trying to understand how much that, you know, is pure demand-driven versus AI. And as I was suggesting earlier, you know, it’s really, there’s a lot of factors right now into it, but early findings are showing the- if you look back in the three-year span when GenAI first came around, we’re starting to see, you know, coming from the index from -5 to -10%, which essentially tells you that was still companies relying heavily on humans, right, on labor arbitrage, traditional labor arbitrage, so now some of them crossing into mid to upper single digits, right, into 5 to 10% that index is going in a positive direction, right?
So now we’re starting to think about, okay, what does that mean? How does that translate to economic value? And we’re trying to look into one economic value factor is profitability, right? More of a concrete measure of success of any of those kind of investments and initiatives that’s coming and go after. So, some companies are a little bit more conservative on changing and taking a hit on the operating profit because that kind of falls into their culture and just the way they are conditioned to operate. And you can see a very flat margin, which again tells you that at least they’re protecting the margins. Some of them are having a dip in their margins. And I think it’s a reflection of their little bit more of a aggressiveness for innovation and reinvestment back in the business. And as they’re trying to build some of their AI kind of IP asset portfolios that they maybe try to find new ways of monetizing that as they look into the next three to five years. And others are, just doing business as usual, right? And they’re more into a kind of an internal reorganization transformation phase more than actually doing, you know, something meaningful that, you know, it’s, I mean, you can look at the, just the raw numbers like, wow, they actually, the indexes are above 10%. But when you start peeling back, you know, and understand why the headcount reduction has been so much. And again, it’s more about that anticipation and trying to maybe offload some of the unprofitable units and the investitures and whatnot, that it’s less about AI, it’s more about making Wall Street happy for the time being, right?
So, this is some of the nuances we’re seeing, but a lot is about leadership, about culture, about how those companies are, you know, conditioning their stakeholders. And there’s another factor that we’re starting to get a little bit better understanding is the impact and the influence of the alliances as well, because they operate in ecosystems, right? So, they have to account for the implications of the partners as well along the way, because they can’t just sell AI products in a vacuum, right? Because they have partners that they do the same thing. So, they have to be very careful how they position that with clients as well.
Rob: Understood. And one thing that’s always interesting is when we’re talking about kind of, do you keep employees or do you increase number of employees to try and take advantage of AI or potentially decrease, you know, if employees are more productive, is it then even more valuable to have more? I think when I chat with econs, they often say to me that labor, they traditionally view it as a game of comparative advantage, where as long as AI plus a person is more beneficial than just AI on its own, we should expect increases in hiring. We should expect your marginal profitability per person should increase. And so, you should actually want to hire more people is often the storyline that they talk to. Is that in alignment with, I guess, your initial findings from the index or? Would you disagree with that?
Boz: No, 100%. I think what the biggest test would be is for the, especially professional services companies, because I think they’re having the biggest kind of implication that AI is putting on their operating and the business models that they’ve been working with, you know, for hundreds of years, some of them, right? Is, you know, if they’re able to start driving revenue beyond the mid to upper mid-single digits, and they maybe crack into the low double-digit growth, I think that’s going to provide a good boost of confidence of what the strategy is. And while historically the headcount would have been expected to grow in parallel in a linear progression. I think this time around with the use of AI and the tools that everybody’s been investing, the net headcount growth will be more like in the low to mid to mid-single digits as long as the revenue grows in the upper mid-single, low double digit, right? So yes, the net effect of the AI is headcount will grow, albeit a slower rate than historical trends and rates, but I think there’ll be net positive effect on the headcount and the employment overall.
Rob: Very interesting. I’m keen to see when you release the full report.
Boz: Yeah.
Patrick: Timing is everything, right?
Boz: Timing is everything, yes.
Patrick: It may come out by the time this podcast comes out. We’ll see. We’ll see.
I do just want to kind of wrap it up. Boz, I’m sorry, Boz, I know you had one more question. Throw that out there and then we’ll and then we’ll wrap things up, Rob. Go ahead, Boz.
What makes an AI lab or a client good to work with
Boz: No, all good. Yeah. My question was because we’ve been talking about all the implications of AI and, you know, you’ve been in the field and you work with clients, you work with partners, you know, you’re kind of, you’re in the weeds so to speak, you know, in the AI. And just curious, I mean, what’s the, what’s the kind of the favorite AI company profile you like to work with? Not a fair question. I get that part, but just, you don’t have to name names. Just thinking about from a, it’s kind of like, what does a real good AI company look like in Rob’s world that you like to work with.
Rob: I think there’s probably two sides to that question. There’s which of the AI labs do I think build maybe better technology, if that’s the lens, or there’s which of our clients have attitudes that lead to the most success with AI? Do you want me to go into both or one of them?
Boz: Well, I’ll give you the consulting answer. Both, I guess. *laughs*
Rob: Yeah, happy to. I think the labs is a particularly interesting question. And I think more and more, a number of the really technical staff of my team, and myself included, are starting to form opinions based on the personalities of the models that they’re creating. There’s a great example at the moment where if you try and build an AI system from Anthropic versus you try and build an AI system from OpenAI, They have taken very different approaches to how you actually try and build reliable, high-confidence AI systems in that they have given their models different personalities from each other. OpenAI has taken this stance of, and you can go and read their documentation, they’ve taken the stance of, you need to be very prescriptive, is how I’ll put it. You need to be very prescriptive of how you’d like the model to act. And if there’s scenarios you don’t like, you just tell it on a prescriptive basis, don’t do X, right? And that’s really simple to work with, and it’s quite nice in that way. But the Anthropic guys have taken quite a different approach. They’ve taken what you would maybe call in a regulatory sense more principle-based, of I’ll give you the general governing principles, and I want you to go out and do these. And hey, Claude, generally, you are a very astute, hardworking, well-intentioned person. Figure out how you should do it in the right way, essentially.
And so when we work on these AI systems, it’s getting harder and harder to build one system that works across multiple providers because they are taking kind of fundamentally, almost like Commonwealth law versus more kind of Americanized style of law – like a principled versus rules-based (prescriptive) approach – in how you actually instruct and teach these models. And not to say that either side is wrong or necessarily better or worse than the other, but they’re very different. And I think for individuals like myself, we probably have a bit of a preference towards one side or the other. And so, I’ve got a number of people on my team who really prefer the one way and a number of people who really prefer the other way. And I think we’re seeing more and more of that over time, which is that the differentiation isn’t necessarily in, well, my model can do X, and its score is 3% higher on this benchmark of impossible PhD questions that no one can even understand. But rather, my model is easier to work with in X scenario because the personality is like Y. And that allows it to be very easy to iterate and get reliability in this case scenario. So, I think the labs to me, I think obviously there’s lots of partnerships and relationships there. But more than that, it’s also they’re creating different things now, which is really exciting to me, because for a while it was all converged. They’re really creating different things now. And that is forming a lot of our preferences.
On the client side of what makes a really awesome client to work with who I’m excited about is that you walk into their boardroom and you start talking about some of these things and they say, hey, I read that report. I read that economic index. I have been playing with this and we’ve actually, we were considering buying this small piece of software and now we’re actually, we just made it in two weeks and we’re not even going to bother going through a four-month-long procurement cycle because we can just make this thing that quickly. And previously, all these things that were, you know, we were putting them on a prioritization matrix and we were going to choose which of these projects we wanted to do. We’ve actually just, instead of doing that, we took the time it would take to have the call to do the prioritization and we just delivered the first project, right? Because that’s how fast we can move when we’re empowered with AI. I think those clients are the most exciting and fun to me because they aren’t held back. They understand the potential and they just want help in actually going out and getting it and achieving it or even accelerating it faster. And when you have an opportunity to work with them, you can really help them push the boundaries and you can get 100x benefit for them rather than just 3x benefit of setting up the initial AI foundations correctly, necessarily. So, I hope that answered both questions.
Boz: Yeah, it’s fascinating just to think about the pace of movement. And I agree, I mean, especially on the client sides, I mean, we look as well and the enterprises, there’s so many of them have been stuck in their own ways, but the ones that really want to do something, they’re not afraid to innovate, they’re not afraid to just to take on that next kind of idea and just embrace it and run with it as well. But the lab side, I didn’t think about it, but something for us to pursue and to go after a little bit more deeper, you know, just as the way you describe it and just think about how the evolution of the labs is going to impact the ecosystem as well.
Rob: Yeah, 100%. And I’m really excited to see where they go because it’s just such different approaches that we’re starting to see now where it was becoming almost commoditized for a while.
Patrick: That’s crazy, Rob, to say it was becoming almost commoditized for a while when this whole field is just still feels so fast moving, so new, so everything is changing all the time. And yet you can still talk about an element of it that was being commoditized recently. It’s crazy.
Rob: Yeah, look, I think it’s- it’s every layer of this field just changes so rapidly. And I think, you know, for a while we started seeing this, oh, the models are just commodities. And then we started getting these agentic harnesses, things like Claude Code and Codex, and then people started saying, okay, well, these things are just commodities. But when you look at these, the one lens I do really enjoy, and from Leopold’s situational awareness paper a few years ago, is almost like you take all these different elements from the model to the harness, to the training it had, to the data it had, to how many millions of hours of GPU compute and billions of dollars were spent on training. You kind of take all these things together, and you just plot them into a single capability line, essentially. So, you have a single metric for capability. And all of these things are essentially just different multipliers on that capability metric. And all of them have so much opportunity for exploration and they all have so much opportunity for improvement. And I think when you talk to people in the labs, the one thing you consistently hear is that just like if I want to increase revenue, I could work on making my business more efficient, or I could work on going out finding new clients, or I could work on global expansion, or I could work on a whole different bunch of areas. And I may find gains in all those areas. I think when you talk to people in the labs, they say we’re finding gains in all the areas, right? There’s 100 different areas and all of them are finding gains at the moment. And I think that is really exciting because the more areas they explore, the more diversity we’ll see in the market of how these different models act and operate. And that means there’s a lot more kind of innovation. And I think it’s a lot more exciting rather than a convergence. I think we’re going to keep seeing a bit more of that divergence.
Patrick: Yeah, that’s a great reminder. I forgot that not that long ago, we were sort of talking about how all the large language models were seemingly becoming so much alike. And you’re right, that opportunity now for innovation, that divergence is a lot stronger now, which convinces me we’re going to have to have this conversation again in six months on season six of TBR Talks and see where things stand.
Final thoughts
Been really fascinating, Rob. I appreciate this time. I feel like we covered so much ground. You talked a lot at the beginning about reliability and you mentioned sort of the governance and the standard product management. And then this idea that people want augmentation from AI, like the clients that you’re talking to. And I think one of the reasons I was really excited to have you come on the podcast is I think you’re working with clients, with PwC’s clients on sort of a day-to-day basis. You really, you get your hands dirty in the AI at client sites. So, your reflections on what they’re actually asking for is just enormously valuable to us. So, I really appreciate that.
Rob: Of course, always happy to.
Patrick: And then I got to end with one last question, because that’s been sort of the theme of this season of TBR Talks. And if you’ve listened to some of the previous episodes, maybe you’ll know what it is. But right now, my daughter is 22, our youngest is 22. She’s graduating from university in a couple of months. Sort of the whole world is in front of her. And it made me in a previous conversation sort of ask some people who are my age or perhaps a little bit younger, but think about what they wanted to do when they were 22 years old. And one answer was a woman who is in technology, but not doing what she really wanted to do at 22, which was to be an engineer on a Formula One team. So now, of course, have to mention PwC does have a relationship with Formula One. Good for PwC, good for all of us. But anyway, so my daughter has no idea what she wants to do with her life. But at 22, Rob, what was your sort of, what did you think you wanted to do with your life? Because at the time you were messing around with actuarial tables at an insurance company, right? So.
Rob: Yeah, I think I’ve always been quite interested in the spectrum of AI. And I think one of the most interesting things was, and something that most people don’t realize is that if you went 10 years back and you went to the biggest AI conference in the world, no one there, not a single person, if you ask them, will we have anything even close to what we have today in 10 years time? Even if you ask them in 50 years time, will we have this? No one would have said, we’ll have this, right? This field has gone so much faster than anyone in the field ever thought it actually would. There’s this concept in the field called out of distribution, essentially, right? And people often today say, like, if AI doesn’t do exactly what I want at this task it’s never seen before, then it’s out of distribution. You know if AI wasn’t trained on this task and it doesn’t perform well. Like 10 years back, out of distribution meant to everyone in the field, it meant that when you use Google Translate, it didn’t have a literal word where this word in English means this word in French. That’s what it meant. Like, we had tables that went this word in English means this word in French, essentially. And we are now at this point where these models are just so incredibly capable and so much further than anyone thought they would be. And so, I was very excited about trying to get these models to even understand a sentence. That was one of my things I was really excited about, was even getting them to be able to understand the semantic meaning between words well. And it turns out that wasn’t that hard.
Patrick: *laughs*
Rob: So, I think I continue just to be really excited by the sheer amount of progress. I think I’m also very concerned about a lot of progress, if I’m being very frank. But I’ve always been interested in what we can actually achieve in this space, and it’s just gone beyond my comprehension of what I thought we’d ever get to. So, it’s very exciting, but it’s going to be a wild ride the next few years. That’s a guarantee.
