Bhavesh Dayalji, Chief AI Officer of S&P Global and CEO of Kensho Technologies, Vlad Barbalat, President, Global Risk & Capital Solutions and CIO of Liberty Mutual Group, and Monica Caldas, Global CIO of Liberty Mutual Insurance, shared bold perspectives with David Reimer and Adam Bryant. Key themes include why hiring for curiosity is now a competitive differentiator, how AI demands cultural transformation rather than project-based change, and which leadership skills will matter most as AI reshapes every function.
FROM OUR CONVERSATION WITH BHAVESH DAYALJI:
Reimer: What are the positive and negative patterns you’re seeing as the technology takes root?
Dayalji: I tend to be a glass-half-full kind of guy. On the positive side, the speed with which you can test ideas has never been faster. Here at S&P Global, we’re seeing product managers — not engineers — using AI to build applications and create user experiences.
We’re seeing lots of great examples like that in which you don’t need to be a coder or a software engineer to create these applications. The limiting factor is only your imagination and your creativity. And that raises questions about whom you hire and how you hire. The answer is that you hire for curiosity. You hire people who are thinking differently and can think outside the box.
Where organizations are struggling is in trying to adopt AI around these very established roles and functions. Many people in these roles are looking at the technology through a narrow lens — how can I use AI to do this role a little faster? They’re not necessarily interested in changing the way they work; they’re just looking to inject AI in certain places to work faster. Many people are taking a proverbial hammer and trying to find nails instead of saying, let’s fundamentally rethink our process and our business.
That’s why leadership and change management matter so much. Some people may not be able to cross that bridge. Who you hire is going to be a differentiator among companies, so that they can leverage AI to its fullest potential. Otherwise, you’re going to be just increasing your cost base, because AI is not necessarily cheap. It will get cheaper over time, but many CIOs find that they’re adding to their cost base right now rather than reducing it. That’s why it’s so crucial that people figure out how to work smarter, rather than just offloading tasks.
Bryant: Let’s get tactical. How do you hire for curiosity?
Dayalji: Ultimately, we’re looking for people who are excited by change. I don’t think corporate America has figured out how to hire the right people, given the transformational changes we’re living through. If you look at boardrooms and management teams, I think there is a lot of focus on AI, innovation, and transformation. But what has amazed me is that there isn’t as much thinking about doing a talent assessment across key leadership positions.
What are we looking for? How is that changing? It’s not just the technology that is changing around you. Everything is changing, so how are you assessing your leaders? How are you assessing how you work? How are you assessing what you see as the key priorities for the business?
Those conversations need to be thought of systematically, as a whole, rather than just assuming the existing team can handle it all and that we don’t need to change how we hire, how we incentivize people, and how we develop people’s skill sets and careers.
It really comes down to your ability to experiment. Are you a tinkerer? Are you a builder? Are you somebody who digs in deep about subjects that are interesting to your company and your role, and are you willing to engage and roll up your sleeves?
Gone are the days when your job as a leader is just to allocate resources and capital. Everyone now has a role to play as an experimenter. Get involved, because this technology allows you to do that.
Some leaders are doing that, yet other leaders tell everybody on their team to use AI even though they’re not using it themselves. You get the impression that they feel like their job is to police the use of AI, instead of leading from the front and saying, “This is how I’m using AI. This is how I use it as my thought partner.”
FROM OUR CONVERSATION WITH MONICA CALDAS AND VLAD BARBALAT:
Bryant: Companies have been rolling out technology implementations as part of their investment and capital strategies for many decades. What’s different now, as you’re having conversations about AI, technology, and the future?
Caldas: I’ve been doing digital transformations my entire career. What is different today is that most prior transformations were treated as individual projects, not as business reinvention. When you orient the body of work as a project, there’s a start, there’s an end, there is a relatively isolated group of people involved, and a technology shift happens.
Sometimes there was an operational shift that took into account the upstream and downstream process changes that were required to fully integrate the technology. What makes this moment different is that we’re approaching it as an ecosystem evolution. There is still a technological shift, there’s still an operational shift, but there is a cultural transformation that has to happen, as well.
Barbalat: I think people became accustomed to technology causing a bit of functional change — a new software package is going to allow me to do A, B, and C a little bit differently. And by the way, the tech department is going to come and install it on my computer, or I’ll download it from the cloud, learn how to use it, and once I master it, this will be my slightly modified workflow.
That is exactly what is not happening with this particular technology, because it is not a vertical technology. It is a horizontal technology. It requires you to treat it as an experiment, treat it as a relationship, and treat it as a version of an assistant or employee. Unless you invest in that relationship, you’re not going to get much back. You need the mindset that this is not just another thing to learn, but rather it is a relationship to explore on an ongoing basis, with the assumption that all kinds of things will change. There will be all kinds of ingrained habits that will evolve. That is very, very different than anything we’ve done to date.
Reimer: How do you drive adoption rates of AI through the organization? What have you learned about the best ways to get people to jump into the cold swimming pool?
Caldas: I’ll start by saying that you don’t learn how to swim by reading about water. You have to jump in, and then you can discover if you have an innate ability or whether you need lessons. Part of my mission is to make sure nobody gets left behind. So we have to figure out the right mechanisms to meet people where they are, based on their different skill levels and learning styles. We talk a lot about “Everyday AI,” which includes sharing modules with employees that show what AI can and cannot do. We also have metrics to monitor the adoption rate of our employees.
Barbalat: This is a team sport, so everything we’ve done within the investment organization here at Liberty Mutual has been in partnership with Monica. We have very high adoption rates, and we’ve also partnered with external AI labs on a continuous basis. So as newer features of the models show up, we’ve been quick to use them.
We’ve also worked hard to lead by example, including asking people which tools they used to produce a body of work. This technology brings tremendous knowledge and capability into everyone’s hands, but it’s the agency of the individuals — to harness it, to explore, and interact with it — that’s critical. We need people to have more agency, rather than just telling them to double-click on a particular feature in a software program.
Bryant: Some people are making the argument that AI will lead to the growing democratization of functional expertise. So which leadership skills will become more important over time?
Barbalat: Some skills will remain important, including the ability to ask the right questions, which requires critical thinking. Unless you ask AI the right questions, you’re not going to get the knowledge deployed in the right way. If you look at articles from when the calculator was introduced, many people were worried that math skills would decay. That didn’t happen, of course, because to use a calculator, you have to know what problem you are solving.
Caldas: Think about engineering, which is much bigger than writing code. You’ve got to architect the system and consider all these macro complexities around governance, security, movement of data, etc. So the engineering field is so much bigger than just writing code. That said, we can use AI to write code, which will raise the baseline productivity everywhere in that particular part of the software development life cycle. That will allow people to spend more time using their judgment.
It shifts the strategic value in each function. Routine work becomes more automated, but engineering is not going away, and software engineers can shift to more complex work. I also think the ability to iterate and to experiment is an increasingly important leadership skill if you want to go fast.
The other skill I’ll mention is portfolio discipline — the ability to know what to prune, what to turn off, and what to double down on as a potential winner. Because everything looks like it could work in the beginning. But you’ve got to have that discipline to know what’s not working and when to shut it off and move on.
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