The AI Impact: Measuring Productivity, Profitability & Business Model Change

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

The AI Impact: Measuring Productivity, Profitability & Business Model Change

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Episode highlights:

• AI maturity and measuring AI in the ecosystem
• Measuring the evolution of business models and productivity improvements
• Analyzing the operating model, commercial model, partner model and delivery model

“The way we’re seeing — again, it’s a broader trend, the way we are seeing from that Human Intensity Reduction Index is that we’re anticipating now, as part of our forecast, is probably a HIRI of about 20 to 25% is where we think is going to be that kind of ‘sweet spot’ for the services organizations, and then margin, profitability, operating profit expanding another 5% to 7% over the next three to five years. That’s going to be a strong indicator of, you know, really a business model change, and the real transformation of how those companies go to market,” said Hristov.

The AI Impact: Measuring Productivity, Profitability & Business Model Change

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

I’m Patrick Heffernan, Principal Analyst, and today we’ll be talking about TBR’s new Human Intensity Reduction Index metric, or HIRI, with Boz Hristov, Principal Analyst for TBR’s Digital Transformation Practice, and Chris Foster, TBR’s President. 

AI maturity and measuring AI in the ecosystem

All right, Boz Hristov, welcome back to TBR Talks, and Chris Foster, welcome for the first time to TBR Talks.

Chris Foster, TBR President: Thank you.

Patrick: Super happy to have you guys here. We’re going to talk about, of course, what we talk about all the time now in the spring of 2026, and that’s artificial intelligence. AI in everything, but it’s also kind of impossible to measure. We’ve been talking about for a while how the companies we cover make announcements about how much they’re investing in AI, or they announce partnerships around AI, but no one has a set definition of what exactly an AI investment is. And it’s really hard to track how much of an impact AI is having on the companies we cover. And because we focus on those companies specifically, we’re trying to figure out a way to measure and evaluate the impact of AI, not just that they’re bringing to their clients, but what they’re actually doing to themselves internally. But to sort of set the stage for that, Chris, what have you seen and what have you heard about AI maturity and measuring AI within the broad technology ecosystem?

Chris: Yeah, I think there are three dimensions that I hear quite a bit about. And I think the dimensions also apply. A lot of things I think about are in the context of our business and being a services business ourselves and serving the IT community. I think we have some of the same opportunities as the broader marketplace does, but three of the things that I hear are one is, how do you consume AI yourself? What tools do you use? How do you use them? What do you use it for? That’s the most tactical thing, which to me is analogous to the tools someone in a trade uses. So, everybody has access to the same tools. So, first and foremost, figuring out which tools you’re going to use and what you’re going to use them for, which lends itself to productivity. So, everybody wants to get more productive, do more with less. The second element or thing that I hear a lot about is how are you helping customers? So, are you providing tools that enable your customers to access, in our case, our research more easily, or in the case of a lot of our customers, how to use their technology more easily? And the third dimension I hear about, and this really applies to our business as well as our customers’ business, but how do you continue to add value in the world of AI? We keep seeing more and more examples of how advanced AI is and the ability to do more and more. And when we’re in a services business where the humans add the value to what we’re doing, how are we going to be able to continue adding value? So, as we look at the maturity curve on AI, two things that I think I see folks struggling with are, are you getting efficiency returns for implementing it? And what value are you as a business, as a technology firm or services firm, going to be able to continue to add as AI moves further and further up the value stack.

Patrick: I want to jump right to the first thing you said about the tactical deployment of AI within our firm, but really within any firm to go after productivity improvements. Have you, so in the last couple of years is where we’ve seen ourselves adopting more AI internally. What are some of the things you think have held us back? And what are some of the things you think we’re going to be doing a year from now, much better than we’re doing right now.

Chris: Yeah, I think it’s the classic things around change and change management. No matter how much you anticipate it, no matter how much you prepare for it, being able to manage change and doing things in a different way is generally harder than actually figuring out the technology or figuring out how to use a new tool. And probably another dimension of that is getting consistency. So having everybody using the tool and using it the same way. And some of the ways around that, I think, just as a small firm, some of the things we’re implementing just around knowledge sharing and the ability to ask your peers how they’re using the tool, what’s working well for them, I think is critical in success. 