Patrick: That’s fantastic and wild and scary. And I think we all definitely share your concerns. I’ll speak for Alex and Boz in saying that we definitely have our concerns about AI. Something we can dive into next time. Rob, thank you so much. Alex and Boz, thank you as well. And it’s really been a pleasure. Thank you, Rob.
Rob: No worries. The pleasure has all been mine. Thank you. Thanks, Patrick. Thanks, Alex. Thanks Boz. Lovely meeting you.
Patrick: Tune in next week for another episode of TBR Talks.
Don’t forget to send us your key intelligence questions on business strategy, ecosystems, and management consulting through the form in the show notes below. Visit tbri.com to learn how we help tech companies, large and small, answer these questions with the research, data, and analysis that my guests bring to this conversation every week. Once again, I’m your host, Patrick Heffernan, Principal Analyst at TBR. Thanks for joining us and see you next week.
TBR Talks: Decoding Strategies and Ecosystems of the Globe’s Top Tech Firms
Join TBR Principal Analyst Patrick Heffernan weekly for conversations on disruptions in the broader technology ecosystem and answers to key intelligence questions TBR analysts hear from executives and business unit leaders among top IT professional services firms, IT vendors, and telecom vendors and operators.
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In this episode of “TBR Talks,” Milan Cooper, head of Product at TWG AI, joins host Patrick Heffernan for a discussion on business process reinvention and implementation of AI-enabled solutions. Milan also discusses the critical role of top-down leadership, particularly starting with the CEO, in successful AI adoption and explains what makes TWG AI’s approach to using AI different from those of peers.
Episode highlights:
The skills required for successful AI adoption
The importance of CEO-led AI programs
Potential points of failure
“AI isn’t really comparable to, you know, cloud or mobile. Those were infrastructure shifts. So, you could delegate them because they didn’t fundamentally change who did the work or how decisions get made. AI does. It changes the shape of jobs. It changes which decisions get automated and which ones don’t, you know, it changes headcount assumptions, and those are CEO questions by definition. You can’t delegate them without the whole thing turning into a science project,” said Cooper.
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TBR Talks is produced by Technology Business Research, Inc.
Edited by Haley Demers
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Art by Amanda Hamilton Sy
The Playbook for AI Reinvention
TBR Talks Host Patrick Heffernan: Welcome to TBR Talks: Decoding Strategies and Ecosystems of the Globe’s Top Tech Firms. Where we talk business model disruption in the broad technology ecosystem from management consultancies to systems integrators, hyperscalers to independent software vendors, telecom operators to network and infrastructure vendors, and chip manufacturers to value-add added resellers. We’ll be answering some of the key intelligence questions we’ve heard from executives and business unit leaders among the leading professional IT services and telecom vendors.
I’m Patrick Heffernan, Principal Analyst, and today we’ll be talking about business process reinvention and implementation of AI-enabled solutions with Milan Cooper, Head of Product at TWG AI.
Meet Milan
All right, Milan, thank you so much for coming on TBR Talks. Really appreciate it. Maybe you could give us a little bit of your background, then we’re going to dive into some of the topics that we’ve already been talking about with respect to AI, but maybe you could tell us a little bit about where you’re at right now and some of the experiences you’ve had professionally over the last decade or so.
Milan Cooper, Head of Product at TWG AI: Yeah, absolutely. Thanks, Patrick. It’s great to be here. So, I’m Milan Cooper. I head up the product and go-to-market team at TWG AI. We’re an AI products and services company, and we work mostly in regulated industries. So, think financial services, insurance, you know, like places where you can’t just ship something and see what breaks. So, before TWG, I spent about four or five years at J.P. Morgan, running AI for software development. And then for about a decade before that, I was at Accenture in technology strategy. I actually started my life as a network engineer, but kind of got sick of being in data centers at 2 A.M., fixing issues on the weekend. So migrated very quickly into strategy and have progressed a career since then. So pretty much my whole career has basically been about getting big, complicated organizations to actually adopt new technology across a number of different waves, which as it turns out is actually the hard part.
What skills are required for successful AI adoption
Patrick: And so, getting big corporations to adopt technology. So, when we think about AI, one thing I’ve heard, and I’ll just, I’ll float this by you and see if it makes any sense, is when it comes to adoption, particularly for a large organization, you need three things. You need the leadership to be bought in, you need the masses, meaning you need adoption or at least experimentation or at least familiarity, willingness across the masses. And then you need, for lack of a better word, a lab. You need people that are actually dedicated to taking that technology, whether it’s, let’s just say it’s AI, taking AI and actually making it useful within the enterprise. Is that, would you agree that those are sort of the three critical components? Is there something else? Is there something that we’re missing?
Milan: No, I think absolutely the top-down leadership is absolutely critical to drive change. The lab component, I would probably describe it as kind of an AI SWAT team. So, the models that exist today are phenomenal, and they’re only getting better. The trick is to have a team that knows how to manipulate those models with context, with data from your business and actually embed them into the workflows of the end users in whatever business line you’re trying to impact. And so, you kind of have to be a little bit of a Swiss army knife. You have to have systems engineering. You have to know how to integrate into systems of record, pull data in. You have to be an expert in the models. You have to know how to manipulate these things and how to build multi-agent systems that work together effectively. And then you have to know enough to be dangerous in the business, and have empathy with users that have existing workflows that kind of run operations today and understand what are the unique things about how they work and what their business is. And then be able to, kind of, embed and integrate that into new reimagined workflows. And so that kind of lab concept of just having research scientists kind of build these things in a dark room is kind of gone. And it’s now this kind of Swiss army knife of skill sets that is required to take something from, you know, that comes out of the, you know, the foundation model providers and actually embed it into an actual business workflow. But, you know, the CEO-led AI transformation is absolutely critical as well.
Patrick: I’m curious, when you talk about all those different components of what you need to be able to bring to the table as an, really as an individual, like how, was it something at Accenture or something at J.P. Morgan that sort of, that helped you develop those skills? And as part of what you’re doing now with TWG AI, is that, is part of that, like you’ve got the skills and the capability, the actual people, the humans, that can do it, and that’s what you’re bringing to your clients?
Milan: Yeah, I think the- I think pattern recognition across working with, you know, tens of companies that are all facing similar challenges kind of pointed us to a clear need to build a team that has these skill sets. So, the big question is, is how do you build an organization that can effectively do this work and what skills do you need? And kind of the experience that we had and myself with consulting and working with lots of different companies and then embedded in financial services industry for a number of years, kind of gave me and us that pattern recognition of, you know, you need all of those things to be effective. It’s not just- you can’t just have a research lab anymore. So yeah, that was kind of a key principle when we were kind of founding this company.
The underlying playbook for AI reinvention
Patrick: So how long have you been with TWG AI then?
Milan: Yeah, so TWG started early last year and we’re part of the overall TWG Global Holdings family of companies. And so, we own asset managers, we own investment banks, insurance companies. We own a huge sports franchise. So, we own the Dodgers, the Lakers, motorsports. We just launched Cadillac Formula One recently. Rodeo is part of our business as well. So, we have a huge, diverse set of of problems and challenges that we have to try and solve with AI. And we’re taking those learnings to market as well. And so, we’re working with other sports franchises, colleges, and then also heavily in financial services and trying to reimagine those businesses with AI.
Patrick: So, you mentioned earlier, you need to have enough knowledge about a business to be dangerous. And yet, I can’t imagine, or maybe you can tell me, where are some of the overlaps that you see between, rodeos and banks or, you know, Cadillac and the Dodgers, like I definitely see where those overlaps exist. But, the regulated industries and, well I guess Formula One would be pretty heavily regulated too. But I’m just curious, how do you have enough knowledge to be dangerous about a business when you’re involved with so many wildly different kinds of companies?
Milan: I think that’s the magic. And so, you know, we ask a lot of questions to our clients. And our clients ask a lot of questions of us. And I think the magic is really when you have a dense technical team with the skill sets that I mentioned earlier around context engineering, manipulation of LLM, and also the experience and the empathy to embed these things within a business. And then you work with SMEs. So, we ask a lot of questions to end users, and we understand what are the nuances of how they work and how they operate. And we learn from them. And those two things coming together is kind of where the magic is. I mean, the interesting thing is we’ve now done this in motorsports and insurance and financial services. And the surface details look completely different, but the underlying playbook is the same every time. A clean data layer that represents the dynamics of your business, clear decision points, human in the loop where it matters, measurable outcomes that you’ve agreed with the CFO before you even start the project. And the fact that the same approach works in the pit lane in a Formula One race and on underwriting desks in an insurance company kind of tells you something important, that this is a methodology problem, not just a technology problem.
The importance of CEO-led AI programs
Patrick: I might agree with that. I’m- actually, I’ll go ahead and say I definitely 100% agree with that, but I don’t know- how much pushback do you get from the companies you’re working with, at least initially, around that assertion that what- they’re not buying technology necessarily, they’re buying a change to their structure and how they work and their processes and their culture.
Milan: I mean, we, it’s an ongoing conversation, but I think one of the things that we evangelize from day one on an engagement is that an AI program needs to be CEO-led. It needs to be top-down. That’s how you drive real change, and that’s how you kind of get out of, you know, pilot purgatory where you’re doing many experiments, but you’re not seeing much impact in the P&L.
Patrick: Right.
Milan: And I think what’s actually changed in the last 12 months is that, kind of, AI has moved from being an opportunity on the CEO’s desk to actually being a liability on the board’s risk register. So, if something goes wrong with an AI deployment in a regulated business, you know, if a model makes a decision it shouldn’t have or customer data ends up somewhere it shouldn’t, an employment tribunal gets interesting. And that lands on the CEO’s desk personally and is something that they have to deal with. So, it’s not a CIO or a technology problem only anymore. And once you understand that, the question of whether the CEO should be involved kind of answers itself. They already are. They just don’t really know it yet. And the thing is, being involved isn’t the same as being in charge. And the test I use to figure out whether a company’s AI program is actually going to work is really simple. Who controls the budget? And if the answer is the CIO or technology or somewhere else, it’s probably going to fail. Not immediately, but eventually. And the answer, you know, if the answer is the CEO or the COO or one of the business unit leaders and P&L owners, it has a chance. The incentives are aligned. And the reason is that AI budgets sitting inside IT get spent on infrastructure, platforms, licenses, sandboxes, and all the stuff that’s easy to procure and impossible to measure. An AI budget that gets to sit with a business owner gets spent on, changing how work gets done, because that’s really what the business owner is judged on. Same money, completely different outcome, productivity, revenue generation, risk reduction.
Potential points of failure to look out for
Patrick: So, one thing I would have to push back on a little bit is you mentioned you were at Accenture for a while doing technology strategy. And during your time, no doubt you advised the CIOs and the CTOs you were talking to that they needed to think of themselves as and they needed to operate as part of the business. They couldn’t just be a cost center. They couldn’t just be somewhere where, you know, like you said, there’s money that just gets spent on procuring infrastructure and things that are easy to measure. So that advice at some point sunk in because the CIOs and the CTOs we talk to today tell us all the time that they’re part of the business, and they see themselves as driving revenue growth, not just cutting costs. But it sounds like maybe we’re hearing one story, but you’re hearing a very different one if what you’re experiencing with respect to an AI-enabled solution implementation isn’t really going to be successful if it’s just the CTO or the CIO in charge, yeah?
Milan: I think so. I mean, there’s so many different failure modes for these projects. This isn’t- this isn’t like software where you plan a big software implementation, you have a budget, you have a timeline, you allocate a team, you have outcomes, and then you just execute. And it’s almost binary. You know, you executed on time, on budget, or you didn’t. Data science is, you know, by definition, an experimental discipline. And so, it requires testing and learning. It takes you to places that you didn’t know you were going to go. You have to solve problems that you didn’t know existed. For example, a lot of companies have AI policies or control procedures that were written 10/15 years ago by kind of people that are no longer with the company. And so those are some of the things that can kill you or can really harm projects. You know, most big companies are kind of silently run by control procedures that were written, 10 to 15 years ago, by people who have long since left. And those controls have kind of permeated a culture to the point where nobody questions them. They’re just how things work. And then you try and do something with AI and you hit a wall and, you know, the wall is actually a policy from like 2014 that no one can remember the reason for. And so, when you kind of start these things in technology, you kind of enter into different, you enter into legal, you enter into the cyber world, you enter into the business world, you enter into audit and reg. You kind of step into all of these different departments and there’s always walls and challenges that you have to solve to actually get these things into production. And that’s really difficult to do from tech, frankly. You kind of need somebody who has influence over all of these departments and they can break down silos and break down walls in all of these different, kind of, diverse functions to achieve the simple outcome of pushing this AI that we built into production. That’s why it’s so difficult.
Telling the success stories
Patrick: But by now, you’ve had some really good successes where you’ve gone from pilot to production a lot, maybe faster than you expected. Is there a good example of where, you know, where a company sort of adopted an AI-enabled solution quicker than you thought and sort of immediately start seeing results?
