TBR Talks: Decoding Strategies and Ecosystems of the Globe's Top Tech Firms

AI and the Workforce: Why Augmentation Is Winning Over Automation

Play Episode Pause Episode

1x

00:00 / 00:57:30

Apple Podcasts Spotify

RSS Feed

Share

Link

Embed

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.

“TBR Talks” is available on all major podcast platforms. Subscribe today!

TBR Talks: Decoding Strategies and Ecosystems of the Globe's Top Tech Firms

Federal IT: AI Adoption, Defense Spending and Projections for the Next 5 Years

Play Episode


Pause Episode

1x

00:00
/

00:35:59

Apple Podcasts


Spotify

RSS Feed

Share

Link

Embed

Federal IT: AI Adoption, Defense Spending and Projections for the Next 5 Years

TBR Talks Host Patrick Heffernan: Welcome to TBR Talks: Decoding Strategies and Ecosystems of the Globe’s Top Tech Firms. Where we talk business model disruption in the broad technology ecosystem from management consultancies to systems integrators, hyperscalers to independent software vendors, telecom operators to network and infrastructure vendors, and chip manufacturers to value-added resellers. We’ll be answering some of the key intelligence questions we’ve heard from executives and business unit leaders among the leading professional IT services and telecom vendors.

I’m Patrick Heffernan, Principal Analyst, and today we’ll be talking about TBR’s brand new Federal IT Services Market Forecast with John Caucis, Senior Analyst for TBR’s Federal IT Services Practice, and James Wichert, Senior Analyst for TBR’s Federal IT Services Practice.

Starting point for the Federal IT Services Market Forecast 

John and James, welcome back to TBR Talks. Really glad to have you guys here because you’ve done something that we have been promising for a long time. We’ve been asked for a long time to do this, and we finally have rolled out Market Forecasts in the IT services space. Always been a challenge. It’s something I’m really happy that we’re finally doing. And John and James, you guys rolled out the first one for the practice, specifically around the U.S. federal government’s IT services space. So, John, you want to tell us a little bit about the forecast?

John Caucis, TBR Senior Analyst: Yeah, it comes at a really interesting time a year after the Trump administration completely upended the market, the Department of Government Efficiencies and then the shutdown at the end of federal fiscal ’25, which further disrupted the overall space. We saw the markets actually expand slightly during fiscal ’25, which wrapped up on September 30th of last year, but mostly because it drafted off of the four-year, five-year bull market that preceded it. So, we took that into account as we were putting together our forecast. We took into account the comments and the interactions that we’ve had with the leading federal systems integrators, Leidos, CACI, Booz Allen, et cetera. And we certainly took into account what we have seen, what we have observed in terms of federal spending priorities, what we saw remaining in place during the Trump administration. It’s going to be AI and defense and national security and intelligence spending in a nutshell. But we put all that together, and we wanted to put together a product, a forward-looking product that really gave the audience, the reader, the perspective from the, you know, through the lens of the vendors that we track. So within that, you will see not only our projections and our analysis around the overall market, where we think the market is headed over the next year, two years, five years, but where the vendors themselves are headed and what are going to be their priorities and how we see them performing over the next five years. And just to kind of put a bow on the quantitative aspects, as I mentioned at the outset, we saw the market expand slightly in 2025, fiscal ’25. That is not going to be the case in ’26. We’re looking at a market contracting anywhere from 3-5%. Buoyed by the defense space and spending in the intelligence markets, but the civil market is going to remain really, really tough during the year. So that will be business as usual in fiscal ’26.

Five-year outlook

Patrick: I want to come back to the assumptions and I want to talk specifically about some of the companies that you’re covering here and the projections that you’re making. But looking out beyond 2026, what’s the five-year picture that you guys came up with?

John: The market will rebound. It’s going to take some time. The defense and intel and spending on national security as not just in the defense and intel spaces, but also civil should remain fairly robust. The Trump administration established that as a priority right up front. Conversely, though, the bull market in civil IT spending, I mean, we were observing double-digit civil growth for three, four, almost five years up until fiscal ’25. That’s over. The party is over in the civil space. That’s where the bulk of the elevated scrutiny on consulting work on behalf of DOGE, the Department of Government Efficiencies, that’s where the bulk of that happened. There’s going to be a lot more emphasis on outcome-based contracting, refocusing on IT modernization, but very constrained. It’s not going to be kind of the free for all that it was. There’s still a lot of investment that needs to happen. So, taking all that into account and taking into account the simple fact that there is still a lot of modernization work that needs to be done across the board, that’s kind of the foundation for our five-year outlook. So, we see the market struggling in ’26 overall with more opportunity on the defense and intel side, obviously, but the civil market should start to pick up again. At least that’s the sense that we’re getting from our observations of the vendors, that by fiscal ’27, certainly by fiscal ’28, the next election year, things should have stabilized in the civil space. And then we see a more moderate pace of expansion, low single digit rates through fiscal ’30.

Market leaders will be taking more market share

Patrick: So, let’s talk then about the companies. And when you talk about that projection next year and then the changes for the next five years, are there, and James, I don’t know whether you want to take the companies you think are going to do well first or the companies you think might struggle first, but who do you look at and say, this is going to be the most challenged in this current environment? And then who do you think is probably best positioned to actually take advantage of what you project is going to happen in the federal IT services space?

John: I’ll start with who I see as leading, who we see as performing the best. And right up front, it’s CACI. In fact, they are projecting for their fiscal ’26, which wraps up on June 30th; they’re projecting still between 8 and 9% growth.

Patrick: Okay.

John: And that’s with a civil business that comprises about 20% of their revenue base. So, they’re still looking at growth this year. They’re still looking at growth over the next two or three years. It will slow down. But with 80% of their business coming from the defense and intel spaces, a large proportion of that from the classified space, from the fact that the bulk of their civil business is in national security, border security, AI-related enablement, they’re going to do well in that respect. So, we see them as being the five-year growth leader. I think we were projecting that their five-year growth, compounded annual growth would be somewhere in the mid-single digit range. So, we see them as being the growth leader.

The other leader, just from the perspective of market share and the size of their revenue base, today it’s Leidos. In five years, it’ll be Leidos. They also have, with a slightly different portfolio, but they also have a very robust defense and intelligence operations. They’re one of the leading vendors that’s going to be participating in the Golden Dome project, the missile defense shield. And they are more exposed to the contraction in the civil space, but they also have a fairly robust health IT business, which slowed down. But, and I think James might talk about this one, when it comes to companies like Maximus, there’s still a lot of activity there, a lot of modernization work. And I think that Leidos will be able to benefit from that. So, we see them not only, and CACI actually, not only retaining their market share that they have today, but actually expanding it a bit.

And I think that suggests also another trend that we’re observing that the companies at the top of the market, the leading federal systems integrators, they’re actually going to be taking, we believe, they’re going to be taking market share from the smaller companies. We’ve seen what’s happened in terms of the 8(a) companies, they’re really under heavy scrutiny right now. A lot of them have gone under over the last year. And we see that coming. We see the top of the market. That being one of the reasons why the top of the market is going to be capturing share from the bottom of the market.

Patrick: Right. And so, I mean, that kind of consolidation is- it seems like it comes and goes in waves. And then, so 10 years from now, we’ll be talking about all the new players that have come in and taken market share. But for the next five years, you’re anticipating that the leaders are going to capture more of the total pie. Yeah. So, all right, James.

James Wichert, TBR Senior Analyst: I mean, just dovetailing on that, I think it’s also worth including General Dynamics Technologies in this conversation. Historically, there’s been little synergy at the top line level between the information technology and the mission system segments under the GDT banner. 3.4% of revenue growth is the largest calendar year improvement we’ve seen from them. And General Dynamics’ leadership team are forecasting fiscal year 2026 sales growing approximately 2.5% over 2025 sales of $13.5 billion. But we feel that GDT is well positioned through 2030 and could expand at a CAGR a little north of 3%. Now, obviously, GDT has a scale advantage over almost all the companies we track, given their annual revenue and General Dynamics’ financial backing.

Patrick: Right.

James: They’re just to better withstand these types of storms that we’ve been seeing in the federal space of late. And like John said, one trend we really noticed when putting together this forecast was that the bigger players were those set up best to expand their market share over the next few years. While they may not be as nimble as their smaller peers who can, at least in theory, more rapidly adjust to sudden changes, GDT certainly has the scale, the expertise, and portfolio necessary to face these challenges head on. And take some short-term disruptions, but thrive in the long-term. You know, DOGE and the government shutdown did negatively impact GDT, but it didn’t cripple them as much as a smaller player like, say, an ICF. Both GDIT and Mission Systems are already well aligned with the Trump administration’s priorities in the defense market. GDIT’s investments in AI, cybersecurity, and other growth areas, they’ve proven fruitful as evidenced by the growing demand for their digital accelerators.

The link between IT services and defense and intelligence businesses

Patrick: So how much is having a strong defense presence going to be critical to having an outperforming IT services company performance in the, an outperforming performance, in the civilian sector? I mean, is it a prerequisite that a CACI or a Leidos or a GDIT has that strong presence and success in the defense space in order to, or is it just- and I’m asking because I’m thinking about companies traditionally, if you’re particularly good in one area, like to go back to like Booz Allen Hamilton is a great example. You guys haven’t mentioned them, but in the federal space, certainly an important player, but they had to split Booz and Company and Booz Allen Hamilton because the two pieces of the business were not operating at the same, with the same kind of success and a whole lot of money. Anyway, it’s a long story there. But the bottom line is when I think about, okay, Leidos has this great defense business and a healthcare business, but here they are doing, how are they going to do in IT services? Is doing well in IT services predicated on having that backstop of a defense and intelligence business?

James: I don’t think it’s a prerequisite technically, but it is extremely helpful. So, looking at another company that, you know, I was just talking about, you know, like the big getting bigger, and I do think that’s true overall, but there is a substantial bull case scenario for Maximus over the next five years. And, you know, they are smaller. They’re heavily entrenched in the federal civilian market. They’ve been largely shielded from DOGE and the government shutdown related disruptions due to the essential nature of their work. And their recent really rapid expansion has been driven by the demand for medical disability examination services. They’re working on the contact center operations contract, parlaying that into IT modernization work. And so that’s all federal civilian, but then very recently, they’ve been successfully making inroads in the defense market.

Like, after years of very minimal wins, all of a sudden they notched two IT contracts, the US Air Force. They’re worth more than $160 million. They recently achieved level 2 cybersecurity maturity model certification. And, you know, they’re showing they’re serious about competing in the defense market. I don’t think it’s unrealistic at all to say they could expand at a CAGR of 4% over the next five years, something like that.

Patrick: Right. Which will exceed what you expect from GDIT. So, yeah.

John: And will exceed the overall market growth rate, which means they’re going to capture share. That’s a great point.

I think in general, the companies that we, Maximus might be the exception although it sounds like they are going to be buoyed somewhat by their defense business, which is gaining traction. But in general, the companies that grow their revenue base overall over the next five years. It’s going to be, the foundation of that is going to be in the defense, intel, and national security spaces. And that’s, as I mentioned, that’s Leidos, that’s CACI. That’s also not in the near term, but in the long term, Booz Allen. We actually see Booz Allen, despite how hard they were hit over the last year. And they’re, I’m guessing, at this point, they haven’t tendered their forecast for their fiscal ’27, which would run to March 31st of next year. I’m guessing it’s not going to be all that spectacular.

Patrick: Right.

John: They were projecting about a 10% decline. I think it’s, you know, after four or five years of double-digit growth, now their sales have turned down, but it’s all on the civil side. I see eventually Booz Allen, their defense business has remained fairly robust. That’s going to be the basis for them to not only buoy what happens, what continues to happen in the civil space, nor pullback in consulting spend, the shift to outcome-based contracting. But Booz Allen is just way too smart to continue to struggle.