But I think a year from now, I think like anything, what we’re doing today and what we were doing two years ago is going to look very rudimentary compared to what we’re doing today. And I think the tools will get easier to use, the expertise will continue to build. We’re seeing obviously more and more in the higher education space around how to use the tools and as we move forward, and I think as everybody in our industry moves forward, you’ll start to see people come into the business that are born with AI tools. Basically, so much of what we saw 15 or 20 years ago where people were being born digital and not having to figure out things were just native in cloud. Things are native in applications. So, I think AI will just become more native, both in terms of on technology as well as in the human brain.

Patrick: And I’m glad you mentioned the knowledge management part of it too, because I think that’s, there’s the change, the consistency, the knowledge management. I think that knowledge management too, we’ve already seen it where people who are new to the firm or just amongst each other, being able to build up our skills and share something that- Boz and I were talking today, and he had just used a particular prompt within ChatGPT to drive a result that was really kind of amazing. And again, it’s something you learn- we’re learning about it every day through that shared thing. 

Introducing HIRI: Measuring the evolution of business models and productivity improvements

So, Boz, turn to you now. So, we- looking at the services companies that we cover, the sort of the idealized model of what AI could do is eliminate the need for any people. You have a billion-dollar company that has one employee or zero employees. Within the services space, services is a people-based business. So, when you started to think about that challenge, what happens to the people within a services space as a services company adopts AI-enabled solutions, you came up with HIRI. So, can you walk us through what that is?

Boz Hristov, TBR Principal Analyst: Sure. I mean, HIRI, just to kind of expand on that, I mean, it’s kind of a working version of our, what we call Human Intensity Reduction Index. And really what it does is we’re trying to better understand how companies are really trying to scale the opportunity around driving a non-linear revenue growth model. Fewer people, more revenue, right? Historically, the outsourcing business has always been labor arbitrage based. The linearity between expanding headcount and revenue has been well documented, right? Ten years ago, when RPA came around, there was a first aha moment and everybody was like, maybe that’s going to crack the code. It really didn’t, right? And now with AI, going beyond just a task-oriented automation, workflow automation, and really providing the opportunity to drive the generation of new content, and really trying to go beyond that kind of just mundane task automation, I think this is where we’re starting to see and hear how the vendors themselves are applying both internally to their own operations, but also how they deliver services. 

So, for us, from a research perspective, we’re trying to look into the evolution of their business models and trying to understand what’s the best way to measure that productivity improvement, right? And is there something that is a unique set of metrics and data that companies think about and use to measure their own success, but also for us from a research perspective that can help us to build a model that can track that evolution and say, okay, how much is that really based on AI versus how much is based on offshore leverage? How much is pricing? How much is the culture of that organization and so forth? So here in the Human Intensity Reduction Index or AI influenced productivity proxy, however you want to call it, I think it’s in a way of like allows us to track that improvement in productivity. Helps us to better understand what the economics looks like for the vendors. How does that translate into profitability, right? Not just the revenue expansion from a non-linear fashion, but profitability. Maybe look into what’s the impact on the day sales outstanding cycle, or maybe their bookings. I mean, there’s other ways to measure. So, for us, it’s about understanding, as companies use more of AI to deliver services, how does that translate into the profitability impact, in the margin profile, to the value that they create to the shareholders, to the stakeholders as a whole. So, it is a way to measure that improvement, it’s a way to measure that evolution of those business models. Again, AI is one element of it. It’s kind of the corner factor of what those companies are aiming to do. We certainly recognize there’s a lot of other variables to be considered, and it’s a moving target.

Patrick: Right. And so, when you talk about HIRI, it’s more than just the revenue per employee. You’re not just looking at how much the revenue per employee has changed and headcount has changed. You do that part, and that’s sort of, in my mind, I see that line. But then you always have to have it paired with or with, so operating margin change or whatever it might be, right?

Boz: Yeah, so you have to see it in the context of the broader economic profile of the organization, right? And operating margin is certainly a common factor, common denominator for all organizations that you can really see the health of the organizations. I mean, you can argue that companies, you know, some companies look at gross margins versus operating margin and so forth. But we, from a services perspective, at least we are looking at the operating profit as a way of the common denominator that’s helping us to understand how those variables are evolving and how they’re changing and what’s the impact. 