Milan: Yeah. So, one of the most recent examples is with one of our asset management clients and they have completely reimagined their investment process with AI. And so that’s from, you know, deal origination and screening to analysis to even in the investment committee and how investment decisions are made. And then also into, you know, once you make the investment, the management of it. And so what was exciting and impressive, and it takes courage to do this, is a complete reimagination of the entire value stream of an investment, not just a point solution at one particular, you know, not just the deal screen and not just the management of the portfolio, but the whole value stream. And you start to see these things kind of comparing because they build momentum. Like when you’re doing point solutions that are disconnected, they all seem like completely uncorrelated problems, and each one has challenges, and they’re all different teams, and nothing’s connected. But when you work around a value stream, you get the same people working on similar problems. It’s all the same context, a lot of the same data. You’re seeing real value compound over time as you move, kind of, downstream within a process. And that builds momentum. And that allows you to actually move quicker. So even though you’re increasing the scope, and it’s kind of scary at first because you’re completely reimagining kind of how you do business in a particular area, you can actually gain huge momentum and huge value by doing that because of the reasons I mentioned. So that’s probably one of the best examples that we’ve had recently of large-scale reimagination and, you know, but also moving really quickly.
Patrick: Right. And when you start working with a client in, like, let’s jump to Formula One. So, Cadillac, new this year in the Formula One space. When you go to them, is it the asset management story that you tell? Or how do you relate what you’re doing with your other clients to clients who are in a completely different industry with a completely different set of industry challenges?
Milan: Yeah, I mean, many of the underlying components of how to do this are the same. So where is the data? What are the decision points that you’re trying to influence? How do you operate today? And we work backwards from the business into the technology. And so even if it’s a net new industry, if it’s a net new business problem, a lot of the components of how you execute this are very similar. You’ve got, you know, large data sets that are either unstructured or structured. We’re trying to get signal from that data and we’re trying to influence a business decision to, you know, do something more effective, improve productivity, better decisions, lower risk. And that’s the same in insurance when you’re underwriting a risk or in a Formula One race where you’re trying to, you know, build a strategy of, you know, for a particular track or based on certain conditions. And so, from a first principles perspective, a lot of what we’re, a lot of what we see across these different industries are very similar. And, we have a proven methodology that we kind of apply to each one and it seems to work.
A new data paradigm
Patrick: And we hear repeatedly that the biggest stumbling block, at least initially, is the data. Is, as you said, it can be structured or unstructured. More importantly, is it, I know Accenture for a while was stressing sort of clean core, this idea of, you know, you get the most out of an AI-enabled solution when you’re starting with clean data. Are you still seeing, well, I’m sure by now most companies understand they got to get their data in shape before they can really leverage AI, but are you seeing that happening on the ground or is it still sort of fits and starts or still sort of depends on where within the business you’re talking about?
Milan: Yeah, I mean, data is obviously the lifeblood of AI. We see a lot of different patterns in customers, but I mean, it’s kind of the boring answer that nobody really wants to hear, but it’s where most of the real work happens and it’s kind of a big failure mode. So, I don’t just mean like cleaning up the data or moving it between systems, although there’s plenty of that. It’s also the governance layer underneath. And so, one of the key failure modes is, actually third-party data contracts, for example. So really unsexy, but, these were signed, years ago and often make up the large majority of the overall data that you consume as a company. So, some of the data is generated inside, a lot of it is procured and sourced from outside. And a lot of these contracts have clauses that say, hey, you can’t use this for AI, you can’t use this for analytics. And so in six months time where you eventually get a phone call from legal that says, hey, you’re not allowed to do this, that’s the kind of you know, data problem that bites you kind of, you know, down the line in these projects and is a big failure mode.
The other thing is kind of the dependency on a large scale data transformation. And so, I think that was the pattern a few years ago where, you know, data is the first thing you need to solve. We have data in siloed places. We have a lot of different source systems. Let’s bring it all together. AI is built in the cloud, so let’s do that. And so, you know, companies would set KPIs and say, hey, like how many terabytes of data do you have in the cloud this month? And it would just be numerator, denominator, and just every month, how much data have you pumped in the cloud? And what a lot of companies created was kind of a swamp, just in a different place. And it didn’t help anything.
Actually, our approach, and I think it’s gaining prevalence at the moment, is don’t, you know, don’t rely on a multi-year data transformation before starting AI. There is still value in cleaning up your data, and that is a separate decision. But from an AI perspective, there’s a need for a platform that can pull in the data from all the different source systems, all the different technologies, and all the different locations into an intelligence layer that can be leveraged from AI. So, you’re almost creating a new layer of data in the organization that specifically supports AI use cases. I think that’s kind of the new paradigm that we’re seeing these days.
Starting with the business process, not the technology
Patrick: Yeah, and then so I’m curious because now you’ve mentioned governance and talking about the risk part of things and a lot of the companies that I spent a lot of time with, these are the consultancies that basically say they do very similar things. They’re focused on risk. They have the governance, risk and compliance layer. They have the consulting and the business industry knowledge and all that. What’s sort of the difference between what you’re doing now and what, you know, what an Accenture or a McKinsey brings to the table?
Milan: Yeah, so our approach starts from quite a different place to most other companies. We don’t start with the technology. We start with a business process. And we ask three questions. Number one, where does that decision get made today? Number two, what data feeds it? And number three, how would we change that decision loop if we could rebuild it from scratch? And only then do we work backwards into what the architecture needs look like and what AI can support that. And we have a joint venture with Palantir. And so that partnership matters because the hardest part of doing this in a real company isn’t the model itself. It’s actually connecting the model to actual entities and workflows and permissions of the business, the context. Who can see what, you know, what counts as a customer, how an underwriting decision flows through five different teams. And kind of Palantir’s Ontology layer, which is what I referenced earlier, is genuinely the best tool I’ve seen for that. And it’s the difference between something that works in a demo and something that works, you know, on a Tuesday afternoon in live operation.
Patrick: And I could only imagine that, I mean, Palantir has had a huge effect on the rest of the consulting and the AI market in the sense that everyone is trying to imitate what Palantir does well with forward deployed engineers and all that, to some degree of success by some of the companies that we cover, but certainly not all. How long is the- is it a joint venture or just a go to market or what’s it- what is it that you’re doing with Palantir and how long has that been going on?
Milan: So, we have a, you know, our leadership has a long history with Palantir. We have a joint venture. We go to market together. One of the best things about Palantir is the Ontology and the data platform. And we selected them as a partner because it allows us to move quickly. So, our data scientists, our AI engineers can drop Palantir into a business, integrate into their data, wherever it lives, whatever it looks like, and actually start building within 30 days. And so, there isn’t this long, drawn-out process of kicking the tires on compliance, of building custom connectors into different technology source systems, you know, connecting to different models. Everything kind of comes out-of-the-box. And so, we can really focus on what matters, which is what is the value stream, what is the business process, asking questions, sitting with SMEs and users, and actually building products that help them with their day-to-day. And so, it’s just an accelerant for us. I think that’s the key differentiator.
Patrick: Yeah. And the accelerant is critical. I mean, I know everyone’s sort of maybe a little bit fatigued with all the hype around AI, but going- that’s getting stalled in the pilot stage and seeing lots of money spent on things that just don’t ever scale, super frustrating.
I want to ask you about, I want to sort of take a step back and look at a long view of technology, because you’ve been around a little while, to say the least. And again, I don’t mean that in a bad way, of course. But before that, I am curious. So, is there a problem set or a sort of a business challenge that you consistently run into with the companies you’re working with that you sort of haven’t seen? Let me come at it a slightly different way. We hear so much about the ability of the companies that we talk to bring AI-enabled solutions to bear on pretty much every problem that exists. And to be a little skeptical, there’s got to be some stuff that’s still too tricky, too knotty, too confusing. And I’m wondering if you’ve run into that like consistently. Is there a certain problem or kind of problem that you see repeatedly at companies that you’re like, you know, AI is not going to solve this anytime soon?
Milan: That’s a really good question. I mean, I think it’s a well-known transformation principle to- like AI is the last thing you want to do as a general rule in transformative process. The first thing you want to do is figure out like why are we doing this process? Like is it a control procedure that was written 15 years ago that no one’s ever really looked at that says that this team needs to write this report? Where does that report go? Who looks at it? How does it improve our business? So, you get rid of everything that’s not required. The second thing is you try and make it more efficient. So, the steps within the process, like are we wasting time? Are those steps required? Do we need to do three reviews? Does it need to have an external team, you know, complete that step? Can we do it, you know, within the team? And so, you improve the process just by, you know, doing classic process re-engineering.
And then you try and automate. So like rules-based stuff. So, if A, then B. And in many cases, those simple kind of automation steps, RPA is all that’s required. And then once you’ve got a clean kind of almost like pre-AI optimized process, you can then look at how to leverage trained systems and large language models to get to the next level. I think if you don’t go through that process, it’s going to be really difficult to get to an end state that is truly optimal. And you’re probably just throwing AI at a problem that doesn’t really necessitate it. That’s, you know, that’s key. And that’s hard work. Like, I’m doing process re-engineering, doing value stream mapping, you know, everyone wants to do go straight to GenAI, wants to go straight to multi-agent systems. But if you take the time to go through those steps, I think the value compounds.
Building safe flexibility into processes
Patrick: So then, so let’s get into the long view of technology by actually looking forwards, not looking backwards. Because a couple of times you’ve mentioned this idea that there are procedures or there’s a process that was put in place 15 years ago and nobody’s, you know, policies nobody’s gone back to look at again. How are you thinking about not laying down those same 15-year mines for the companies you’re working with? So that 15 years from now, somebody’s not coming in and saying, who is this Milan guy and why did he set up this policy here? Like, why are we cleaning this mess up? Like, how are you thinking about making sure that you’re not doing that yourself?
Milan: That’s a really good question. I think it’s tricky, right? You can only hit what you can see. And so, as we rewrite policies for the AI world, you know, we’re making sure that the kind of legacy software development lifecycle type paradigms are completely broken down and they’re fit for a world that requires, you know, fast iteration, experimental approaches. I think that’s the main focus for us at the moment. Like, I think that’s going to be, you know, the future. I think that’s going to be how businesses operate for a long time. And I think probably building in safe flexibility into these control procedures. And specifically what I mean by that is kind of the ability to experiment on model training and data that is real. Like historically, that’s been a big no-no. Like you cannot use real data to, you know, in a system until you’ve got through kind of 10 steps and you’ve done all this stuff and it, you know, the system is proven and hardened and all of that kind of stuff. But that’s just completely backwards in the world of AI. Like the first thing you need to do is look at data to see if there’s signal. So, if you can’t access the data on day one, then you’re kind of in a catch-22 situation. And so, I think built-in flexibility into these control procedures built around an AI world is probably going to, you know, probably going to be valid for quite a while.
Patrick: For a while, right. Yeah, that makes sense. And I think back to the number of times that I’ve sat with, consultants who have said, they’ll spend two or three weeks trying to just get access to their client’s data because within the client, there’s all different kinds of people that own the data and have to sign off on the data. And so, they’ll start an engagement and three weeks later, they’re still waiting for access to the data, which is insane. And that just doesn’t, that can’t fly. That can’t be true anymore in an AI world. It just can’t.
Milan: That’s exactly right. Yeah. Squeezing that timeline between, you know, how quickly can you get real data in the hands of our data scientists is a huge accelerant. That is probably the number one failure mode of, you know, especially in the early stages of these projects, is just how long it takes to get access to data. And again, going back to CEO led, sometimes data lives across departments. And it’s not just one tech person or one business person that needs to kind of approve access to this stuff, it’s many. And then who do you get to kind of, you know, knock heads together to make sure that happens?
Patrick: Yeah, it all comes back to, like you said at the beginning, that question of like, who actually makes decisions and, what data are they using to make those decisions and can they make the- are they empowered to make the decisions?
What makes AI evolution different from previous technology evolutions
I do want to look backwards a little bit and sort of put the longitudinal view of technology in the context of where you sit today, could you, 10 years ago, when you were with Accenture doing advising, doing tech strategy and advising clients, did you imagine sort of where we are today? I mean, how and how much have things, I guess I’m answering the question, no, you didn’t, maybe you’ll say yes, you did, but how much of what you’re seeing today is sort of, is more than just an evolution of RPA, more than just an evolution of, you know, even blockchain or some of the other technologies that were so hot and emerging not that long ago, and how much of it is just truly revolutionary, truly different from what you expected 10 years ago?
Milan: Yeah, it’s a good question. I mean, some of the evolution and some of the technology waves that I’ve been a part of over the last kind of, you know, 15, 16, 17 years have been almost by accident. So, I started off right at the bottom of the stack as a network engineer. And so, I was in hardware. Then all of a sudden, kind of the early parts of last decade, all the infrastructure people at Accenture just became cloud people. They literally just changed our job title. So, on Friday, I was an infrastructure guy, and on Monday, I was a cloud engineer.
Patrick: Right.
Milan: So, some of these things just happen to you, some of them you get to make conscious decisions on. But the thing is, AI isn’t really comparable to cloud or mobile. Those are infrastructure shifts. So, you could delegate them because they didn’t fundamentally change who did the work or how decisions get made. AI does. It changes the shape of jobs. It changes which decisions get automated and which ones don’t. It changes headcount assumptions. And those are CEO questions by definition. You can’t delegate them without the whole thing turning into a science project. So, I think that’s really what’s different with this evolution is this isn’t just an infrastructure shift, which, you know, it isn’t just a kind of like cloud, for example, was a paradigm shift in how to optimize infrastructure and workloads. This is really introducing an intelligence layer into your organization and how you kind of leverage that is a key strategic question for all these companies.