Patrick: Right.

John: They’re already starting to figure things out. They were just rolling with the market for the last five years. I mean, they were well positioned to capture the type of spend that agencies in the civil space were, you know, that match with their spending patterns. That has changed quickly, radically over the last year, but Booz will adapt. And I actually see them over the next five year period, over the next five years capturing market share. You know, leaning on their advisory heritage, that is evolving as well. But in general, the companies that have a robust defense business, robust intelligence business, presence in national security in the civil side, we see them as performing the strongest over the next five years.

Patrick: Yeah, and betting against Booz would be like betting against McKinsey. It’s just foolish because, I mean, they’re too smart, they’re too well run. If consulting in particular, but IT services as well, really depend on client retention. I mean, I think Booz has done an exceptional job over the year at retaining their clients across the different agencies in the defense space.

Companies there are questions around 

One more question specifically to the companies. So we don’t need to get into who you think is going to fall apart, but I am curious if there are certain companies you look at and think, and I know this is true when we look at the broader IT services space, you just think, I don’t know where they’re going. Like when I look out five years, they’re a bit of a mystery. The strategy isn’t clear. Where they’re trying to place their bets isn’t clear. Can they execute isn’t clear. That doesn’t mean they’re necessarily going to fail. You just don’t know what is really going to become of this company. Are there any like that in the federal IT services space?

James: I mean, KBR’s MTS spin-off is interesting in that sense, but I do think overall there’s still a vision there that makes sense. I think for the vendors I track, ICF is probably the one that most baffles me a little bit.

Patrick: Yeah.

James: They had this huge wave of M&A activity between like 2020 and 2022. And then they stopped that to allow their business to grow. And it was doing that. And then, their federal revenue cratered by more than 35% year over year in 4Q25. And while their revenue comparisons certainly won’t be that bad in the first half of 2026, they still won’t be pretty.

Patrick: Right.

James: Their overall headcount declined by close to 10% year to year. Most of their workforce reduction efforts, you know, that’s been more programmatic, consulting-oriented, and those are the aspects of the business that DOGE’s contract terminations hammered and the Trump administration’s moving away from funding. So, I mean, there’s still an opportunity in IT, but ICF as a whole just appointed a new company president whose background is heavily tied to the energy, environmental, and commercial part of the business. The vast majority of ICF’s recent moves have been related to grid modernization, you know, other energy-related plays. Their federal IT business will bounce back and return to growth in 2026, but there’s just more hard times coming for their overall federal business. And it doesn’t look like their management’s particularly interested in investing further in them at the moment. Barely any partnership activities, no M&A, there’s no major deal wins. It just doesn’t look great for ICF right now. I’m not entirely sure what their plan is. I don’t know if they want to look at divesting something even further into commercial to hedge their bets, but it just seems very chaotic.

Patrick: Yeah, so that’ll be interesting to come back to in the next year or two years. John, how about you? Any mystery companies out there?

John: SAIC.

Patrick: Huh.

John: Last October, they, gosh, their C-suite is- half the people that were there six months ago are no longer there. Their former CEO is gone. The chief innovation officer that she hired has departed the company. There were multiple other C-suite level and senior executive departures. They’re restructuring the business again. For the second time, in about 3 years. What that says to me is that they haven’t evolved away from the commodity IT types of services, despite making some good acquisitions over the last five years. The last acquisition was in 2021, despite really being aggressive in enhancing their alliance ecosystem, enhancing their partnerships, particularly with the hyperscalers. We saw a lot of great moves there. They introduced some fairly robust solutions in the cloud arena. Their messaging was good. Their messaging was strong. But then just almost overnight to see the upheaval that we’ve seen, and it wasn’t just caused by the market, because companies like Accenture Federal Services, IBM’s federal business, CGI, Booz Allen, I’ve mentioned already, they were hit harder.

Patrick: Right.

John: They had a much more severe impact on their top line from DOGE and the government shutdown than SAIC did. But it’s like SAIC is hitting the reset button again. And to me, that’s an indication that the previous leadership, at least those that departed, the board looked at their performance and did not see the evolution of the company happening as fast as it should have.

Patrick: Right.

John: But this is a really bad time to be resetting your strategies.

Patrick: Yeah.

John: So, I think there’s a lot of questions. If you read our report, that’s the analysis that we kind of wove around SAIC, that they are the most, the most questions really revolve around SAIC, where they’re going and how they’re going to get there, more importantly. They’re going to have to move fast. And they actually have started in one respect. They made an acquisition. of a company that enhances their portfolio in agentic AI, which is a good move. It’s not going to move the needle a whole lot in terms of revenue, but at least they have the capabilities that they can scale across the remainder of their portfolio. So that was great. If that’s an indication of what they’re going to do. Their profitability has improved, which is which is one of the goals of the new restructuring program, so they can plow those profit dollars back into the company in terms of investments, M&A and new solutions and whatnot. But there’s still a lot of questions around SAIC in my mind.

Discussing potential scenarios for the next five years

Patrick: So, I want to come back to the acquisitions and AI. But you mentioned the report. And I think one thing that we tried to do in the forecast for IT services was not just give a top line or a big number and say, okay, this is, the market’s going to grow at this amount over this number of years. We wanted to talk about what- once we’ve done that, and we can talk about how you guys came up with that number, but once we said, this is what the market is going to look like, this is what the growth projection is for the next five years, what are the things that could change that? What are the things that could be kind of the wild cards? And the pandemic was a reminder to all of us that these things can happen. And honestly, what happened with DOGE last year was another example of where can chaos come from? So maybe just highlights of a couple of the sort of the scenarios that you have in the report and how they play out.

John: We’ll start with a potential best case scenario, which would be, I think fiscal ’26 is already a foregone conclusion in the civil market. But a rebound, a sooner than expected rebound in civil spending would certainly be welcomed by, especially by folks like Booz Allen and Accenture and CGI and IBM. And there’s still a lot of modernization work that needs to happen. If the rebound in civil happens sooner than expected, if the stabilization happens, and we are actually seeing some signs of that now. I think the Trump administration recognizes that, as I mentioned, that there’s still a lot of baseline IT enhancement and enhancement of IT infrastructures that has to happen before they can start executing on their priority of implementing AI across the civil space, as well as defense and intel.

Patrick: And that’s true with- when we’re looking at on the commercial side, like every single enterprise has to go through IT modernization, data readiness. You can’t just flip the switch and turn on your AI-enabled solutions and think they’re going to work. So same is going to be true across federal government.

John: Yeah. So best case scenario, the recognition of that drives a rebound sooner than expected.

Patrick: Yeah.

John: Immediately off the top of my head, that’s what I’m thinking. I mean, another best case scenario would be that defense spending is even more robust than it’s expected to be. I think the Trump administration requested a defense, an overall defense budget north of a trillion for the first time.

Patrick: Right.

John: And we’re expecting to see that grow. Some are saying as high as $1.5 trillion by the end of the Trump administration, Trump 2.0. We’ll see if that plays out. But there’s going to be a huge IT component in that.

Patrick: Yeah.

John: So, the folks that are doing well now, because they have a footprint in defense intel and national security, they’re going to continue to do well. And that’s honestly why I see CACI being the growth leader over the next five years.

Patrick: Gets back to what you said earlier, that the bigger companies, the ones that have been successful now going into this, are simply going to gather up more market share. Any of the scenarios jumped out for you, James?

James: Well, to build off on what John was just talking about, I mean, yeah, the Trump administration has openly talked about increasing defense spending to $1.5 trillion in federal fiscal year 2027. And with the ongoing conflict in Iran, it’s yeah, it’s looking very likely that defense spending will be growing more rapidly than many of us would have expected a few months ago, which would provide additional opportunities to FSIs. Just looking at one vendor in particular, you know, GDT, they stand to benefit. Beyond mission systems, defense electronics being increasingly in demand, GDIT have been aggressive about fostering an expanded relationship with the DOD or the Department of War under Pete Hegseth and, you know, the DOD have been pushing vendors to self-organize, take on more risks during development, deliver results faster. And GDIT have certainly been showing a willingness to do that. Just a few months ago, GDIT launched a Mission Emerge Center in Springfield, Virginia, where the Pentagon and intelligence communities can closely monitor the co-development of innovative military solutions that GDIT and their partners are working on. And speaking of partners, yeah, GDIT have continued ramping up their partnership activity. Lately, their collaborations with AWS and Google, they’ve notably centered around defense needs. And I think that’s just one more scale advantage to GDT, just being able to build up new facilities and just actively collaborate with these companies.

Patrick: Right, they have the financial backing and they have the partnerships in place.

The impact of AI on the next five years

So, let’s wrap with AI, because everything always goes back to AI these days. Fair enough. And so, how did you think about the five-year federal IT services forecast in terms of what application of or adoption of AI could do both to these companies and then to sort of the overall government spending. And I’ll lay it out in the way I’ve been thinking about it, which is lots of companies are anticipating that they’re going to spend money on AI now, and they’re going to be saving money on their operations in three, four, five years, however long it takes. And they want to see that return on the investment as soon as possible. Government shouldn’t be designed around return on investment. It isn’t a business. But if you think about government spending, is the idea going to be the more AI enabled the IT environments are within an agency, the less money they’re going to be able to need in order to operate? So, you could actually see AI depressing government spending long-term, five years, maybe 10 years out. How do you see that playing? What was your thinking going into this forecast with respect to AI?

John: One of the vendors that comes to mind in this regard is Leidos, because I think that the way that they’re approaching this question is really smart. No agency is going to be able to, or be willing to, because of security concerns, because of the ethics around AI, how it’s going to disrupt the federal workforce, and other concerns as well. No agency is just going to overnight, as you kind of used your analogy earlier, flip the switch and go agency-wide with AI. They’re not going to AI enable their agency, end to end, comprehensively overnight. What Leidos is doing is, and this is also in response to the market, the shift in procurement approaches towards more outcome-based, and that’s still kind of questionable. I think it’s going to be more of a fixed price rather than, I think outcomes are, that’s a really nebulous concept.

Patrick: Yes, it is.

John: What does that mean? Whereas a fixed price, I mean, that’s on paper.

Patrick: Right.

John: That’s set in stone. So, what Leidos is doing is they’ve stepped back, and they’ve said, and they’ve got the flexibility to do this because they’ve improved their profitability far beyond what I thought was even possible over the last two years. So now they can, they’ve got that buffer to work with and they’ve said, we are willing to break up either existing contracts into smaller modules and AI enable that piece of the contract, this one particular function in an agency, this one particular department within an agency, take a piecemeal approach, show you what we can do. And even if it costs us more in the near term, in the long term, they’re setting themselves up for the downstream work, to take that AI implementation agency-wide.

That’s a slightly different approach than what I see in Accenture doing right now. Accenture, I think they’re falling back more on the messaging that I see in the global commercial space, where they’re emphasizing their ability to go enterprise-wide right from the get-go. And they certainly have the chops to do it. But I don’t think the maturity or the willingness on the part of the agencies is in place yet. And the budgets aren’t there yet either. There’s too much risk. Risk in terms of cybersecurity, risk in terms of workforce upheaval, a lot of unanswered questions. And I think that IT decision makers and agencies are- they want to see what can be done on a small scale before they commit to going larger.

Patrick: Yeah, and when you say outcomes-based is nebulous, it’s because of what you said at the end there about risk. The reason why outcomes-based contracts are so difficult for companies and their providers, IT services companies and enterprises or agencies to agree on is because who takes on the risk? If it goes badly, then a company, an IT services provider can walk away and go to the next company and try and cut their losses. But if it goes badly and an agency is stuck with an IT environment that doesn’t work, or an AI system or enabled solution that doesn’t work, then they’re the ones that took on that downside risk. So that’s a real challenge there. Any closing thoughts on AI, James?

