In this week’s episode of “TBR Talks: Decoding Strategies and Ecosystems of the Globe’s Top Tech Firms,” senior analysts Kevin Collupy and Kelly Lesiczka join host Patrick Heffernan to debate India-centric IT services vendors Tata Consultancy Services (TCS) versus HCLTech. This discussion is part of TBR’s Vendor Showdown Series, where TBR analysts assume the role of head of strategy for one of the companies they cover and use TBR research and data to build a competitive intelligence case for that company’s position in the market.
“For India-centric vendors, it doesn’t, to me, make a ton of sense to overcommit to outcome-based. Being an efficient, fast follower, as I mentioned, is what got them to this scale, got them to this success. You know, fixed-price, SLA-based contracts are really going to benefit from AI and automation, and that’s going to help buffer those margins, help that profitability to reinvest it into what clients are demanding from AI. So, to me, that’s the best approach, that mixed commercial model,” said TBR Senior Kevin Collupy.
Episode highlights:
Differences in AI investments and strategies for TCS and HCLTech
Weaknesses observed within each company
2027 predictions for TCS and HCLTech
This episode is now available on demand across all major streaming platforms. Listen and learn today!
TCS vs. HCLTech Showdown: AI-driven Disruption
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’ve got an episode of TBR’s Vendor Showdown Series, where we take our newest AI research portfolio and apply it through the lens of competitive intelligence. Today we’ll be looking at TCS versus HCLTech with Kevin Collupy and Kelly Lesiczka, both Senior Analysts for TBR’s Professional Services Practice.
How the Showdown works
All right, welcome to the TBR Showdown Series, as we’re calling it, where we gather a couple of analysts to talk about two companies that they report on, that they cover, and put them head-to-head and say, what’s their strategy now? What are some weaknesses they see in the other company? And where do they see them going in 2027? So, this will be three rounds. First round, each of the analysts will take on the persona of the head of strategy for the company that they cover, and they’ll make the case to the board of why the strategy is working, why the strategy is the right place for them to be, how they’re leading the market and why. Round two will be an opportunity to talk about weaknesses, either in their own company and how they’re overcoming them, a little sort of jujitsu, hey, I’m not as weak as I look kind of thing, or straight up attack the other company and say this is where they’re weak. And then the last round, it’ll be a prediction for 2027. At the end of which I will then declare a winner. So today we have Kevin Collupy and Kelly Lesiczka. Kevin is going to be arguing on behalf of TCS, Kelly, on behalf of HCLTech. And you know what, Kevin, since you’ve been dreading this more than anybody else, we’re going to let you go first.
Kevin Collupy, TBR Senior Analyst: Yeah, absolutely.
Round 1: Making the case for TCS and for HCLTech
Patrick: So round one. Take it away, Kevin.
Kevin: So why TCS stands out, their strengths, why they have the advantage. Three things immediately come to mind. It’s been true for over a decade. They’ve got scale. They’ve got excellent resource management. They’ve got great financial resilience/profitability. The scale, $30 billion company, really only second in the entire professional services market, IT services market to Accenture, neck and neck with NTT Data. Longstanding client relationships. They’re one of the few that’s still capturing those mega deals out there, those billion dollar plus deals. They’re truly enterprise ready and they run at a severe discount to those global players such as the Big Four.
You know, as far as resource management, longstanding strength of management, workforce has steadily been around that 600,000 figure. It will jump up 10,000, it will jump down 10,000 here and there, but it’s been hovering for about two years pretty consistently at 600,000. However, with that scale, their attrition remains on par and below a lot of the global GSIs, but on par with the India-Centric peers, low teens, which is pretty impressive being two to three times the size of a lot of the other India-Centric peers. And from an AI Disruption Index and TBR’s HIRI, Human Intensity Reduction Index, that’s key to improving skills. That productivity at that massive scale, it’s not about simply reducing headcount. TCS has really been doing well lately. Last fiscal year, they reported close to 70 million learning hours across their workforce, 5 million competencies gained. In the latest quarter, they noted that over 300,000 of its workforce have been, how did they word it, been trained in a higher proficiency of AI and ML. So, whatever that may mean, but it draws the attention to the resource management that they know it’s important. They’re putting the effort in on their staff and they are training.
And lastly is the financial resilience. They’ve stabilized their revenues. They had a bit of a choppy few quarters post that COVID boom, but they’ve really kind of found their way. They’re at the low single digit margin, low single digit year to year growth in terms of revenue. However, their margins, they’re pretty incredible to think it’s a 600,000 person organization based in India selling IT services and they’re operating at 25% operating margin. And management’s even said they have aspirational goals to be between 26% and 28%, which is pretty crazy. And that margin strength, it gives room for TCS to invest in that next wave from the internet to the cloud, now it looks like AI is going to be that next big thing and they’re getting ahead of it and they’re investing heavily. And that financial resilience and stability gives them that ability. And just last year now, TCS I want to say last October announced, how did they word it? They want to become the world’s largest AI-led technology services company. So, they’re kind of drawing the line in the sand, and they’re not necessarily being a fast follower in this case, which they’ve done so well in the past. They’re trying to be a leader. So that’s, yeah, that’s kind of my case, that financial capacity, labor force, scale, discipline, client access. It’s hard to replicate.
Patrick: And that last point on how they’re positioning themselves as an AI leader is what sort of puts them high up in our, in TBR’s AI Disruption Index when it comes to the y-axis of that little compass chart that we have.
Kevin: Yes.
Patrick: So, all right, that’s the case for TCS. Kelly, HCLTech.
Kelly Lesiczka, TBR Senior Analyst: Yeah, sure. It’s hard to follow, Kevin, your intimidating statistics there, but we’ll start. I’ll do the best I can. So, HCL has kind of shifted their go-to-market strategy similar to all the other companies following the surge in AI, how it’s impacting their clients and what they need to do moving forward. HCL’s partner ecosystem has been a vital part of their strategy for a long time now. It’s really grown and evolved beyond just their core, but they are closely aligning themselves with AWS, Google, and Microsoft. Google Cloud has been a long-term close partner for HCL, but it’s now further strengthened by the launch of its Gemini business unit, which they launched in April. It’s helping them to enhance their AI technology offerings, as well as focus more on some of those industry-oriented offerings within manufacturing, retail, and technology. Technology services has been a growing vertical for HCL over the past few years. And so, I think this close alignment is helping them to penetrate more around those targeted solutions, focusing on more specific needs and workflows. HCL’s primary goal is to become an AI orchestrator, at least for now, not prioritizing or favoring any platforms, but really focusing on the client needs. That’s always been a strength of the company is to really align themselves with their clients and give them what they need, even if it doesn’t necessarily align with the company’s portfolio, but to do- not giving them services that they shouldn’t have, but just making sure that they can meet the client where they are.
One more notable investment that I’ve found is around HCL’s data center investment. And it’s a strategic move for HCL. It’s really pushing them to work better with the local clients in India, particularly government and other regulated industries. Competition in the region is really ramping up both from IT services vendors as well as from now the consultancies are really making a stronger push there. And I think the way that HCL is approaching it is really strong for two main reasons. And the first one is just the capabilities that it’s providing. They’re getting the compute capacity as well as increasing their ability to deliver engineering software and managed services in the region. The investment centers on training AI and SLMs to help their clients create their own internal AI stacks, so going back to that orchestration role. And HCL’s approach is focused on more of a tiered approach, so looking at the smaller models that are more specific to problems or workflows within enterprises and then distributing different tasks to different models depending on what the needs are. So, it’s not just using one platform for all of it, but making sure that the workloads are better distributed.
The second reason that is pretty strong is around the financial strategy and the way that it’s supporting this, it’s not just putting forth all of that investment itself. It’s working with partners looking for different funding. And for example, HCL’s working with Sarvam to build out that physical infrastructure and it’s including a technology center as well as an AI data center in the region- so, in India. The additional resources are really helping to enhance HCL’s position as that orchestrator and provider. And it just I think it gives them a leg up, whereas they already are well known in India, it helps to ward off some of the competitive pressures there.
The last main strength that I wanted to call out for HCL is definitely their engineering and R&D. A lot of its competitors, both within India-centric vendors, as well as just some of the IT services have been investing in engineering services over the past few years, but it’s an advantage that HCL has already had in terms of building out those proprietary platforms, which it now has AI Force, and it’s creating more of the industry solutions based within this platform, so for example, their Agent Store. And so, it’s long-term investment and just experience around engineering is really helping it to I think get better grounding around AI as it can now do some of the more AI design and deployment tasks that it couldn’t, or it has an advantage doing it more quickly than it peers.
