Strategy Summit 2026: Why AI Transformation Needs a Human Touch

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0:59Amy Bernstein:I'm Adi Ignatius, and this is the HBR IdeaCast.

1:11Amy Bernstein:A few weeks ago, Harvard Business Review hosted a day-long event looking at the cutting edge of Strategy Research and Practice, the HBR Strategy Summit 2026. The day was filled with expert advice and guidance from both executives and academics. And for the next four Thursdays, we'll be sharing some of the best conversations with you on IdeaCast. First up, a conversation between HBR Editor-in-Chief Amy Bernstein and Nigel Vaz, the CEO of Publicis Sapient. The company is in the digital transformation business, helping organizations modernize and adopt artificial intelligence to their existing models.

1:46Amy Bernstein:That means he has had a front row seat to digital transformation at all kinds of organizations, and he shared his thought on what companies really need to do now around AI before it's too late. You'll hear him argue why AI should be thought of as an operating system, not a tool, how linear thinking is holding leaders back, and the most exciting opportunities he sees AI offering now. Here's that conversation between Amy Bernstein and Nigel Foss.

2:14Amy Bernstein:You have had a ringside seat for strategy making all over the place, all over the world, many different kinds of companies. You have been doing it for years, so you have the long view. How has AI affected all of that, all the strategy making, all the thinking about strategy? If you could sort of boil it down.

2:35Nigel Vaz:Look, I think AI is far more an operating system for how a business needs to operate than it is a technology, right? Because I think we're at the beginning of a fundamental transformation where AI has been talked about as a technological trend, but it fundamentally is reshaping how businesses create and deliver value, much like the internet did in the 90s. So for me, it's not so much about how AI is changing the process of strategy, but it's more how AI is changing how decisions are made and how work gets done. And if you think about how decisions are made and how work gets done evolving, then very quickly you are, you know, having to change very simply the tempo of strategy, right?

3:19Nigel Vaz:So, you know, do annual strategy cycles work? Do planning shifts, you know, come in long multi-year cycles? Do budgets get decided on an annualized basis? And do those create competitive advantage, which is the primary purpose of strategy? Or is it actually about how business needs to operate differently? So similar to what we saw in the advent of digital, for me, AI isn't about making strategies smarter, right? It's mostly about how it forces organizations to be rethought, particularly in the context of how quickly they move.

3:55Amy Bernstein:So it's a question of speed, but you also talked about value creation and value capture. So it's also about business model. It sounds as if you're talking about, you're saying that organizations really need to rethink their entire business models. Is that accurate? it? Absolutely.

4:14Nigel Vaz:Because I think, you know, when you think about like how, you know, organizational innovation has evolved over the last, you know, so many years, right? We're really starting to see that organizations find the ability to innovate in small pockets, but they find a real challenge in how they scale these innovations across the company. And the reason that that breaks down is because largely experimentation doesn't scale unless you really reimagine how things are going to work. And I think we are seeing that real big shift in the context of AI. So when you think about a lot of these proof of concepts, which don't scale, they're largely because the kinds of problems they're solving are functional problems in a specific part of the organization that can deliver narrow streams of value versus the broader shift for the organization requires a bigger rethink in the context of the what and the how of how a business operates, which I think is most of the conversations we're in with a lot of companies around the world, like tackling really big, meaningful problems.

5:27Nigel Vaz:Can you take a car that would take 18 months to redesign and bring that down to 18 weeks? And if you could, what are the choices strategically does that offer you?

5:37Amy Bernstein:Right. And as always, it comes down to the choices. And you can tell the hard thing about strategy is what you say no to. But it sounds as if part of what you're getting at here is that for organizations that in the last decades have moved into a bunch of different businesses, and that's happened across many organizations. It sounds as if what you're calling for is focus. And I'm wondering how you get organizations to focus strategically.