James: I mean, what you were just talking about there, I guess you’d say that’s like a worst case scenario too for vendors. Just this environment where everyone’s taking on increased risk. I mean, Peraton’s big win with the FAA, the brand new air traffic control system contract, you know, their compensation’s being tied to performance benchmarks. So just more accountability and risk there. And if we see that bleeding elsewhere into the market in other contracts. But with AI, I mean, that’s a very interesting point on whether it would compress, spending over time. I don’t think it would in the next five years. And even in that case, I still think, you know, Neil Young famously once said, rust never sleeps. You always need to keep adapting and evolving.

Patrick: Yeah.

James: It’s like those systems always need to grow. And so. I think there’ll always be a market. It’s not like it’ll dramatically ever fall off the cliff or anything. I think it maybe expands less rapidly in say like 10 years, but yeah.

Patrick: Yeah, that’s why I’m still very bullish on the consulting market because all of this chaos just feeds the need for somebody to come in and help you figure out what to do. And AI, the shift towards agentic, or this push around, the hype around agentic right now, this idea that you can have a robot that will do things for you, and the robots never sleep, and that’s great, but then the robots eventually get bad at their jobs. Eventually they start doing things they shouldn’t do, or more importantly, they just aren’t needed anymore. And then you gotta retire them. Well, that process is not, it’s not as simple as just, oh, I’m not gonna check that e-mail account anymore. That’s not quite the way it works.

John and James reflect on what they wanted to do at age 22

All right, I want to wrap. This has been a little long, but it’s been super good. And I just want to wrap with the same question I’ve been asking everybody here in season five. This is season five of TBR Talks which is kind of amazing. So right now, as we’re recording this, my youngest child is in her last semester of college. And so, for her, the whole world is open. She can imagine all the possibilities of what she could do with her life. And so, it made me think back to, all right, when I was just graduating college, what did I want to do with my life? What was my sort of this is my- and part of this is also inspired by a conversation I had when I was in Toronto, where a woman who’s a very successful executive in an IT services company said if she could go back and do it all again, she would be an engineer for a Formula One race car team, so with that in mind, John, I’ll let you go first. James, I already know the answer. You already, when you were graduating college, all you wanted to do was come work at TBR because it wasn’t that long ago.

James: That was my dream ever since I was a child.

Patrick: There we go. But I know there’s- I know you were also a professional photographer, so we can come back to that in a minute. But John, when you were just about to graduate from college, what did you, what was your dream job, dream career?

John: Well, I thought I was going to go to law school, and I almost did. I almost did.

Patrick: Wow.

John: I applied and I got accepted. And then I decided to go to business school instead. And that was the right choice. And I think there’s still a lot of opportunity there because of how broad a business education is and how flexible it is. You’re not locked into one discipline or another. You have the focus on finance, on marketing, on accounting, global business, even IT to an extent.

Patrick: But when you were 22, were you thinking you wanted to be an attorney that was prosecuting criminals or you wanted to be like, general counsel for ExxonMobil? I mean, what was your-?

John: That was a long time ago.

Patrick: It was a long time ago.

John: I’m not going to say how long ago it was, but I actually thought about coming back and perhaps teaching.

Patrick: Okay.

John: You know, teaching like pre-law in my alma mater, but are moving into more like the constitutional law area.

Patrick: Okay.

John: Obviously, that’s not what happened. But what I’m encouraging the kids in my family now is, you know, look for a career that is not going to be disrupted by AI. And really, that’s the trades. I have three nephews, for example, who’ve already, I’ve had this conversation with, they’re not college age yet, but we’re encouraging them to either, and that could be the traditional trades or it could be college level trades, engineering, STEM, or business.

Patrick: Yeah.

John: I mean, business is essentially trade. But I think overall, I mean, I recently saw a podcast with the chief technology officer at Palantir, and he said, we got to pump the brakes on the fear, the fear mongering with AI, because there’s, and he might be overly optimistic, but I don’t think so. The disruption- there is going to be disruption, but it’s going to be more of a reset and an enabler. So maybe a traditional college degree will lead to something that we can’t even imagine at this point, an opportunity path that creates new opportunity avenues for folks.

Patrick: Right. All right, James, when you were 22, what was 22-year-old James wanting, thinking he was going to do with his life?

James: From a young age, I’ve always wanted to provide value to shareholders. That was everything I was focusing on day one.

Patrick: *laughs*

James: Yeah, when I was 22, honestly, when I was 22, I was here. So, anyway. *laughs*

Patrick: Yeah. But you took, but you had-

James: I mean, if we go earlier back, I don’t know.

Patrick: You have been paid for photography. That is-

James: Yeah.

Patrick: Yeah, you have been a professional photographer. So that was, but you didn’t imagine yourself like being a photojournalist out covering a war somewhere.

James: No.

Patrick: That wasn’t your, yeah.

James: I mean, when I was 21, I was here. But when I was younger, when I was in high school, I started a photography business. I was very big into marketing. I used to just do a lot of sports photography. So, I would go out to major league soccer matches. I worked with the US Women’s National Team. And that was very fun. I was, I mean, I still like taking photos. I still love doing that, like going out on hikes and just capturing that experience. You know, I guess I always thought it would have been cool to make something work out of that because I used to do like friends’ headshots and people’s like professional photos and stuff.

Patrick: Right.

James: And I guess like that’s what I was like looking at doing when I was going into college. And it’s like- held on to that for a while. And then, it’s all about the shareholders. It’s all about it.

Final thoughts 

Patrick: *laughs* Excellent. Gentlemen, thank you so much. What we’re going to do is have you back in season six and see how the early part of the forecast held up and see how the companies you talked about are doing. Thanks for coming on.

John: Thank you.

Patrick: Tune in next week for another episode of TBR Talks.

Don’t forget to send us your key intelligence questions on business strategy, ecosystems, and management consulting through the form in the show notes below. Visit tbri.com to learn how we help tech companies, large and small, answer these questions with the research, data, and analysis that my guests bring to this conversation every week.

Once again, I’m your host, Patrick Heffernan, Principal Analyst at TBR. Thanks for joining us, and see you next week.

TBR Talks: Decoding Strategies and Ecosystems of the Globe’s Top Tech Firms

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!

TBR Talks: Decoding Strategies and Ecosystems of the Globe's Top Tech Firms

Why AI Is Fundamentally Reshaping Enterprise Operations

Play Episode


Pause Episode

1x

00:00
/

00:48:52

Apple Podcasts


Spotify

RSS Feed

Share

Link

Embed

In this episode of “TBR Talks,” Sid Nair, Cloud Growth lead at Accenture Americas, joins host Patrick Heffernan and TBR Principal Analyst Boz Hristov to discuss why AI is fundamentally reshaping enterprise operations, shifting from a technology initiative to a business-led transformation. They unpack the critical enablers of successful AI adoption — including data readiness, governance, talent and change management — and explore how buyers are navigating complex purchasing decisions across ecosystems of partners and platforms.
 
The conversation also examines the future of alliances, evolving commercial models, and how ecosystem-driven partnerships will redefine value creation in the AI era.
 
Episode highlights:

• The framework for successful AI adoption within enterprises
• How buyers are looking to purchase AI
• The role of AI and agentic AI in shifting the alliance ecosystem

“Those top 10 partners may evolve to a slightly different set moving forward, especially with the new data AI partnerships coming in. Because today, what we do with the Anthropics, the OpenAIs, the Metas of the world, it’s very, very small, right? In comparison to what we do with the SAPs, the AWSs of the world, right? So, I do believe that the penetration index will increase for us in terms of, we’ll do more with partners. But I think that mix of those partners in the top 10, it might definitely look a lot different by 2030,” said Nair.
 

Why AI Is Fundamentally Reshaping Enterprise Operations

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 challenges enterprises face in scaling AI and changes in the broader technology ecosystem with Sid Nair, Cloud Growth Lead at Accenture Americas.

AI & agentic process transformation will be business-led

All right, Sid, welcome very much. Welcome, welcome, welcome to TBR Talks. Very excited to have you joining us. And I’m here with Boz to have this conversation, really two conversations. The first one, I really want to talk to you about the long view of technology. This is sort of the theme for this season. I’m trying to talk to people who’ve been around for a while who have seen some changes because we spent last season and all of last year, it feels like, just talking about AI and talking about it in such a hyped way. Everything felt so new. Everything was so different. And I want to take a step back and say, okay, for people that have been around technology for a long time, is this really new? Is this really so groundbreaking and different? Maybe it is, maybe it isn’t, but I just wanted to get your thoughts on the longitudinal view of technology. So maybe- and then I’d like to talk to you about alliances, because this is something we’ve spent a lot of time talking about. And it is also something that’s changing and that we hear a lot about in the marketplace and what’s different. So, we’ll get to alliances second, but let’s start with that longitudinal view of technology and sort of the basic question that we have at the beginning of 2026, which is the hype around AI, is it legit? Is AI really going to be that sort of dramatic, transformational, generational change in enterprise, in technology, in business?

Sid Nair, Cloud Growth Lead at Accenture Americas: No, and this is a great start to a wonderful session today, and it’s good to see both of you, and thank you for having me. And I think the topics that you’ve chosen, at least the first one, is on everyone’s mind. I mean, on one hand, you have the markets today, you know, the stock markets are being defined by AI in a certain way, right? Every day, Claude comes out with a new model, the market takes a dip, or the SaaS companies go for a toss, or whatever you have it, right? So, from that perspective, AI is definitely having a much more impact globally from a macro perspective, more than cloud or more than anything else that happened in the past, say, 20/30 years, for sure, right? There’s no doubt about it. On the other hand, when you know, when we talk and you guys talk to clients and partners, we’re doing the same thing. I’ve been at a bunch of sessions with CEOs of large technology companies and everybody, right, including obviously our own CEO, it’s all about AI right now, and rightly so. And I’m not saying that just because everyone’s saying it’s- that’s the fact, but AI is truly defining the way in which businesses would have to operate in the future. And when I say future, that future is now, that future is today. And with some of the advancements that have come out, we all thought OpenAI, ChatGPT was the rock star and that was the gold standard. But over the last two and a half years, now you’ve seen I mean, there’s Anthropic is out there, and of course, NVIDIA’s building a lot of the infrastructure with the GPUs, but it’s not just about the GPUs, it’s about the models. It’s about the actual, you know, the models that Claude especially is just redefining so many different things.

Now, it’s also scary on one hand because- and I say it’s scary because a lot of our customers, our traditional customers, have really not been used to this kind of a change. I mean, whether you’re an airline, you’re hospital, a bank, you know, there’s so many new use cases that are popping up across the board to say, AI can do this, AI can do that. But these companies are still struggling to figure out, how does AI really help? You just can’t go sign a 100-seat license with Anthropic and say, oh, I’ve got the model now, but what do I do with that model?

So, on one hand, you have GSIs like Accenture, obviously, we do a lot of work with our traditional partners, like say an AWS, a Microsoft, a GCP, Salesforce, SAP, you name them. But all of those big software companies also have agentic AI built into the new delivery mechanisms, which they didn’t have a few years back. So, when you talk about, all right, on one hand, all our minds have investments in these big technology platforms. And now they’re getting inundated with, okay, this is what AI can do for you. This is what agentic can do for you. And a lot of that, it’s falling on the desk of the CIO traditionally, or it has in the past, because CIO’s got to decide, oh, there’s a technology issue, I’ve got to deal with it. But what we’ve realized is, and that’s part of the reinvention that customers would have to do to embrace AI, is that this is not just the CIO’s decision or the CDIO’s decision. Because for a client or for a company to actually truly embrace AI and agentic, it now goes down to so many different decision points in the firm. And I’m not saying the entire company has to decide, but the chief operating officer would now have to start thinking about, hey, how do I look at my supply chain and engineering? And what are the kind of models that I need to, kind of, bring in? And it’s not just about technology, it’s about those use cases around supply chain and around engineering. The CFO would have to start thinking about, hey, I have to do some kind of finance transformation to truly embrace AI. What are the use cases that I need to start thinking about?

