Making Magic With Gen AI: Capital One’s Prem Natarajan

**Sam Ransbotham** (0:02)
Generative AI requires organizations to carefully balance product innovation, science, and engineering. On today's episode, a leader in the financial services industry shares his experience with these challenges.

**Prem Natarajan** (0:16)
I'm Prem Natarajan from Capital One, and you're listening to Me, Myself, and AI.

**Sam Ransbotham** (0:23)
Welcome to Me, Myself, and AI, a podcast on artificial intelligence and business.
Each episode, we introduce you to someone innovating with AI. I'm Sam Ransbotham, Professor of Analytics at Boston College. I'm also the AI and Business Strategy guest editor at MIT Sloan Management Review.

**Shervin Khodabandeh** (0:42)
And I'm Shervin Khodabandeh, senior partner with BCG and one of the leaders of our AI business. Together, Mit Smr and BCG have been researching and publishing on AI since 2017, interviewing hundreds of practitioners and surveying thousands of companies on what it takes to build and to deploy and scale AI capabilities and really transform the way organizations operate.
Hi, everyone. Today, Sam and I are talking with Prem Natarajan, chief scientist and head of enterprise AI at Capital One. Prem, thank you for joining our show today. Let's get started.

**Prem Natarajan** (1:20)
Delighted to be here, Sam and Shervin.

**Shervin Khodabandeh** (1:22)
Describe your role at Capital One and the history of how you got there, please.

**Prem Natarajan** (1:27)
My role at Capital One, if we just stick to the AI aspects of it, is to build upon, like, Capital One has a legacy of being a very tech-forward enterprise. It was the first bank and I think one of the only major enterprises worldwide that is all in on a single public cloud.
That kind of transformation takes both a deep belief in the power of technology and a willingness to mobilize the enterprise, if you will, around that kind of vision. It takes the vision and the willingness to execute and the energy. And that, I think, puts us on a great footing to then harness the power of machine learning, artificial intelligence and all of that. And Capital One is both integrating technology using technology as a transformative tool, but also in machine learning.
And so my role right now is to strengthen that kind of history, build upon that history of early adoption of a lot of technology. We are in this kind of historical inflection point in AI with transformers and generative AI and all of that. And one way I see my role is to bring the power of all of this new technology to deliver value to the business, to deliver magical experiences, valuable experiences, everyday conveniences to our 100 million plus customers to help all of those folks.

**Shervin Khodabandeh** (2:55)
You said inflection point and I agree we're at an inflection point. Why do you think we're at an inflection point though?

**Prem Natarajan** (3:05)
I mean, this is not the first inflection point, but it does feel historical in some sense to me. In the past few decades, there have been a few such points. In colloquial AI history, if you will, people like to think of them as AI spring, followed by AI winter, followed by AI spring, followed by AI winter.
I feel each of those transition points between those eras is kind of an inflection point. Initially, it was all these expert systems and all of that. Then we said, oh, they don't really scale because they require so much human input. Then the whole probabilistic set of things came in, Bayesian models. Then they later became, in some context, hidden Markov models and all of that for speech and language processing, et cetera. Those have all been inflection points where we said, oh, this thing. And even though sometimes people feel like AI has always been promising, in many ways in my mind, the previous inflection points in AI history have actually become commoditized, which is the true sign of success.
Like 20, 25 years ago, using speech recognition in standard industry practice, whether it's for interactive voice responses, seemed novel. Now all of us kind of expect it is there. And so once it is there all the time, we kind of don't think of it as AI. Honestly, like we thought, oh, that's just speech recognition. But there was a time when it was like the forefront of machine learning and AI. And so now this new inflection point, though, I'd say if we think about it as a stack, as a science stack, a capability stack, we go from being able to take phenomena and convert them into some representation, like a speech signal into the sequence of words. And the next step up is kind of interpreting some of that transduction into something meaningful, like maybe some level of semantic interpretation, et cetera. We keep moving up that slide. Right now we're in this place where we've built all of these systems.