# AI as Operating System: Rethinking Strategy

**Podcast:** HBR IdeaCast
**Published:** 2026-03-12

## Transcript

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I'm Audie Ignatius, and this is the HBR Idea Cast.
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 Voz, the CEO of Public's Sapien.
The company is in the digital transformation business, helping organizations modernize and adopt artificial intelligence to their existing models.
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 Foz.
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.
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 um, you know, having to change very simply the tempo of strategy, right?
So, you know, do annual strategy cycles work?
Do planning shifts, you know, come in long multi-year cycles?
Do budgets uh, you know, 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 uh to be rethought, particularly in the context of how quickly they move.
So it's a question of speed, but you also talked about value creation and value capture.
So it's also about business model.
You're it sounds as if you're talking about you're saying that that organizations really need to rethink their entire business models.
Is that accurate?
Absolutely.
Because I think, you know, when 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, you know, experimentation doesn't scale unless you really reimagine uh how you know things are going to work, right?
And I think we are seeing that real big shift in the context of AI.
So wait, when you think about uh a lot of these proof of concepts, you know, which don't scale, um, 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 of how a business operates, which I think is most of the the conversations we're in with a lot of companies around the world, like tackling really big, meaningful problems.
Like, can you take a car that would take 18 months to redesign and bring that down to to kind of 18 weeks?
And if you could, what are the choices strategically does that offer you?
Right.
And as always, it comes down to the choices and it and it and it you can tell the 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 a is focus.
And I'm wondering how you get organizations to focus strategically.
In my experience, the 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, right?
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, you know, so many aspects of how we engage with customers, how we drive growth, how we take cost out of the business.
But at the same time, it's also not so big that it just does not deliver value quickly enough and very quickly, you know, um, the organization kind of moves on to the next thing.
So, how do you advise your clients to find that sweet spot?
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 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 um, you know, clients who are in the midst of large-scale technological transformations and they're basically looking at building new digital platforms and tools as they get out of the traditional software ecosystem, which means that all of their business processes are baked into these software platforms that are monolithic and haven't changed or don't change frequently enough, right?
Now, rather than basically saying, hey, we're gonna get rid of our ERP systems and we're gonna get rid of uh, you know, 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, um, 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, you know, technology to an agentic, you know, agent first orchestration, you prove the model, and now you can kind of start to say, you know what, we can we need to go on a broader modernization effort across our organization to replicate the learnings from here, almost on an uh incremental basis until we are no longer bound by the business processes that are unconstrained by the technology that we are are leading.
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 what is the most common error in thinking that you've come across?
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 gonna do this, then we're gonna do that, then we're gonna do that, then we're gonna do that.
We'll review the outputs and then we'll go around the loop again, right?
And and this idea of the linear baton passing, you know, functional separation of strategies.
So our we've got a corporate strategy, then we've got our finance strategy, we've got our marketing strategy, we've got a product strategy, we've got our 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?
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.
Yeah, and and what you're saying is reminds me of a couple of themes that we've heard already today about, you know, the importance of trial and error 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 the KPIs, your OKRs, whatever they may be.
I think this is one of the biggest challenges, right?
I think this idea of like a strategic uh, you know, planning exercise that is separate from an executional exercise is part of that traditional model, right?
I think so much of strategy today, whether it's around growth or whether it's around cost out 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, you know, idea of, well, look, we're gonna come up with this incredible strategy, we're gonna spend all this time developing a strategic hypothesis, but then you know, we're gonna 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 uh in order to iterate, right?
So much of this today is about measuring strategy in in 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 uh progress is in the direction that you want.
You know, like I was using the the technology examples rather than waiting for a project report at the end of a milestone.
What is the cost per release?
What's the cycle time per for per feature?
What's the defect um escape rate?
You know, because ultimately we're in the business of helping companies transform digitally, but what that primarily means is deploying technology in order to enable um either driving growth or solving cost and efficiency challenges or 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 uh strategic choices.
So you mentioned driving growth and you mentioned uh driving efficiency.
Uh you know, we we 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 when you're looking across your roster of clients at those that are really have really kind of cracked the code, what is it that they are doing differently?
I would say very few companies across the world would say that they've cracked the code and and you know, we agree with that perspective.
But what I can tell you about 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, whether it's in the context of 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, um, 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, you know, not being done uh historically.
So I I want to go to some of the questions that have been coming in, Nigel.
You're you've got you you've clearly touched a nerve with a lot of folks.
One question that's gotten a lot of upvotes comes from Stacy.
She says the theme throughout this summit is rethinking how we do work with.
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 uh relationship with leaders, experts, and individual contributors.
Yeah, I think that's a fantastic um 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 Stacey'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 context of the internet uh conversations, and we were a company that built some of the first online banks and allowed you to pick a seat on an airplane.
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 uh very similar to kind of where we find ourselves with AI today.
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 Daisy's question, organizations that are more successful than others are not having this conversation in the context of technology.
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.
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.
And look, I think there's two levels of conversation here, right?
I mean, one of the challenges, you know, about what makes this different than the traditional ethical discussions of the past, uh, 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, you know, 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?
What choices are we making in the context of open source models where we can understand how models have been trained and the weighting and closed models?