So, it’s almost becoming a business-led AI agentic transformation where each of the business leaders now have to be more smarter, more educated on the applicability of AI and agentic into their function and not just outsource it to the CIO. Literally, it’s like within the organization, I want to outsource it to my CIO, and my CIO is now going to outsource it to another provider. But now each of these different, you know, I like say, for example, I’ve been a chief customer officer or a chief sales officer in multiple lives before. But if I was in the CSO role in a large company, I would now have to think about, okay, for customer service transformation, customer experience transformation, what are those use cases that I need to think about to actually bring in the power of agentic and AI, whether it’s through my call centers, whether it’s in terms of how I do marketing.

So, I think it’s- AI is kind of becoming all pervasive. In the past, I mean, at least the cloud hype cycle was, hey, move your stuff to the cloud and it’s going to cost you less and you can transform and it’ll save you a lot of money. You can work faster, cheaper, better. And it was all a technology transformation. But now this true business process transformation that’s going to, that, you know, it’s all pervasive, right? It’s kind of hitting every aspect of the organization. Even HR, I mean, the CHRO has now got to start thinking about how do I use agentic and AI to make my human resources processes, right? Whether it’s from recruiting, whether it’s performance management, whether it’s training. I mean, so I think it’s exciting. There’s so many opportunities. So, when Accenture talks about reinvention, we are obviously reinventing ourselves within the firm, but also for our customers. They have to start thinking about reinventing the way they work, because unless they do that, you really can’t change the way in which you service your customers. I know that was a long-winded answer, but I just wanted to kind of cover everything that’s been- that I’ve seen going on so far.

Patrick: Yeah, Sid, you touched on so much that we need to come back to. And I want to start first with, you mentioned cloud, and you mentioned that the sort of the transformation that cloud was hyped up to provide. That is, you know, you get to move from CapEx to OpEx, you get to change, you get to move faster. It’s funny you said, you know, it’ll be cheaper. It turns out cloud was actually more expensive. But anyway, we’ll set that aside for a minute. But at the time that cloud was being introduced and hyped, when there was the hype cycle around cloud, it was presented as a business change, not a technology change, and yet we now look back and say, okay, it was really a technology change. You don’t think we’re going to have that same conversation five years from now about AI, right? This is truly a business change.

Sid: I truly believe it’s a business change. And the reason I also say that is, at least in the past, the way that, you know, we’ve all sold cloud, I mean, I’ve sold a lot of cloud. You kind of position cloud, even all the hyperscalers position cloud as a business change, but it was primarily, you know, it was, you know, you’re moving your infrastructure, you know, to a different platform at the same time, you truly get the benefits if you modernize. But what happened was everyone did lift and shift. So, if you had modernized your applications before you moved it, then it would have been a true business change, but that never happened. It happened in a few cases. But out here in this world of agentic and AI, you don’t get any of those benefits unless there is some kind of business process redefinition, or you reinvent the way you work. And that’s why some of the use cases that I’ve just briefly mentioned, whether it’s finance or HR or talent or the customer side, you’ve got to look at those use cases, figure out with those use cases the applicability of that within your business, and only then can you start introducing those agents and LLMs to actually transform your business. It’s not about, oh, let me just move my data to this model, you know-

Patrick: Right.

Sid: -this agentic model and it’ll happen. No, it’s not going to happen on its own. You first have to embrace the change, agree to, this is going to be the new way that I’m going to run my business, and then the agents come in, then you use the right models to kind of transform the business, so.

The framework for successful AI adoption within enterprise

Patrick: Right, and change management ends up being so fundamentally important to AI adoption, and I want to ask a question, and then I’ll turn to Boz: when you think about- you mentioned a bunch of the different buyers, there’s a bunch of different people within the enterprise that have the opportunity to adopt AI, and we should all raise a glass to the poor chief human resource officers, because since the pandemic, they’ve had nothing but the world thrown at them between a pandemic, quiet quitting, then we had all these layoffs, and now they have to adjust to it. Anyway, that’s my own little sob story for them. But when we think about enterprise adoption of AI, one framework that I had, and I’d love to get your reaction to this, was for it to be successful, you need to have three different sets of people who are bought in. First is leadership. You can’t do it unless leaders believe you’re actually going to get transformation. The other is you need the masses. You need everybody sort of understanding what the opportunities are here. You need everyone having both that combination of excitement about the opportunity, but also maybe a little bit of fear of what the change could be, because fear can sometimes be a good motivating factor. And then you need the lab. That is, you need people who are truly dedicated to just being able to make sure that you’re not letting AI run amok. You’re not doing things that are going to be damaging to your enterprise long-term.

Sid: Yeah.

Patrick: And you need people who are super specialized. So, in your experience, when you’re working with clients, do you see those sort of those three groups of people within very successful AI adoption?

Sid: Yeah, absolutely, absolutely. In fact, I’ll just take it to a slightly different level as well, right, Patrick? So, when we actually talk to our clients, we talk about a couple of different things, everything that you spoke about, right? So, we start with responsible AI and governance. We say, hey, you’ve got to think through what is your commitment to responsible AI? Because trust is very essential to deriving value within the company and also the branding for the company. You don’t want AI to go amok, if you will, right? So that is something that we really pride on as a firm and we go and talk to clients about what is that model going to look like. And then your data readiness, right? Because what part of this, of your, I mean, a lot of clients, as you know, even back in the cloud days, I just said back in the cloud days, we’re still in the cloud days.

Both: *laughs*

Sid: But we used to move everything to the cloud, but a lot of times the core data platforms would still, the unstructured data, would still be sitting on-prem. So we’ve really got to build those scalable and secure platforms, data platforms, where the data- your enterprise data is secure because a lot of times you kind of, you don’t want to mix public data with your own enterprise data for an organization because that becomes very difficult.

Patrick: Right.

Sid: So, then that leads to, once you create that secure and scalable platform, then you start talking about data readiness and you’ve got to acknowledge that data readiness is actually the biggest challenge in my view, right? Because if we talk about, we wanted all these things, so like I said, unless you’ve got data readiness in place, it’s going to be really, really tough.

And then I would just talk, you spoke about talent and change management, so critical, right? It’s a significant barrier, honestly, because, I mean, we are scaling up big time. We’ve got about over 75,000+ people around data and AI, but now it’s not just data and AI, it’s agentic. Now, people may think, you just need agents. No, you still need people to create those agents, to run those agents. In fact, we also are going to be launching agentic, you know, like we talk about AMS and IMS. You need a managed services model to manage agents. So that’s one of the services that we’re going to start offering as well. One is you’ve got to build them, and then you’ve got to manage those agents. You just can’t let the agents run everything, right? So that’s kind of funny, right? Create agents and then have people manage those agents. So, but that’s going to be critical.

And then, within an organization, I mean, the talent gap within an organization, as you know, is huge. I’m not talking about service providers like us, but there’s a shortage of skill sets within companies, so we can train those people. They need to kind of understand how to use AI. I mean, you can put in all these great models, but if your employees don’t use AI, then what’s- it’s pointless, all the investments that you make. And then I would absolutely double down on what you said on organization adoption, right? The organization adoption, and I would also call out value realization kind of linked to that becomes super critical because each of those owners of the business, right, whether it’s, you’re the COO, the CFO, the CHRO, you know, the chief sales officer or what have you, they’ve identified a business need, they’ve identified how they want to implement this new change using agentic and AI, but they’ve also got to clearly figure out what is the value case, am I really getting the bang for the buck because it is still an investment, and once they prioritize the right use cases and all of that. So yeah, I think fully agree with what you said.

Patrick: Yeah, excellent. Boz.

How buyers are looking to buy AI

Boz: Yeah, great conversation so far. Fascinating just listening to you Sid, as always. Just thinking about, I mean, you spoke about, just a moment ago about developing agents, launching, like, managed services, the different opportunity areas, right, that companies like Accenture see in front of themselves, but also kind of the way the market is moving towards. I think earlier you mentioned how a lot of the reinvention services you’re also applying to yourself and as you’re bringing up those to the clients. So, all great use cases, thinking about how the market is evolving, but one area that I’m very curious, and we get a lot of questions as well is, how are buyers looking to buy AI? It’s kind of like the flip side of the question, how are companies, like Accenture, looking to sell, right? So, it’s kind of like a- but I’m trying to get your perspective because you interact with buyers on a daily basis, right? So very curious, what’s your sense? What’s your view? What’s the feedback from the clients you guys are getting? How do they feel comfortable? Because they’ve been conditioned to procure services on times and material basis for years now, right? We’ve seen some cases, fixed price models, you know, being introduced. Outcome-based services has become almost a dirty word, you know, kind of everybody talks about outcomes and nobody defines outcomes these days, right?

Sid: Yeah.

Boz: So, I’m very curious, what’s your perspective on, how do you think buyers want to buy AI now and in the future?

Sid: So, we’ve seen, you know, again, Accenture as a firm is so heavily focused on industries, right? So, we’ve got different stories that we take to different industries just because each industry buys slightly different, right? But having said that, you know, so the use cases that we- the AI kind of use cases that we’ve built are kind of very different for each of the industries because we feel that is where they can get the maximum bang for the buck to start off with. That’s on one side.

On the other side, we still are going to clients, and that goes back to the old way of doing business, and I don’t mean old in a bad way, which is, hey, you still need to move to the cloud, or you still need to get your data ready. You can’t have your data sitting in silos. You can’t have them sitting in 100 data lakes. You’ve got to get your data modernized. If your data is not modernized, there’s no point in figuring out how you’re going to use some of these agentic AI models. So, we still have a lot of data modernization that we are kind of selling as foundational elements for clients who really want to do agentic AI on an enterprise level.

And then, you know, the interesting part is while we’re trying to go in and set up, you know, AI/agentic COEs for clients and giving them a journey to say, hey, we can build, we call it the switchboard that we’ve now built. We allow customers now, if they kind of use us as their data COE partner, we give them the switchboard that they get a kind of access to playing with multiple LLMs, they get access to tool sets from all the big hyperscalers, and they get the secure environments that we build, and we give them different use cases. So, they can play around and see which use case with which partner or which LLM makes sense. And then we help them kind of score in between because no client is like diving in and saying, oh, this is what I want to do versus this because there are so many different use cases, so many different LLMs, hundreds of LLMs out there in the market. And then the hyperscalers also come with their own LLMs, right? And they allow you to play with. So, we’re kind of building these sandpits, if you will, different templates using the switchboard model where they can, it’s almost like you can plug and play and figure out which model suits you best. So, we’re giving them that opportunity.

But what’s also coming at us, from a lessee perspective almost, is a lot of clients are talking directly to OpenAI, directly to Anthropic, and they’re signing up for licenses. But now they’re like, okay, I’ve got this 100-user license, now, how do I use Claude, right? So, now we have our RDE services that we’ve launched, and we’re working directly with some of these big partners like OpenAI and Anthropic, and we are providing the engineering capability to help these big AI partners to actually go in and get their platforms used by clients. So, we’re actually hitting it across so many, so it’s not like this oh, in the past, you want to migrate to the cloud, okay, which cloud do you want to choose? *laughs*

Boz: Yeah. Yeah.

Sid: A straight play, and then we would say, you want to modernize or migrate, and then how do we run it? But today it’s quite complex in terms of, it goes right from, let’s modernize your data to whether you want to, play around with multiple LLMs using like the switchboard that Accenture brings to the table. Or then now, okay, you’ve already gone and bought a bunch of licenses, thinking like an easy play switch. They almost think about the, you know, ChatGPT kind of a model. Oh, I just have to buy it and AI starts working. But no, it’s not that simple. You know, public data and your private data is very different. Your private enterprise data has to be ready to actually start leveraging some of these tools, Boz. So that’s what we’re seeing today.