Patrick: Excellent. So, end of round one, what I’m hearing is TCS big and profitable. That’s basically what they are.
Kevin: Yep.
Patrick: And HCLTech is all about ecosystem orchestration and engineering. One thing I want to point out for people from TCS, among those 70 million learning hours, some of them could be listening to TBR Talks podcast. That could count.
Round 2: Looking at weaknesses
So round two. Now it’s an opportunity to either tell us why the other company has some weaknesses they won’t be able to address or talk about some other weakness either in your company that you’re covering or the market that’s forcing all companies to address some kind of challenge or weakness. So, we’ll flip it around now. Kelly, you get to go first. Take it away.
Kelly: Thank you. So, a weakness, I’m not going to attack TCS, I’ll focus on HCL. But for HCL, I think a challenge that they’ve had is around visibility outside of their existing clients as well as traditional services. HCL has executed on a large marketing effort program a few years ago. They’ve also been working with MetLife Stadium to help grow the brand recognition outside India and just around. I think to more modernize the brand, showing off some of the digital and experience capabilities that they have. There is still some limited recognition of HCL’s full digital and AI portfolio, especially outside of these established places, which can, I think it can slow them down in some spots. Even as HCL is seeking to evolve its portfolio to better align with AI needs, including adding different talent, its market perception as an AI orchestrator could use some more investment there. I think growing in the talent, using some of those partners, but again, you don’t want to rely too heavily on partners. But I think just kind of focusing on building their brand and recognition to show the full scope of their portfolio. Looking at the HCL software, I think sometimes it’s kind of forgotten in a sense, but it’s a strength that they have. It might just need a little bit more of a push.
Patrick: So would you say sort of thinking about like if they have a credible differentiation around, I hate that word but still, around AI, they have not yet monetized that they’re credible, what they’re doing in AI, they haven’t fully monetized in part because they just haven’t had the marketing behind their brand, right.
Kelly: Yeah, I would say so. I think they’re kind of shifting there. I think that the MetLife Stadium project is a good example, and it’s showing what they’re able to do, not just behind the scenes, but also the user interface and how people are interacting with it.
Patrick: Okay, Kevin, what do you got?
Kevin: Absolutely. So, kind of take the consulting approach here. I’m going to both try and tear down HCL and pick up TCS. We’ll see how it goes.
Patrick: We’ll see how long I let you do that, but let’s go.
Kevin: So, HCLTech, in my opinion, I think it’s got to be careful balancing its ambitions with the actual real-world economics. HCLTech, as Kelly had mentioned, definite strengths in engineering and proprietary AI and IP. But the challenge is its lower margin base and the need for simultaneous investments in a moving market that are necessary to keep up in AI. It could be a challenge. And to me, it’s noticeably among the India-Centric peers been focusing a greater amount on that outcome-based pricing that we hear so much about, and that creates a lot of risk. You know, it passes the risk from the client back to the service provider.
So alternatively, from TCS’s point of view, they’re happy with fixed price, fixed capacity, time and materials, transaction pricing, you know, for a majority of their contracts. It’s what’s gotten them to this point.
Patrick: True.
Kevin: And selectively, they do offer outcome, how do they term it, they term it as output-based commitments. You know, when TCS has enough visibility or pricing control to give it a proper risk management to that contract. For India-centric vendors, it doesn’t, to me, make a ton of sense to overcommit to outcome-based. Being an efficient, fast follower, as I mentioned, is what got them to this scale, got them to this success. You know, fixed price, SLA-based contracts are really going to benefit from AI and automation, and that’s going to help buffer those margins, help that profitability to reinvest it into what clients are demanding from AI. So, to me, that’s the best approach, that mixed commercial model. And I think TCS is doing it well. And I have pause right now on HCLTech’s strategy.
Patrick: Fair enough. Okay. And that wraps up round two.
Round 3: Predictions for TCS and HCLTech in 2027
So, round three. Let’s hear some predictions about what you- now, you can just be a TBR analyst. You don’t need to be the head of strategy. Take that hat off. So, you’re the TBR analyst looking ahead at these two companies. And what- sort of, the prediction can be anything from sort of basically, are they going to grow as fast? And what do you expect is going to change? Or what’s some kind of big bet that they might be making in 2027? So, Kevin, you get to go first again.
Kevin: Perfect. Yeah. So TCS in 2027. It’s going to look a lot like TCS in 2027.
Patrick: *laughs*
Kevin: And that’s a good thing. I think from the internal workings, they’re going to be, or hopefully be, less labor linear. That’s the biggest key for TCS to keep pace in that the next wave of what IT services, professional services is bringing. I don’t expect a major headcount collapse. I do expect slow, low mid-single digits revenue growth year to year. I expect more output from its existing labor force. You know, really the goal is revenue growth without that proportional headcount growth. That’s going to be the kind of the golden goose. And automation absorbs more of the coding, testing, monitoring, process work, while the employee skills are going to have to shift a little bit, which TCS has been trying to get ahead of, you know, towards engineering, orchestration, governance, you know, domain expertise, you know, more AI will be embedded into its internal IP, such as in WisdomNext, in BaNCS, in Cognix, all TCS products. You know, as Kelly had mentioned, HCLTech has been investing in infrastructure. Same with TCS. In the last year, maybe 18 months, announced TCS HyperVault, you know, AI-enabled data centers. And that helps expand, you know, TCS’s breadth. It’s not going to be, you know, a main profit driver, a main growth driver, but it’s just that ongoing necessary expansion that TCS is going to have to take, you know, to remain where they are.
And as far as the four models, kind of the way we look at in our AIDI disruption reports, from operating commercial delivery and partner models, the operating model is still people heavy, but increasingly human AI and skill centric. The commercial model, definitely going to still be times and material, fixed price, SLA, with a little bit of that outcome-based pricing where it makes sense from an economic point of view. Delivery model using Agentic, using standardized and repeatable solutions and platform-led delivery. Partner model, maybe a shift away from just the focus on the hyperscalers, now moving to the AI models, to silicon, to the software providers, to infrastructure, TCS can really play that sweet- the sweet spot for them is that AI-enabled technology orchestrator and at a discount to the Big Four, to the GSIs, to Accenture, but still with a high quality and know-how. The main risk is, you know, AI revenue could be project-based, could be shorter, could be lumpier. So, the key for TCS will really be converting those projects to that managed services. Really, that’s the happy place for TCS, that ongoing revenue stream. And that’s my thoughts.
Patrick: Take away that word discount and weirdly, I think you just described TCS as the Bain.
Kevin: Ha, yeah.
Patrick: Bain, B-A-I-N, the Bain Consulting of the IT services world. Never would have put those two together.
Kevin: Yeah. Discount, value.
Patrick: Take away the discount, talk about quality, talk about staying the same, just executing on what they do well. All right, Kelly, prediction for HCLTech for 2027.
Kelly: Yeah, sure. I think HCL, not to copy Kevin at all, but I don’t think we’ll see too many significant changes. I think at the core they’ll still be doing what they’re currently doing. I think they have a lot of investments in place that they need to kind of pan out, like the data center investment, for example. I think HCL Software is a good opportunity for them that they can leverage. There’s been more innovation within the practice, you could say, over the past few years, and I think that’ll help them, especially as they’re coming out with the Agent Store, for example, those industry solutions, I think they’ll see more. I think that’s a good avenue for them to go down and to really push. While its delivery model, HCL’s delivery model, has not really caught up to the shift in portfolio to account for all these new offerings and all, HCL does pay close attention to its talent profiles to help it better, I think deliver better on projects, especially as now they’re looking to pursue the AI-assisted delivery. Paying attention to those talent profiles helps. I liked, Kevin, your use of linear, oh what was it?
Kevin: Non-linear labor growth. Or revenue growth.
Patrick: Yeah, non-linear revenue growth.
Kevin: Yeah, so, you don’t have to sell more product and hire more bodies. That’s, you know, if you can disconnect those two, that’s going to be the key.
Patrick: Right.
