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

Speaker A: 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.

Speaker B: I am Bernard Hampton from Bank of America, and you're listening to Me, Myself in AI.

Speaker C: Welcome to Me, Myself and AI, uh, a podcast from MIT Sloan Management Review exploring the future of artificial intelligence. I'm Sam Randsbotham, professor of analytics at uh, 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, reskill, and prepare talent for the use of AI. Uh, Bernard, welcome to the show.

Speaker B: Hey, Sam, thanks so much. Great to meet you.

Speaker C: 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?

Speaker B: Yes, certainly. The Academy's 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. Uh, at the Academy, we're laser focused on workforce agility. And specifically by that, I mean it's about building the right skills in 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, upskilling and readiness of really an AI enabled workforce.

Speaker C: So, uh, 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?

Speaker B: Uh, close.

Speaker C: 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?

Speaker B: Our Academy pathways are really central to technical skills, data and AI literacy. Client facing excellence, leadership capabilities that scale. And so 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. So 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.

Speaker C: 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. And then it just feels like these topics are coming along so quickly. And 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 uh, a process that can respond to that level of agility?

Speaker B: 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. And so when we use AI based learning modules, it's not about saying, oh, 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.

Speaker C: You mentioned things like communication skills. At the same time you also mentioned AI technical skills. If you think about this 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?

Speaker B: Ultimately it's been incredibly important that we keep both of them top of mind. I mean it is easy today and AI, uh, dominates most news cycles. It dominates what you read online. It's the fun thing to talk about when the reality is it is not like a toy. Companies who treat that really seriously are going to start kind of top down in leadership and developing skill in the space so that it flows through the organization. Those that maybe not so serious are going to treat it like a toy that you play with for a while and then you put it away. At the same time, what's operating in the background is this concept that we believe that humans should always take the lead with AI. And so our ability to talk about both simultaneously says that the importance m of human skills continues to be really important and a critical differentiator when you start to think about things like empathy, listening, judgment and decision making continue to become incredibly important, while at the same time technical affluency in AI becomes incredibly important. The other two things that I'd say are a backdrop really across both of those is AI will continue to change the way that we work at a faster and faster pace, as we all can imagine, if we're embracing the technology for what it can ultimately do for us, while at the same time we have to continue to consider what career mobility looks like and where is the workforce in their skills journey. As work changes, you may need fewer people to do certain things, but at the end of the day, we are a client business and we want not only more people to face off with clients, but think about what could more people bring to clients if they were spending less time administratively or on task type functions, but really go support and understand what the needs, the goals, the objectives of the client are wherever they are in the spectrum.

Speaker C: I'm going to get this stat wrong and so you can correct me, but I think I read somewhere you fill something like 40% of your roles internally. That requires a lot of upskilling and reskilling, I would guess. Is that where the challenge is? Or is that a goal to use more internal or more external? Or how are you thinking about that mix?

Speaker B: Yeah, so you get really close. That's 20,000 people we hired last year. That includes 2,000 of our student campus hires. 45% of open roles last year almost were filled internally. Which further leans into why skilling and upskilling are so important across the organization.

Speaker C: I was reading somewhere where you're talking about the desire to redeploy talent versus reduce headcount. I think there's certainly a headline out there right now when it, uh, seems like every time I look at the news, there's Company X has reduced headcount by, uh, thousands of people, all because of AI. Well, one, I'm suspicious in the first place that that's actually due to AI, but I think you're on the record of trying a different approach versus that reduction. What's your thinking there?

Speaker B: Yeah, our CEO has been quite clear and really this is about our clients, it's about our teammates and it's about communities. So when you think about an employer of our size and scale, the knowledge of the organization and our client connectivity becomes really important. So for our teammates, yes, we've said that over time you may need less of people to do certain functions in the organization, but at the same time there's opportunities for reinvestment. And so our opportunity is the recognition that our employees bring a lot of value to the organization. They've had a commitment and a level of loyalty. And those that want to continue to learn, that are curious, that are agile, we continue to provide them tools to be able to have a career full of as much mobility as they would like over the course of their careers. But at the same time they carry with them a level of acumen and experience that's beneficial for our clients and the organization. Whether it's working in risk or if it's working in a client facing role, for instance, we want them to be able to continue to bring their best and enjoy doing so. And our employee engagement results bear that out as well.

Speaker C: You've got a, uh, massive variety of people within your organization, from super technical to super non technical. How do you figure out who needs to know what? That's timey's me.

