US Bank AI Chief on Preparing Your Business for GenAI

Srini Nallasivan, executive vice president and chief AI and analytics officer at US Bank, recently detailed how the company’s efforts to build a rock-solid AI and data analytics team has paid off as generative AI enterprise use skyrockets.

Nallasivan served as a keynote speaker at the Forrester Data Strategy & Insights event last week in Austin, Texas. He was joined onstage by Forrester Vice President Srividya Sridharan -- who served as interviewer for the session.

Traditionally, the executive functions of a chief data officer at a large bank focused on data lineage, data quality, and data strategy, Nallasivan explained. His role marries artificial intelligence and data analytics to deliver improved customer experience, revenue growth, and more. “We really need to understand transaction by transaction what is going on so that we can support from a different side and a lot of work goes into supporting that -- then comes automation and efficiency,” he said.

US Bank has been working with AI for several years, but its generative artificial intelligence (GenAI) capabilities really accelerated this year with the public introduction of OpenAI’s ChatGPT.

Nallasivan said the bank’s GenAI program needed a boost from the top to help focus on four critical areas: collaboration, infrastructure, quality data, and responsible AI. “The journey started with seed funding from our CEO. It’s not just about hiring the right data scientists -- because if you have money, you can go hire the right data scientists -- but it’s a combination of multiple things. I told him that I needed a sponsorship, which is crucial … it’s not a bottoms-up approach.”

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A Hybrid Approach to Team Building

To deal with the growing importance of data analytics and AI needs, Nallasivan said he has quadrupled the size of the AI and analytics team over the past five years. He says he likes to hire a mix of grizzled data scientists and some that may have graduated just recently. “Going after the right talent in this market isn’t easy,” he said. “You can either go for a very experienced data scientist hire, which is going to cost you a lot more, or try to go after a graduate with a couple years of experience and train them and make sure they have to life experience of working on some real-life use cases. So, we pretty much have a hybrid strategy.”

Managing that team and its many responsibilities throughout the organization is no small task. Efficiently prioritizing needs is key, he said. “There’s no silver bullet,” he said. “And if you work for a big company that has so many different business lines, and every business line wants to prioritize their own use cases, which comes to the top?”                

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Nallasivan said care must be taken to “make sure that you think about the change management aspects of all these ingredients and need to come back to make sure that you prioritize a good use case.”

Advice for AI Implementation

AI was a recurring theme throughout the event’s agenda. Forrester’s Sridharan asked Nallasivan to give advice to attendees as the look to quickly implement generative AI for their organizations.

“I’ll give two pieces of advice,” he said. “The first one: Always get your senior management sponsorship. You really need to make sure that your CEOs and pretty much all senior management understand what GenAI is, so they can be sponsors. And you really cannot build a model and take it to the front lines, and say, 'OK, go use it.' You really need sponsorship from the CEO and to try to constantly educate yourself. There is no right answer in this space because this field is continuously evolving.”

Understanding and keeping up with changes in GenAI will be crucial going forward, he said. “The rate at which AI is changing is very dramatic. So, try to make sure you understand what’s going on and how the industry is evolving. You have to understand the regulations because every country has a new regulation coming out.”

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