Kelly: Yeah, HCL had a goal, I think it was last year, they had come out with a goal to increase revenue without increasing headcount. Currently, they’re not quite there yet, but I think the moves that they’re taking around AI could help them improve. And their HIRI, as Kevin had mentioned, has actually been above the India-Centric peer average. So, they’ve always been very good about not hiring ahead of demand and making sure that it’s not hiring too many people. And they have a good balance. And I think that’s partially what helps them stick close with its- keep its portfolio relevant while not stretching margins too tight.
The main note I think I wanted to call out for HCL was really it has been good at keeping its portfolio close with demand. I think I mentioned it a bit before. Their core capabilities always seem to be close to what client needs. A lot of their strengths in application services, IT optimization, data utilization, and engineering. And now it’s all- it all needs to be addressed as clients are looking into this AI project. So, they’ve just been good at keeping the core tight and relevant, I think is a good way to put it.
Determining the Showdown winner
Patrick: Excellent. All right. Thank you very much. So now we’ll wrap it up with the final decision. Who is the winner in this battle? And I have to say, so you guys know me well enough, there are certain things that just resonate with me, so, ecosystem, orchestration, engineering. Knowing that HCLTech’s HIRI, the Human Intensity Reduction Index, is actually in the not only market leading, but in the positive side, all of that is indicators to me of a company that is absolutely doing everything right. And orchestration and ecosystem and engineering is what so many companies, so many companies that are doing well are doing well, but also so many clients are looking for exactly that. Like that’s what we hear repeatedly across the entirety of everything we cover at TBR is that people are looking for those ecosystem plays. Engineering has become such a hot thing. And then orchestration is exactly where companies should be playing.
And then when Kevin brought up that HCLTech is pushing the envelope on outcomes-based pricing. I’m like, well, that takes it over the top. There’s no way I can’t pick HCLTech for the winner here until Kevin then explained how TCS has this idea of output-based commitments and is actually moving its commercial model towards that. And so, I think short term for maybe 2027, I would say HCLTech is definitely going to outpace TCS, but probably the rest of the India-Centric peers. But at the end of the day, you know, big and profitable is almost always going to win. And if big and profitable can actually shift its commercial model and embrace that outcomes-based pricing, even if they have to call it output-based commitments, it doesn’t matter. It’s still the way that the market is going. And I have to say long term, unquestionably, I think TCS is going to be the winner of this particular battle.
Kevin: Excellent.
Patrick: Are you happy with that decision?
Kelly: I’m content. I think it was the right decision.
Kevin: Yeah, I think it’s, as our favorite sports radio analysts would say, it’s kind of a scoreboard. If you’re in business, you need to make money and you need to be profitable and TCS does that well.
Final thoughts
Patrick: Fantastic. All right, thank you both so much.
Kelly: Thanks for having me.
Patrick: Next week, we’ll be taking a brief break from the Showdown Series when I’ll be speaking with Chris Antlitz about TBR’s upcoming coverage of Starlink.
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.
https://tbri.com/wp-content/uploads/2026/10/TBR-Talks-S6E3-Promo-Image.png12001200TBRhttps://tbri.com/wp-content/uploads/2021/09/TBR-Insight-Center-Logo.pngTBR2026-10-16 13:36:042026-10-16 13:36:04TCS vs. HCLTech Showdown: AI-driven Disruption
In this week’s episode of “TBR Talks: Decoding Strategies and Ecosystems of the Globe’s Top Tech Firms,” senior analysts Catie Merrill and Alex Demeule join host Patrick Heffernan to examine whether ServiceNow or Salesforce currently holds the stronger position in AI adoption. This discussion kicks off TBR’s Vendor Showdown Series. In this format, TBR analysts assume the role of head of strategy for one of the companies they cover and use TBR research and data to build a competitive intelligence case for that company’s position in the market.
“Both companies, I think, have a lot in their corner. You know, when we talk about the right to play, I think that they both have the right to play in this new world. We talk about what’s going on, like obviously what happened in equity markets with the SaaS-pocalypse back in 2025 through the beginning of 2026. You know, obviously so much of the opportunity is moving to the model layer, but we talk about complexity, and complexity brings in more money,” said TBR Senior Analyst Alex Demeule.
“These vendors, I think, are in a position to sort of navigate that complexity on behalf of buyers and being an intelligence broker that’s capable of stitching together what is increasingly becoming a really broad market and being able to sort of cut through that noise and be able to be the ones that are actually figuring out how we take this large language model capability and turn it into the outcome,” said Demeule.
Episode highlights:
Changes in revenue per employee and operating leverage for ServiceNow vs. Salesforce
Weaknesses seen within the companies and across the technology space
2027 predictions for ServiceNow and Salesforce
This episode is now available on demand across all major streaming platforms. Listen and learn today!
ServiceNow vs. Salesforce Showdown: AI Adoption
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’ve got the first episode of TBR’s Vendor Showdown series, where we take our newest AI research portfolio and apply it through the lens of competitive intelligence. Today we’ll be looking at ServiceNow versus Salesforce with Catie Merrill and Alex Demeule, both Senior Analysts for TBR’s Cloud and Software Practice.
How the Showdown works
Welcome to the TBR Showdown Series, where our analysts take on the role of the head of strategy of one of the companies that they cover. And they’re going to use TBR’s research to make a CI case for why their particular company, in this case, has the best or the better right to play in the market.
And so, I’m joined by Catie Merrill and Alex Demeule, and we’re going to talk about ServiceNow and Salesforce. So that’s the Showdown. That’s the head-to-head ServiceNow versus Salesforce. So, what I’m going to ask in round one is that they each make their case for why ServiceNow and why Salesforce have a better right to play in this space. And then in the second round, we’ll ask for a weakness. They can talk about the company they cover; they can talk about the space overall; they can talk about whatever they want in the weakness. And then in the third round, I’ll ask them to make a prediction about their company heading into 2027. All, of course, within the context of what AI is doing, both within their company and within the broader space, the disruption that adoption of AI is causing these companies and causing all of their clients and their ecosystem partners. And then at the end, we’re going to declare a winner.
Round 1: Making the case for ServiceNow and for Salesforce
So, with that, round one, Catie, why don’t we start with you? Let’s make the case for ServiceNow.
Catie Merrill, TBR Senior Analyst: Sure. So, I think looking in the context of our AI disruption research where we’re looking at HIRI, which is the Human Intensity Reduction Index and changes over revenue per employee and operating leverage. So, we do have ServiceNow ahead of Salesforce in our HIRI metric. They’re still trailing below their peer group, but that’s largely a function of operational maturity if you look at some of the big names that they’re stacked against. But then bigger picture in our AI business model matrix, that’s where we’re looking at a couple different things. So, disruptions in commercial model, partner model, sales operations model, and R&D, which is interesting because we’re not just looking at how AI is impacting the cost structure per se, but how it’s impacting their ability to innovate faster and get to market faster. So, looking at those four areas and then positioning them where they’re placed currently in the AI space and where they’ll be in the future based on our own conversations with customers and our own research.
So currently within that matrix, we have ServiceNow leading its peer group and slightly above Salesforce just for the current position. And we can talk about the future, but that’s really, again, several factors are feeding that, but I think simply if you just look at their growth profile, they’re growing much faster than their peer group, at least organically, 20 percent plus subscription revenue growth. And then we also look at the role the platform plays in their ability to expand in these new markets. And that’s always been an advantage for ServiceNow, but in the AI era, I think specifically, it’s really allowing them to expand heavily and capture growth in these new areas. So, it’s both vertically in the stack, so not areas that influence AI per se, but areas that enable AI, which are really the bottlenecks that we continue to see. So, that’s security, observability, AI governance. And then it’s also letting them expand horizontally as well into these different business functions. And I think the hot topic one is, of course, CRM and ServiceNow has big ambitions to expand in CRM as well. And that has implications for a company like Salesforce.
Patrick: Excellent. All right, Alex, you’ve got to make the case for Salesforce.
Alex Demeule, TBR Senior Analyst: Yeah, absolutely. And I think that these two names right now are it’s just a really interesting dichotomy to cover because historically these wouldn’t be names that we would be sort of juxtaposing as competitors and when we’ve seen this AI era, this massive pivot from two vendors that predate this AI era and how they’re both trying to come from their historical domains into what is now becoming a more similar opportunity for them, that being sort of this layer on top between the application layer and the model layer that can be that agentic execution and agentic management. And I think that the thing that has always come back to me with Salesforce and how they’re positioned ahead of this opportunity, is just that I feel that the strategy that they- the strategic pivot that they really took towards the end of the 2010s, we’ll say, back when they’re acquiring MuleSoft and then Slack, they’ve really been able to bring together a really strong portfolio of still somewhat disjointed apps. And I think that’s something that I could talk about during this weakness segment. But they really capture and hit a lot of the parts of that AI value chain in a way that I don’t think that ServiceNow is necessarily able to match, at least today.
