AI Upskilling at Scale: Bank of America’s Bernard Hampton

**SPEAKER_1** (0:02)
What learning and development lessons can we take away from an organization upskilling hundreds of thousands of employees on the use of AI? Find out on today's episode.

**Bernard Hampton** (0:12)
I am Bernard Hampton from Bank of America, and you're listening to Me, Myself, and AI.

**Sam Ransbotham** (0:17)
Welcome to Me, Myself, and AI, a podcast from MIT Sloan Management Review, exploring the future of artificial intelligence. I'm Sam Ransbotham, Professor of Analytics at Boston College. I've been researching Data Analytics and AI at MIT SMR since 2014, with research articles, annual industry reports, case studies, and now 13 seasons of podcast episodes.
In each episode, corporate leaders, cutting-edge researchers, and AI policymakers join us to break down what separates AI hype from AI success.
Welcome back to Me, Myself, and AI. Today, we're joined by Bernard Hampton, Head of the Academy at Bank of America. The Academy is one of the largest learning and onboarding organizations in corporate America, supporting more than 200,000 employees worldwide. Bernard has a central role in the bank's effort to upskill, re-skill, and prepare talent for the use of AI. Bernard, welcome to the show.

**Bernard Hampton** (1:16)
Hey, Sam, thanks so much. Great to meet you.

**Sam Ransbotham** (1:18)
I'm guessing most listeners are pretty familiar with Bank of America. It's pretty huge. It's one of the world's largest financial institutions. I looked up 70 million clients, 35 countries.
It's huge. But I'm guessing most people may not be familiar with the Academy, which you lead. So can you tell us a little bit about the Academy and how that relates to Bank of America?

**Bernard Hampton** (1:40)
Yes, certainly. The Academy has existed since 2017 It replaced our legacy learning organization, and it's Bank of America's award-winning onboarding, education, and professional development organization. That's really dedicated to the growth and success of teammates across the enterprise.
At the Academy, we're laser-focused on workforce agility. And specifically by that, I mean, it's about building the right skills and the right roles faster. And we continuously process, improve, and look for opportunities for operational excellence or to bring in new technology or modalities to be able to hit that mark. And that's really about the mobility, upscaling, and readiness of an AI-enabled workforce.

**Sam Ransbotham** (2:23)
So we're kind of the same. I teach a couple hundred students a year and you've got 200,000. That's about the same, right?

**Bernard Hampton** (2:29)
Close.

**Sam Ransbotham** (2:30)
The scale seems kind of staggering. The scale combined with the speed of change of everything going on, how do you manage those two things at the same time?

**Bernard Hampton** (2:41)
Our Academy pathways are really central to technical skills, data and AI literacy, client-facing excellence, leadership capabilities that scale.
At the end of the day, when we think about those shifting priorities across the organization for specific populations, we do a couple of things. Number one, we have an internal, traditional learning skilled organization, but at the same time, we match that with subject matter expertise from the business. Within my organization over the last few years, some 750 people have moved from the line of business into the Academy and become a full-fledged Academy teammate contributing that real world intelligence to the organization.

**Sam Ransbotham** (3:23)
That sounds good and I like the idea, but it just seems really hard.
I think about a year ago, prompt engineering, everybody needs to learn prompt engineering, and then RAG was the latest thing. Then it just feels like these topics are coming along so quickly. Actually, I could pick the topic of today, but we're recording about a month before this broadcast, and so it'll probably be old hat by then. How do you keep up with that? How do you design a process that can respond to that level of agility?

**Bernard Hampton** (3:55)
AI certainly has created quite a bit of runway and opportunity for us. It shifted the learning priorities really towards faster proficiency in core roles, better critical thinking and decision-making, as you can imagine, stronger communication and relationship skills, and then practical affluency in AI tied to daily work. When we use AI-based learning modules, it's not about saying, we're putting an AI tool in front of someone to help aid learning. It's thinking in real practical ways about ultimately, who do we serve, what are we trying to accomplish, and then work backwards and determine the best solution that allows us at scale to be able to be practical, fact-based, help somebody focus on and develop core skills in a way that is psychologically safe, but also engaging.

**Sam Ransbotham** (4:41)
You mentioned things like communication skills. At the same time, you also mentioned AI technical skills. If you think about the spectrum from super soft skills versus the more technical skills, where are your challenges more? What are you having more trouble with, or how do the challenges differ for each of those types of learning experiences?