The revenue mix of the future

Boz: Yeah. That’s very helpful. I mean, just thinking what we hear in the market, and one word that is constant is, as it comes to AI and the way AI gets sold is around transparency, right? Clients do appreciate transparency. They want to know what they’re paying for, right? Exactly. What’s the benefit when they spend money on either a tool or service or a wrapper around it, right? So, we always kind of, we’re trying to kind of look a little bit, and Patrick asked questions about the history of technology, or maybe look into maybe the next five years. I’m not even going to go 15 or more, but more next five years. In your sense, what’s the right model for professional services companies from a revenue mix perspective? I think I’ve heard, you know, Julie speaking on one of the previous calls, I think she said about sometimes 60% fixed price, you know, the revenues, fixed price services for Accenture. But what’s the kind of the revenue mix for professional services organizations in the future, in the next five years or seven years, as we’re thinking about the transparency that buyers are also looking for as you are talking about AI?

Sid: Yes. So, I firmly believe that we still, so some of the contracts that we continue to sign- and it’s also, we continue to compete in the market with a bunch of competitors out there. So, I think that old traditional model of give me a hundred FTEs and give me P times Q, I think that’s gone. We still have some deals pop up.

Boz: Yeah.

Sid: There are some customers that are still all about the rate card. I don’t think those plans are thinking about the future in my humble opinion, because everyone else is kind of leapfrogged. But I do believe we’re seeing a lot more risk-reward kind of fixed price outcome models come up. I mean, we’ve been doing that in the past.

Boz: Yup.

Sid: But I think what’s interesting now is these are not these fixed price, you know, risk-reward outcome deals that are just based on, oh, yes, just take this book of business and run it. No. We are now being asked to, we do a lot of these, create a new platform for me, transform the way I run my business, and now kind of train my people and help me run my business in the new model. And we’ve done a bunch of those massive deals, massive transformations. There’s true transformation, right? So, while on one hand we talk about reinventing ourselves, reinventing our clients, now these deals are reinvention deals. You’re reinventing the way, you know, we’re reinventing commercial models with clients where, you know, we bring in a bunch of assets that we have built in. We’ve also got platforms. As you know, Accenture has, we have the Accenture platform and products team that there’s IP that we built, whether it’s our life insurance platform, we’ve got the media platform. So, there are many more things like that we will continue to build using AI with specific industry applicability, because like I said, we go each industry with a specific offering. So I do believe that gone are those days where, you know, if $100 of revenue that we earned was just based on, you know, 800,000 people, But I think moving forward, those $100 of revenue, the split would be, there would be a productized service. There would be a bunch of RDE services where you’re actually building agents and you’re actually- it’s not about the productivity of 100 FTE, but it’s the productivity of what 100 people are doing to build 1000 agents. So, I think those are models, I believe, we’re already seeing in play. So, I think it’s exciting times. The commercial models are changing. In fact, you’ve probably heard about some of the new things that we’ve launched. We’ve actually got a new chief commercial officer in the firm.

Boz: Yes.

Sid: And we never had that before. And the reason we have commercial officers, we’re talking about end-to-end right from the first conversation you have with the client to figuring out what is the best commercial model that fits in to what the client needs, whether it’s reasonable assets, whether it’s some of the platforms that we have, whether it’s some of the partner investments or some of the partner platforms that they want to kind of co-build with us. So, that’s one of the reasons that we’re excited that as we reinvent Accenture and as we try to reinvent our clients, some of these new commercial models are going to be be very critical.

Boz: Yeah, this is super helpful. Thank you, Patrick.

How partnering has changed over the last few years

Patrick: Sid, that’s a great transition into the alliance discussion because as you build those commercial models differently with your partners, you’re reflecting how much the ecosystem strategies and how much alliances have changed just in the last few years. I mean, you’ve been doing this for a while, so you’ve definitely got, again, that longitudinal view on what alliances have been like. But what, in addition to talking about commercial models differently, what are some of the other things that, in your view, have changed over the last few years with respect to how Accenture and really how all of the companies that the professional services space, IT services space, how they partner with technology companies differently than you used to?

Sid: You know, it’s just been amazing, Patrick, just looking at how some of these partnerships have evolved. I mean, in the past, you would work with, say, one CSP, and it’s like a one-way- I shouldn’t say one-way, it’s like a single-threaded kind of conversation around this is what the CSP offers. But today, as an example, if you’re working with AWS, and by the way, Accenture is the number one partner across the top platforms. I mean, and it’s not just me saying it, it’s, you know, the partners will say that too. Now- but now it is, it’s about, hey, we talked to AWS. It’s not just about AWS, but it’s about what does AWS do with NVIDIA? What does AWS do with Anthropic? What does AWS do with Salesforce? So, this conversation- what does AWS do with SAP, right?

So, we have partnerships with all of those firms that I spoke to you about. But at the same time, there are things that AWS as an example, or Microsoft as an example, they do with all these other partners as well. So, for us, previously, we would only think about, okay, here’s our go-to-market with AWS. But now we’ve got these other go-to-market motions, right? Like, you know, Microsoft Databricks, very, very, you know, the bunch of solutions that we actually take to the market together. I mean, even with NVIDIA and Dell, I mean, we actually announced something called AND, not the most innovative name, but it’s Accenture NVIDIA and Dell. *laughs*

Patrick: Right.

Sid: Right, So, it’s Dell hardware, NVIDIA chips, but- and we have our software stack, you know, our data management software stack that’s sitting on it. So, it’s so interesting that, previously it would be just taking one of these partners and go out there. So, it’s becoming a little more complex. At the same time, I think it’s exciting for the client because now the client’s also thinking about, I don’t have to really talk to three different partners, but if I go talk to an Accenture, who’s my lead GSI, they will bring in the best of, say, you know, okay, if they want to build up a private AI data center, they’ll bring in Dell, NVIDIA, they’re bring in the side, and I have one solution provider to go to and not talk to five different partners. So, I think that’s the true benefit that we’re seeing, and that’s where some of our go-to-market motions have changed as well.

The role of AI and agentic in shifting the alliance ecosystem

Patrick: Do you think the change happened because- well out of all the different factors that played a part in that, so part of it was clients demanding or clients asking for more multi-party alliances, more of you guys cooperating and bringing to the table the services, the software, the hardware, the platform all together. Part of it was the technology itself changing. I mean, just the nature of cloud, you know, became hybrid cloud, multi-cloud became, you know, an imperative, not just an idea. And then also the companies themselves, the leadership changes. Do you think, were there other factors that sort of contributed to how the ecosystem looks so different today. The alliances- by ecosystem, I mean how you individually partner differently with these other companies.

Sid: You know, four or five years back, we still had different- the traditional relationships where we would talk to the CSPs and say, hey, it’s all about migration and modernization, and we would just leave it at that. Then we would have these data conversations with, say, the Snowflakes, the Databricks, and figure out what we kind of do with them.

But soon we understood that, hey, a lot of the customers, if they really want the true value of agentic and AI, you can’t just do it on-prem because it’s going to be super expensive, especially with the cost of GPUs and the cost of compute and all of that. So then suddenly, the hyperscalers became a more important conversation to have, but then they did not have the stacks like, Snowflake and Databricks and Anthropics and, OpenAI. So now you had to bring the hyperscalers and these big data companies or the AI companies together because either of them could not exist on their own, right? And then you have NVIDIA sitting at the bottom saying, hey, my GPUs are expensive and I can’t just do compute if I don’t have a platform and if I don’t have a software stack, right? What are the use cases I’m building? You know, there’s nothing much I can do. And that’s what I think everyone saw the value of, you’ve got to, if we need to make this agentic AI model work, you know, we know the challenges. Compute’s going to be a massive challenge. Basic electricity is going to be a massive challenge. Everyone can’t go setting up their own data centers. And then you talk about the tech stack, and then you need a GSI who can kind of put all of this together. So, I don’t think, you know, that the GSIs were kind of, they kind of led in this conversation, meaning they didn’t bring all of them together, but I do, we’ve had so many conversations with all of these big partners. And we’ve told them, here are the challenges. And I do believe that someone had this wake-up call and they started talking to each other and they’re like, hey, you know what, this is the, we all have to work together. And now GSIs have to figure out the model to kind of stitch them together as well when we go to market. And we’ve been doing that over the last year or so pretty well.

Creating alignment in partnerships 

Patrick: Yeah, it’s amazing how much has changed just in the last couple of years. I’m going to let Boz come in with a question in a minute, but one thing I just want to run by you, when we talk to your counterparts, that is people running the alliances programs or alliances practices at the different GSIs and the consultancies, there are sort of two main challenges that come up again and again. The first one is they don’t know what we do. That is, our partners are not well enough versed in what we do to be able to distinguish between us and everybody else out there that’s like us. Of course, Sid, there’s nobody like you and there’s nobody like Accenture. But still, they say, how do we distinguish? And our partners don’t know us well enough to know the differences. The other thing that they often bring up is alignment around sales. And I don’t mean incentives necessarily, but like you have companies that are more focused on the relationship and companies that are more focused on the quarter by quarter, even month by month sales numbers. So how in your experience, how have you- are those two of your biggest problems and how have you tackled those problems?

Sid: Yeah. So, we do take pride at Accenture that we were one of the pioneers of creating the business groups way back in the day. I’m talking about 20/25 years back, way back before my time at Accenture. Where the business group as a concept, it was almost like a partnership where, and this is a partnership in a true sense where you would have like an SAP or an AWS on one side and then an Accenture team. Both companies would dedicate people on both sides just to work in that partnership. So, it’s not like a joint venture, but it’s a real partnership where you have a scorecard of accounts that you want to go work together. You have investments that you both make in terms of people, in terms of joint solutions, in terms of go-to-market capability. And then, I mean, there’s a bunch of top to top that happens. When I say top to top, the CEOs meet on a regular basis. They look at joint metrics to see what’s making sense, what’s not making sense. And those metrics then flow downwards to every business leader to say, hey, if these are our top 10 partnerships, and we have 10 business groups, if we say as an example, then each of the P&L leaders would have a metric that kind of go against those top 10 partnerships, right? So, if you have to do $100 of revenue, you’ve got to make sure at least 50% to 60% of that, as an example, comes from these top 10 partners. And each partner would have like a little target within that P&L leader’s view. So that’s how we’ve kind of successfully been running that whole book of business and that whole model all this while.

Now, at the same time, the BGs or the business groups have primarily focused on partnership development, partnership management, building joint offerings, and go-to-market strategies. But they worked very closely with the sales team to make sure that you had sellers aligned to the BGs. And in this case, we need to have sellers who understand AWS or SAP or Oracle. And then our sellers have to work hand in hand with sellers from an SAP, an Oracle, right? And then when we go to market together, the sales teams of the two companies, whether it’s the partner or whether it’s the GSI in our case, we’re kind of, we understand what the customer is trying to solve. And then that’s how we’ve been together a lot more. So, but when we started off way back in the day, we really didn’t have a dedicated sales structure that would kind of focus on each of these partner motions. We would have more the partnership and alliance management structure. But we’ve evolved quite a bit and we’ve got dedicated sellers for each of the platforms and that is making a big difference.

Patrick: Yeah, that’s super helpful, Boz.

Aligning on commercial models in go-to-market partnerships 

Boz: Yeah, I just want to expand on that last point about the sales structure and the sales element of your relationship with the key partners. Just going back to my ask earlier about the kind of the future commercial model, the revenue mix for the professional service organizations. One thing that we have heard in the market is around how professional services firms like Accenture, you know, you guys are talking about outcomes, talking about risk-reward, and kind of have that conversation with the clients, especially as it pertains to AI or agentic AI. Yet some of your key technology partners continue to measure and try to sell more on transaction-based, either SaaS-based, kind of subscription-based, or even license-based models. So, clients get caught in the middle, right? On one side, you sell outcomes, on the other side, you’ve still got to sell a transaction. I mean, how fast do you think technology partners are and how fast, I mean, how fast is that relationship evolving that, you know, what’s the happy medium, I guess? Because I understand that it will be very hard for either party to go as close to the other because you don’t want to also, as you said, to become a competition. It’s more like a co-petitive relationship in some cases. So, what’s the happy medium when it pertains to your alliance’s go-to-market commercial model so that the clients don’t feel like, okay, what are we paying for outcomes? Are we paying for licenses and kind of like, what’s the kind of conversation look like?