So again, Slack being this sort of digital HQ, and that was something that Marc Benioff was calling it back when they were acquiring Slack. Slack as a conversational interface is one, an established interface that has a robust install base. And two, as we’ve seen AI come into the fold, Slackbot was one of the first parts of Salesforce’s AI product story that really hit its stride in seeing sort of rapid adoption. And part of that can be down to sort of how they’re pricing these and sort of offering limited capabilities within free tiers and stuff like that. But a lot of the feedback that we’ve kind of seen out in the market has been relatively positive for Slackbot, especially when we compare it to some of the other still emerging platforms that Salesforce has. So, I think that what they’ve been able to do with this UI that is becoming a bigger part of that AI value chain, it does set the stage for being able to capture that control interface. And I think that the downstream opportunities from that are where it flows next.
And the other side of this kind of gets into more recent acquisitions with Informatica. And when I talk about sort of this broader portfolio that Salesforce has been able to put together, Informatica was kind of that sort of closing deal that brought it all together and bringing in that data management piece. You know, both Salesforce and ServiceNow are marketing themselves on the context that they’re able to bring to AI. And so, the ability to bring context isn’t necessarily a differentiator, but where that data is stored and how close that is stored to the agentic execution layer, that does matter. And with Informatica, Salesforce gained a mature offering that already had its own mature install base. And it’s an install base that captures both Salesforce data and non-Salesforce data. And so that ability to come to market with a UI in Slack that’s integrated across different application vendors and can be used for managing agentic tasks and then being able to bring in data across Salesforce and non-Salesforce estates, it really does give Salesforce a really well-rounded platform position from which to get into more of the reasoning engine and the more Agentforce related tools that are really what they’re after long-term. And this starts to get to be a bit more speculative because I think that in both Salesforce and ServiceNow’s case, the financial metrics and the reporting, it’s still a small amount of their overall revenue. And we’re still dealing with a case where these are emerging platforms. And so, a lot of it right now is coming down to figuring out, I guess, as you put it, the right to play and who has put the pieces together in the right way to go after these opportunities.
Patrick: Excellent. So, as we wrap round one, what I’m hearing is we’ve got the leader in terms of TBR’s metrics, looking at AI disruption and where it’s heading and the leader in terms of growth. And we’ve got on the other side, we’ve got a company that’s been acquiring like crazy to build up this very broad platform.
Round 2: Looking at weaknesses
Round two. Alex, this time we’ll start with you. Give us a weakness that you see, and you kind of hinted at it already with Salesforce, or a weakness across the technology space entirely. Something that will have an effect on what Salesforce is able to do.
Alex: Yeah, Salesforce’s challenge in Informatica, I think, was a response and an acknowledgement of this challenge. So, Salesforce, obviously, they’ve been an acquirer for a very long time at this point. And a big criticism has always been, the disjointed nature of this increasingly broad portfolio of solutions and trying to create a through line and bring that into a cohesive platform portfolio is something that has plagued the company for a long period. I think that Informatica, it starts to get into sort of that data governance and trying to overcome a lot of those challenges and bring into structuring data under similar formatting, creating that sort of unified data pipeline across these different application suites and being able to-
Patrick: Let me stop you there before you go too far on how great Informatica is at solving that problem. Let me frame it a different way. So, when thinking about this and looking at our research on Salesforce, the breadth of capabilities that you talked about with all the different acquisitions, Slack and Informatica and the rest, leads to complexity, which means longer deployment engagements, which makes it cost more and potentially delays consumption. So how does, in addition to Informatica, how does Salesforce, or more importantly, is it just too complex for its own good? Let’s put it that way.
Alex: I think it is complex. I think that this is sort of the trade-off of complexity rising alongside breadth. And so, Salesforce’s strength does create that weakness. I think that it’s a weakness and an issue that is not a- ultimately, I think when it comes to AI, I don’t think that it’s a Salesforce specific issue. You know, like this summer we’ve seen the talk around Forward Deployed Engineering. And I think that ultimately is a response to, you know, and again, I don’t want to overstate the role of a Forward Deployed Engineer, but I think that what we’ve seen in sort of hearing from vendors is that this understanding that AI capability has come a long way in the past two years, but that’s not necessarily translating into outcomes at the end user all the time. And so that comes down to the complexity issue and AI kind of forcing this ahead and forcing all vendors to confront how do you take this technology that touches on a bunch of different parts of the stack and how do you deliver it in a way that delivers reliable value generative outcomes every single time. And so maybe it’s a non-answer, but Salesforce and everyone, Salesforce is focusing on bringing in more Forward Deployed Engineering. It was their hottest role of 2025. It’s an area where we look at the ecosystem where they’re expanding very broadly in terms of how they’re trying to get services participants on board and bringing that all together to sort of bring in field learning to be able to improve the way that they deliver this complex portfolio. So, I think that it is a weakness for Salesforce, but I don’t think that that’s a weakness that is specific to them. And I think that it’s something that a lot of vendors are dealing with right now.
Patrick: All right, fair enough. I’ll buy that. And now, Catie, ServiceNow, give us either a weakness or maybe play on the idea that there’s weaknesses across or challenges across the entire space that ServiceNow has to deal with.
Catie: Sure. So, I’ll start ServiceNow specific. So, it’s actually also the strength that I said before, which was the platform that lets them expand into all these areas. And they can capture a lot of growth in a lot of different ways. But I also think it’s diminishing their ability to be this neutral layer within the stack. They’ve historically done that very well. And I don’t think we’re seeing that. I don’t think they can sustain that as much as they used to. So, I think there’s areas in the stack where they could be partnering better from a technology perspective, but it’s not just about who you integrate with and from an engineering perspective, but from a go-to-market perspective. So, it’s kind of this trade-off between how much do they really want to control and how much do they want to let partners take over. And I think it begs this broader question with ServiceNow, the platform has been so key to their success historically, but is there this future where they kind of get absorbed into the application layer. And so, I think ServiceNow needs to decide, right? Do we really want to compete at the platform? Where are we competing at the platform? And where are we competing at the application? So, I think I would say that’s something they still need to figure out and become more definitive on strategically.
More specifically for a weakness and a challenge. So, Alex hit it on the head. I think with data governance, I think we can all agree Informatica was a big strategic win for Salesforce. I think as we head into the last quarter of 2026, context is the center of every vendor message. Every vendor wants to be the context engine or the system of context, whatever you want to call it. And Salesforce certainly enables that with Data 360, but ServiceNow does as well to a very similar extent with Workflow Data Fabric. So, in my opinion, a stronger message for Salesforce is not about context, it’s about trust and putting trust in the data that feeds that context. And that’s where Informatica comes into play. And they’re a master in their craft. I think a challenge too though is there’s still a large legacy component with Informatica. So, there’s still a lot of MDM modernization work that needs to happen. And it’s all going to come down to execution. I think it fills a big piece of the portfolio, like Alex mentioned. But whether Salesforce can really sell it with the rest of the portfolio is still a big question, right? So, there’s still- that execution still needs to happen.
Patrick: Yeah, you anticipated my question about sort of ServiceNow having that workforce and governance platform just kind of like everybody else. So how does it maintain any pricing power because it’s just like everybody else? And how does it attract its ecosystems, partners best opportunities, not just the opportunity to partner, but the opportunity to partner where it’s going to be most beneficial to both parties.
Catie: Right. And I think that’s something we’re looking, we’ll be looking at that for ServiceNow and whether they can execute in 2027, because we’re not just looking if they can cross-sell and upsell more SKUs, but whether they can drive usage on the platform and attract partners not just implementing ServiceNow Now Assist, but are they building on the platform and using the platform to drive managed services opportunities? I think that’s what we’ll be looking for within the partner ecosystem. And can ServiceNow and Salesforce, right, as they both implement similar strategies at the platform layer, can they make it compelling enough for partners to do that?