Patrick: So, if you had to sort of balance like where we are now, where we’re going next on exactly that point of that sort of it’s- that intelligence layer is a key strategic part of any business. Are we at the point now where if companies have not already defined their AI strategy, already begun making investments in how they’re changing their business with that intelligence layer, that we’re going to see those companies left behind? I mean, is there a- do you see in looking at the companies you look at and then thinking about them in the context of their competitive landscape, are you seeing where that intelligence layer and that investment in the strategy now is going to make all the difference over the next, you know, five, six years to separate the companies that actually will succeed and those that won’t?
Milan: Yeah, I think so. I mean, it’s almost like, I can’t remember what the saying is on bankruptcy, where it happens slowly, then all at once.
Patrick: Right, yeah, a run on the bank, yeah.
Milan: Yeah. That’s it. So, you know, it’s, I think that’s- that day is coming soon, right? I think this isn’t, you know, typical, you know, productivity enhancements where you kind of take, you know, low single digit percent off your OpEx or whatever. This could be an enormous transformation shift in how companies operate. And so, you’re starting to see early signs of that where some companies are kind of completely re-imagining how they do business. There’s a lot of froth in the market at the moment. There’s a lot of dollars and capital flowing into point solutions that are promising big things. And I think at this point in the game, I think you need to have, you need to be way beyond just having or thinking about your AI strategy. I think there needs to be, you need to be pretty far down the path in fundamental business transformation using AI, whether it’s other traditional competitors that are just moving faster than you, or whether it’s net new companies that haven’t even been built yet that are going to kind of bite you at the heels and then ultimately take over large parts of your business. I don’t think it’s, overdone to kind of pose those questions to businesses and understand, how, like what’s the best way to defend against that?
Technology evolution vs business model evolution
Patrick: Yeah, and I think back to like how we, for years, we talked to companies about digital transformation. That was the buzzword before AI. Everything was about digital transformation. And we would hear these use cases where companies and their consultancies would tell us all about the digital transformation. And at the end of the day, it was, they adopted S4HANA, or they adopted Salesforce or something. And the business hadn’t changed. It’s just that the underlying technology had changed. Like their technology environment was different and improved and better, whatever, sure. But there wasn’t a business model change. And I think what we’re seeing now, and I think of what I heard you say is that AI is different in that way and that it’s allowing for a transformation that’s business model that’s fundamental rather than just technological.
Milan: Yeah, I think that’s right. You know, I think the saying back in the day was no one gets fired for buying IBM, I think. You know, you can kind of see a similar thing happening now with the foundation, with the large labs. Everyone’s, you know, the obvious thing to do is just go buy a chatbot.
Patrick: Right.
Milan: And introduce that into your business and, kind of hope for the best. And I think that is, that strategy is- and there’s been some recent research on kind of how, what the impact of that is on organizations. So, for example, I think there was a recent article in HBR that talked about how chatbots don’t improve productivity for work. They just increase intensity. And so, everyone’s kind of experiencing task expansion, blurred boundaries between work and non-work and a lot more multitasking. And the intensity of work is just increasing. So, the underlying process hasn’t been transformed. We just dropped the chatbot on top of it and hope for the best. And so, I think, there’s emerging research that’s kind of calling that out as a finding. I think we’re starting to see more and more of that. And I think that’s going to continue to point towards the need for underlying process reimagination.
Patrick: Yeah, it’s funny you use the word intensity. We’re about to roll out some new research that we’ve come up with that looks at what we’re calling human intensity reduction index. So, the human intensity reduction index looks at how well a company can continue to make profitable dollars with fewer people. How do you reduce the intensity of the humans that are going into, you know, and we’re looking primarily right now at services firms and services- consulting and IT services is a people business, but the sort of hype and promise around AI is that you’d be able to do more with fewer people. And so, the question is, can you actually reduce the human intensity within your organization and continue to make as much or more money as profitably as before.
Milan: Yeah, I think that’s right. And I think that’s, you know, this is all a really important signal for executives because it tells you that just deploying AI tools and waiting for productivity to show up in the P&L is naive. And if you don’t redesign the work itself, AI just becomes a tax on the people that you already have. And so that’s actually one of the reasons we’re so focused at TWG on embedding AI into core processes, rather than handing work as a chat bot and just hoping for the best.
Patrick: Yeah, that’s a really, that’s just a great way to think about it.
Following interesting problems and interesting mentors
All right, I have Milan, I’ve got one more question, and it’s a question I’ve been asking everybody this season on TBR Talks. And it’s about, to give you the context for the question, our youngest child, our daughter, is going to graduate from college in a month. And she’s going to be so happy when I no longer talk about her on this podcast. But anyway, she’s graduating, she’s 22, and she’s at that point in life, you know, 22 years old, you’re finishing up university and you look around, you imagine what you could do with your whole life, what you want to do with your life, where you want to be in the world. And talking a bit, you know, I know what I was doing when I was 22, but I’m really curious. So here you are now working for TWG AI, you’re doing this incredible work with these companies like Cadillac and Formula One and the Dodgers and all that. And when you were 22 about to leave university, did you think to yourself, I want to be an AI- I want to help companies use AI to change their businesses? Is that what you thought you were going to be doing at this point in your career? Or what was 22-year-old Milan thinking he was going to do?
Milan: That’s a really good question. I think the story I told earlier around how things have happened to me versus being conscious decisions is probably a theme throughout my career. I’ve been led by one, really interesting problems and two, awesome people. And so those are my two kind of like decision points and in whatever I do is do they solve one or both of those things? And so, there’s no planning involved here. It’s really reactive. But I think 22-year-old me is just asking open questions and being curious about the world and being curious about people and how companies work and kind of asking the questions that maybe some people are kind of afraid or embarrassed to ask because they might make you look stupid or uninformed or whatever. But I think that was a principle that I held. That’s how I do business. And I think you can kind of operate without a rigid plan if you just, kind of, ask questions, follow amazing leaders around the world and look for interesting things to work on.
Patrick: Yeah, that’s, I love the follow interesting leaders part of it too, because I mean, being open-minded and asking questions others don’t want to ask and looking for difficult problems is great, but man, you got to surround yourself with good people because that makes all the difference, doesn’t it?
Milan: Oh yeah. I mean, look, I was at J.P. Morgan and Jamie Dimon evangelized the apprenticeship model. It’s why he pulled everyone back into the office probably earlier than a lot of companies did, because being around colleagues, being around leaders, learning behaviors, good and bad, is really what shapes you professionally and personally. And finding those people that have followership and are inspirational and teach you continuously is really hard and it happens by accident. There’s no, you can’t just go out on a Monday and say, I’m going to go like build a really deep relationship with a phenomenal leader. It just happens over time. It happens by accident, happens through fortuitous interactions. And you got to, kind of got to put yourself in the game to take advantage of that.
Patrick: Yeah, 100%. And that the apprenticeship model has been so important to consultancies, to so many different kinds of companies that yeah, I’m glad. I’m glad we’re back in the office as much as we are now.
Final thoughts
All right, I lied, I’ve got one last question for you. I have to ask, having started to watch and follow Formula One a lot more this year, how many years until Cadillac is going to be the, you know, the manufacturer’s champion in Formula One?
Milan: *laughs* Yeah, so Dan Tarras would probably have a very good answer for this, the head of motorsports. As soon as possible, Patrick, is my answer.
Patrick: As soon- so, you’re not going to give me, it’s not going to be this season, it’s not going to be next season. Well, it can’t be this season. It’s too late already.
Milan: I think what the team are doing, what the team are doing back there from a standing start, you know, building a car and getting it on the track with all the regulations that exist within Formula One is a monumental achievement. I think, you know, step one, step two is winning races.
Patrick: Yeah, and it’s incredible. And this year they’ve got, they got the two races got cancelled, so now they have more time to look at all the data and make all the tweaks and stuff. So, it’s going to be a really, it’ll be, it’s going to be a good season. We’ll see how Cadillac ends up at the end of it.
Milan, thank you so much for coming on. This has been really- I’ve enjoyed this conversation immensely. And you’ve said some things that I think I’m going to- when I share this with my colleagues, they’re going to, there’s going to be a lot of, well, we may have to follow up with another conversation once my colleagues hear this. So, this has been really, really great.
Milan: Thank you, Patrick. Great to be here.
Patrick: Excellent. Thanks.
Tune in next week for another episode of TBR Talks.
Don’t forget to send us your key intelligence questions on business strategy, ecosystems, and management consulting through the form in the show notes below. Visit tbri.com to learn how we help tech companies, large and small, answer these questions with the research, data, and analysis that my guests bring to this conversation every week. Once again, I’m your host, Patrick Heffernan, Principal Analyst at TBR. Thanks for joining us and see you next week.
TBR Talks: Decoding Strategies and Ecosystems of the Globe’s Top Tech Firms
Join TBR Principal Analyst Patrick Heffernan weekly for conversations on disruptions in the broader technology ecosystem and answers to key intelligence questions TBR analysts hear from executives and business unit leaders among top IT professional services firms, IT vendors, and telecom vendors and operators.
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https://tbri.com/wp-content/uploads/2026/04/the-playbook-for-ai-reinvention-cover-1-scaled.jpg25602560TBRhttps://tbri.com/wp-content/uploads/2021/09/TBR-Insight-Center-Logo.pngTBR2026-05-04 12:43:442026-05-04 12:43:49The Playbook for AI Reinvention
In this episode of “TBR Talks,” host Patrick Heffernan is joined by TBR’s federal IT services experts, Senior Analyst John Caucis and Senior Analyst James Wichert, to discuss the current state of federal IT services and their projections for the next five years. The pair shares their thoughts on how the adoption and application of AI will impact federal IT companies overall and, subsequently, government spending, and whether a strong presence in the defense sector will become critical to the performance of IT services companies in the civilian sector.
This episode also highlights one of TBR’s newest research reports, the Federal IT Services Market Forecast,which is currently available in TBR Insight Center™.
Episode highlights:
The five-year outlook for federal IT
The link between IT services and defense and intelligence businesses
The expected impact of AI over the next five years
“There’s going to be a lot more emphasis on outcome-based contracting, you know, refocusing on IT modernization, but very constrained. It’s not going to be kind of the free-for-all that it was. There’s still a lot of investment that needs to happen. So, taking all that into account and taking into account the simple fact that there is still a lot of modernization work that needs to be done across the board, that’s kind of the foundation for our five-year outlook,” said Caucis.
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TBR Talks is produced by Technology Business Research, Inc.
Edited by Haley Demers
Music by Burty Sounds via Pixabay
Art by Amanda Hamilton Sy
Federal IT: AI Adoption, Defense Spending and Projections for the Next 5 Years
TBR Talks Host Patrick Heffernan: Welcome to TBR Talks: Decoding Strategies and Ecosystems of the Globe’s Top Tech Firms. Where we talk business model disruption in the broad technology ecosystem from management consultancies to systems integrators, hyperscalers to independent software vendors, telecom operators to network and infrastructure vendors, and chip manufacturers to value-added resellers. We’ll be answering some of the key intelligence questions we’ve heard from executives and business unit leaders among the leading professional IT services and telecom vendors.
I’m Patrick Heffernan, Principal Analyst, and today we’ll be talking about TBR’s brand new Federal IT Services Market Forecast with John Caucis, Senior Analyst for TBR’s Federal IT Services Practice, and James Wichert, Senior Analyst for TBR’s Federal IT Services Practice.
Starting point for the Federal IT Services Market Forecast
John and James, welcome back to TBR Talks. Really glad to have you guys here because you’ve done something that we have been promising for a long time. We’ve been asked for a long time to do this, and we finally have rolled out Market Forecasts in the IT services space. Always been a challenge. It’s something I’m really happy that we’re finally doing. And John and James, you guys rolled out the first one for the practice, specifically around the U.S. federal government’s IT services space. So, John, you want to tell us a little bit about the forecast?
John Caucis, TBR Senior Analyst: Yeah, it comes at a really interesting time a year after the Trump administration completely upended the market, the Department of Government Efficiencies and then the shutdown at the end of federal fiscal ’25, which further disrupted the overall space. We saw the markets actually expand slightly during fiscal ’25, which wrapped up on September 30th of last year, but mostly because it drafted off of the four-year, five-year bull market that preceded it. So, we took that into account as we were putting together our forecast. We took into account the comments and the interactions that we’ve had with the leading federal systems integrators, Leidos, CACI, Booz Allen, et cetera. And we certainly took into account what we have seen, what we have observed in terms of federal spending priorities, what we saw remaining in place during the Trump administration. It’s going to be AI and defense and national security and intelligence spending in a nutshell. But we put all that together, and we wanted to put together a product, a forward-looking product that really gave the audience, the reader, the perspective from the, you know, through the lens of the vendors that we track. So within that, you will see not only our projections and our analysis around the overall market, where we think the market is headed over the next year, two years, five years, but where the vendors themselves are headed and what are going to be their priorities and how we see them performing over the next five years. And just to kind of put a bow on the quantitative aspects, as I mentioned at the outset, we saw the market expand slightly in 2025, fiscal ’25. That is not going to be the case in ’26. We’re looking at a market contracting anywhere from 3-5%. Buoyed by the defense space and spending in the intelligence markets, but the civil market is going to remain really, really tough during the year. So that will be business as usual in fiscal ’26.
