Joining me now is Nigel Vaz, who's the CEO of Publicis Sapient, which is a very kind of high-growth digital transformation business that's born out of Sapient and the Publicis Group. And Nigel's advised some of the world's largest businesses across geographies for over 20 years. And we've learned a lot about Nigel and his firm in recent years and some of the major organizations. In fact, we were just talking about one right now where his company is advising. He's one of Consulting Magazine's top 25 global leaders, and he's always in major business media and news media like BBC, Bloomberg, Wall Street Journal. And he's also author of a book on digital business transformation. So welcome, Nigel. It's great to have you here with the HFS audience. And maybe, I don't know if anyone saw it in a video with Nigel last year. He's got some very poignant views on Gen.AI in particular and the work they're doing. But how do you anticipate Gen.AI evolving considering the substantial investments your firm's made? You did some JVs with like Tequila, PS Hummingbird, as well as some niche acquisitions in the space. So what is the big plan for driving these AI intelligence systems and your core AI strategy for businesses like Sapient? There's a lot in there. A lot of them.
I'd say, you know, if we can just take a step back for a second, right? And while we're doing a lot in the Gen.AI space, I think it's worth probably just pointing out how incredibly exciting it is to be at a point in time where we are seeing the fundamental augmenting of people and organizations in the context of intelligence in a way that we just have not seen ever. And I think that creates just a huge opportunity. So a lot of the work that we're spending our time thinking about is how do you fundamentally not just incrementally move things forward, but really reimagine things in the context of a world that now has organizations and people within those organizations have capabilities that were just fundamentally not possible only a couple of years ago, right? In thinking about something as basic as somebody who couldn't write a line of code can now produce decent code because they have a concept for how to talk to a computer and, you know, what that interface looks like. When you start to think about Gen.AI in the context of industry-specific opportunities, we're making lots of investments in building tools and platforms that allow companies to start to make the right choices not only on the basis of which model is right for them, so a model hub kind of architecture, but also is it a large language model, is it a small language model, is it something that is semantic search-driven or, you know, natural language conversational interface-driven or is it computer vision-driven? And every one of these architectural changes augments people at a fundamentally different level. So, you know, we work with companies where all of a sudden a security guard can now see way more than they were able to see in the context of patrolling a mall and they're being augmented that way. A doctor is getting information from systems that are telling him not only what's happening with the patient today but are assessing probabilities and rates of progression for how their health might evolve in the next few hours, determining patient care. You know, something as simple as thinking about how you augment people in their everyday internal working tasks. If you're a journalist or you're a writer or you're a designer, how are you actually able to produce and create more in very specific contexts? So for us, I think the idea here is that we're just at the beginning of this very transformational set of changes and whilst we're seeing applications that are now at scale, the potential to just fundamentally reimagine how a lot of this is done lies in front, which is I think what's really exciting.
So you think this is much more of a cultural shift than a technological one?
I think it's both, right? I think you have to have the evolution in technology as we make continued progress toward this kind of increasingly certain idea that we will get to some form of artificial general intelligence over the course of the next few years. And then that's the technological implication. Of course, the cultural implication is how do you think about risk? How do you think about ethics? How do you think about making sure you're not reinforcing bias but you are being really thoughtful about reinforcing the unlearning of bias in a lot of these models, right? So I think there is a shift both in terms of technology and what's going to be possible, but I think that shift of how we as people consider the idea that in this case, the ability to use these tools and operate as a quote-unquote augmented human is almost as important as historical experience ideas. I think we're at that point now where very soon I think we'll start to see the transition from this idea that this person's got a lot of experience to this person can operate in this new environment. And I think that's going to be more important.
Yes, like the adage about you're not going to be replaced by AI, but you might be replaced by somebody who can...
Who uses AI better than you. Yeah, for sure.
And do you think this is going to lead to people being held accountable for their ability to extend themselves into AI environments?
Yeah, I feel like when people ask me today just in our own organization, what is the most important skill? I was talking to some of our early careers teams and for me, I think this idea of learning, unlearning, and relearning is probably the most critical capability set or skill set I think that you can start to master at an individual level and potentially at an organizational level because there are fundamental things that you have learned that have made you who you are that you're going to have to unlearn because if you persist with those, you are going to go down a path that is not as productive as unlearning that skill. And then of course you have to learn something else and then that constant unlearning and learning needs to relearning. So it's kind of how do you learn, unlearn, and relearn? I think that's probably the most significant skill at an individual level and potentially at an organizational level, which is very hard to do, right? Because people are conditioned to only be able to tolerate a certain amount of change. Everything in nature, right? I mean, I was talking to a doctor years ago who said this to me. So he said, babies were born as teenagers, most parents would abandon them almost instantaneously. It's because they give you time in nature to get to grips with what you're going to have to deal with later on, right? Anything linear in nature, right? I mean, I was talking to plants and babies and anything exponential is typically bad. So think volcanoes and of course most recently or earthquakes or most recently pandemics, right? We as sort of humans aren't geared to kind of cope with that. So now if you take that into an organizational context, where not only do you have the rate of change that is increasing, but the scale of change is getting to be exponential, the only thing you can do as a person is get comfortable with this idea that in that changing landscape of constant change, your ability to learn, unlearn, and relearn becomes your greatest asset. Because frankly, what you knew to be true yesterday is no longer true today.
Right. So how do we bring this into a broader technology context? A lot of enterprises went through tremendous investments in cloud and migrations and now we're seeing a lot of pilots being funded through loose change. But we're starting to see ambitious C-suites starting to put serious money into this. How fast is this moving in your opinion or is this more of a slow burn as we figure it out?
Well, it's interesting that you started with cloud, right? Because if you think about cloud, cloud was primarily, you know, for the most part, an infrastructure play, right? And we saw huge growth in cloud services, but a lot of people were simply migrating their existing legacy onto a fundamentally different infrastructural platform. And where you didn't actually see that transformation and that, you know, cloud native architecture promise come to life, what you saw is a lot of organizations really essentially assessing whether the value that they are getting was commensurate with the investment they made. And I can tell you, certainly, a lot of CEOs and CFOs that I talked to said, well, we're spending X many millions on this, you know, this journey, but the promise of what this was going to unlock has yet to come. And I feel like as we've thought about transformations of our clients' businesses and their organizations, we often talk about this framework called SPEED that we use and it's an acronym for, you know, strategy. And so being really clear on what value you're trying to unlock. The P is product. So rather than thinking about a project, a cloud migration project or an AI project, how do you constantly evolve these, you know, over a period of time on an iterative basis in the same way that software iterates, right? The E is for experience. So how do you actually design experiences for employees, for customers, for citizens that basically benefit from what you're building? The next E is engineering. So how do you think about engineering basically not being about IT, which was all about risk and cost and keep it, you know, like lights on, keep the lights on, but actually really re-engineering what it is that you're trying to imagine in the context of these experiences. And then the D is data and AI, which is essentially our belief that, you know, in a world that we live in today where in, you know, the early days, data was all about structured data and this idea of a very, you know, rigorous management of the quality of the data. Today, all AI, particularly Gen AI, is mostly fueled by unstructured data. So how do you actually manage the quality of that so it's not garbage in, garbage out? So the speed architecture helps