Round 3: Predictions for ServiceNow and Salesforce in 2027
Patrick: Excellent. All right, round three. Now we’ll start with Catie again. You hinted at it already. You mentioned 2027 and what you expect. So just give us a couple of ideas, a couple of predictions for where ServiceNow is going to be in 2027.
Catie: Sure. So, I think we will continue to see their HIRI metric rise. Their growth profile looks very strong, especially with these new pricing and packaging changes. We may continue to see some tuck-in acquisitions in certain areas. I think data and analytics, the pressure is going to be on for them to expand in that area. But they also need to do it in a way where it’s not this kind of building block approach that we talked about earlier with Salesforce. And they’re making sure it’s really integrated into the platform layer in a way where they can expand appropriately across the stack. And so, I think we’ll see HIRI rise too as they’ve been pretty clear that they’ll start the new year with the pre-acquisition headcount. So, I expect a pretty steady rise in HIRI. And again, I think the thing we’re just looking for is if they’ve been, I think both companies have been messaging to the market for several months now about these agentic use cases and driving consumption on the platform, but I don’t think we’ve really seen that from either one, you know, meaningfully anyway. So, I think that’s what we’ll be looking for in 2027.
Patrick: Excellent. Alex.
Alex: Yeah. And yeah, I think that on the Salesforce side, there’s already been- it’s still a small base as far as we talk about agentic driven revenue, but I think that some of the metrics that they’ve shared in the last earnings call, especially around what existing customers, like customers who started using Agentforce early on in the adoption cycle, how their usage has trended, doubling. That’s been a very positive metric. And so, I think a lot of what they’ve been able to share around how existing customers are doing with the platform has been very positive. And I think that sort of points towards a new logo and existing logo growth trajectory. And so that’s more of a sustainable growth trajectory. And so, I think that some of the growth rates that we’ve seen from Agentforce, which have obviously been high given the small base, I don’t see those tapering off anytime soon.
One of the things that I think extend across a lot of these players that are sort of targeting the agentic execution layer is how they’re partnering with model labs. One of the things that I think Salesforce has been a front leader in is being really aggressive into opening up integrations. You know Claudforce, that was a really big announcement coming into their 2Q26 earnings call. And that kind of points towards a world where we see more and more doors open between these software incumbents and these new model labs that are rising pretty dramatically.
I think that we’ll also see a lot of movement in terms of growing model diversity. So, one of the things that I’ve been talking about for a long time now, and maybe it’s a wrong take that I should be reconsidering, but just the role of smaller language models and open weight models and how these vendors that are going after the execution layer can be an intelligence broker. Like how do we solve the cost curve in AI? And I think a big part of that is going to be coming from coming to customers with the best model for the job at the best price. And I think that when you’re talking about ServiceNow and Salesforce and their position in front of these large install bases and the opportunity that they’re going after, being a vendor that can expand those options and have domain-specific models that are tuned to the tasks that they’re serving, that’s going to continue to be something that they’re targeting and expanding while they grow new ties with the model labs and the larger leaders now.
Patrick: Excellent. And you brought in Claude, you brought in the model to the small language models, which I’m a huge fan of, so all right.
Determining the Showdown winner
And now we get to declare the winner of this Showdown, this company Showdown. And I think I’m going to try and say this a way that doesn’t sound as maybe direct and painful as I’m hoping it will be. Look, in the short term, I think Salesforce, it’s hard to argue with a strategy that is acquire stuff that makes you better and grow faster and expand your breadth and complexity. And yeah sure, complexity might slow things down, but it also brings you more revenue eventually. And just that they’ve jumped on the Forward Deployed Engineers model, as Catie pointed out, they’ve got context with Informatica. I think in the short term, it’s hard to argue against Salesforce. But in the long term, I always come back to liking a company that does what it does well and sticks to what it does well. And if you’re ServiceNow, as Catie, sorry, but as kind of boring as they sound right now, but they’re still executing well enough to be leading across the metrics that we have. So, I got to say long term, my bet will be on ServiceNow. Any complaints about the ruling?
Alex: That’s interesting. Because when we were talking about this, we were the opposite.
Patrick: I wouldn’t be surprised.
Alex: So, like, both companies, I think, have a lot in their corner. You know, when we talk about the right to play, I think that they both have the right to play in this new world. We talk about what’s going on, like obviously what happened in equity markets with the SaaS-pocalypse back in 2025 through the beginning of 2026. You know, obviously so much of the opportunity is moving to the model layer, but we talk about complexity and complexity brings in more money. These vendors, I think, are in a position to sort of navigate that complexity on behalf of buyers and being an intelligence broker that’s capable to stitch together what is increasingly becoming a really broad market and being able to sort of cut through that noise and be able to be the ones that are actually figuring out how we take this large language model capability and turn it into the outcome. And obviously, they’re not even alone in that. The Forward Deployed Engineering model kind of takes that an extra step forward.
Patrick: Right.
Alex: And one of the things that I always come back to with ServiceNow is just their proximity to the IT service department.
Patrick: Yeah. The people that actually live with the decisions that are made about IT budgets, that are made about AI,
Alex: Right.
Patrick: The people that have to implement the AI-enabled solutions that somebody higher up in the chain decided they had to have, the people that have to actually manage the sort of rogue AI or the bring your own AI that’s happening across enterprises, ServiceNow is a lot closer to them, which to me, long-term is the better play. But Catie, do you think short-term ServiceNow is a good play as well?
Catie: Yeah, I think right now, if you ask the winner, we have ServiceNow very marginally above Salesforce.
Patrick: Yeah.
Catie: And long-term, I think we have in our matrix, we have Salesforce slightly ahead of ServiceNow. I think I think we’ll be looking at culture and their ability to kind of stay focused in a certain area. I think that’s one thing. Yes, Salesforce is acquiring and putting different pieces together, but they’re very good at what they do. They’re focused on, they are a system of record, but they’re putting the data underneath it. ServiceNow, I think, and I think with the messaging, we see so much around AI governance and for sure, but we don’t hear about data governance and data governance is still the key enabler to AI governance. I think we have to figure out the AI models have proven they’re incredibly capable, but how do we turn that capability into something that can scale at enterprise levels and do so in a secure and trusted way? So, I think, yeah, it just comes down to focus and again, deciding where they want to play. Do they want to be the platform? Do they want to be the application? I think that’s kind of, that focus is something I think we’re going to be looking for from ServiceNow.
Final thoughts
Patrick: Excellent. Well, thank you both very much for participating in this head-to-head clash. I think it’s actually kind of perfectly fitting for my role here at TBR that the two analysts come in, have this head-to-head discussion, and then immediately disagree with my judgment at the end of it. So that’s beautiful. Thank you both very much.
Next week, we’ll be talking about TCS versus HCLTech with Kevin Collupy and Kelly Lesiczka.
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.
https://tbri.com/wp-content/uploads/2026/10/TBR-Talks-S6E2-Promo-Image.png12001200TBRhttps://tbri.com/wp-content/uploads/2021/09/TBR-Insight-Center-Logo.pngTBR2026-10-09 11:53:152026-10-13 12:02:55ServiceNow vs. Salesforce Showdown: AI Adoption
In the Season 6 premiere of “TBR Talks: Decoding Strategies and Ecosystems of the Globe’s Top Tech Firms,” industry expert and thought leader Nav Thethi and TBR Senior Analyst Stephanie Long join host Patrick Heffernan for a discussion on measuring success in AI transformations.
“I am running my own podcast show, and I speak to executive leaders from mid to large companies all the time — all executive-level conversations — and these things keep coming up, that there is a lot of education gap with AI right now at all levels,” says Thethi. “IT or hard-core tech people might understand that better, but people who are driving the strategy, who are mandating their teams to adopt this change are not very well educated, okay? And that force [is] actually pushing teams to run faster because there is a FOMO in the industry to compete against competitors and industry standard to chase this AI transformation. And that’s the part I find very difficult for businesses and leaders to understand and to write about it.”
Episode highlights:
The difference between transformation and adaptation
The areas where AI is hindering adoption
The definition of a successful transformation
This episode is now available on demand across all major streaming platforms. Listen and learn today!
If you have insights to share on technology alliances, leading vendors, emerging technologies, disruptive business models or ecosystem intelligence and would like to be a guest on “TBR Talks,” contact us today.