Five-year outlook
Patrick: I want to come back to the assumptions and I want to talk specifically about some of the companies that you’re covering here and the projections that you’re making. But looking out beyond 2026, what’s the five-year picture that you guys came up with?
John: The market will rebound. It’s going to take some time. The defense and intel and spending on national security as not just in the defense and intel spaces, but also civil should remain fairly robust. The Trump administration established that as a priority right up front. Conversely, though, the bull market in civil IT spending, I mean, we were observing double-digit civil growth for three, four, almost five years up until fiscal ’25. That’s over. The party is over in the civil space. That’s where the bulk of the elevated scrutiny on consulting work on behalf of DOGE, the Department of Government Efficiencies, that’s where the bulk of that happened. There’s going to be a lot more emphasis on outcome-based contracting, refocusing on IT modernization, but very constrained. It’s not going to be kind of the free for all that it was. There’s still a lot of investment that needs to happen. So, taking all that into account and taking into account the simple fact that there is still a lot of modernization work that needs to be done across the board, that’s kind of the foundation for our five-year outlook. So, we see the market struggling in ’26 overall with more opportunity on the defense and intel side, obviously, but the civil market should start to pick up again. At least that’s the sense that we’re getting from our observations of the vendors, that by fiscal ’27, certainly by fiscal ’28, the next election year, things should have stabilized in the civil space. And then we see a more moderate pace of expansion, low single digit rates through fiscal ’30.
Market leaders will be taking more market share
Patrick: So, let’s talk then about the companies. And when you talk about that projection next year and then the changes for the next five years, are there, and James, I don’t know whether you want to take the companies you think are going to do well first or the companies you think might struggle first, but who do you look at and say, this is going to be the most challenged in this current environment? And then who do you think is probably best positioned to actually take advantage of what you project is going to happen in the federal IT services space?
John: I’ll start with who I see as leading, who we see as performing the best. And right up front, it’s CACI. In fact, they are projecting for their fiscal ’26, which wraps up on June 30th; they’re projecting still between 8 and 9% growth.
Patrick: Okay.
John: And that’s with a civil business that comprises about 20% of their revenue base. So, they’re still looking at growth this year. They’re still looking at growth over the next two or three years. It will slow down. But with 80% of their business coming from the defense and intel spaces, a large proportion of that from the classified space, from the fact that the bulk of their civil business is in national security, border security, AI-related enablement, they’re going to do well in that respect. So, we see them as being the five-year growth leader. I think we were projecting that their five-year growth, compounded annual growth would be somewhere in the mid-single digit range. So, we see them as being the growth leader.
The other leader, just from the perspective of market share and the size of their revenue base, today it’s Leidos. In five years, it’ll be Leidos. They also have, with a slightly different portfolio, but they also have a very robust defense and intelligence operations. They’re one of the leading vendors that’s going to be participating in the Golden Dome project, the missile defense shield. And they are more exposed to the contraction in the civil space, but they also have a fairly robust health IT business, which slowed down. But, and I think James might talk about this one, when it comes to companies like Maximus, there’s still a lot of activity there, a lot of modernization work. And I think that Leidos will be able to benefit from that. So, we see them not only, and CACI actually, not only retaining their market share that they have today, but actually expanding it a bit.
And I think that suggests also another trend that we’re observing that the companies at the top of the market, the leading federal systems integrators, they’re actually going to be taking, we believe, they’re going to be taking market share from the smaller companies. We’ve seen what’s happened in terms of the 8(a) companies, they’re really under heavy scrutiny right now. A lot of them have gone under over the last year. And we see that coming. We see the top of the market. That being one of the reasons why the top of the market is going to be capturing share from the bottom of the market.
Patrick: Right. And so, I mean, that kind of consolidation is- it seems like it comes and goes in waves. And then, so 10 years from now, we’ll be talking about all the new players that have come in and taken market share. But for the next five years, you’re anticipating that the leaders are going to capture more of the total pie. Yeah. So, all right, James.
James Wichert, TBR Senior Analyst: I mean, just dovetailing on that, I think it’s also worth including General Dynamics Technologies in this conversation. Historically, there’s been little synergy at the top line level between the information technology and the mission system segments under the GDT banner. 3.4% of revenue growth is the largest calendar year improvement we’ve seen from them. And General Dynamics’ leadership team are forecasting fiscal year 2026 sales growing approximately 2.5% over 2025 sales of $13.5 billion. But we feel that GDT is well positioned through 2030 and could expand at a CAGR a little north of 3%. Now, obviously, GDT has a scale advantage over almost all the companies we track, given their annual revenue and General Dynamics’ financial backing.
Patrick: Right.
James: They’re just to better withstand these types of storms that we’ve been seeing in the federal space of late. And like John said, one trend we really noticed when putting together this forecast was that the bigger players were those set up best to expand their market share over the next few years. While they may not be as nimble as their smaller peers who can, at least in theory, more rapidly adjust to sudden changes, GDT certainly has the scale, the expertise, and portfolio necessary to face these challenges head on. And take some short-term disruptions, but thrive in the long-term. You know, DOGE and the government shutdown did negatively impact GDT, but it didn’t cripple them as much as a smaller player like, say, an ICF. Both GDIT and Mission Systems are already well aligned with the Trump administration’s priorities in the defense market. GDIT’s investments in AI, cybersecurity, and other growth areas, they’ve proven fruitful as evidenced by the growing demand for their digital accelerators.
The link between IT services and defense and intelligence businesses
Patrick: So how much is having a strong defense presence going to be critical to having an outperforming IT services company performance in the, an outperforming performance, in the civilian sector? I mean, is it a prerequisite that a CACI or a Leidos or a GDIT has that strong presence and success in the defense space in order to, or is it just- and I’m asking because I’m thinking about companies traditionally, if you’re particularly good in one area, like to go back to like Booz Allen Hamilton is a great example. You guys haven’t mentioned them, but in the federal space, certainly an important player, but they had to split Booz and Company and Booz Allen Hamilton because the two pieces of the business were not operating at the same, with the same kind of success and a whole lot of money. Anyway, it’s a long story there. But the bottom line is when I think about, okay, Leidos has this great defense business and a healthcare business, but here they are doing, how are they going to do in IT services? Is doing well in IT services predicated on having that backstop of a defense and intelligence business?
James: I don’t think it’s a prerequisite technically, but it is extremely helpful. So, looking at another company that, you know, I was just talking about, you know, like the big getting bigger, and I do think that’s true overall, but there is a substantial bull case scenario for Maximus over the next five years. And, you know, they are smaller. They’re heavily entrenched in the federal civilian market. They’ve been largely shielded from DOGE and the government shutdown related disruptions due to the essential nature of their work. And their recent really rapid expansion has been driven by the demand for medical disability examination services. They’re working on the contact center operations contract, parlaying that into IT modernization work. And so that’s all federal civilian, but then very recently, they’ve been successfully making inroads in the defense market.
Like, after years of very minimal wins, all of a sudden they notched two IT contracts, the US Air Force. They’re worth more than $160 million. They recently achieved level 2 cybersecurity maturity model certification. And, you know, they’re showing they’re serious about competing in the defense market. I don’t think it’s unrealistic at all to say they could expand at a CAGR of 4% over the next five years, something like that.
Patrick: Right. Which will exceed what you expect from GDIT. So, yeah.
John: And will exceed the overall market growth rate, which means they’re going to capture share. That’s a great point.
I think in general, the companies that we, Maximus might be the exception although it sounds like they are going to be buoyed somewhat by their defense business, which is gaining traction. But in general, the companies that grow their revenue base overall over the next five years. It’s going to be, the foundation of that is going to be in the defense, intel, and national security spaces. And that’s, as I mentioned, that’s Leidos, that’s CACI. That’s also not in the near term, but in the long term, Booz Allen. We actually see Booz Allen, despite how hard they were hit over the last year. And they’re, I’m guessing, at this point, they haven’t tendered their forecast for their fiscal ’27, which would run to March 31st of next year. I’m guessing it’s not going to be all that spectacular.
Patrick: Right.
John: They were projecting about a 10% decline. I think it’s, you know, after four or five years of double-digit growth, now their sales have turned down, but it’s all on the civil side. I see eventually Booz Allen, their defense business has remained fairly robust. That’s going to be the basis for them to not only buoy what happens, what continues to happen in the civil space, nor pullback in consulting spend, the shift to outcome-based contracting. But Booz Allen is just way too smart to continue to struggle.
Patrick: Right.
John: They’re already starting to figure things out. They were just rolling with the market for the last five years. I mean, they were well positioned to capture the type of spend that agencies in the civil space were, you know, that match with their spending patterns. That has changed quickly, radically over the last year, but Booz will adapt. And I actually see them over the next five year period, over the next five years capturing market share. You know, leaning on their advisory heritage, that is evolving as well. But in general, the companies that have a robust defense business, robust intelligence business, presence in national security in the civil side, we see them as performing the strongest over the next five years.
Patrick: Yeah, and betting against Booz would be like betting against McKinsey. It’s just foolish because, I mean, they’re too smart, they’re too well run. If consulting in particular, but IT services as well, really depend on client retention. I mean, I think Booz has done an exceptional job over the year at retaining their clients across the different agencies in the defense space.
Companies there are questions around
One more question specifically to the companies. So we don’t need to get into who you think is going to fall apart, but I am curious if there are certain companies you look at and think, and I know this is true when we look at the broader IT services space, you just think, I don’t know where they’re going. Like when I look out five years, they’re a bit of a mystery. The strategy isn’t clear. Where they’re trying to place their bets isn’t clear. Can they execute isn’t clear. That doesn’t mean they’re necessarily going to fail. You just don’t know what is really going to become of this company. Are there any like that in the federal IT services space?
James: I mean, KBR’s MTS spin-off is interesting in that sense, but I do think overall there’s still a vision there that makes sense. I think for the vendors I track, ICF is probably the one that most baffles me a little bit.
Patrick: Yeah.
James: They had this huge wave of M&A activity between like 2020 and 2022. And then they stopped that to allow their business to grow. And it was doing that. And then, their federal revenue cratered by more than 35% year over year in 4Q25. And while their revenue comparisons certainly won’t be that bad in the first half of 2026, they still won’t be pretty.
Patrick: Right.
James: Their overall headcount declined by close to 10% year to year. Most of their workforce reduction efforts, you know, that’s been more programmatic, consulting-oriented, and those are the aspects of the business that DOGE’s contract terminations hammered and the Trump administration’s moving away from funding. So, I mean, there’s still an opportunity in IT, but ICF as a whole just appointed a new company president whose background is heavily tied to the energy, environmental, and commercial part of the business. The vast majority of ICF’s recent moves have been related to grid modernization, you know, other energy-related plays. Their federal IT business will bounce back and return to growth in 2026, but there’s just more hard times coming for their overall federal business. And it doesn’t look like their management’s particularly interested in investing further in them at the moment. Barely any partnership activities, no M&A, there’s no major deal wins. It just doesn’t look great for ICF right now. I’m not entirely sure what their plan is. I don’t know if they want to look at divesting something even further into commercial to hedge their bets, but it just seems very chaotic.
Patrick: Yeah, so that’ll be interesting to come back to in the next year or two years. John, how about you? Any mystery companies out there?
John: SAIC.
Patrick: Huh.
John: Last October, they, gosh, their C-suite is- half the people that were there six months ago are no longer there. Their former CEO is gone. The chief innovation officer that she hired has departed the company. There were multiple other C-suite level and senior executive departures. They’re restructuring the business again. For the second time, in about 3 years. What that says to me is that they haven’t evolved away from the commodity IT types of services, despite making some good acquisitions over the last five years. The last acquisition was in 2021, despite really being aggressive in enhancing their alliance ecosystem, enhancing their partnerships, particularly with the hyperscalers. We saw a lot of great moves there. They introduced some fairly robust solutions in the cloud arena. Their messaging was good. Their messaging was strong. But then just almost overnight to see the upheaval that we’ve seen, and it wasn’t just caused by the market, because companies like Accenture Federal Services, IBM’s federal business, CGI, Booz Allen, I’ve mentioned already, they were hit harder.
Patrick: Right.
John: They had a much more severe impact on their top line from DOGE and the government shutdown than SAIC did. But it’s like SAIC is hitting the reset button again. And to me, that’s an indication that the previous leadership, at least those that departed, the board looked at their performance and did not see the evolution of the company happening as fast as it should have.
Patrick: Right.
John: But this is a really bad time to be resetting your strategies.
Patrick: Yeah.
John: So, I think there’s a lot of questions. If you read our report, that’s the analysis that we kind of wove around SAIC, that they are the most, the most questions really revolve around SAIC, where they’re going and how they’re going to get there, more importantly. They’re going to have to move fast. And they actually have started in one respect. They made an acquisition. of a company that enhances their portfolio in agentic AI, which is a good move. It’s not going to move the needle a whole lot in terms of revenue, but at least they have the capabilities that they can scale across the remainder of their portfolio. So that was great. If that’s an indication of what they’re going to do. Their profitability has improved, which is which is one of the goals of the new restructuring program, so they can plow those profit dollars back into the company in terms of investments, M&A and new solutions and whatnot. But there’s still a lot of questions around SAIC in my mind.