Speaker B: In short, it's everyone needs to know something. I think in its simplest form, we think about AI in three different levels. Call it at level one. That is about every role and function. That's about your personal use of tools. Uh, it's about personal productivity. And so everyone has access to some version of AI today to be able to enhance what they do today and think about things like where I need to write or analyze information, maybe prepare something, the task oriented or administrative type functions. And we want them to feel confident and capable to be able to use AI in creative ways to make their workload simpler. Now on one side of that is you can certainly say, oh, I save a bunch of time. I can take a deep breath and kick back. But the reality is the best measures of that is what do you turn that increased capability into? And the way that we think about it is how do we measure the transition of people doing everything from upskilling themselves to expending that time in more accretive, uh, activities that are beneficial to the client, that are beneficial to the productivity of the organization, or supporting someone else who takes care of a client. And then there's a secondary function of AI or a secondary level that is really about functions. We'll take unique systems. Maybe there's one group that needs to use one system most often and we curate Using agents, the ability to be able to decipher, pull together, aggregate information that simplifies this one function across a particular group. And then there's level three where we think about large workflows, multiple data sources, multiple agents involved, and that's usually large scale and horizontal across the organization. Well, each one of those has pretty big wins for the organization at the end of the day. That builds a picture of productivity that allows us over time to begin to, as that productivity ramps up, begin to decide where are opportunities for redeployment or where are opportunities that maybe you don't replace a role, but it doesn't mean you need to go into a situation as in some companies that generate large scale layoffs at the end of the day.

Speaker C: Yeah, those levels are interesting and I'm glad you mentioned measurement, particularly in the first one. I'm uh, sure bank of America, like everywhere else has a whole bunch of KPIs that they measure what's going on and measure productivity and efficiency and those things. As I think about it, I worry that the prevalence of those sorts of measures are going to lead us towards really focusing on what you call that level one, which is much easier to measure. It's going to fit well with the existing KPIs versus that level 3 which seems more cross cutting. It has the chance of changing balance within organizations. How do you keep from just making everything, uh, a level one type, hey, let's get more efficient and more productive.

Speaker B: From our perspective it means that one, you do them all simultaneously. Two, on the other side of that, a big part of the work that we do in the academy is not just aiding in the development of tools and resources to help bring AI affluency across the organization and readiness to be able to use those tools, but also our uh, skills library that helps us continually provide opportunities for teammates to invest in themselves. There's a balance of what you measure. Sometimes that measurement M may be about legacy systems that you want to be able to sunset in terms of newer systems that are more AI forward or technology that allows you to better communicate with clients. The other part of that is measuring, well, what's the time that we spend on high value work. And that's not necessarily a function of only measuring productivity. You're uh, at a couple of different triggers. You've got the one that says people will move to do things on their own. And the other is you reduce the number of people who do that work to be what's appropriate for the volume of what's left over.

Speaker C: Are There things that you've looked at on paper that hey, this might be a good place to use AI, but you've decided that the risk didn't pay out or how are you kind of instilling some of those guidelines and where we should be using tools whether than we could be using tools?

Speaker B: Yes. So think about what AI is really good at. AI is really good at research, it's really good at writing administrative functions, it's good at task. AI is not good at judgment. That requires a human in the loop. So when we think about the what and the how in our training process, we deploy AI to make learning one more practical, two more relevant and three scalable. That includes AI, uh, enabled learning experiences such as simulations, guided practice. The Academy leverages AI conversation simulators to help teammates build and strengthen soft skills through interactive role play and coaching, strengthening along the way. AI guided by the way as well. And then it's designed to accelerate readiness for teammates across various roles, support career mobility and ensure human oversight remains central to the learning experience. So in doing so, we begin to somewhat be able to say, hey, here's what AI is really capable of doing very well and we want to put people in a situation where they experience and improve their skill. The one I'm talking about in particular is an interactive platform that enables teammate to practice real world scenarios. That's a great use of AI in thinking about how do I immerse somebody in a situation in a safe simulated environment. Ultimately it builds pride, proficiency and professionalism.

Speaker C: Ooh, I really love that. We did some research a couple years ago where we framed it as self determination. If you felt like you had more authority, if you felt more confident, if you felt like you had better relationships with people, if you felt good about what you're doing, you're more likely to use these tools even though you might think that they may be tools that quote, replace us as humans. And that's not at all the perspective. And I love that simulation aspect. Do you watch the Good Place, this TV show? I don't know. I recommend it. I think it's hilarious. But one of the scenarios in the Good Place is they have someone go through a simulation of breaking up with his girlfriend. And you know, because you just don't get that many chances to break up with your girlfriend and you want to do it right. And you know, for the lovers out there, I'll say that he finds there's no good way to do it. You know that it's going to be painful no matter what, but what you're talking about there is having people practice things that are hard in safe places, things we don't get to practice very much. What kinds of things are you putting through this interactive simulation environment? I'm curious about the actual things that people can practice.

Speaker B: These are mostly used in our high volume, high paced environment. So think about our contact centers. Think about the 3,500 financial centers around the country and the peaks that happen at different time periods and at every one of them. Whether it's a slow paced time or fast paced time. We need people that exercise great judgment, have a client feel that they're listened to by somebody who's empathetic and is working to be able to help them, and at the end of the day is focused on what they want to accomplish. And so it could be anything from cashing a check to performing a complex transaction. And we put people in real life situations that allow them to respond to an avatar that looks like a, um, live walking, talking, breathing client. And you get to engage in various scenarios and get feedback real time on your handling of it, on your use of tools and resources in the process and how you exercise judgment. So that's 1, 2. When I think about human in the lead, one of the key questions behind developing, training and use of technology correctly is in situations where, let's say you processing a high risk transaction in five times in a row. If you were using AI of sorts and it said yes, yes, yes, the key question has to be, well, what happens that sixth time? Does Sam look at that and say, well, it said yes five times in a row. This is probably the same. And the ability to recognize human in the lead is to use scenarios that prepare people to say, what is it that I should be using as the human to validate the accuracy of the recommended action that's taking place in that moment. And so we have to always have those considerations in mind to both protect our clients, protect the organization, and have the client have a great experience.