The headcount movement, you know, as it changes, as we look into it, we saw probably in the last 18 to 24 months a decline in headcount, which was for the first time since we started tracking the kind of outsourcing business 20 plus years ago in the services industry. So that was the early indicator. The big question was like how much of that is AI automation, how much is a slower demand, right? So that was kind of the big- and then I think while the initial 12 to 18 months maybe have been a lot more of that slower demand and discretionary slowing down. I think we kind of have hit that inflection point. We’re starting to see more of the influence of AI impacting the Human Intensity Reduction Index. So, we saw those indexes for some of the larger systems integrators and consulting services hovering in the –5% to -10% about three years ago when GenAI came around. We’re now seeing it hovering at like a +5%, +7%, you know, essentially. While their margins are either staying flat, some of them are improving, in some cases, margins are declining, and there’s a reason for that because some of those companies are a little bit more innovative in terms of they want to reinvest back some of the savings that they generate as a result of fewer employees. So, they’re putting back into different tools and how they’re actually applying those tools in your service delivery and trying to monetize that. 

So, the way we’re seeing, again, it’s a broader trend, the way we are seeing from that Human Intensity Reduction Index is that we’re anticipating now, as part of our forecast, is probably a HIRI of about 20 to 25% is where we think is going to be that kind of “sweet spot” for the services organizations, and then margin, profitability, operating profit, expanding another 5 to 7% over the next three to five years. That’s going to be a strong indicator of, you know, really a business model change, and the real transformation of how those companies go to market, how much are they using AI internally but also how they’re using for service delivery with their clients.

Patrick: Yeah, and two aspects of that too is you started looking at HIRI or you started taking the metrics from right when GenAI launched.

Boz: Yes.

Patrick: And then because we can do it quarterly, we’re going to be able to see, not- it’s benchmarking, but it’s sort of real-time benchmarking how that change is happening. 

Analyzing the operating model, commercial model, partner model and delivery model

So, the other piece to AI maturity, so that’s the data side, the sort of qualitative side, is around the business models.

Boz: Yes.

Patrick: Is how are the companies changing their business models because of adopting AI at scale? And so out of the- so we identify the four business models of the operating model, commercial model, partner model and delivery model. So out of those four, just thinking ahead to the next, this year, so through the rest of 2026, which of those four you think are going to be, is going to be the most disrupted, the ones where companies are going to focus the most attention on making the changes that Chris was talking about are so essential when you’re trying to adopt AI at scale? Is it commercial? Is it delivery? Is it operations? Is it partner?

Boz: I would say the commercial is the hardest, but the most important out of all four, because essentially that’s the transaction. That’s where those companies get paid, because they’re not operating in a vacuum on their own. They’re operating in an ecosystem, right? So, they have to think about the partners, think about the stakeholders on the client side. So, we’re starting to see some evolution in the commercial models, but we also understand that there’s a lot of moving pieces and no one has really cracked the code on the right commercial model moving forward. There’s some early indications of what the model could look like in the next three to five years, more the stable model and the way it’s shifting from more of a times and materials away from the traditional model. 

I think what we’re going to see is the operations model is where we’re going to start seeing a little bit more, you know, companies trying to set the foundation because the operation model is culture, it’s leadership, it’s governance. It’s trying to think about who is actually involved and how is an organization going to be able to drive the business internally with our own stakeholders because there’s a lot of partners and salespeople and delivery people that have been doing business one way, and now they have to collapse silos and they have to do all these things differently and think about how much is that tool going to impact my job and think beyond that, essentially, right? So, there’s a generational shift in some of the larger organizations, some reorganizational efforts and programs that have been going on even prior to AI. So, I think this is operational model, I think it’s a critical kind of a building block as those companies while they’re working on the other three. 

The partner model, as I said, everyone’s operating an ecosystem. I think it is an opportunity between the partners to get closer together. I think the commercial model, as I mentioned a moment ago, I think will be that kind of a sticking glue because services companies continue to talk about outcomes and think about delivering outcomes to clients, milestone-based or project-based or scope, whatever the case might be. There’s kind of the broader outcome discussion. One technology partners continue to talk about subscription and consumption, right? So there’s a little bit of a gap between the messaging and the execution and what’s actually how you measure value because services companies really focus on client retention and they’ll do anything and everything versus a technology company typically is like the next sale, close the quarter and that’s it and move forward, right? 

Patrick: Right.

Boz: So, if that can start, the partner mode starts to be a little bit more innovative of thinking and going to market, and that’s why we’re seeing some of the larger services providers tapping really heavily on the AI native companies and expand the ecosystem, right? You know that driving revenue from the established partner ecosystem is important for the ongoing business, but now expanding into like the Anthropic and the OpenAI or the Mistral AI of the world, you know, those are the companies in the cursor, you know, that will actually provide that next wave of opportunities for services companies because I think those AI native companies, AI native partners, I think will be likely more open and easier to see the value to that outcome focus versus the maybe more traditional partners that a little bit more still have a bigger, large businesses to run.