Discussing potential scenarios for the next five years
Patrick: So, I want to come back to the acquisitions and AI. But you mentioned the report. And I think one thing that we tried to do in the forecast for IT services was not just give a top line or a big number and say, okay, this is, the market’s going to grow at this amount over this number of years. We wanted to talk about what- once we’ve done that, and we can talk about how you guys came up with that number, but once we said, this is what the market is going to look like, this is what the growth projection is for the next five years, what are the things that could change that? What are the things that could be kind of the wild cards? And the pandemic was a reminder to all of us that these things can happen. And honestly, what happened with DOGE last year was another example of where can chaos come from? So maybe just highlights of a couple of the sort of the scenarios that you have in the report and how they play out.
John: We’ll start with a potential best case scenario, which would be, I think fiscal ’26 is already a foregone conclusion in the civil market. But a rebound, a sooner than expected rebound in civil spending would certainly be welcomed by, especially by folks like Booz Allen and Accenture and CGI and IBM. And there’s still a lot of modernization work that needs to happen. If the rebound in civil happens sooner than expected, if the stabilization happens, and we are actually seeing some signs of that now. I think the Trump administration recognizes that, as I mentioned, that there’s still a lot of baseline IT enhancement and enhancement of IT infrastructures that has to happen before they can start executing on their priority of implementing AI across the civil space, as well as defense and intel.
Patrick: And that’s true with- when we’re looking at on the commercial side, like every single enterprise has to go through IT modernization, data readiness. You can’t just flip the switch and turn on your AI-enabled solutions and think they’re going to work. So same is going to be true across federal government.
John: Yeah. So best case scenario, the recognition of that drives a rebound sooner than expected.
Patrick: Yeah.
John: Immediately off the top of my head, that’s what I’m thinking. I mean, another best case scenario would be that defense spending is even more robust than it’s expected to be. I think the Trump administration requested a defense, an overall defense budget north of a trillion for the first time.
Patrick: Right.
John: And we’re expecting to see that grow. Some are saying as high as $1.5 trillion by the end of the Trump administration, Trump 2.0. We’ll see if that plays out. But there’s going to be a huge IT component in that.
Patrick: Yeah.
John: So, the folks that are doing well now, because they have a footprint in defense intel and national security, they’re going to continue to do well. And that’s honestly why I see CACI being the growth leader over the next five years.
Patrick: Gets back to what you said earlier, that the bigger companies, the ones that have been successful now going into this, are simply going to gather up more market share. Any of the scenarios jumped out for you, James?
James: Well, to build off on what John was just talking about, I mean, yeah, the Trump administration has openly talked about increasing defense spending to $1.5 trillion in federal fiscal year 2027. And with the ongoing conflict in Iran, it’s yeah, it’s looking very likely that defense spending will be growing more rapidly than many of us would have expected a few months ago, which would provide additional opportunities to FSIs. Just looking at one vendor in particular, you know, GDT, they stand to benefit. Beyond mission systems, defense electronics being increasingly in demand, GDIT have been aggressive about fostering an expanded relationship with the DOD or the Department of War under Pete Hegseth and, you know, the DOD have been pushing vendors to self-organize, take on more risks during development, deliver results faster. And GDIT have certainly been showing a willingness to do that. Just a few months ago, GDIT launched a Mission Emerge Center in Springfield, Virginia, where the Pentagon and intelligence communities can closely monitor the co-development of innovative military solutions that GDIT and their partners are working on. And speaking of partners, yeah, GDIT have continued ramping up their partnership activity. Lately, their collaborations with AWS and Google, they’ve notably centered around defense needs. And I think that’s just one more scale advantage to GDT, just being able to build up new facilities and just actively collaborate with these companies.
Patrick: Right, they have the financial backing and they have the partnerships in place.
The impact of AI on the next five years
So, let’s wrap with AI, because everything always goes back to AI these days. Fair enough. And so, how did you think about the five-year federal IT services forecast in terms of what application of or adoption of AI could do both to these companies and then to sort of the overall government spending. And I’ll lay it out in the way I’ve been thinking about it, which is lots of companies are anticipating that they’re going to spend money on AI now, and they’re going to be saving money on their operations in three, four, five years, however long it takes. And they want to see that return on the investment as soon as possible. Government shouldn’t be designed around return on investment. It isn’t a business. But if you think about government spending, is the idea going to be the more AI enabled the IT environments are within an agency, the less money they’re going to be able to need in order to operate? So, you could actually see AI depressing government spending long-term, five years, maybe 10 years out. How do you see that playing? What was your thinking going into this forecast with respect to AI?
John: One of the vendors that comes to mind in this regard is Leidos, because I think that the way that they’re approaching this question is really smart. No agency is going to be able to, or be willing to, because of security concerns, because of the ethics around AI, how it’s going to disrupt the federal workforce, and other concerns as well. No agency is just going to overnight, as you kind of used your analogy earlier, flip the switch and go agency-wide with AI. They’re not going to AI enable their agency, end to end, comprehensively overnight. What Leidos is doing is, and this is also in response to the market, the shift in procurement approaches towards more outcome-based, and that’s still kind of questionable. I think it’s going to be more of a fixed price rather than, I think outcomes are, that’s a really nebulous concept.
Patrick: Yes, it is.
John: What does that mean? Whereas a fixed price, I mean, that’s on paper.
Patrick: Right.
John: That’s set in stone. So, what Leidos is doing is they’ve stepped back, and they’ve said, and they’ve got the flexibility to do this because they’ve improved their profitability far beyond what I thought was even possible over the last two years. So now they can, they’ve got that buffer to work with and they’ve said, we are willing to break up either existing contracts into smaller modules and AI enable that piece of the contract, this one particular function in an agency, this one particular department within an agency, take a piecemeal approach, show you what we can do. And even if it costs us more in the near term, in the long term, they’re setting themselves up for the downstream work, to take that AI implementation agency-wide.
That’s a slightly different approach than what I see in Accenture doing right now. Accenture, I think they’re falling back more on the messaging that I see in the global commercial space, where they’re emphasizing their ability to go enterprise-wide right from the get-go. And they certainly have the chops to do it. But I don’t think the maturity or the willingness on the part of the agencies is in place yet. And the budgets aren’t there yet either. There’s too much risk. Risk in terms of cybersecurity, risk in terms of workforce upheaval, a lot of unanswered questions. And I think that IT decision makers and agencies are- they want to see what can be done on a small scale before they commit to going larger.
Patrick: Yeah, and when you say outcomes-based is nebulous, it’s because of what you said at the end there about risk. The reason why outcomes-based contracts are so difficult for companies and their providers, IT services companies and enterprises or agencies to agree on is because who takes on the risk? If it goes badly, then a company, an IT services provider can walk away and go to the next company and try and cut their losses. But if it goes badly and an agency is stuck with an IT environment that doesn’t work, or an AI system or enabled solution that doesn’t work, then they’re the ones that took on that downside risk. So that’s a real challenge there. Any closing thoughts on AI, James?
James: I mean, what you were just talking about there, I guess you’d say that’s like a worst case scenario too for vendors. Just this environment where everyone’s taking on increased risk. I mean, Peraton’s big win with the FAA, the brand new air traffic control system contract, you know, their compensation’s being tied to performance benchmarks. So just more accountability and risk there. And if we see that bleeding elsewhere into the market in other contracts. But with AI, I mean, that’s a very interesting point on whether it would compress, spending over time. I don’t think it would in the next five years. And even in that case, I still think, you know, Neil Young famously once said, rust never sleeps. You always need to keep adapting and evolving.
Patrick: Yeah.
James: It’s like those systems always need to grow. And so. I think there’ll always be a market. It’s not like it’ll dramatically ever fall off the cliff or anything. I think it maybe expands less rapidly in say like 10 years, but yeah.
Patrick: Yeah, that’s why I’m still very bullish on the consulting market because all of this chaos just feeds the need for somebody to come in and help you figure out what to do. And AI, the shift towards agentic, or this push around, the hype around agentic right now, this idea that you can have a robot that will do things for you, and the robots never sleep, and that’s great, but then the robots eventually get bad at their jobs. Eventually they start doing things they shouldn’t do, or more importantly, they just aren’t needed anymore. And then you gotta retire them. Well, that process is not, it’s not as simple as just, oh, I’m not gonna check that e-mail account anymore. That’s not quite the way it works.
John and James reflect on what they wanted to do at age 22
All right, I want to wrap. This has been a little long, but it’s been super good. And I just want to wrap with the same question I’ve been asking everybody here in season five. This is season five of TBR Talks which is kind of amazing. So right now, as we’re recording this, my youngest child is in her last semester of college. And so, for her, the whole world is open. She can imagine all the possibilities of what she could do with her life. And so, it made me think back to, all right, when I was just graduating college, what did I want to do with my life? What was my sort of this is my- and part of this is also inspired by a conversation I had when I was in Toronto, where a woman who’s a very successful executive in an IT services company said if she could go back and do it all again, she would be an engineer for a Formula One race car team, so with that in mind, John, I’ll let you go first. James, I already know the answer. You already, when you were graduating college, all you wanted to do was come work at TBR because it wasn’t that long ago.
James: That was my dream ever since I was a child.
Patrick: There we go. But I know there’s- I know you were also a professional photographer, so we can come back to that in a minute. But John, when you were just about to graduate from college, what did you, what was your dream job, dream career?
John: Well, I thought I was going to go to law school, and I almost did. I almost did.
Patrick: Wow.
John: I applied and I got accepted. And then I decided to go to business school instead. And that was the right choice. And I think there’s still a lot of opportunity there because of how broad a business education is and how flexible it is. You’re not locked into one discipline or another. You have the focus on finance, on marketing, on accounting, global business, even IT to an extent.
Patrick: But when you were 22, were you thinking you wanted to be an attorney that was prosecuting criminals or you wanted to be like, general counsel for ExxonMobil? I mean, what was your-?
John: That was a long time ago.
Patrick: It was a long time ago.
John: I’m not going to say how long ago it was, but I actually thought about coming back and perhaps teaching.
Patrick: Okay.
John: You know, teaching like pre-law in my alma mater, but are moving into more like the constitutional law area.
Patrick: Okay.
John: Obviously, that’s not what happened. But what I’m encouraging the kids in my family now is, you know, look for a career that is not going to be disrupted by AI. And really, that’s the trades. I have three nephews, for example, who’ve already, I’ve had this conversation with, they’re not college age yet, but we’re encouraging them to either, and that could be the traditional trades or it could be college level trades, engineering, STEM, or business.
Patrick: Yeah.
John: I mean, business is essentially trade. But I think overall, I mean, I recently saw a podcast with the chief technology officer at Palantir, and he said, we got to pump the brakes on the fear, the fear mongering with AI, because there’s, and he might be overly optimistic, but I don’t think so. The disruption- there is going to be disruption, but it’s going to be more of a reset and an enabler. So maybe a traditional college degree will lead to something that we can’t even imagine at this point, an opportunity path that creates new opportunity avenues for folks.
Patrick: Right. All right, James, when you were 22, what was 22-year-old James wanting, thinking he was going to do with his life?
James: From a young age, I’ve always wanted to provide value to shareholders. That was everything I was focusing on day one.
Patrick: *laughs*
James: Yeah, when I was 22, honestly, when I was 22, I was here. So, anyway. *laughs*
Patrick: Yeah. But you took, but you had-
James: I mean, if we go earlier back, I don’t know.
Patrick: You have been paid for photography. That is-
James: Yeah.
Patrick: Yeah, you have been a professional photographer. So that was, but you didn’t imagine yourself like being a photojournalist out covering a war somewhere.
James: No.
Patrick: That wasn’t your, yeah.
James: I mean, when I was 21, I was here. But when I was younger, when I was in high school, I started a photography business. I was very big into marketing. I used to just do a lot of sports photography. So, I would go out to major league soccer matches. I worked with the US Women’s National Team. And that was very fun. I was, I mean, I still like taking photos. I still love doing that, like going out on hikes and just capturing that experience. You know, I guess I always thought it would have been cool to make something work out of that because I used to do like friends’ headshots and people’s like professional photos and stuff.
Patrick: Right.
James: And I guess like that’s what I was like looking at doing when I was going into college. And it’s like- held on to that for a while. And then, it’s all about the shareholders. It’s all about it.
Final thoughts
Patrick: *laughs* Excellent. Gentlemen, thank you so much. What we’re going to do is have you back in season six and see how the early part of the forecast held up and see how the companies you talked about are doing. Thanks for coming on.
John: Thank you.
Patrick: Tune in next week for another episode of TBR Talks.
Don’t forget to send us your key intelligence questions on business strategy, ecosystems, and management consulting through the form in the show notes below. Visit tbri.com to learn how we help tech companies, large and small, answer these questions with the research, data, and analysis that my guests bring to this conversation every week.
Once again, I’m your host, Patrick Heffernan, Principal Analyst at TBR. Thanks for joining us, and see you next week.