Defining Success in AI and Digital Transformations
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 digital transformation, the roadblocks to successful AI adoption, and measuring that success with Nav Thethi, industry expert and thought leader, as well as Stephanie Long, Senior Analyst for TBR’s Telecom Practice.
Nav Thethi’s background defining customer experience
All right, Nav, welcome to TBR Talks. I’m sitting here in the studio with my colleague Stephanie, and we’re excited to have you on today.
Nav Thethi, Industry Expert & Thought Leader: Thank you, Patrick. Thank you, Stephanie, and pleasure to be here.
Patrick: Yeah, maybe so we’re kicking off season six, and one of the things we want to try and talk about as much as possible is the bigger challenges around AI. A lot of people are kind of lost in the weeds of what AI can do for them directly. A lot of people want to talk about sort of scaling AI, the sort of the great big picture, but really we want to talk about what’s happening now, how to overcome some of the hurdles, what are the challenges that people are seeing day-to-day. And I know you’re in a great position to talk about that. Maybe you could start off by telling us, I know it sounds weird, but maybe tell us what did you do yesterday? Give us a sense of what you’re working on, kind of, every day right now. And then also maybe a little bit about, sort of, how you got to where you are right now.
Nav: Sure, that’s a great start. Okay, my journey has all along around customer experience. I have over 25 years experience in industry and my core expertise is digital experience. But on this digital experience, my focus is all about customers. Overall this journey, I have worked through big brands like Quark, Fidelity, Hitachi, still involved with leadership and defining the customer experience around all the digital customer facing touch points. I take care of back end and front end. Whatever touches and influences customer experience, I am involved. I am taking care of every single aspect.
Okay, but what I did yesterday was like how to create a robust back-end customer profiling that can deliver in the future post-AI era customer experience. How to understand those customer sentiments today when customers are very well educated, more educated than ever before, information is available at their handheld, and they come very well prepared. They know more than the sales team will sometimes know about the product. How to engage with them, how to motivate them and influence them to conversion path, and ultimately win business from them. So, I do this every day.
Patrick: And so that puts you in touch every day with leaders at companies that are struggling with how to get the most out of the investments they’ve made, not only in AI, but in any of their customer experience platforms and any of the sort of digital transformations that have happened in the last decade, right?
Nav: Absolutely. And you mentioned digital transformation, and transformation 20 years ago was different than today. Back then, just having a website and probably a database from the form fill is a digital transformation. Now, that was not even an awareness anymore. So, the transformation standards have changed. So does the guidelines and the growth path.
The difference between transformation and adaptation
Patrick: Let me ask you a quick question about that because we have always at TBR, we’ve always been a little bit persnickety I guess about the word transformation where what we want to see is not just what you just described, which is like, transformation could just be, you know, adopting a new piece of software. We’re always looking for transformation as a transformation in the business model, meaning a company is no longer pursuing the same business model they were pursuing prior to whatever technology, whatever changes, change management, whatever new strategy they took on. So, in your experience, how often does that kind of transformation take place and how often is it just sort of an adaptation or a transition to a new way of working?
Nav: Absolutely. Okay, so I call it strategic maturity of organizations, and that’s the maturity code that I have created, which has five stages. So, what happens is like, the very first stage is awareness. So, wherever the business is, whether it is a digital or strategic transformation, where first of all, there is an awareness phase, like there are some sort of processes in place. And then it comes to the first one is awareness and the second one is experimentation. Then you start playing around with this thing on the growth towards transformation. So, what I’m getting to is what transformation really means today versus 20-30 years back. So, at any given time with the growth, you are aware of something, you are aware of your current state and future state, and then you start experimenting things towards that growth. And then once you find success, for example, through experimentation, through pilots, through sample of your customers or data or employees on few things and you get success. And then you start integrating that to a larger scale or different teams within the organizations or larger number of processes. And then the fourth stage becomes optimization. You start settling it down, you start finding benefits from it. You start either saving cost, or increasing revenue from that. And then you start getting personalization with your audiences. And then finally, the transformation where you actually have highly mature business state. Could call it Six Sigma, could call it like, the stage five of maturity model from top-notch organizations or institutions that have defined the standard. So that is what the five stages of any maturity scale. That is what I defined a transformation means. For example, like 30 years ago, as I mentioned, having a database was a digital transformation because most websites were sending form fill to an e-mail address. Having a database was giving a lot of flexibility to analyze that data. Today, having a CRM is not even a digital transformation. Having a personalized customer data platform is not a digital transformation. How are you using it? Tools don’t define transformation anymore. It’s the maturity to utilize value from those tools and orchestrate that to the best potential is what defines digital transformation.
Defining successful transformation
Patrick: So, I want Stephanie to jump in here, but I got to ask you a quick question before she does. In your description there of those five stages, one of the key elements was success. And I think the challenge that we see right now for a lot of enterprises is defining success, measuring success, and then mapping that success to a return on the investment. So success, if it’s defined in an AI-enabled solution as a certain number of employees within an organization are using AI, or if it’s defined as a certain number of employees in an organization are building their own agents and having those agents take on part of their work and so they’re more productive, that’s all great, but that’s not necessarily one really success in terms of transformation, nor is it too necessarily a return on the investment, which was significant.
Nav: Yeah.
Patrick: So can you talk a little bit about how you help people define what success actually means in that framework that you laid out.
Nav: Yes. There is a study by McKinsey that says if we talk about today’s transformation, which is all about AI, 88% of all companies surveyed says they have AI transformation in place. They are scaling on top of that, and they are satisfied with the output. On the other side, PwC has published another report interviewing 1,000 CEOs from mid to large organizations, and only 55% of those CEOs claim to have either revenue growth or cost reduction in the process. So, what does it tell us? It tells us that almost just half of all AI investments are actually giving any value to their businesses. And the same study by PwC says only 12% of CEOs claim that they do have value. Okay. So, the majority are not seeing any value in the AI investments. Gartner have published another report, over 90% of AI investments are failing. And the reason is not technology, it’s leadership. Okay.
Patrick: Yeah.
Nav: And this means like, we are getting better on productivity. We are doing more than before. We have more options to work for us, to be more productive and be more efficient, but it is not really tuning into the balance sheet. It is not influencing the growth in any possible way. And we are stuck in transformation theater. Okay, but what success metric really is to your question, Patrick. So, the success here really is from all these studies, they’re pointing to either revenue growth or cost reduction. So, it means value improvement. So that is the success factor right now that industry is looking for in this transformation theater or productivity paradox of AI investments. How to define your success metric is totally based on businesses’ own preferences and internal structure, but it ultimately gets down to monetary value, cost reduction, or revenue growth.
Areas where AI adoption hinders organizations
Stephanie Long, TBR Senior Analyst: Right. So Nav, we’ve been talking about digital transformation, AI, and frequently when we talk about those topics, as it’s come up here, we talk about what are the advantages, the ROI, the success factors that you glean from those investments. But I kind of want to spin that around on you and ask, from your opinion and from your vantage point, how has the availability of AI actually made your job more difficult as it relates to sort of this customer experience, customer insights role?
Nav: What I am finding is there are blind spots in terms of data sharing to these large language models, to these frontier models. And there have been talks all around it. Like, are we training our competitors? Are we giving over business secrets to a public domain that might come back and bite us? There is a lot of gray area around it. I’m not claiming that there is a leak, but there are possibilities that we don’t have visibility where all that data is going, how it will actually impact us down the line in the future. So that’s one thing, but it doesn’t mean we should not use it.
How to address that. That is why industry is moving towards small language models, SLMs and local models. So have your own model, train it, and then transform around it to achieve that AI sovereignty. This is something that businesses have already started looking at. This is the future. Having small language models, any single business doesn’t need billions of parameters to run their processes around AI. Some do, but not everyone. Whatever is required for LLMs level can go public, public as in like frontier models. And by the time that industry will also mature to the point that it is safer. But I would recommend definitely and as early as possible, businesses should start exploring SLMs and start working towards it to have that control over their own data and privacy factors. What I’m hearing is even like mid leadership giving business information, strategic documentation to LLMs to be prepared for next leadership update meetings. Okay.
I am running my own podcast show and I speak to executive leaders from mid to large companies all the time, all executive level conversations and these things keep coming up, that there is a lot of education gap with AI right now at all levels. IT or hard-core tech people might understand that better, but people who are driving the strategy, who are mandating their teams to adopt this change are not very well educated, okay? And that force actually pushing teams to run faster because there is a FOMO in the industry to compete against competitors and industry standard to chase this AI transformation. And that’s the part I find very difficult for businesses and leaders to understand and to write about it.