Patrick: Yeah, and I think from our perspective, to get back to one of Chris’s points about the value that we provide, because the ecosystem is changing in the way that you just described, applying the way that we look at the world and the way that we provide value to our clients to include these AI native companies and how they’re partnering with the services companies is something we’re going to have to do an increasing amount over the next couple of years.

Boz: 100%, yeah. As we’ve discussed in the past, in other episodes here on the podcast, I mean, we have built an ecosystem intelligence research coverage over the last three+ years, and we continue to grow that coverage both from a vendor-specific coverage and the relationship, but also the depth of the existing one, as we’ve discussed. You know, looking at the services companies across nine technology partners, we started expanding that on a geo level, growing now the ecosystem with adding those AI native companies, because there is going to be a discussion around those companies being on a two-dimensional discussion where is it just an Accenture plus Anthropic or do we need to include them part of the Accenture plus Microsoft plus Anthropic, whatever the three or four-way partnership is.

Patrick: Right.

Boz: And we’re starting to do right now as we’re building out the next iteration of those research reports around the data management companies, the Snowflake and the Databricks of the world, and looking into that three-way of relationship between the SI and the hyperscaler and data management companies. So, there’s a- we are kind of building the pathway for those AI-native companies, because naturally they’re just going to come up and be as important, if not even more than some of those legacy partners in the past.

Patrick: Yeah, I can predict now there’s a season 6 podcast episode on exactly that topic. 

Adding the value layer of the human element

So, Chris, so everything Boz has been saying from the TBR research agenda, thinking, the way that we’re approaching this challenge around AI and maturity and measuring it. How much does that resonate and reflect what you’re hearing from our clients as they’re asking about questions around AI?

Chris: Yeah, I think living in the world in which we do, we hear things from clients. The question is really about the value we’re contributing. And as I’m listening to Boz speak, I start to think about this, the value proposition and the layer of value that any firm adds to an engagement gets skinnier and skinnier or thinner and thinner. So, it’s really for us to be aware of that value that we’re adding on top. And I mean, the question when we have two types of research, we have off the shelf kind of research, which to me, it’s really important that that be forward-looking with predictions and insights built by humans that can’t be generated easily through AI. And then the custom engagements that we do that get down to answering very precise questions. 

But I do think that we have to be very cognizant of that human layer that we’re adding on top that adds particularly the forward-looking elements into the engagements that we do. And I think it’s true for, you know, Boz talks a lot about efficiency and we look at the maturity of these AI models, but those are really value proper rotors that things that were done that customers are willing to pay for that added value to engagements are able to be done through technology. So, I just, my under arching or overarching concern, or I think opportunity, is for firms to add value on top of what the technology can do. So, I think going forward, it’s about being very cognizant while you’re using AI to make yourself more efficient that the real priority is the value layer that you’re still adding on top of all the technology that’s being implemented.

Patrick: Right. There’s still the knowledge, the experience, the ability to talk about trends that couldn’t be picked up by AI because they’re in your head, and you know them because you’ve been looking at this field for so long.

Chris: Right. Or in my experience, and tell me if you see this differently, but AI is somewhat linear in the way it operates, and it’s very good at coming up with the next thing in a sequence, but what’s beyond that next thing? That’s where I think the human elements are still super valuable.

Extrapolating beyond the services industry

Patrick: Yeah, I want to come back to that. And I want to actually ask Boz a question about what you just said. But before that, so we’ve talked almost exclusively so far about services. And I guess being honest, TBR Talks, we tend to talk about services a lot. But from your perspective, Chris, looking at the firm as a whole, these AI issues and the challenges around metrics that Boz brought up, you’re seeing those across cloud and telco and infrastructure and devices as well?

Chris: I think when I look at our business and coverage of the IT market, whether it’s hardware, software, services, telco being the areas that we go deep into, they’re all a little bit different. So, I think you guys know very well what happens in the services business and it’s human intensive. But if I look at the hardware space as an example, the hardware space is more technology intensive than it’s ever been. I look at a company like Dell that’s had massive headcount reductions over the last couple of years and really become a very close partner of companies like NVIDIA where it’s really building your business off of a demand model. So, I think AI in the race to build out the infrastructure, I think the challenges are a little bit different there. I think if I look at the cloud companies and the hyperscalers, in some ways it’s all around the power question and how efficient they can be in build outs of data centers. So, I think it’s the question or the challenges are a little different in all the spaces. And telecom, for instance, is you’re looking at a business where the service providers, the Verizon’s AT&Ts, are very mature, and it’s about how do you get super efficient in a highly scaled business that is super competitive? So, the challenges are all a little different, but it still comes down to the question of how do you maintain differentiation in models that become highly scaled and highly efficient.