6:08Nigel Vaz:In my experience, the way I think you think about focus is you've got to pick things that you can test and learn from undoubtedly, but you have to pick those things in a way that allow you to take those learnings and make sure that those learnings actually are applicable to the broader organization. So you want to pick a problem that's not so small that it can be dismissed as irrelevant in the context of a broader transformation, but not so big that you never get out of the blocks in terms of how you actually are solving it. And you want to find yourself in that sweet spot of saying, this is a decent enough problem that the organization will see it as representative of how we actually solve the bigger challenge that AI presents itself, because it's forcing us to rethink so many aspects of how we engage with customers, how we drive growth, how we take cost out of the business.

6:57Nigel Vaz:But at the same time, it's also not so big that it just does not deliver value quickly enough. And very quickly, the organization kind of moves on to the next thing.

7:09Amy Bernstein:So how do you advise your clients to find that sweet spot?

7:12Nigel Vaz:The first thing you have to get really clear headed about is what problems are you trying to solve at an organizational level? And then what are the precursors to those problems that become candidates to validate that strategy? Right. Some of those questions are what questions? So what what are we doing? Right. Many of those questions are how questions. I'll I'll I'll illustrate, you know, a kind of an example. Right. We have lots of clients who are in the midst of large scale technological transformations. And they're basically looking at building new digital platforms and tools that they get out of the traditional software ecosystem, which means that all of their business processes are baked into the software platforms that are monolithic and haven't changed or don't change frequently enough.

7:58Nigel Vaz:Right now, rather than basically saying, hey, we're going to get rid of our ERP systems and we're going to get rid of a lot of our core technology. What you're basically saying is what are the precursors to that? So maybe we'll take a functional area, which is an older application that's difficult to change, that's harder to move. And we will deploy an AI modernization effort on that area of the business. Suddenly you move from slow moving technology to an agentic agent first orchestration. You prove the model and now you can kind of start to say, you know what, we need to go on a broader modernization effort across our organization to replicate the learnings from here almost on an incremental basis until we are no longer bound by the business process or aren't constrained by the technology that we are leading.

8:52Amy Bernstein:So, you know, you're talking about very new ways of thinking about organizational strategy and business strategy. And I'm wondering, when you're dealing with clients, what is the thing you listen for that will most reliably kill a strategy in the age of AI? I mean, what is the most common error in thinking that you've come across?

9:17Nigel Vaz:I think probably the single biggest thing is the ability to follow a linear thought process before you get going. So this classic idea of, you know, we are going to do this, then we're going to do that, then we're going to do that, then we're going to do that, we'll review the outputs, and then we'll go around the loop again, right? And this idea of the linear baton passing, functional separation of strategy. So we've got a corporate strategy, then we've got our finance strategy, we've got a marketing strategy, we've got a product strategy, we've got a manufacturing strategy, right? And not really focusing on thinking about how data flows across the organization and how work will get done and how these interdisciplinary tasks that create connections between sales and marketing that are historically not common, but now really valuable if you could connect those data sets in the context of solving potentially a manufacturing question, not sales or marketing, right?

10:20Nigel Vaz:And being intentional around thinking about those kinds of challenges is probably the one I would highlight because it's almost like all of the success of strategic processes thus far are the very things that to some extent limit your ability to get value. in terms of being intentional about how you design for an AI-first world, primarily around people and context and OKRs, not just technology.

10:50Amy Bernstein:Yeah, and what you're saying reminds me of a couple of themes that we've heard already today about the importance of trial and error, getting away from this linear waterfall approach to strategy making, having to hammer everything to perfection before you move on to the next step. And now I'm wondering, how do you know that your new strategy is working before you start getting, you know, the numbers that prove it, the KPIs, your OKRs, whatever they may be?

11:22Nigel Vaz:I think this is one of the biggest challenges, right? I think this idea of like a strategic, planning exercise that is separate from an executional exercise is part of that traditional model. I think so much of strategy today, whether it's around growth or whether it's around cost innovation or whether it's around operational acceleration, has to come from having a strategic set of principles and approaches, but then also from how that connects into the organization in the context of real execution, providing input back into that process so that you don't have this idea of, look, we're going to come up with this incredible strategy.