Patrick: So, I want to build on that a little bit because as you were talking, I was sort of writing a couple notes down, thinking about the different, and Stephanie, I’m glad you pivoted to the like, what is going wrong with AI and what are the challenges? I think it’s so easy to get caught up in, you know, sort of all the magic of it. And Nav, you brought up some really good points about the challenges. And I sort of, initially I had three buckets and the more you talked, I kept adding buckets. But I feel like the challenges to adopting AI at scale in an enterprise and what I’d love to hear you- I’d love to hear Nav, your reaction to these five buckets, and also which of these is the one when you talk to leaders on your podcast, which is the one that comes up the most? So, there’s, you mentioned knowledge gaps, so like the lack of, or a lack of talent, and that falls into the kind of the knowledge gap. There’s lousy data. You talked about small language models, even large language models. At the end of the day, it’s data, and sometimes it’s just lousy data that causes AI to be inefficient and not worth the time and the investment. There’s also the cost. I mean, we see the stories all the time now about how much more expensive having an agent is than hiring a person at the moment.
Nav: Correct.
Patrick: But cost is there. And then the last two, there’s, and you mentioned this as well, there’s risk. There’s definitely a governance, risk, and compliance. Now you could call that a cost, but I think it’s independently more of a risk in that you can have a spread of AI throughout an enterprise that’s unchecked. You can have your data being spread to places where it shouldn’t go. And then I would say the last one would be kind of a distraction element where AI and trying to adopt AI distracts time that leaders should be spending on other parts of running their organization that are more vital than just, are we adopting the best new AI solution? So out of distraction, governance, risk and compliance, cost, lousy data and knowledge, which of those five do you think is the one that really is primarily getting in the way for a lot of the leaders that you talk to?
Nav: Okay, that’s a great point. Thanks for asking. And that reminds me another acronym that I go by is I’m calling it Fear Factor. Okay, and this is F as in FOMO, of course. Leaders are curious to adopt AI fast and having that system in place. There is a push from the board also to be AI enabled end to end. And then E is an education gap where at all levels, there is something that people don’t understand about AI. And from strategic perspective, what AI capabilities are and their limitations are, they don’t understand. Some leaders do understand AI just as another automation tool and not the whole ecosystem. And A is an alignment gap. Whatever programs businesses are running with AI are experiments or just to explore what it can do, not really aligned with the actual business or customer related outcome. That is why we have been hearing pilot purgatory more often and keep coming back in different conversations that AI is just have been another pilot purgatory or transformation theater because there is an alignment gap. And that’s been coming over and over in conversation with different leaders that I sit with and speak for very long conversations. And last one (R) is roadmap void. What this means is businesses do know what they want. They have money, they have strategy, they have decisions, they have teams. But how to get to the finish line is something is not clear because of that education gap, alignment gap, and FOMO. Companies are just moving forward, just trying to do what the best they can do and keep chasing it.
From those five buckets that you had defined, Patrick, with all this, I would pick the leadership is the main thing to start with because that accountability needs to be there. Okay, just making a mandate and pushing teams to bring in results is not enough. Leaders today, especially in today’s time with AI, when the horizon is changing, the way customers are researching for purchasing products or even researching about businesses to deal with, they are educating themselves. Why not leaders educate themselves to the point that they understand this AI technology and how to enable their teams, equip them with the right tools and services to be enabled. So, leadership is number one thing. And education, again, having leaders self-educate, get themselves trained. They don’t have to know like how to develop an agent and how to deploy LLMs or SLMs. No, not every level of team member leaders need to understand that, but at least to the point they understand what are the challenges that the next layer or next level of team might face and be ready to support that.
And then data. I am running a survey right now on the strategic maturity and I have a handful of responses. But whatever the sample I have received so far, it determines the strategic maturity of businesses on five pillars: technology, data, customer experience, leadership, and culture. Out of that, what I have found so far, talent and culture. That has been like the most, like the highest standard in the organization. Like all companies, majority of them do have strong talent and culture in place already. Where they are lacking to get higher on strategic maturity. Number one, leadership. Number two, data. So, these two are the weakest links right now, that I have studied so far in my research. And I kind of agree with that based on my conversations with other senior leaders also on my podcast, even otherwise on different settings.
The AI yield crisis and what to do about it
Stephanie: When I was listening to you talk about those things, it brought up a couple of things in my mind. You know, you talked about things like pilot purgatory and SLMs and FOMO and the leadership gaps and all these things together sort of send up the warning bells in my head about the silos and how that can actually reduce the efficiency and productivity of some of these investments that companies are making. And to your point before, data and leadership could be solutions to that challenge if done correctly.
Nav: Correct. Correct. What I have analyzed just recently, I mean, it’s like three, four months now. Gartner published an estimated prediction for AI investments will exceed two and a half trillion dollars by the end of 2026. And RAND Company published other report that 42% of all AI investments, all AI experiments are being halted because they’re not possible to scale or they’re not successful. So, all these experiments are failing. And out of that $2.5 trillion estimated spend on AI investments, nearly $1 trillion is just about AI solutions, software and service investments. The rest is infrastructure and logistics.
Okay, so based on this, I have analyzed there are some other numbers in the calculation, but what I have analyzed is $383 billion is being spent in AI is already stuck and it is predicted by end of 2026 to be at $383 billion in AI yield crisis. And Stephanie, to your point is this AI investment, $383 billion is going to get stuck in AI investments by end of 2026, and that’s global. Okay, so what does it mean? Because pilot purgatory, because transformation theater, because alignment gap, knowledge gap, roadmap void, and all these things are supporting that AI yield crisis that is set to grow at $383 billion. How to get out of it is where I’m spending my time right now. What are those key reasons that this investment either made incorrectly, or it is impossible to yield or reclaim this? And as we discussed, leadership and data are the two top reasons based on my findings and I’m also seeing that a pattern from other reports and interviews as well. Measurement is very important. What we don’t do is that we don’t define benchmarks.
And Patrick, to your point, like what success metric should be is something that is missing as well. Okay, we are relying on those traditional metrics to determine our success. For example, if we speak about websites, we see website as recently, of course, it has become a demand gen tool and we do see websites as our awareness property. How many people are visiting to the website has been a very successful metric for a very, very long time. Okay. For example, 50,000 visitors in June and now in July, we have already exceeded 50,000 and we still have more than a week’s time and we are set to generate over 60,000 unique visitors to our website this month. This metric doesn’t matter anymore because the market has shifted. Right now, customers are researching about businesses on GenAI. They are finding context, they are looking at vendors, looking at their solutions to their problems on GenAI. A lot of traffic is directly going to GenAI. They’re not going to search engines and that keep’s growing. And visitors coming to the website has reduced over the last couple of years. Most businesses are still looking at visitors’ traffic to their website, but that is not a highly relevant metric anymore. Now it has been observed that whatever traffic is coming to the website is highly intent. They are very likely to convert because they are already coming educated. They’re already aware of your product, your services, your other customers and business model. Means you just have to divert them to the right person as fast as possible. I come back to the point, measure, defining your benchmarks. So those benchmarks have to be revised right now. I started with that AI yield crisis in billion dollars. If you have a $10,000 investment on AI program and six months later you kill it because you don’t see value or you don’t see scalability on that program means you are also contributing to that $383 billion of AI yield crisis this year. You can only save it if you define your benchmark first, where the current state of your business is, what your goals are, what your success metric looks like, and then adopt technology later. Okay, so it starts with the leadership. It starts with a vision and define your goals. Again, same practice as we used to have like many, many years ago. It’s just that we stopped doing it. And now the framework has improved a little bit with the technology, with the industry norms, but baseline is still the same. Define your benchmark, define your goal, and then have your roadmap in place and then start working on it. And that’s how we can save from growing that AI yield crisis number to multi-billion dollars.
Pivoting operations to embrace the future
Patrick: I want to use the example that you gave just a couple of minutes ago about the sort of search engines and search and GenAI. And I want to look at it through two different frames, sort of the today right now frame and the year from now frame. Stephanie, like, did you want to sort of frame it up as what’s happening now? Like, sort of the- what maybe, because everything you just said, I think I understand, but I also am not sure that most people who aren’t where you are seeing that. Are you- Stephanie, do you know what I’m saying?