Patrick: Right. And that’s, I guess, part of the beauty of- is you brought up so many topics that we touch on in the services space, like the power question when you talk about the hyperscalers. We touch on them, but we touch on them primarily from how are the services companies helping the companies that are challenged with that. We haven’t been thinking it through like all the way through what’s going to happen to the services companies themselves because of those exact challenges. One last question for you, Boz. Chris mentioned sort of the AI tends to be linear. Is that your experience too?

Boz: I mean, I think from a technology perspective, yes, in terms of the first degree of response. I think he was alluding to the second or third degree of implications where the humans step in and can actually provide much more value. I think from that perspective, yes. I think the opportunity for AI is to drive non-linear revenue growth, the scalar of that. But I think I agree with him in terms of when it comes to, you know, it’s good as based on what you ask him, you know, and you know, the thinking that, you know, the ingenuity of the human mind, you know, and just kind of trying to be a little bit more creative about asking a question a different way. Again, second, third, fourth degree, you know, derivative implications of a particular question. Unless you ask that question, it’s going to be more- it’s not going to be as creative as a human mind might be, at least for the time being.

Patrick: Yeah, and I’m hearing-

Boz: For the time being, I put that disclaimer.

Chris: Yeah, right. And the one thing, we’ll see- but the one thing that AI probably isn’t very good at is, as long as there’s an element of human behavior involved, AI is never going to account for all the possible human behaviors.

Boz: Yeah.

Patrick: Right, it’s like economics would be a perfect pure science if humans didn’t get in the way. 

Chris: Right.

Patrick: And we talk about technology is never the problem. It’s always humans. You can flip that around with AI and say, technology, AI, is never the problem. It’s that humans have the opportunity to be ingenuous and creative and all that kind of thing. 

Reflecting back on being a 22-year-old about to enter the workforce

All right, this has been excellent. Chris, I have one last question for you because this is a question I’m asking everybody in season five. And this whole season for TBR Talks, we’ve talked a lot about longevity in technology. So not getting too caught up in the hype around everything that’s new with AI, but talking more about the long view. And part of that long view comes back to thinking about yourself when you were 22 years old. And I’m specifically picking out 22 years old because our daughter is about to graduate from college and the whole world is in front of her. And she can imagine what she wants to do and where she wants to be in 20, 30 years. So, thinking that through, I’ve had a lot of conversations with people about what were they thinking at 22? What did they think they were going to be doing when they were 22 years old? And I’ll give you an example of that. One woman told me she got a degree in mechanical engineering. She’s now in IT, but she wishes she had just joined a Formula One racing team and been a mechanic in the labs with them. So that was her like dream job at 22. That’s where she thought she might end up. So, 22-year-old Chris Foster, what was the dream job that you thought you were going to be doing?

Chris: Being right here with you. 

Patrick: *laughs*

Patrick: *laughs*

Chris: That’s what I imagined. I’m not sure if you’re asking the question about providing advice to a 22-year-old. At 22, like a lot of people, I didn’t have that passion to be a fireman or want to be an astronaut, but I always liked business and I always wanted to be around business. So, I did, I feel like I did end up in a place over the years where always around business and how business works and business operates. But my advice to that 22-year-old is follow your passion. If it is F1, go to F1, you’ll find your way. And if you go to a profession, this is cliche, but if your occupation is your passion, you’ll never work a day in your life.

Patrick: And I will say, people have answered that question with advice. And a lot of it follows in the same track of like, sort of, if you think you want to go do it, go do it and execute, you know, be good at it, show up every day, be responsive, be responsible, all that kind of stuff. So, and I mean, if you wanted to be in business when you were 22, you’re running the firm now, that’s pretty great.

Chris: Yeah, and I think it’s about following that. And I think the other thing is, don’t do stuff just because you’re trying to please somebody else.

Patrick: Amen to that. Yeah, absolutely. 

Final thoughts 

Chris, thank you very much for finally coming on the podcast. I appreciate it, Boz. You’re welcome every time, of course. And we’ll talk again soon.

Chris: Thank you.

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

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

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

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