TBR Talks: Decoding Strategies and Ecosystems of the Globe’s Top Tech Firms
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https://tbri.com/wp-content/uploads/2026/04/federal-it-ai-adoption-defense-spending-and-projections-for-the-next-5-years-cover-scaled.jpg25602560TBRhttps://tbri.com/wp-content/uploads/2021/09/TBR-Insight-Center-Logo.pngTBR2026-04-27 16:55:362026-04-27 16:55:40Federal IT: AI Adoption, Defense Spending and Projections for the Next 5 Years
In this episode of “TBR Talks,” Patrick Heffernan is joined by Alan Flower, Global Head, AI Labs at HCLTech, to explore why AI represents a true inflection point on par with the internet, cloud and smartphones. Alan shares frontline insights from hundreds of enterprise AI engagements, explaining how organizations are moving beyond experimentation to large-scale deployment focused on productivity, simplification and growth — not just cost-cutting.
The conversation also examines the evolving role of talent, partnerships and innovation, and why the AI era is creating unprecedented opportunity for both enterprises and the next generation of builders.
“I think a lot of senior business executives are clearly thinking in terms of productivity, which is the flip side of the efficiency coin, of course. They’re thinking more broadly in terms of, if we can get AI to do the boring work, right? If we could get AI to do the things that are a distraction, then maybe my employees would have additional capacity to grow my business, right? So in general, the language seems to be one around growth and speed and innovation,” said Flower.
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TBR Talks is produced by Technology Business Research, Inc.
Edited by Haley Demers
Music by Burty Sounds via Pixabay
Art by Amanda Hamilton Sy
The Framework for Successful AI Adoption Within Enterprise
TBR Talks Host Patrick Heffernan: Welcome to TBR Talks: Decoding Strategies and Ecosystems of the Globe’s Top Tech Firms. Where we talk business model disruption in the broad technology ecosystem from management consultancies to systems integrators, hyperscalers to independent software vendors, telecom operators to network and infrastructure vendors, and chip manufacturers to value-added resellers. We’ll be answering some of the key intelligence questions we’ve heard from executives and business unit leaders among the leading professional IT services and telecom vendors.
I’m Patrick Heffernan, Principal Analyst, and today we’ll be talking about artificial intelligence, alliances, and the long view of technology with Alan Flower, Global Head, AI Labs at HCLTech.
The inflection point of AI
Alan, welcome to TBR Talks. This is season five, and I’m really excited that you’re with us today. I wish I was sitting in your office in London having this chat with you live, but we do what we can these days. And this is always the challenge, but also the blessing of having this kind of technology.
Alan Flower, Executive Vice President – CTO & Global Head, AI & Cloud Native Labs at HCLTech: Yeah, well, Patrick, I’m delighted that we meet again, albeit virtually, of course. I really enjoyed the last time we met in person in London. And of course, hasn’t our world changed so much since we last met?
Patrick: Yes.
Alan: So, I’m looking forward to discussing what we’re actually seeing out there.
Patrick: I think our world has changed since we last exchanged emails last week, but let’s dive into it. The two things I really want to talk about today, Alan, we’re- this season on TBR Talks, we’re focused on the longitudinal view of technology. So I wanted to talk to people who could put in perspective what we’re seeing changing with respect to AI, with respect to technology overall, with respect to enterprises and how they’re run differently now than they were when all of us, and I’ll put us in the same kind of age category, when we all started in business, when we all started our working lives, how much technology has changed. And the reason I want to focus on the longitudinal view is because we spent the last couple of years very much in an AI hype cycle. And so maybe we come out the other side of it and we think, that was just like all the other hype cycles, or maybe things have really completely changed. But only having that longitudinal view, only having that longer perspective can tell us what we might be looking forward to in the next phase as we go through the next evolution of the age of AI.
And I also want to talk about alliances. It’s an area that we do a lot of research on. I know you’re deeply involved in, and it’s another area that has changed a lot over the last five years in the way that companies behave in the ecosystem and the way that they partner is different than it was a few years ago. So, let’s start with the longitudinal view, the longitudinal view of technology and what you think of where we are with AI. Is it- are we ending a hype cycle and going into a real change or are we ending a hype cycle and going back into something else?
Alan: That’s a really good question, you know, Patrick, right? And I’ll give you my view and you delicately brought attention to the fact that maybe you and I have been in this industry for rather a long time, right, Patrick? And I would say, when I look at my career, right, my career started writing software for the original IBM PC.
Patrick: Wow.
Alan: Or indeed the PC that came before the IBM PC, right? So, my professional career, right, as a creator of solutions extends over 40 years. Now, during my career, let me just be really kind of straightforward, I think I’ve only seen four, maybe five genuine inflection points. An inflection point being basically a point in time after which nothing is the same ever again, right? So just think about those just briefly, right? The introduction of the original PC, that was just explosive, right, in terms of its impact, right?
Patrick: Right.
Alan: Think about the emergence of the internet followed by the World Wide Web. That was a massive one, you know, wasn’t it, right? Then we had the smartphone, right? Think about the iPhone, for example, that transformed computing for everyone, right? What came next, right? Maybe we could say the cloud, right? The cloud came next, right? And now we are in this fifth inflection point, which is AI. And when I look at those five inflection points in my career, the really big ones, think about it, right? The really big ones, the emergence of the internet and the World Wide Web, the smartphone, and now AI, right?
AI might be the biggest one of all, you know, Patrick. And so why do I say that, right? Why do I say to you that I don’t think this is a bubble, all right? Now, I’m kind of somewhat lucky, right? Fortunate. I lead our global AI labs here in HCLTech. You know us really well, you know, Patrick, right? But I’ve got six AI labs around the world. I’m actually speaking to you today from our AI lab in London, right, which is one of our busiest right now. We’ve delivered pretty much nearly 1,000 advanced AI engagements from these labs with clients around the world. So, this is GenAI and agentic AI. We’ve had that advantage of doing around about 1000 engagements with over 500 of the world’s biggest companies, right? And the remarkable thing is we’ve gained a huge amount of insight into not just what works and what doesn’t work, right? But we’ve gained a huge amount of insight into what the world’s leading businesses intend to do next.
Now, whilst I lead our AI labs, you would be kind of forgiven, right, for thinking that the people that come into an AI lab are probably technologists, they want to kick the tires, ask us our opinion of the latest models or whatever, Patrick, right? And that was certainly the case, right? Maybe three years ago, when the GenAI era started, we saw a lot of that technical exploration, right? But here, I think, is probably the most compelling change that I’ve seen, you know, Patrick, and certainly over the last year, but in particular the last six months, right? The people that are coming into our AI labs are the world’s most senior business leaders, chief executives, chief operating officers, CFOs, as well, of course, as maybe the more traditional CIO audience as well. So, when a chief executive rocks up inside an AI lab, they tend to have a huge amount of vision in terms of how they expect AI to impact their business. And I would say to you, Patrick, the reason for, I guess, my confidence around the genuine nature of this inflection point is it’s really clear when you speak to the world’s business leaders, they’re going to re-engineer their entire company around this premise. That AI is going to significantly augment the way that work- that way that work is done. And whilst we’ve got this combination of technology leaders and business leaders coming into the labs, there’s one really important change that I’d love to share with you really, Patrick. And that is, I think for most of the companies that we’ve supported over this kind of this recent period, for most of them, the experimentation has stopped, right? They got to a level of confidence probably around about the middle of last year in many cases, they got to a level of confidence where they believe that the impact is genuine and significant.
So, what this really means from my perspective is most of the work that we’re doing in our AI labs at HCLTech now, it’s not experimentation, right? These are clients saying to us, I want to deploy autonomous agents into production. I want to use AI to transform the way that my key value streams operate in my company. And I just share this with you because we’ve got this ringside seat, right, into what the world’s businesses seem to be doing. And I think just in summary, to answer your question, I think the evidence really in terms of what we’re seeing, the things that we’re building today, the things that we see running in production, I think there’s no doubt in my mind that the impact of AI is genuine and will be sustained, right?
The only thing, I guess, final comment I would make on this, you know, Patrick, and I’m sure you’ve got a strong view on this as well. From my vantage point, this journey is going far faster than we ever expected, far quicker, far, far quicker, right? So, the level of confidence that we see in business leaders in particular, far exceeds what I expected at this point in time in the journey. And along with that confidence, I think comes, you know, quite a concrete instruction to deploy this where it’s viable, you know, to do so, right? So, it’s moving more quickly. I think it’s absolutely a solid inflection point that’s here to change. And as I said, I think we see real evidence that clients want to deploy this where it’s appropriate to do so.
Cost cutting, optimization, productivity and efficiency
Patrick: So that, Alan, that opens up so many questions. Let me start with this one. We have been thinking about and hearing about the primary benefit of AI being about cost cutting, being about optimization, being about how many, to be very direct, how many people can you get rid of because people are expensive. When you meet with, especially when you meet with CEOs that come into the AI labs, are they talking just about cost cutting, or are you seeing a true business model reinvention, a real true transformation where there’s growth opportunities associated with their implementation of AI-enabled solutions?
Alan: Yeah, another good question. See, I think, right, I think it would be particularly short-sighted of a chief executive to be only thinking in those extreme terms that you shared, right, in terms of efficiency and cost cutting. And what I will say is I probably see the opposite, quite honestly, you know, Patrick. I think a lot of senior business executives are clearly thinking in terms of productivity, which is the flip side of the efficiency coin, of course. They’re thinking more broadly in terms of, if we could get AI to do the boring work, right? If we could get AI to do the things that are a distraction, then maybe my employees would have additional capacity to grow my business, right? So, in general, the language seems to be one around growth and speed and innovation. I think we should take a huge amount of reassurance from that, right? That the world’s business leaders seem to be looking at this in a more positive mindset. And if I think, and we’ve got so many examples, Patrick, of where modern AI, agentic AI in particular now is being deployed into the heart of these businesses. There are a number of themes that kind of spring to mind. I’ve mentioned productivity to you, simplification, right? The world’s businesses have an awful lot of complexity, complex processes in particular. So many examples now of where we’ve deployed advanced AI into those complex business processes to bring simplification. Humans love simplification, Patrick, quite frankly so this is a key area, anything to do with improving the customer or the client experience. That’s an obvious one where you would expect AI to be bringing impact today. And, you know, in general, this expectation that we will all be given the opportunity to delegate work to AI agents in particular. You know, this kind of assumption, I think, from a lot of businesses now that to improve the productivity of my most valued kind of employees, I want them to have the opportunity to delegate work to AI agents to just remove some of the load on them, right? So, I think overall, you know, the overwhelming message that we hear from clients is they just see this as a terrific opportunity, right, to drive growth, improve the customer experience. Clearly, it’s going to bring efficiency as well, you know, Patrick, that’s pretty obvious. But I’m generally encouraged by the overwhelming kind of positive theme of the conversations we’re having.
Patrick: I love the idea of framing the AI helps with the efficiency and the productivity, which frees up the humans to do the growth part of it. Because the productivity is very easy to understand. How much can we simplify? Oh, I should say, I think simplification might be the word for 2026. I spent a few days in Australia with a Big Four firm recently and every single client used the word simplify or simplification during those sessions. So clearly you guys have tapped into exactly the right thing. Simplification is going to be the catchphrase for 2026. But I love the idea of the humans bring the growth and the AI brings the efficiency and the productivity.
The framework for successful AI adoption within enterprise
I’m curious too, I want to run something by you. This idea that adoption within an enterprise- that you can have, you can have the best sort of business case for it, but what you need enterprise-wide is you need leadership buy-in, you need the masses to buy-in. That is, you need lots of people within your enterprise who are willing and open, maybe see AI as a little bit scary, but are willing to try and try it out and experiment with it. And then you need the lab, literally a lab like you guys have, or at least within an enterprise, you need a dedicated group of people who are working on AI, not only full-time, but working on it as a way of bringing it to scale within the enterprise. You have to have all three elements, the leaders, the masses, and the lab for AI adoption at scale within an enterprise to actually be successful. Is that framework work for you? Is that what you’re seeing when you talk to clients now?
Alan: Yeah, I like the way you framed it, Patrick. I do, and I’m going to give you a really good example of where that has worked, you know, in practice, right? And we’ve got so many of these examples, right? But I think time’s going to limit the number I can share with you. But let me give you one from the States, right?
So, we are in the process in the US. We are deploying an AI clinical advisor. This is AI in the consultation room. It sits alongside your clinician, your doctor, right? And it’s being deployed- it’s being deployed to 20,000 clinicians in the States as we speak. And it’s been, you know, the deployment’s been going on for quite some time. But the really interesting thing about why this particular project has been so successful was it started off as like an innovation idea, right, from someone inside the client’s clinical kind of workforce. And, you know, our role as the AI lab inside HCLTech, the client came to us to say, we’ve got a great idea, would it work, right? And we spend a lot of time helping clients understand maybe the art of the possible with AI. But anyway, we built an early-stage AI advisor. And guess what, Patrick, right? We handed it over to our client and it was quickly picked up by the chief nursing officer for one of the US’s largest healthcare providers, right? And this individual saw value, started sharing it with a few clinicians. Guess what? They started using it in the consultation room, right? And so, the remarkable thing was the frontline staff, right? The nursing staff, the doctors, the clinicians. The reason why they saw so much value in this AI clinical advisor, Patrick, was because it is reducing clinician burnout.