Sid: Yeah. So, I think, the focus that our sales team has and our go-to-market has always been outcome-based, right? Meaning, is it about modernizing a platform? Is it about migrating? Is it building a new business process service delivery model? That’s always been the way we do it. Now, how do we do it? Hey, you need a software stack, you need a hardware stack, you probably need a cloud partner. And then we don’t get into how each of our partners sell to the client, right? Because we know that, you know, the hardware seller is only going to focus on selling that hardware contract and then software seller is trying to get in there, and the cloud partners are trying to just get compute in there, which is perfectly fine. So, when we go in with the construct, we clearly, from a GSI perspective, we communicate to the client that you need these different things, right? This is the infrastructure, that basic infrastructure you need, whether it’s hardware, software, CSP, what have you. And then here are the services that we will kind of provide, but we also show them the outcome. So, we show them the collective outcome of what the customer is trying to do, and then so each of the partners, they go do their pitches right on their own, and many times the client will tell us, hey, can you help me choose which is the best partner on whether that’s software, hardware, what have you. And when they ask us, we then, we do have to give them that choice, right?

And we give them the choice based on best fit, because we’re partners with all of them. We do good work with everyone, but for each client, I mean, if a client is spending 80% of their budget with one partner, with one CSP, and they still have not- they have some unused credits, we’re like, why would you go choose another partner? You already have best pricing with this existing partner, and you still have commitments to meet. We can absolutely make those commitments meet because just by shifting to another partner, they’re not going to take you to the moon or Mars. You’re still going to have it.

Boz: Yup.

Sid: So that’s it, Boz, we’ve actually seen, we have actually seen, we don’t- the partners go in and do their pitches. At the same time, if we are working together on a deal, then they know what we are trying to do on that deal because we are trying to sell the client an end-to-end kind of deal to transform them. And the partner clearly understands that, hey, this is my role or my- what I’m trying to really pitch as part of your solution, and that’s one of the reasons why a lot of the partners kind of want to work with the firm like us, because they know that Accenture is not just going there and giving the client like, here’s 100 FTEs and do what you want. We are actually going in with an outcome-based solution and every partner wants their solution or their service to be part of what we’re trying to pitch.

Boz: Got it. That’s very helpful. I think you mentioned something how clients sometimes come to you and ask you for recommendations, they solicit feedback, which technology partner to go with. Do you see clients kind of moving in a direction where they’ll prefer to have a one vendor orchestrator or partner orchestrator where they’ll come to Accenture and be like, okay, you are managing the ecosystem stack, and we hold you accountable. Do you think that’s actually something that buyers are interested at all moving forward, looking at the whole stack evolving and consolidating and all that?

Sid: I think it’s evolving. So, you know, we also have a resale business. I know we’ve had some conversations with you in the past. So, I do believe we’ve got a bunch of clients who come in and say, hey, we’d like to use you for procurement as a service. And so, and that is an offering that we have. So, they say, hey, this is how much money that I’m spending across different partners. Number one, I’d like you to help me reduce my spend. Number two, help me optimize my spend. I mean, look at all the licenses that I have. In many cases, there are some licenses that we don’t use. So, help me cut those contracts. And number two, we may be having the same, we may be having three partners providing the same service. Why do I need three? I just need one. And then anything that’s coming up in renewals, they’ll ask us, you have the bigger purchasing power of Accenture. Can you help us, you know, with better pricing? So yeah, so that definitely is happening just in terms of procurement overall. Now, we also do a lot of technology strategy work for clients. So, when you’re the tech strategy provider, we do define the technology strategy for the client. That’s in terms of, hey, here’s the stack that you need, here’s what you probably need for whether it’s data AI, here’s what you need from a pure compute perspective. And then we get into all of that end to end. So yeah, we do have a lot of those conversations, and not just conversations, we do have a lot of those clients that we actually do that work. But that’s still not like 70, 80% of our work is still project-based, end-to-end outcome opportunities is still what we do the most.

Boz: Got it. Thank you. Patrick?

Looking forward at the next few years of partnerships

Patrick: Yeah, just to pull all this together then, so as we’re looking- so we looked back at the start, I want to sort of look forward now. And given all your experience, when you think about by 2030, what does creative alliance management look like at that point? If you were doing your job as creatively as possible four or five years from now, what would that look like? What would be different?

Sid: That’s a great question. You know, we’ve started, I’ll say a few things, right? We’ve started measuring an ecosystem penetration index. You guys are analysts, right? You like indexes a lot.

Patrick: Yes. Ecosystem penetration index?

Sid: Yes.

Patrick: I like that. Okay.

Sid: So that’s something that I termed, okay, I take full credit to this. *laughs*

Patrick: Okay.

Sid: So, but that’s something we’ve been circulating internally at the firm to talk about ecosystem penetration index now for each of our businesses. And I think this is public information, Julie’s probably mentioned this in one of the analyst meetings either last quarter or the quarter before, that 60% of Accenture’s business comes from ecosystem partners. Okay. And obviously, Julie, super, very clear with the street that our success moving forward is directly related to increasing that ecosystem penetration index. So, from 60%, we’ve got to go to 70% or 80% or what have you. So I think by 2030, in the next three to four years, I think that ecosystem penetration index at Accenture that we would have would probably go from 60% to maybe, I don’t know, 70% or 80%, but that’s still massive, meaning, because we are a $70 billion company, roughly. And imagine that even at 60%, you’re talking about $40 to $43 billion, coming through some of our big partnerships. And then when you break that down, our top 10 partners take the lion’s share, right? So, it’s not like, yeah, of course, we’ve got hundreds of partners, but our top 10, the top 10 ecosystem partners of that 60%, I’m not going to give exact numbers, but it’s a big portion, right?

Boz: Yup.

Sid: And so, what’s happening is- but that would change the, those top 10 partners may evolve to a slightly different set moving forward, especially with the new data AI partnerships coming in. Because today what we do with the Anthropics, the OpenAIs, the Metas of the world, it’s very, very small, right? In comparison to what we do with the SAPs, the AWSs of the world, right? So, I do believe that the penetration index will increase for us in terms of we’ll do more with partners. But I think that mix of those partners in the top 10, it might definitely look a lot different by 2030.

Patrick: Yeah. Excellent. And then when you think about like, so the business groups, you mentioned 25 years ago, that was kind of a new idea that Accenture came up with. Is there sort of the next new big idea in terms of how you actually creatively manage your relationships other than measuring them better and maybe measuring them with different partners? Is there another sort of big idea that’s sort of waiting to be launched?

Sid: I think we are- what we’re trying to do is the traditional 10 big partners that we’ve had that you all know about, I think the model was kind of the same model that we built many years back, but I do believe that with these new partnerships, right, especially on the data AI side, that’s going to look very different. We’re still forming them. I mean, we’ve created those relationships, but I think it’s going to look so, so different. The metrics are going to be very, very different. It cannot be the same traditional metrics that we’ve had before. So more to come on that, because like I said, that is still just a few months old right now. But I’m excited about the potential of how those relationships evolve and the impact that they have on how we manage a traditional relationship, because there’s a lot of synergies, right? What each of these new data AI relationships would bring to the existing top 10 relationships?

Reflections on entering the workforce and advice for new hires today

Patrick: Right. So, Sid, I got one last question, and it goes back to something you said at the very beginning. You mentioned AI is scary is the word you used, actually. So, right now, I have my youngest child is 22. And she’s going to be graduating from college soon. And for her, AI is pretty scary. And the workforce is a scary place to be going into. But I actually want to talk, I wanted to relate that to the longitudinal view on technology by saying, she’s 22. At 22, did you think you would be sitting in that chair talking to two guys like us about technology? Did you imagine that you would be in the role that you’re in today when you were a 22-year-old and coming out of university? Like, what did Sid Nair think Sid was going to be doing 50, well, not 50, sorry, Sid

Both: *laughs*

Patrick: However many years it’s been since you were 22. What was 22-year-old Sid Nair thinking?

Sid: You know, I was number one happy I had a job. *laughs* And I got into the tech space right away. And those days of technology, I’m talking about in the mid-90s, was just go sell a client basic stuff, right? It’s like a computer, right? And some basic software, right? I remember I used to sell Netscape browsers, though, back in the day. We were setting up enterprise servers, proxy servers, and the internet was still coming up. Never, ever did I ever imagine that we would be in this day and age where you have agents kind of doing so many things for you, where you could literally have these automated tasks and you could potentially have in the next year or so, you could have robots in your house for $5,000 or less, right? Thanks to Elon Musk. I mean, that was the space age. I mean, those are things that we watched in the movie, right? The flying cars and robotaxis and all of that, autonomous cars. I mean, we have Waymo now in Texas. Never, ever, I mean, those are things that we would watch in like Back of the Future or something.

So, and I do agree. I mean, I’ve got a 25-year-old and a 23-year-old- one’s just about, one’s already in the workforce and the younger guy is about to go into the workforce and working for a bank. I do believe it’s a little scary for them because the impact that AI has had on all of us, our generation, is slightly different because we’ve done so many different things and our roles are slightly different. But when you get into the workspace, if you’re in supply chain or in banking, and if each of your employers are doing this massive scale of agentic and AI, and an entry-level employee, if you’re coming in as, you know, to kind of do some research work for a bank in terms of M&A transactions, an agent could do all the research that you as a new employee can bring to the table.

Patrick: Right.

Sid: Same thing if you’re in the supply chain space, you’re managing logistics, the whole process of managing, let’s say, 200 logistics providers like my son’s doing at Dell. I’m sure Dell’s going to put in a huge agentic play where you can automate a lot of that and have agents kind of do that. So, I’m sure it’s super scary for entering the workforce, the traditional workforce, right, and traditional roles.

Patrick: Right.

Sid: But hey, if you come in as a cloud engineer or if you come in as an agentic expert, as someone who can look at a use case and build agents and manage those agents, I think you’ll be in a much better spot. So not much better spot, I think you’ll be less concerned about what the future holds for you. You know, the three of us, we’re probably not going to do this for the next 20 years.

Patrick: I’m definitely giving up this before 20 years goes by, no doubt.

Final thoughts

Although this is enormously fun. So, 20 years from now, I hope we can at least sit down and maybe over a dinner instead of just over a Teams call. So excellent. Sid, thank you so much for your time today. It’s been tremendous, Boz, thank you for helping out with this. Appreciate it. And we will definitely get ourselves out to Texas to see you soon.

Sid: Absolutely. And it’s so good to see both of you again. And thank you for your time. And yeah, the Texas dinner is due, all right.

Boz: Absolutely. Thank you, Sid. Good to see you as always. Thank you.

Patrick: Take care, Sid.

Sid: Appreciate it. Have a good one. Bye.

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!

TBR Talks: Decoding Strategies and Ecosystems of the Globe's Top Tech Firms

AI Client Use Cases Done Right: Avoiding the Two Biggest Mistakes

Play Episode


Pause Episode

1x

00:00
/

00:28:20

Apple Podcasts


Spotify

RSS Feed

Share

Link

Embed

In this episode of “TBR Talks,” Principal Analyst Boz Hristov joins Patrick to break down key insights from recent analyst events with Fujitsu, Infosys and PwC. They explore what companies are getting right — and wrong — in presenting client use cases, as well as why in-person engagement remains critical for understanding strategy, culture and execution.
 