Um, what are the geographic considerations in the context of sovereignty around uh AI and data governance, uh, which is becoming an important consideration in the context of all of the conversations uh around the geopolitical landscape and you know, changing so rapidly with tariffs and 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?
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 you know an error.
You think, you know, three years into this iteration of AI, we would have, you know, uh 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.
And they simply copy that question, stick it into a public um AI chatbot and ask the question, sharing perhaps some of the information the customer's given them in order that they might provide that customer with a better response.
But that data now, of course, has been uh, you know, um uh is been exposed uh to a public domain context where you don't know exactly where and how that will, you know, propagate further.
Um, these are some basic things that I think you have to recognize aren't just now about these guidelines about saying how we want ethical use of data, how we deal with misinformation and disinformation, how we deal with um, 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.
Because I think in this day and age, that is where the difference gets made.
You know, so do you have some ability to watermark or to highlight um, you know, AI usage in the context of um, you know, creative outputs, uh, uh 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.
Yeah, that that that makes a lot of sense.
Our our next question actually is kind of adjacent to this.
It's it's from a CEO who asks for organizations serving vulnerable communities, what safeguards are essential so AI does not unintentionally reinforce inequities and access, voice or outcomes?
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.
And I think actually understanding how you're addressing misinformation, disinformation bias in the context of the data sets becomes critical.
And then I think it's making sure that you have the appropriate safeguards where you are building in reinforcement on a consistent basis around the things that you want to ensure are held true.
Because I think in the context of vulnerable communities, or indeed in the context of providing equitable experiences for people, making the choice for what you want to, you know, limit is almost as important as what you want to reinforce.
Yeah, a lot of decision making, a lot of choices.
You have to be really focused and mindful on all of that.
Rich Hua, who's a founder and CEO asks what is the most important human attribute that leaders must exhibit to successfully drive AI transformation?
What social and emotional factors are leaders not thinking about enough?
This is a big HBR question.
You know, we have to kind of recognize that there's something, you know, we're at a point now where there's been a lot of conversation about AI in a in a kind of broad generic sense, right?
But if you start back to this wave of you know generative AI maybe, you know, three odd years ago, right?
You know, we started with chat, right?
And then we saw reasoning models um start to evolve.
And now we are in a world where we're starting to see agents, you know, very practically.
I I have a few on my computer now doing work while I'm having this conversation, right?
And then eventually we'll evolve to having kind of AI coworkers.
And I think we have to recognize this idea that the very nature of how we work as an organization is going to start to change to this interaction between us as people and the dependence and the direction and the agency that we will provide, you know, these AI tools to work on our behalf.
And I think we have to start to think about this in the context of how we think about people, where you aren't necessarily just gonna give them a task and they go off over a very long period of time and just kind of continue to execute that task, but you're actively able to engage with them, you know, redirect, course correct, nudge, uh, and and evolve, right?
And so that persistence around the memory of these agents, the interaction, I think will define uh, I think very strategically how we have to start to think about work in the context of organizations, because that is very different from then how work got done just with you know people uh engaging with each other.
To the second question, uh I think what people aren't doing is necessarily thinking as strategically about what are the guardrails, what are the expectations?
If you are a CHRO in an organization working for a CEO like you, you know, or a human resources leader, or as we call it in SAP and a people success leader, somebody whose job it is to make people successful, how do you actually start to think about making these people successful in the context of how you enable agents to interact with these people?
You know, we as a business are an enterprise AI technology company, in addition to you know, a services company, right?
So we think of ourselves as people and product together.
And so that coexistence in organizations doesn't just exist in ours because we're in the business of providing that service.
That exists in the context of every business, whether you're a retailer or a telecommunications company or an airline, um, where your people are gonna be working alongside these these AI tools, which have moved from being just entirely directed by people to in some cases, you know, uh operating autonomously.
So, how do you then in the context of that ensure that both sides of that you know equation, the human and the AI coworker, as it were, are working together in a system that creates um you know value and minimizes risk uh to the organization because I think that that will be the frontier that you know we will find ourselves in very, very quickly.
So we have time for one last question.
And this one got a lot of upvotes.
It's from Katerina, who's a co-founder.
Can you give an example of effective usage of AI in strategy development or execution and what was critical to that success?
I'll pick an example of uh a strategic choice.
Uh, you know, uh an automotive company in this instance was making on a big, you know, strategy question around how do we actually do three things at the same time.
First, move quicker in a more agile environment.
Second, make sure that we are being more responsive to customers changing behaviors, and third, organizing operationally our supply chains in order to be responsive to this, right?
And the first thing I think they had to do was to strategically weight the balance of what this question was going to um, you know, what this resolution was going to affect the most.
And in this case, they prioritized making sure that they were being more relevant to their customers, right?
And so one of the things that they did is shifted the process of you know, predetermining a lot of the answers uh and starting to basically build what was a strategy frame around the cost, the the demographic, the the type of uh you know automobile that they were producing, uh, and then like on an almost on an iterative basis, engaging with markets to basically say, okay, if we add this camera that allows you to reverse, uh, you know, now that means uh the cost for Malaysia, which is a big audience, suddenly becomes too high, and then they're not going to participate in that model's uh, you know, consumption.
So, how do we use that um, you know, information early enough that we can start to design the dash with or without a camera so that there's optional variants created.
Then that you know feeds into the supply chain, uh, you know, in terms of how they're sourcing, pricing.
So it's a good example of you know, AI being used to drive large strategic decisions, you know, which then enable lots of small strategic decisions that ultimately get them to a faster design car that they're able to pivot from more quickly if they don't see as much uh engagement from the markets before it even hits the actual consumer um whose perspectives have been fed in uh you know through this entire process.
That was Nigel Vod, CEO of Publicis Sapient, speaking to HBR editor-in-chief Amy Bernstein at the 2026 HBR Strategy Summit.
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I'm Audie Ignatius.