You can imagine if you’re a doctor today, whether you work in the States or with me in the UK, there’s overwhelming demand, right? And clinicians are getting burnt out. And this was an example where modern AI can start to reduce the burden on the clinician, right? It’s doing the obvious things like transcribing medical notes, but it’s ordering medicines from the pharmacy, for example, it’s updating medical records, but most importantly, it’s given the clinician access to the world’s leading medical research, right? It’s helping the doctor make better decisions. You go in to see your doctor with some sort of ailment, you know, Patrick, you hope, right? You hope that your doctor is going to prescribe, you know, the best medication for you and fully understands the impact on your health and so on, right? So, our AI clinical advisor is tapping into all of this research, right?
Now, overwhelmingly, right, the users, the beneficiaries of this keep asking for more, right? Patrick, you made this point, right? Having a user base that are basically screaming, demanding the benefits of this has been really helpful. Now, think about this from the business’s perspective, the stakeholder perspective, because you refer to this kind of, you know, the need to have this kind of top-down approach, right? This AI clinical advisor, typically, it’s giving back three-minutes for each consultation. Now, when I say to you, AI is giving a doctor three-minutes, it doesn’t sound like much, right? But here in the UK, a typical consultation with your doctor in the local surgery, you’ll be lucky to get more than 10 minutes, Patrick, with a GP in some countries, right? So, giving back three-minutes is- that’s absolutely a massive dividend, right? Now, in the case of our particular client, that three-minute bonus translates to a minimal annual saving of $200 million, right?
Patrick: Wow.
Alan: This AI clinical advisor is giving back $200 million worth of productivity, right? So, there’s the example, right? Where you have, let me call it a frontline workforce who are looking at AI as reducing the burden on them, AI doing the less interesting work. You’ve then got the senior leadership looking at this in commercial terms. And then of course, our role at HCLTech in the AI lab, helping the client rapidly build this solution. That’s a good example, Patrick, right, of where those three elements that you described come together quite well.
Patrick: Yeah, that’s an impressive number, no doubt, and that’s a great example.
The changing of roles and AI proficiency
I’m curious too, because you could start to think about how that use case will evolve. And at some point, the masses become so adept at the technology and adept at using AI, it just sort of becomes, you know, it becomes, well, it’s already embedded in e-mail, but it becomes just sort of part of the everyday. And that’s where I wonder sort of long-term, do- well, short-term, do companies, are the enterprise, the clients you’re working with now, short-term, right now, do they have the talent on hand that can implement and scale and then manage, and decommission agents as needed? You know, every agent’s going to need some support and maintenance. So, do they have the talent on hand now? And do you see a future where you don’t even think about AI talent as something special or unique, sort of every human in the organization has their own, proficiency with AI to a degree that, I don’t know if they build their own agents and all that. We’ll get to that later. But do you sort of see a future where, and I guess I’m asking a really hard question because it’s, do you see a future where your firm’s role changes a lot because you’re no longer providing that expertise that you’re providing now? You’re providing something else because everybody’s got that expertise.
Alan: It’s a good question, right, Patrick? And I, you know, there’s several elements for that question, but the first part, of course, was around this kind of belief that everyone will need to be AI native, right, in terms of their skills. And I think, yeah, that’s absolutely the case, right? There is, you see evidence today, quite frankly, Patrick, of almost a two-tier workforce emerging in some sectors where you see those earlier doctors maybe with a growth mindset, for example, they latch on. They latch on to the power that these tools can provide them. They latch on to the competitive advantage that these tools can offer. And they kind of supercharge their journey, right? And they’ll often come to us at HCLTech to guide that adoption of tools, right? But then you see that second group of people who haven’t quite invested in the journey yet at scale, right? And what we’ve got to, I guess, bring focus to is to get everyone up to the same level, you know, Patrick, so they’re able to fully utilize it. And this is no different, by the way, if you were employing someone to join your team, Patrick, as an example. You almost take it for granted that they know how to use Office Suites, for example. You just assume that everyone knows how to use PowerPoint or Excel, right? But it’s the case even with Office Suites today that most of us don’t really tap into the full depth of capability, right? We can survive with Excel.
Patrick: Right.
Alan: And every once in a while, every once in a while, you’ll come across someone who knows how to really drive Excel an example, right?
Patrick: Right.
Alan: Now, in this realm of AI, you know, I think really, each of us- there is an incumbent expectation on each of us that we learn how to really, really drive the power that these tools can offer us. And again, when I look around, you know, organizations today, you see these rock stars emerging, Patrick, people who are super productive, whether it’s the 10x developer or the salesperson that just seems to be able to issue more proposals than anyone else. There is really good evidence that people are starting to dig deep into the capability, but I will draw your attention. There was this recent report from one of the AI companies, Anthropic, where they’ve looked at, so what are people using Claude for, right? Who are they, what sort of work do they do, what are they asking Claude to do, but in summary, you know, they’ve kind of concluded that the average user of Anthropic’s products is barely tapping, you know, barely tapping into the surface, right, of the power. In other words, if you’ve got a Claude subscription, most people with a Claude subscription might only be using 10% of its capability, right? So, there’s an assumption, I think, Patrick, that over time, we all become a lot more conversant, use of these tools becomes a lot more habitual. And then that sort of significant uptick in productivity is going to be more widespread than maybe it is today.
Patrick: Yeah, and that’s really encouraging because when you think about it, if we’re only using 10% of this capability, that when we see it, as you mentioned, sort of those superstars, when you see AI used in its, even if it’s not full, even in its 50% or 75% capacity, what it can really do, it is absolutely astonishing. And I guess we’re all just heading in that direction. It’s just going to be at very different paces, of course.
Alan: Yeah, it is. And you asked the question about the impact on companies like HCLTech, right?
Patrick: Yeah.
Alan: And you know as well as I do, Patrick, that under the surface AI is a ferociously complex and sophisticated environment, right? You know this, right?
Patrick: Oh, yeah.
Alan: There is so much complexity there, right? And what we’re seeing, of course, right, is, you know, clients that come into us to ask us to, you know, design and build these capabilities. They need our help to implement and govern, of course, the consumption of, you know, modern AI across their organizations. It is, you know, it is revealing, right, significant additional demand for the services that HCLTech offers.
How HCLTech is partnering differently
Patrick: Yeah, and so let’s actually, let’s use that and think about the complexity. And I do want to, as I mentioned at the beginning, talk about the ecosystem and alliances. And just sort of very quickly, can you reflect a little bit on how much you’ve seen change in the last five years and sort of how HCLTech is partnering differently than maybe they did a few years ago, especially not just around cloud, but also around AI?
Alan: Yeah, it’s another kind of interesting question, Patrick, to reflect on. I think one of the great things about this company, right, HCLTech, is partnering and the, you know, the curation of an ecosystem of collaborative partners has always been at the heart of everything that we do, right? So as a company, we tend to partner quite naturally. And I think, you know, if there’s one thing I’ve learned, Patrick, during my career, if you want to move quickly and tap into new growth markets, partner is the way to do it, of course, right? Combine your strengths with that of a partner to go after the bigger opportunity.
And I think back in the past, right, before this AI kind of era, right? I think sometimes partners may have looked at a company like HCLTech as a channel to market. In other words, we want you to resell. We want you to resell our product, right? Now, our clients, of course, they want a more strategic relationship with a company like HCLTech. They see us as their transformation partner. They see us as that partner that will support, right, the complete reinvention of their business with AI. And that again is then reflected, I think, in the maturity and the evolution of the ecosystem relationships that we have. And if you look across what I would call the ecosystem of AI partners, companies like OpenAI, for example, and others, right, where there is great demand, just as an example, right, is whilst many of us clearly can see the benefit in using a product like ChatGPT, right? The benefits are obvious. Under the surface, if you want to integrate ChatGPT with your enterprise systems, right? Whether it’s single sign-on or more importantly, access to your data sources and other systems inside your company, right? There is a huge amount of work to be done, Patrick, right? To get that smooth integration between a product like ChatGPT Enterprise and a typical kind of complex kind of corporate IT environment, right? And that is a really good example of where, you know, our relationship with OpenAI as an example, brings this kind of two-way kind of benefit to both companies, right? In terms of, you know, we help the world’s largest enterprises integrate modern AI, not just OpenAI, of course, but a broad range of offerings. We help modern enterprises integrate advanced AI with their kind of corporate infrastructure. And then the flip side of that, again, is for the technology company, the AI provider, many of those companies don’t really have the ability to integrate their products with these complex legacy IT estates. So, it’s a mutually beneficial relationship, right? A company like OpenAI has the benefit of knowing that HCLTech, with all of our great kind of expertise and reach, we can help clients obtain maximum value from their use of a product like OpenAI.
Patrick: Right. And you said it exactly right at the beginning. Everybody needs a partner in order to grow. Nobody does end to end. Nobody serves every single client need. Everyone needs a partner in an ecosystem.
Career aspirations at 22 years old
Alan, this has been great. I want to wrap up with one quick question I’ve been asking everybody this season, and I’m reflecting on two things. One, my own- my daughter’s about to finish university. So, she’s, you know, 22 and the world is in front of her. And also that longitudinal view of technology that we were talking about before. So, I’m just curious. So, the 22-year-old Alan, what is it you wanted to be? And did you ever expect you’d be sitting in an AI lab when you were 22? But most importantly, when you were 22, what was it you looked at the world and thought, okay, the world’s in front of me. This is what I want to do.
Alan: What a really good question that I didn’t expect, Patrick, but it’s a really good question and I’m going to answer it, right? So, you know, when I was younger, right, when I was a teenager, when I was in my early 20s, there were only two things I wanted to do. I wanted to be an entrepreneur, right? I came from a family of businesspeople, right? So, I knew that I was going to create my own career, right? I’ll go and start a business was my belief. But the second thing, I knew that I wanted to work with computers, but software in particular. I knew as a teenager, that I’d figure out a way to run a business that involved software, right? Now, I will say that when I was 22, I can tell you what I was- I can tell you exactly, Patrick, what I was doing as a 22-year-old, right? I went back into the office unpaid every Saturday and Sunday for a year. In fact, it was longer than a year. Every weekend, I went back to the office unpaid. And you might ask, why on earth did you go back to the office as a 20-year-old?
Patrick: Yeah.
Alan: I went back to the office as a 20-year-old to teach myself then, new capabilities that would make me more productive. So as an example, this is probably showing my age, right? I taught myself the C language every weekend for a year, right? I used to write software products in Assembly Language, right? That’s going back quite some time, Patrick, right? But I realized that if I could master the C language, and of course, subsequent languages since then, I realized that I could actually create products far quicker than I was, right? And that investment of time as a 22-year-old enabled me to launch my first business, right? I was soon running a software company, for example, right? Now, I share that example with you, Patrick, right? Because, you know, my advice to your daughter or any other, you know, kind of ambitious young person is, it’s never been easier.
Patrick: Ha
Alan: It’s never been easier to start a business or launch a product or convert a good idea. And I met- this is my final story for you, Patrick, but we’ve got so many. I met a medical student from New Zealand, right? And she was probably the age of your daughter, right? And during her eight-week experience in India, she and the other students were asked to create an app, okay? And so, whilst I was there, I was at the New Zealand High Commission at the time, she showed me the application that she had created, right? She is a trainee surgeon. She knows nothing about technology, but she had built this app that basically knew pretty much everything around resolving common medical issues. If you injured yourself, was out hiking, this app would take a video of your injury and recommend treatment and then, you know, talked to various medical services, right? And I called over the New Zealand, I called over the New Zealand Minister for AI and I said, you’ve got to look at this, right? This is a medical student from New Zealand and just look at this app that she’s built, right? Now the remarkable thing, right? I just told you about me having to invest a year, right, just to learn a new programming language back when I was that age. This student built this product in two days.
Patrick: Oh my God.
Alan: In two days, right?
Patrick: Yeah.
Alan: So, if I was a 22-year-old today, Patrick, I would be innovating like crazy, right? The ability now to convert all of your good ideas into products and services that you can monetize and take to market, it’s never been easier, right? So, I think any young person, Patrick, now, they should be encouraged, right? Look with a positive view at all of the capability that’s now been made available to you. And if you’ve got the ambition to do so, leverage these tools, launch a business, launch a product. It’s never been easier.
Final thoughts
Patrick: Alan, I got to tell you, I’ve had a lot of conversations about AI. I can’t think of one that’s been more positive overall. You’ve just got, you’ve got just a great outlook on how these tools are- a great outlook based on experience on how AI and these tools are just so great for us. So, I really, I appreciate that. It is really refreshing to hear that kind of perspective. So, thank you.
Alan: Well, thank you, Patrick. It’s always nice to spend time with you. Thank you very much.
Patrick: Great. And we’ll be in person as soon as we can be. I will see you soon, Alan. Thank you so much for coming on the podcast.
Alan: Yeah, take care. Bye.
Patrick: Great. Take care, Alan. Bye-bye.
Tune in next week for another episode of TBR Talks.
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Once again, I’m your host, Patrick Heffernan, Principal Analyst at TBR. Thanks for joining us and see you next week.
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