The conversation also looks ahead to how analyst events may evolve over the next five years, from more practitioner-led discussions to the rise of AI-native companies hosting their own events.
 
Episode highlights:

• Client use case mistakes to avoid
• The value of being in-person at events
• Predictions for the next five years of analyst events

 
“I think there’s everyone from the GSI side, the services, professional services side, has been focused so much about reskilling and retraining their existing professional services, their delivery staff, which is, you know, the right approach. But I think given the nature of AI and agentic AI, and the need for selling more technology in a different way than traditional services, I think there’s an opportunity for companies to start talking about what they’re doing and how they’re actually training their personnel on selling the technology,” said Hristov.
 
Listen and learn with TBR Talks!
 

Submit your Key Intelligence Questions for Patrick and his guests
 
Connect with Patrick on LinkedIn

Learn more about TBR at ⁠⁠⁠⁠⁠https://tbri.com/⁠⁠⁠⁠

 
TBR Talks is produced by Technology Business Research, Inc.
Edited by Haley Demers
Music by Burty Sounds via Pixabay
Art by Amanda Hamilton Sy

‘TBR Talks’ on Demand — AI Client Use Cases Done Right: Avoiding the Two Biggest Mistakes

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 some recent travel with Fujitsu, Infosys, PwC and Salesforce with Boz Hristov, Principal Analyst for TBR’s Digital Transformation practice.

Toronto Fujitsu analyst event: discussions around partnerships

Patrick: Boz, welcome back to TBR Talks season five.

Bozhidar Hristov, TBR Principal Analyst: Wow, five seasons already?

Patrick: It’s kind of amazing. We’re on our fifth season of TBR Talks.

Boz: Wow.

Patrick: I think you might be the most frequent guest, but I’m not going to do the math, but that’s probably true.

I want to talk about travel and the events that we’ve been going to. This year has started off. We’ve each been to a few events. We got a lot more on our calendar coming up in next month and the month after and the month after, actually. A lot of stuff I’m looking forward to, but let’s take a few minutes today to do two things. Look at and talk about the events that we’ve been to, and then talk about some things that we see overall and what those reflect about the market that we’re covering. So, the professional services, IT services, digital transformation, AI market that we’re in.

We did both go to Fujitsu in Toronto, and we had a fantastic podcast you can listen to when we sat and interviewed Asif Poonja the day after. Absolutely fantastic discussion with him. So, I don’t think we need to go into that anymore, except I will say one quick thing about that I really enjoyed was how they brought everybody together in Toronto. And Toronto is not an obvious choice for an analyst event, even for Fujitsu, and I realized that, the Canadian and US business are important. I thought that choice of location was really important.

Boz: It was a great location, great event. I agree. Yeah, it wasn’t an obvious choice, especially in January in Toronto. Not everyone is fond of being, you know, of traveling in the middle of the winter to the Great North. But I think it worked out well, not just logistically, but also the content.

Yeah, Fujitsu was, like I said, the episode we recorded with the America’s CEO was great. But just the kind of overall take from the event, you know, it’s probably the, I’m probably going to miscount the times, probably the last 5th or 6th event, Fujitsu event in the last two and a half/three years that you and I have attended.

Patrick: True.

Boz: So, we kind of see the progression of the company’s efforts to expand its America’s business, which includes Canada and the US and obviously some Caribbean business, but those are the main two markets that they’re looking to. And they have a very deliberate strategy, that they know what they do well, they know where they play well, and they go really deep. And as they’re trying to expand that business, they are leaning on partners, both establishing new ones that are trying to help them to grow that business, you know, both on the industry vertical side, you know, as well as on the technology side. We know Fujitsu’s background in technology and networking, you know, has always been a strength for them. They’re coming from that position, thinking about how they are organizing their value proposition around AI. I think it’s an important element. We talked in the past about amalgamation of AI. I think that story continues to help them to navigate the hype around technology. It’s certainly, you know, there’s some aspirations as every company does, looking to the future, building up their consulting business, with the Wayfinders looking to do a little bit more with some of the technology partners, you know, and get into the use cases. They’re very transparent, which I certainly appreciate that, and they’re very humble in their own way, and really trying to do things the right way and they’re very upfront. So, which is, I’m not saying that others are not, but I think it comes through much sooner in the conversations, both during the presentations and, offline, with executives and the partners and the clients. So that’s definitely a huge help to understand the story better.

There’s a way to go for them, obviously. We spoke about how much, you know, relationships and the use cases around the likes with ServiceNow could be of help, you know, having ServiceNow executives in the past on stage alongside with Fujitsu is great. Having now executives and talking from some of the AI partners, that’s always of help. So that’s definitely, like I said, an evolving story, small, incremental, positive changes. I think it’s kind of the way to summarize Fujitsu. Like I said, the podcast we did and the event perspective we put together goes a little bit more in detail about that. But I would say that, you know, looking forward to the next one, you know, to hear more about some of the initiatives that they have embarked on, both in Canada as well in the US and really trying to see, you know, learn more about how much more Japan is supporting, you know, and see North America/Americas as a region that can actually change the perception of who Fujitsu is and how can actually be a competitive peer to some of its more, the larger, more unknown names here in the US and in Canada.

Infosys Analyst Day event: discussions around talent

Patrick: Right. You said a lot of things there. You probably saw me taking notes. I want to come back to a couple of those things, but let’s pivot real quickly to, maybe not real quickly, we’ll see, to the last event that you attended, which was Infosys down in New York City.

Boz: Right. Yeah. So, Infosys event just last week, you know, and Infosys- and I’ve been attending that event for the last several years as well. I can see Infosys does two kind of main events where analysts have a great exposure to executives, partners, and clients. One is the Infosys Analyst and Advisor Day springtime, and then the second one is the Confluence event, which is the larger client event where they also host analyst sessions that we have attended in the past and written about and discussed in the past.

I think what I saw from Infosys this time around that was nuanced and the evolution was about, of course, AI, AI enterprise, AI strategy was a big topic, right? That continues to be a narrative that everyone’s expanding on. I think there was a lot more discussion around domain, and they brought in the use cases for a lot more focus on the industry specific, kind of like going a little deeper. The panel discussions told the story. Less so being- we still had some of the executives presenting and updating, the traditional kind of analyst event, the strategy and whatnot, but the panel discussions told the story. The panel discussions were a mix of clients that were not just a traditional CIO client, but the CMOs, the business, the operations. So that was a good kind of a presentation of expansion into the enterprise and building trust with various personas. So, the domain discussion was definitely great and nuanced that, you know, it was, they’ve done in the past, but I felt like there was a little bit more like emphasis on the domain discussion.

The second piece that was, more of a culmination, I would say, it’s a topic around the talent. I think everyone talks about talent these days and there has always been, but even more these days with AI. Infosys outlined a very clear path about the career progression and the various parts of the pyramid, how they’re training them, how they’re actually trying to ensure that they’re- the people that are involved in development, deployment, management of any AI tools, you know, gets prepared to do that. Very clear, you know, we’ll go into details, I’ll be working on an event perspective shortly, so we’ll get into more details and clients can come to us and we have those conversations separately, but very clear path, which is important. I think there’s an opportunity for them to re-emphasize a little bit more the sales component of the talent pyramid, I would say.

And it’s not just Infosys. I think there’s everyone from the GSI side, the services- professional services side, has been focused so much about reskilling and retraining their existing professional services, their delivery staff, which is, you know, the right approach. But I think given the nature of AI and agentic AI and the need for selling more technology in a different way, than traditional services. I think there’s an opportunity for companies to start talking about what they’re doing and how they’re actually training their personnel on selling the technology. I think we may see that as kind of the next wave of focus and expansion and announcements from the companies about “AI sellers,” quote-unquote, just kind of to frame that. You know, what does that mean? You can argue that maybe the forward-deployed engineers is probably that model that’s kind of the bridge into that, maybe. Maybe AI sellers is too hard. Maybe you have to be managing the narrative with the technology partners so they don’t want to go too far into the selling AI for the AI sake. I get that part. But I think we, as analysts, would appreciate a little bit more transparency about what exactly companies are doing beyond the FTE label, put it this way, and how does that fit into the broader talent pyramid discussion.

Patrick: Right.

Boz: So, Infosys, going back to Infosys, more domain, more clear to clients, and a greater exposure to personas that are beyond just the CIO that were brought into stage, and partners. In previous events like that, they would bring in traditional partners like SAP, Microsoft, and Oracle. Now they had some AI partners that were on stage that actually are starting to talk about, you know, which is kind of like you can kind of gradually see that pivot into introducing the new personas, the new partners that are important for them from moving forward.

Patrick: Right. I’m curious, so when I went to the PwC Australia event, and at one point when the leaders were speaking, they outlined three or four challenges that they are facing or challenges to growth, things that they, in their own minds, they need to put down their strategy to how they’re going to tackle these things. And critical to how they were going to tackle them was talent. They came back to that a few times. And it’s sort of, I can picture in my notebook where I have a big star around the word talent, because I’m like huh, the problems that they’re identifying are ones that are going to be addressed by talent management. So, it sounds like Infosys maybe had a similar mindset.

Boz: Yeah, yes, I think, and I think we have heard from Infosys kind of the pieces have been put together over the last couple of years. They’ve talked about power programmers. They’ve talked about the various career paths for the engineers that are involved into developing the small language models that they have, or the agents that are attached to the people that are actually selling the outcome-based and the value-based services that they sell versus the traditional software engineers. So, we have heard some of those. I think it has come a little bit more, has crystallized the talent strategy and I think they kind of understand what needs to happen next as they have experienced and they tested some of those models over the last couple of years and they know exactly. And it’s- I think it will be easier when they’re looking to recruit new talent as well. Because if you are someone who wants to be involved in AI more than somebody else, you know what your career path is and you can kind of know what does that entail, what’s your day-to-day work going to look like. So, I think that’s- agents are great. You still need people.

Patrick: You still need people, yeah.

PwC Analyst Summit: discussions around client use cases

And I will say so, a couple things out of the PwC Australia trip, because you know PwC really well. You spent a lot of time with them yourself. One thing that really struck me, they managed to do two seemingly contradictory things at the same time. One was they had both, they had people there from PwC Global. I know they’re, you know, PwC member firm, but people that have a global role.

Boz: Yup.

Patrick: They had them there and they talked about in almost every client use case, they talked about bringing in assets and people or leveraging assets and people from around the world, from PwC Poland, PwC UK, PwC India, PwC US. So, there was that global, this is how we’re a global firm. At the same time, it was so heavily focused on Australia, New Zealand clients, the Australia, New Zealand delivery, the Australia, New Zealand problems in the market and how they’re helping solve them. So, they managed to be both very global, which is a challenge for any of the Big Four firms, and simultaneously very focused on Australia and New Zealand.

Boz: Yeah.

Patrick: Which I thought was kind of neat. I hadn’t seen it, I hadn’t felt it that much before, even when we were in Toronto with Fujitsu, you know, that was still always a challenge. I think we’ve seen it with PwC many times in the past.

The other thing that I thought was curious, and I’m going to try to pay attention to this at all the events we go forward. You know how we’ve sat there and we’ve heard client stories. And one of the first question is always, what did you actually do? But the second question is often, is this an established client or was this a new client?

Boz: Mmhm

Patrick: PwC had a good mix of the established clients, then what I would call sort of new clients that they earned through reputation, through whatever word of mouth order. And then the, I can’t think of a better term than the won clients, meaning there was an RFP that they actually competitively bid for. And they were, PwC was very good about explaining up front, like that’s what this type of client was as they launch into the use case. Overall, just an excellent event and a really good opportunity to see what PwC is doing well in Australia.

Client use case mistakes to avoid

But I want to now- so that was Fujitsu Infosys PwC. I want to talk about a couple things now, sort of use cases, client use cases, because they feature so prominently in these events that we go to. And I want to talk about the value of being in person.