12:04Nigel Vaz:We're going to spend all this time developing a strategic hypothesis, but then we're going to sort of then deploy that validation of that hypothesis into a very linear process of measurement again. And then we'll review it at the end of next year when we do next budgeting cycle in order to iterate, right? So much of this today is about measuring strategy in unit economics, not just activity. You know, thinking about the smallest possible things you can measure and using those to allow you to infer whether your strategic progress is in the direction that you want. You know, like I was using the technology examples rather than waiting for a project report at the end of a milestone.

12:49Nigel Vaz:What is the cost per release? What's the cycle time per feature? What's the defect escape rate? Because ultimately, we're in the business of helping companies transform digitally. But what that primarily means is deploying technology in order to enable either driving growth or solving cost and efficiency challenges or customer experience improvements in an organization. A lot of this comes down to how you are measuring in increments and then using that to infer or validate your hypothesis around strategic choices.

13:25Amy Bernstein:So you mentioned driving growth and you mentioned driving efficiency. You know, we have talked a lot about the efficiency piece of this. And I'm wondering, when you see an organization that's really using AI to drive growth, what is it doing when you're looking across your roster of clients at those that have really kind of cracked the code? What is it that they are doing differently?

13:55Nigel Vaz:I would say very few companies across the world would say that they've cracked the code. And, you know, we agree with that perspective. But what I can tell you what the people who are leading in the current context, right, there are a few things that they're doing that are really different. The first is recognizing that the traditional idea of software systems encapsulating a lot of the differentiation from a process perspective is now moving to a data ecosystem of connecting different sets of data in order to start to understand how those data connections enable them to serve customers better.

14:34Nigel Vaz:whether it's in the context of, you know, improving basket sizes in a retail context, by using predictive analytics on what's in that basket, and perhaps what you might be looking to create on the basis of the things you have, and telling you about the few things you might have missed through to, you know, accelerating the process of drug discovery by looking at adjacencies to the primary areas of research that the company is focused on, leveraging all of the data sets from previous failed trials. Every one of these is an innovative use of connecting data into an AI-first approach to creating value for end patients, citizens, customers in a way that was just not being done historically.

15:32Amy Bernstein:So I want to go to some of the questions that have been coming in, Nigel. You've clearly touched a nerve with a lot of folks. One question that's gotten a lot of up votes comes from Stacey. She says, the theme throughout the summit is rethinking how we do work. With that, where do you think AI strategy should live? Is it living in IT right now most of the time, it seems as if there needs to be cross of cross functional relationship with leaders, experts and individual contributors.

16:08Nigel Vaz:Yeah, I think that's a fantastic observation, right? And this is where I started off right at the beginning. AI even today is talked about in the context of technology. And I have an analogy here to respond to Stacy's question, you know, of going back to the 90s, right? When the most valuable technology companies in the late 90s, early 2000s were companies like Cisco, because where we were in that curve of the internet getting established was moving packets faster between organizations. So the whole complex of the internet conversations, and we were a company that built some of the first online banks and allowed you to pick a seat on an airplane.

16:46Nigel Vaz:And those were not technological problems. Those were problems of business innovation and recognizing that an airline that allows you to pick your own seat not only makes things at an airport more efficient and not only makes things at a call center more efficient so you're not calling up and saying where's 32B because you can't see a map in front of you. It also means that it creates one of the largest revenue streams for an airline today where people are willing to pay for the privilege of picking a seat, right? And I think this is very similar to kind of where we find ourselves with AI today.

17:18Nigel Vaz:So much of the conversation is around compute. So much of the conversation today is around the technological manifestation of AI in the context of which model is better. But the reality is, whether it's compute or models, there are foundational architectural components of AI when the real conversation about AI ought to be held at a business level because most of the value will get created on the applications, on the business processes, on the new offers we build. on top of the compute and the models, right? And so to, I guess, stage this question, organizations that are more successful than others are not having this conversation in the context of technology.