Stephanie: Yeah, so like for example, Nav, to your point before about like web traffic and people visiting your website and now we’re pivoting toward GenAI, how or do we measure at all, do we bother to measure, for example, bot traffic and how that might correlate to some sort of a productive metric toward an AI investment or maybe it doesn’t? Or maybe a website needs to be redesigned for the bot traffic instead of the human traffic. How do we sort of pivot from where we are today to embrace that near-term futuristic state where GenAI is, and subsequent agents are in some ways the users of these traditional business websites?
Nav: Websites don’t need revamps with this change. It’s just that, like, the customer intent has shifted. They are shifted from in context to intent based search. Okay. When customers go on GenAI, they ask long questions and they keep interacting with that. And then AI, GenAI based on their indexed data across the internet generates the result. We all know that. We all do that every day, every hour. Okay. So, what we have to do now is to make sure that we have those intent awareness, what customers are looking for, and have our website answering those questions in one way or the other. It could be either from FAQs, which has been already used for even Google-based search engine optimization process. But that has to improve now. The content- the content structure is needed to change now in order to be more GenAI friendly. This is important. And second thing you mentioned about bot friendly website, I don’t think that is necessary right off the bat. It addresses some use cases and we have a protocol MCP for that, which is Model Context Protocol, totally for bots. That is a different scenario. But having just the content update to be more GenAI friendly is low hanging fruit. It’s just, like, answer questions, answer intent-based questions on the website is important. And even writing page structure from top to bottom in the story based on intent signal, based on intent-driven search terms. And your other aspect of the question is, I’m trying to remember, and that is about how to get there, right?
Patrick: Yeah, how much does that transition need to happen now? How much of it are you seeing because of your actual physical location in the valley, but also sort of your place in the ecosystem? And how do you think folks that are not as immersed in AI or maybe not as fully aware, what is really happening and at what speed?
Nav: Go to data. Go check analytics. Go check analytics. One thing I would suggest, another area to look at is whatever visitors are coming to your website, we used to have a traditional metric called bounce rate. Okay, if you have 50,000 visitors to the website and your bounce rate is 25%, has that been the same with reduced number of visitors or it’s been increased or whatever? So, if your- since the data has proven that whatever visitors today are coming to the website are high intent means you should have lesser bounce rates.
Patrick: Right.
Nav: You should have more people converting ultimately to the goal.
Patrick: Right.
Nav: If that’s not happening. you already see where the gaps are on the digital properties. I mean, that customer journey needs to be connected all the way from the first touch point coming from GenAI to conversion. That path should be shorter now because customers are already aware and highly intent.
Patrick: Right.
Nav: Okay. So, data. Understanding your data is very, very important. And again, it comes back to the same point you mentioned earlier, Patrick, the success metric. The success metrics need to be redefined now based on the current circumstances and customer education.
Patrick: Yeah, and I’m glad you said data because of course we’re Technology Business Research, so we sit on top of 30 years worth of data. So, it’s not just the data that people generate themselves, it’s the data that’s out there. As you’ve mentioned a bunch of times so far with some of the studies.
I want to wrap Nav with just a question about the future. And so, a year from now in the summer of 2027, when it comes to AI, technology, leadership, enterprises, adoption, scale, all of those sort of things that are constantly top of mind today, what of those are going to be top of mind in 2027? And is there something else when you sort of look into your crystal ball, when you look ahead and you say, this is going to be the topic of 2027 in the summer, what comes to mind for you?
Nav: Okay. We started this race in 2022 when ChatGPT was first announced in 2022. And it’s been four years and next year, five years. And I’m really anticipating right now that we’ll be quite settled by next year. We’ll be very much clear about what AI can and is doing for us. And we will start actually implementing stable projects with this technology, and we’ll be productive. We will be productive and we’ll have something new to chase. It’s been five years.
Patrick: Yeah. And five years is that- it’s a lot of time for any technology trend.
Hear more from Nav
Nav, what’s the, you’ve mentioned a couple things that you’re working on. What’s the easiest way for people who are interested in hearing more about what you’re doing and your thoughts and your podcast? What’s the easiest way for people to reach you?
Nav: Sure, yeah, my website, navthethi.com is my bio and it has pretty much everything that I do every day and my podcasts, my articles, my knowledge base, I keep it updated. Otherwise, LinkedIn, I will share my LinkedIn URL also and I’ll be happy to connect. I’ll be happy to share my knowledge, like recorded, non-recorded, either way and like, yeah, I do speak and I can give free advice too, if anyone wants to learn from my experience, not just my experience, like I have inherited a lot of leadership experience from my podcast and talking to different leaders. I’d be more than happy to exchange knowledge and experiences.
Final thoughts
Patrick: Yeah, and I got to say, we’ve had a few conversations I’ve really enjoyed, every time we’re done talking, I’ve got something new in my notebook that I go back to again and again. So, it’s been really, really insightful, really appreciate it. Stephanie, thank you for helping out. And as anyone who’s ever listened to this podcast before knows, I’ve always got one last question, even after I say last question. So, here is truly the last question. And I’m going to be asking this all through season six. So, we’ll see what kind of answers we get. But if there’s, and here’s where you got to be, Nav, you got to be really creative. If there is one thing that you wish AI could do just for you, what would that one thing be? And actually, you know what, Nav, I’m going to give you a break. I’m going to let you marinate on that for a second. Stephanie, you got to share what that one thing is.
Stephanie: You know, I was thinking if you could program AI to do household tasks, but exactly how you like them done, that would be a game changer because we all have our own particular ways we like to do certain things around our own houses and to have that uniquely just for you programmed something to do it for you would be really nice.
Patrick: See, the only thing that would challenge me there is like, is mixing a cocktail a household task because, so I like to do it a very specific way. And I actually might be afraid if AI came in and did it for me, then I would lose that sort of Zen that comes with that. But that’s not a household task, you’re talking about folding laundry, right?
Stephanie: Folding laundry, yeah. Things like that, which we all have our own particular way of doing.
Patrick: So Nav, what is the one thing, one thing you wish AI could do just for you?
Nav: At this point, if I want to use AI for the very first thing, I wanted to make decisions on my behalf for my podcast workflow.
Patrick: Oh, okay.
Nav: What does it mean all the way from guest research, post-production and publishing to social media based on the cadence, analyze and research. Again, I am a big supporter for human-in-the-loop aspect. But it does do- it should do the due diligence for me and make everything and bring in front of me just for approval and does everything else.
Patrick: Right, Yeah, that would free up a lot of your time and a lot of your mental space as well, no doubt.
Nav: Yeah. And at scale, if I want to talk from corporate perspective, yeah, I would, I would leave AI to understand customer sentiments to the very one-on-one personalized basis. It can do that, but there are hallucinations. There are customer frustrations involved with AI. You talk to a chatbot and even if it is AI enabled, it keeps sending you in loops. If you call a bot answered phone line, oh, you still have to talk to a human today. And from a corporate perspective, I think, that is the element I would look for my very first preference that customer experience can be optimized and it should address that customer sentimental element as soon as possible.
Patrick: Yeah, that’s a great one. That’s a great one. So, Nav, thank you so much for coming on the podcast. Stephanie, thank you for hosting with me and we will definitely chat soon.
Nav: Thank you. Thank you. It was a pleasure to talk to 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.
https://tbri.com/wp-content/uploads/2026/10/TBR-Talks-S6E1-Promo-Image.png12001200TBRhttps://tbri.com/wp-content/uploads/2021/09/TBR-Insight-Center-Logo.pngTBR2026-10-02 14:02:092026-10-13 11:52:32Defining Success in AI and Digital Transformations
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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Art by Amanda Hamilton Sy
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.
“TBR Talks” is available on all major podcast platforms. Subscribe today!
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.
“TBR Talks” is available on all major podcast platforms. Subscribe today!
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.
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
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.
“TBR Talks” is available on all major podcast platforms. Subscribe today!
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.
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
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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https://tbri.com/wp-content/uploads/2026/05/TBR-Talks-S5E11-Cover.png13501080TBRhttps://tbri.com/wp-content/uploads/2021/09/TBR-Insight-Center-Logo.pngTBR2026-05-26 09:26:042026-05-26 09:31:03IT Services Market Forecast: The Levers to Pull to Accelerate Growth
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.
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
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.
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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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