Boz: Yes.

Patrick: That is after all, what these events are all about. And then I want to look out five years and say, what is our travel schedule and what are the things that we’re going to be doing in five years is going to look like. So, starting with client use cases, there’s two mistakes everybody makes, both at these events and in thought leadership that we read. I shouldn’t say everybody, because I’m to say they didn’t make- PwC didn’t make these mistakes. But the two most common mistakes are, you don’t know what the consultancy or the IT services company actually did. You hear a great story, but you’re like, so what did you do? The second common mistake is so much time gets spent on describing the client themselves, how big they are, what market they’re in, what challenges they face, all of which is important to know as context, but there’s a limit to context before context just becomes the entirety of the story.

So, PwC was extremely good about, it seems like they prepped their clients well to ensure that they talked about the PwC part of the story, and they didn’t spend too much time setting the stage.

Boz: Yup.

Patrick: I think they had a big advantage because, with the exception of two of us, all the analysts in the room were Australia and New Zealand analysts or local to the region analysts. And so, they knew those companies. They didn’t have to explain who those companies were. At one point, one of the Australian analysts, as a presentation started, reached over to me and showed me his notebook because he had written that the company was the equivalent of AAA.

Boz: Oh *laughs*

Patrick: So, what was funny is he didn’t write AAA. He wrote AA. But at that point, I knew it was an insurance company.

Boz: Yeah.

Patrick: And I’m like, I’m pretty sure we’re not talking about Alcoholics Anonymous here. I’m pretty sure this is AAA. So, he was giving me the context of the, not quite getting it right, but it was pretty funny. But I think that part, they solved both of those problems really well. I don’t know what your experience was at Infosys. Similar, where they had those challenges and they overcome them or no?

Boz: To a degree. I think a little bit of both. I would say there were some clients that were very explicit about how they work with Infosys, but not all. I think there’s room for improvement, I would say.

Patrick: Yeah.

Boz: And like you said in the beginning, it’s not a challenge just for Infosys. I think most clients, most companies that we attend at the events to is exactly clients come on stage and they start talking about the problems, but sometimes even they forget to mention the name of the vendor that is hosting them and actually they work with. And I understand partially because they don’t work with only one vendor.

Patrick: Correct.

Boz: So, they work with a vast majority and it’s kind of how they have to manage that carefully, right? But you are the vendor event and you’re hosted by that vendor. So please, if you guys are listening to us, do us a favor. Be very specific about, you work with company X and this is how they helped us. That’s very helpful because then it changes our way of thinking for questions and understanding a little bit more, amplifying the value of the company that you guys work with. Why did you choose them? Also, what went wrong, you know?

Patrick: Yeah.

Boz: How did the company overcome that? I know nobody wants to talk about what went wrong, but we know all projects, something goes wrong.

Patrick: Yup.

Boz: There’s not a perfect project ever, period. So, opportunity for having more of an enriched discussion about how you work with vendors, why did you choose them? How did you guys set up the next stage, the next phases of the project? So again, Infosys is starting to address some of those challenges through some of the presentations, but not all of them were.

Patrick: Yeah, fair enough. Very, very rare that it’s done extremely well.

The value of being in-person

How about sort of the in-person aspect of this? And I was reflecting on this on the way back from Australia, which is a long way and a lot of time to reflect. How things have changed a lot since the pandemic, and there’s an even higher expectation, I feel like, to be in person. And so, what is it that you get out of being in person? And I’ll jump ahead a little bit and say, you mentioned earlier that panel discussions told the story. I would argue that a panel discussion in person is 1000 times more valuable than a panel discussion virtually.

Boz: 100%

Patrick: So, in addition to panel discussions telling the story, what else do you get out of being a person?

Boz: I mean, self-serving: connections. *laughs*

Patrick: Yeah.

Boz: You know, just putting a face to the name and just having that, building new relationships and understanding. You know, nothing can substitute a sidebar conversation before or after the event, you know, either the cocktail hour or dinner or whatnot, or at the game as we were with Infosys, watching the Rangers and having the suite for select clients and partners and just making those connections. And so that’s, like I said before, I mean, agents are great, humans are always still in need, still in demand, and I think that cannot be substituted no matter what. So, I think it’s always about having those experiences, hearing firsthand people just like to talk to people. You know, and as much as we all appreciate the technology that can help us to make our everyday lives easier, there’s still parts of our brains that are getting a little bit more of that socialization aspect that we all crave for.

So, connection and socialization, really learning from the body language. That’s part of it. The unspoken part of it is equally important to the spoken one, that people discuss it on stage or otherwise, and you can kind of get that sense of who’s doing what and why, just getting into that. Also looking at our peers as well.

Patrick: Yup.

Boz: Learning from them, what are the questions that they’re asking as well and trying to think about the questions we’re getting from our clients as well. So that’s an element that, you know, we know there’s some people that really like to ask questions and it’s just great, so, that’s- we learn from those along the way. So that’s kind of some of the aspects that we’re always looking for. A great venue, doesn’t hurt.

Patrick: It’s always nice.

Boz: Always nice.

Patrick: It’s always nice. I think who stays in the room is really important too. I think for years now we’ve been looking at when somebody isn’t speaking and they’re- one of the executives or really one of the clients is always fascinating too. When they stay in the room and they still seem engaged in what’s going on, that tells you a lot about it.

At PwC Australia, they had a newly promoted manager. So not a senior manager, not a principal, not- just a newly appointed manager who kind of served as the MC all day. She spoke the- she spoke at the beginning, but then most importantly, she spoke right after the executives, the leadership of PwC Australia outlined their strategy and, you know, where the firm is and all that kind of stuff. And then she reflected on, okay, that’s what this feels like. Everything they said, that’s what it feels like down here at the manager level. That’s what it feels like working day to day in this firm. It brought the culture out so much. I mean, everyone talks about culture, but it’s sometimes it’s really hard to feel.

Boz: Yeah.

Patrick: And then all day long, she, either before or after a client session, she reflected on, I’ve worked with a similar client on a similar problem, or this is what that team really went. So that was enormously helpful. And I think being in person made all the difference there. I would also say to go back to Fujitsu for a moment, from what you were saying earlier, one of the things that you mentioned, we’ve been to so many events with them in the last two years or whatever it’s been. Being in person allows us to see the consistency of the message and the consistency of the strategy. And at the same time, the progress-

Boz: Yup.

Patrick: The sometimes incremental and sometimes a lot faster than incremental progress that they’re making. And I don’t think you get that if you’re not in person having those conversations again and again. So, let’s keep doing the in-person stuff.

Predictions for the next five years of analyst events

So now let’s look out five years. I think we can look out one year and say over 2026, we’re going to see a lot more- we’ve been asking for SAP and Microsoft and AWS to be at the table with the companies that we cover.

Boz: Yes.

Patrick: I think 2026, we’re going to see a whole lot of AI companies.

Boz: Yes, of course.

Patrick: We’re going to see Anthropic at the table. We’re going to see all of them brought in. Fair enough. Got that. But if you look out five years, what do you think going to analyst events or traveling to see our clients is going to look like five years from now. Keeping in mind, five years ago, we didn’t know if we were going to do anything in person because we were still in the throes of the pandemic. But what do you- and I’ll go first to give you a second to think about it since I gave you no warning. Generally usually do, but I’ll do it this time.

I think we’ll see more smaller focused events, events in quotation marks, because I think they won’t feel like the big 100 analyst events or the big 10,000 people events. I went to Salesforce in Australia as well.

Boz: Yeah.

Patrick: Massive event. I think the biggest- my biggest takeaway from that, and we will have a special report on that that’ll come out, everybody can read the rest of what I think. But one of the things that, the sort of epiphany I had sitting in a room with 10,000 people was how locked in Salesforce is to the seller. You were talking about salespeople-

Boz: Yeah.

Patrick: And sort of forward deployed sales engineers, maybe. Maybe we’re coming to forward deployed sales engineers. Salesforce has that market locked.

Boz: Yeah.

Patrick: And everything they do is focused on addressing the needs of and extracting dollars from salespeople within an organization. But that was a 10,000 person, big, huge event. I think we’re going to see a lot more smaller, focused, let’s gather. And PwC Australia was kind of a good example of that because there were- I don’t think there were two dozen analysts. It was pretty small.

Boz: Yup.

Patrick: And there was even a time at the Salesforce event where we had a sort of breakout with public sector focused analysts and Salesforce. And that was just a handful of people that all knew each other.

Boz: Sure.

Patrick: With the exception of me.

Boz: *laughs*

Patrick: I didn’t know any of them. And so, I think we’re going to see more of those kind of- because it’s just, it allows for just such a greater conversation. And I think as analyst relations teams get better at understanding what each analyst and each individual firm brings to the table, they’ll be able to cater the events to that. And then I think, I hope, knock on wood, literally, I want to see, I hope to see in five years, a return to the experience centers.

Boz: Yes.

Patrick: To use the PwC phrase. Being in an office is great.

Boz: Yup.

Patrick: Being in an event space is kind of not great. But being in those experience centers is just, it was tremendous when we used to go to those, because you see, again, you have the face-to-face, in-person, interaction with people who are delivering to clients all the time.

Boz: Yeah.

Patrick: You see the space that a client walks into, so you have a little bit of feel of what the clients are going through. And you also see the investment, the innovation, the forward-lookingness of the company that you’re there with. So, I’m hoping that we see more of that moving forward. But what about you?

Boz: Yeah, maybe using that last point about the experience centers and the kind of innovation spaces that we’ve attended in the past and kind of like gone through a small lull right now, but it’s more like the event spaces these days. But more of the practitioners led discussions, events. As you mentioned, PwC’s example, newly promoted manager, MC-ing and connecting that, I think hearing from practitioners, I mean, all due respect to the C-suite executives, you know, we would love to hear their story. But for us, it’s about understanding and connecting it to the everyday operations as well.

Patrick: Yeah.

Boz: So, it’s good to, and I know we’ve experienced some of that in the past with some events, but having a practitioner being involved. And I know those folks, those professionals get excited because they are rarely heard outside within the boundaries of the organization.

Patrick: True.

Boz: So, it’s an experience for them. And I think it just creates a different dynamic. And I think that’s, if we can hear more of that, if we had more of those practitioner let events and involved events, I think it will be great. I’ve been very curious and I don’t know if that’s going to happen. That’s going to be a signal to exactly how the market is changing- an Anthropic or a Palantir or OpenAI analyst event.

Patrick: Mmhm

Boz: Because there’s this narrative in the market about the disruption of all those companies disrupting, including our industry, analyst industry, right?

Patrick: Right.

Boz: If we see a Palantir, if we’re going to see an OpenAI or Anthropic, I do believe we will, because every cycle goes through a hype and every, you know, they have to look for where the new growth opportunity will be coming from. So, I think we were going to see that. Which we’re very curious to thinking about an in-person analyst event run by born on the AI companies, basically, right? Because you can- they can just do it with agents. Why do you need the analyst?

Patrick: Right.

Boz: So, my bet is that we’re going to see that in five years. We’re going to see those companies running analyst- they’re going to start developing their own analyst relations teams.

Patrick: Analyst relation- analyst relations agents, can’t even say it. Okay

Boz: So they’ll start doing that type of stuff.

Patrick: Excellent.

Boz: Perfect example, Nvidia, two dozen Nvidia needs, you know, any further promotion?

Patrick: Not really.

Boz: Not really. They do have a very strong analyst relations team.

Patrick: Exceptionally good analyst relations team.

Boz: So that’s an example of a company that is doing exceptionally well and still, you know, relies and leans on the analyst community as good as they are. So, yeah.

Final thoughts

Patrick: Excellent. Thank you, Boz, for coming in. Appreciate it very much.

Boz: Thanks for having me.

Patrick: Next week I’ll be speaking with Chris Antlitz about Mobile World Congress 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!