Why AI Is Fundamentally Reshaping Enterprise Operations

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.

 

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