18:01Nigel Vaz:They're having this in the context of what kind of changes are possible for us with our customers, with our employees, with our partners and how we interact with them in the context of what is possible technologically as opposed to a technology-led strategy for AI, which is fine to have at a CIO level, but that's not where the primary value unlock, I think, will come for the broader organization.

18:27Amy Bernstein:That makes a lot of sense. Another question that's had a lot of upvotes comes from Suzanne, who asks, when you talk about AI transformation with the human touch, what ethical red lines do you believe every organization should define before deploying AI at scale? How do you advise CEOs to balance the pressure for speed and cost savings with the need for responsible ethics first AI, especially when the short term ROI is unclear. And I'll just add when there's so much pressure to show ROI. Yeah.

19:05Nigel Vaz:And look, I think there's two levels of conversation here, right? I mean, one of the challenges about what makes this different than the traditional ethical discussions of the past are the fact that these ethical considerations have to be grounded in the technology. Because if you don't actually ground them in the technology, all they become is a set of ethical guidelines and principles that you put out as an organization to make yourself feel better. And what I mean by that is having a clear perspective on how are we using data that's been given to us in the context of one thing for another. What are the expectations of what kind of data we want to allow, leave our organization to potentially interact with which kinds of models.

19:54Nigel Vaz:What choices are we making in the context of open source models where we can understand how models have been trained in the waiting and closed models? What are the geographic considerations in the context of sovereignty around AI and data governance, which is becoming an important consideration in the context of all of the conversations around the geopolitical landscape, you know, changing so rapidly with tariffs and other considerations, right? All of these are not just principles that can be agreed. They have to also drive a very specific set of technological decisions. So I'll give you an example of this, right?

20:34Nigel Vaz:Saying we actually want to protect our customers' data, but then allowing your employees to experiment on AI tools that are not in a sandbox, which is a pretty basic example, and where that data might potentially be enriching models in the public domain is an error. You think three years into this iteration of AI, we would have not seen happen. But it still happens because companies aren't necessarily providing their employees tools to enable them to be the most productive that they can be. And so you are seeing somebody who's trying to help a customer in the context of a customer service problem and is finding it really hard to find the information on their own website or on the own systems they've been given.

21:18Nigel Vaz:And they simply copy that question, stick it into a public AI chatbot and ask the question, sharing perhaps some of the information the customer has given them in order that they might provide that customer with a better response. But that data now, of course, has been exposed to a public domain context where you don't know exactly where and how that will, you know, propagate further. These are some basic things that I think you have to recognize, aren't just now about these guidelines about seeing how we want ethical use of data, how we deal with misinformation and disinformation, how we deal with, you know, AI swap in the context of outputs, how do we deal with fakes and really helping, you know, guide in the context of marketing, perhaps, or social media, what's fake and what's not, right, all of these choices have to be then embedded in system, you know, in systems and systemic ways of working in the technological approaches you choose.

22:17Nigel Vaz:Because I think in this day and age, that is where the difference gets made, you know, to do you have some ability to watermark or to highlight, you know, AI usage in the context of, you know, creative outputs, marketing communications, you know, all of these things, I think, are where the distinction of whether you're truly living the values that you preach around responsible AI, I think matter.

22:42Amy Bernstein:Yeah, that makes a lot of sense. Our next question actually is kind of adjacent to this. It's from a CEO who asks, for organizations serving vulnerable communities, what safeguards are essential so AI does not unintentionally reinforce inequities in access, voice, or outcomes?

23:01Nigel Vaz:The most critical thing there is recognizing the data that models that you're using have been trained on in the context of services that you're providing, right? Because we should all be clear, AI is only as good as the data that it's trained on. So if your data has all of the biases and all of the concerning components that you want eliminated from the interactions with these vulnerable communities, I think you have to start with the data sets that are being used in order to provide services. Because I think all of the things that you build on that foundation will either only compound or potentially could be mitigated in the way that the models have been trained.