As chief operating, information, and digital officer at TIAA, Sastry Durvasula oversees four interconnected pillars — technology, digital and client experience, operations, and shared services — powering one of the most trusted institutions in financial services. With a track record of leading transformation at big brand organizations, Durvasula is known for his ability to write new chapters for century-old companies. His leadership story is one of vision, reinvention, and impact, spanning 40-plus patents and AI-powered breakthroughs.
On a recent episode of the Tech Whisperers podcast, we unpacked Durvasula’s journey from engineer to Fortune 100 COO and the leadership playbook that has formed the foundation of his success. In a moment when AI disruption is overwhelming many organizations, he offers a blueprint for remaining focused, deliberate, and deeply human.
One of Durvasula’s operational principles is what he calls the historian’s advantage: the ability to learn and recognize patterns from the past and connect them to possibilities for the future. In a follow-up discussion after the show wrapped, we spent more time exploring his framework for anticipating what’s next and leading transformation at scale in the era of AI. What follows is that conversation, edited for length and clarity.
Dan Roberts: You’ve said one way leaders give their organizations an advantage is by being a ‘historian.’ What does that look like in practice for you, and how does it help you as a leader?
Sastry Durvasula: I’m a student of technology trends. I study a lot about companies. I study their annual reports, and it’s even easier now with AI. I look for industry trends. I have a folder on my phone called Geek It Out that has five or six apps that are following industry and technology trends.
The previous histories of technological revolutions — be it electricity or the internet or mobile or social media — are so useful to learn from because the change aspects of the AI revolution that we are going through now will be very similar to the change aspects of some of these big technology changes that happened in the past.
For instance, I was in Washington, D.C., recently for our Futurewise conference, and I was studying the history of lamplighters in the White House. Before electricity, the job of the lamplighter was to light the lamps in the White House, using natural gas. In 1891, electricity came into the White House and the lamplighter job got decommissioned. Essentially, they moved on to other crafts. Another interesting factoid is, adoption was a major issue, and in the early days staff were assigned to operate the switches due to fear of electrocution. Other crafts and roles went through massive changes. A master blacksmith had to reskill to become an effective manager of electric-powered metalworking equipment. The head baker role completely changed with electric mixers, ovens, and refrigeration.
Fast-forward to now, will AI replace jobs? Yes, it will replace jobs. Will we reskill? Yes, we will reskill. Will there be transient roles like prompt engineers as we all learn how to use it and get comfortable with it? Yes. In the end, will it give new opportunities for humans to perform in a different scale? One hundred percent.
Another story I often reference is BlackBerry as an example of a company that didn’t scale with the times. It grew from $3 billion in 2007 to close to $20 billion in 2011, despite the iPhone’s release, despite recession. And then, in five years it was down to $2 billion. Now it’s a historical study. There are other examples like AOL and Yahoo that were the pioneers in the past tech revolutions. So I think history matters a lot. Do I believe some of the pioneers of the current AI revolution will be the eventual BlackBerry, AOL, or Yahoo? Yes.
It also helps with managing your stakeholders, especially the board, when you have to present these big transformation programs. Sharing these anecdotes from history helps set the stage and motivate people when the complexity of transformation becomes so tough and dense. You can use it as a telescope as well, not just as a past. For example, as we think about the future, if AI were to be this way five years from now, what history are we going to set at this point as we tread the path?
What’s your advice for leaders who want to build teams and cultures that can telescope out and see around corners?
My general rule of thumb is to follow the customer. It’s one of the things that I guide my teams with and that I try to practice personally as well, because ultimately, where the customer is and where the customer will be is where your company will be, or what it will be driven by.
For example, the generations are shifting as we speak. In TIAA, we have millions of customers, which we call participants, and they encompass the retired population, people who are still investing, and Gen Zers coming into workforce. The Gen Z participants have a different set of needs and a different set of expectations from companies. A lot of them are leveraging AI in day-to-day life for so many things. If the customer is on the phone all the time, and they’re going through stores, and you’re a payment card company, you have to ask, are you going to be relevant on the phone or not? When I was with American Express, we made the decision to be relevant, and that’s how you tap the phone, and you can use American Express.
Or there’s the iconic currency of membership rewards points that American Express has, which was reserved for all these big, exotic cruises and vacations and stuff. We asked ourselves, are we going to be relevant and make this currency useful in the day-to-day life of our customers? The answer was yes, so if you go to Amazon or McDonalds or to pay for a taxi in New York City, you can use membership rewards points if you have an AmEx card.
So I try to use that as a guiding principle: Do you know where your customer is, and do you know where your customer will be? Especially with AI, now more than ever, we have to predict where the customer will be and be relevant in that area. I think a lot of companies, including TIAA, will have to go through that major transition as we go through this AI revolution.
Yogs Jayaprakasam, a former colleague of yours at American Express who is now the chief technology and digital officer of Deluxe, says one of the things that stuck with him is your ability to simplify how you articulate technology’s value. How do you articulate the value of AI to all stakeholders, not just the ones who understand it?
Half of my organization is operations. They’re not technologists. You have to explain the value of AI to non-technical folks and, frankly, even to technologists, because they have varying degrees of understanding of AI. Some just look at it as pure-play tech, some look at it as a little bit more than that, and some just look at it as a bunch of large language models that we’re going to use from different companies.
People compare AI with electricity, and I agree with that, because you don’t think about electricity as a technology, right? It’s just part of our life. We think of electricity in our daily life only when there’s a big power outage. All the change that happened during its early days got the world to the stage we are in now. So, if you apply that to AI, it’s not the technology you need to explain; it’s the change we are going through with AI and the power it brings as you go through this major transformation that needs to be made explainable. Here’s my hypothesis: Unlocking the true power of AI is one-third technology and two-thirds a change management challenge.
First, we have to look at it as a business rewiring opportunity. I use AI as a leapfrog opportunity. There are a lot of things that we could have done better in technology that we didn’t. AI comes in and levels the playing field, so you could almost become the very best in your craft despite having all this technical debt or other debts that we’ve been carrying in our processes and experiences, because it fundamentally rewires the company in a very different way.
The second thing is the workforce of the future. I think AI is a driving force to determine what the future workforce will be for any company. The comparison for that would be the pandemic.
I was at McKinsey back then, and you think about a global management consulting firm that spends all its time on travel and working in conjunction with the clients at the client’s site having to do that craft remotely at global scale. Digital collaboration was a thing, but it was not operating at scale in any company. And all of a sudden, we dropped everything and became the most digitally collaborative firm and digitally collaborative society. I believe AI is that forcing mechanism at this point to recraft and rewire the workforce of the future.
And third, I always say, you’ve got to solve the boring problems to get to scale, not just the sizzle side of the house. Because just giving a cool interface to clients or our colleagues is not going to cut it if you can’t build the underlying foundation. So those are the things I talk about when it comes to AI: rewiring the workflows, workforce of the future, and solving the boring problems of the company, where you have to fundamentally pay off your debt in data, in technology, in processes so that when you get to scale with AI, you have the whole company transitioning into that new scale, not just parts of the company.
You’ve said middle managers are pivotal to the success of any transformation and that they bear the greatest burden. Can you expand on that?
I’m a big advocate for elevating the learnings, accountability, and empowerment of middle management. Middle managers is where change either makes or breaks. Now, with AI, where there are all these hypotheses that the pyramid structure is going to be replaced by the diamond structure and the entry level is going to shrink, you wonder, how are you going to establish the new normal for middle management?
Elaborating further on change and adoption, I always say these things follow Newton’s Laws of motion in large companies. Everything continues to be in a state of rest of uniform motion unless it’s compelled by an external force. And every action has an equal and opposite reaction. I think AI change management will go through this. Top-down mandate and the bottom-up innovation will act as the forces, but it’s the middle management that will need to turn into actional outcomes.
When middle management takes charge in rewiring the workflows of the future, defining the workforce of the future, and solving the boring problems, innovating with the power of AI, it takes it to a different level.
Once you’ve set your vision in motion and communicated the why, how do you sustain momentum and keep stakeholders engaged over time?
The early years of any big transformation program is the honeymoon stage. You get a lot of visibility, you do these big partnerships, you release some early wins, and then the complexity sinks in. I often talk about ‘leadership stamina.’ I think it’s very important to have that level of stamina to execute through these complex initiatives because transformation is a lot about vision but a lot more about execution. Seeing through that execution with a level of operational focus is as important as being strategic and visionary at the beginning of transformation to activate the transformation. As you assemble the team, you have to pick and choose the players in a way that the team has visionaries and also great execution leaders. You have to have a team that collectively has that leadership stamina and an execution arm of the team that really gravitates to hardcore execution.
In terms of the organizational patience, every year you have to have an investment into this program. How do you ask for investment in a tough year? You have to have a strategy. And what happens if you don’t get all the investment? You have to have a contingency plan. And what happens if a partner you’re working with is not working out? Then you have to have another option. A lot of these lines of defense, whether it’s risk management or audit and control, legal and compliance, with all the general complexity that’s going to come in, you have to lead with a design in mind for these things so that when you hit the surprising aspects of a transformation, they’re knowables. Because we should predict the knowables and have an option that we will activate when we hit them, because every transformation will have complexity; we just have to design for it.
By studying the patterns of past revolutions, seeing around corners, simplifying the complex, and empowering the middle, Durvasula shows how great leaders prepare their organizations to shape the future, not simply survive today’s disruption. For more insights from Durvasula’s transformation playbook, tune in to the Tech Whisperers.
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Read More from This Article: TIAA’s Sastry Durvasula offers CIOs a blueprint for engineering what’s next
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