Speaker C: You've got a huge organization, you've got a lot of resources and scale that you can put to that. What's the advice to people that may not have those resources for developing that level of infrastructure? Do you any of these things work well in small chunks or do they need big scale to work?

Speaker B: You know what? They absolutely do. In fact, there's a few other routines that we have. Yes, there's some additional things that we measure, like systems that we want to sunset for another, we measure how many prompts an organization is writing by line of business. So what is the kind of culture of AI adoption by individual group around the organization. Some of those things most would have access to depending on the AI tools that they've chosen and what their infrastructure looks like. But some of the best advice that we get actually happens at multiple levels. We recently did this at a senior level group around the organization is met with different parts of the organization other than our own cross functional groups at multiple levels and did hundreds of these listening sessions where we brought together 20, 30 people at a time and began to engage them about what are their thoughts about AI, what are they finding helpful, what are they still curious about, whether they need help. All those ideas and feedback generate everything from feedback from my group as we're building and developing training feedback for our technology group and council as we think about what's next or the filtration of what are the prioritized major projects and initiatives for the company to invest in next to be able to support our teammates. And so anybody can just simply talk and listen to people when you deploy a tool and out of that you get the opportunity to prioritize and that's a value regardless of the size of the organization.

Speaker C: Yeah, I think that's a great way to think about that. That most of those things like you say, listening and basic things that don't require a lot of resources to implement it seems entirely within the realm of most organizations.

Speaker B: I talk to companies of all sizes on this journey. Being curious has been important to me. It's been important to my leadership team as well. So uh, we meet with some of the largest companies around the world, but we also meet with uh, several mid size and small organizations because out of that I'm thinking about how are our clients potentially thinking about it and what do they need? How am I thinking about groups that may be of different size and scale than another? What might they be missing? What might we be missing? And so just that general curiosity and learning from missteps and learning from successes of other companies is just an important place to engage just in dialogue and being thoughtful about what do you do first and next so that you're not just experimenting but you're being really intentional about what do you adopt, what's the reasons why that gives people confidence on the other end of why am I experiencing X, Y or Z next?

Speaker C: Actually I, like you mentioned missteps. I think we're always so hesitant to admit that we've ever done anything wrong and most people other than me have done things wrong in the past. But the idea of getting feedback is really huge. And you mentioned that in the simulation part, I find students, uh, don't actually mind tests as much as you think they do because they see the things that they don't know well and where they can improve. We all, we very much like to improve. And I think that's been a theme that's come through. What you're talking about is that let people know what areas to improve and how to improve. And people generally like that. I mention my students, though. You know, Everybody. You mentioned 40, 45% of your people are internal. If I'm doing the math right, that means that 55 or 60 come from external. What kind of advice can you give to people who are entering the workforce now? What kinds of skills should they be thinking about to be an active part of a workforce now?

Speaker B: One, I think you said the right operative word when you mentioned skills. Because one thing that's become clear is that technical skills or the half life of technical skills have become shorter at any time, probably in my lifetime as an adult. And to recognize things that we continually talk about in our company, which is clarity, learning, agility and intellectual curiosity. And so continuing to keep those things at the forefront are incredibly important attributes. We talk about them not only quite a bit here at bank of America, but specifically curiosity keeps you relevant, and that's including about new technologies. When I think about AI today, it should not be a fear of the unknown, but the opportunity to embrace something that will be in today and tomorrow's environment. Just as important as using the telephone or email to be able to do business. I would say to anybody thinking about their professional life ahead is to be intentional about challenging yourself. Pick, stretch work when you have an opportunity that builds a skill that you can reuse and then be a great teammate by learning from and sharing with other people. Collaboration is such a important trait in this work environment that across companies, typically the days of working in a silo, particularly if you work in a client business, your need for others and thinking about the power of the organization with the client at the center could not be more important. And then finally, I'd say just continuing to develop human skills and recognize that strength of ethics and judgment and decision making continue to be ultra important.

Speaker C: I like that you've thought about a lot of these things, maybe in a lot more depth than I have. One of the things we sometimes do on the show, and I think it'd be fun for you, is to just ask you a bunch of rapid fire questions. What do you think people are getting wrong about artificial intelligence right now? You See a lot of people learning about this technology. What are they getting wrong?

Speaker B: Probably two things come to mind. One is it will go away, that it's a fad. And then, number two, to think that it within itself means everybody's job's going to go away.

Speaker C: What's moving faster or slower about AI than you thought?

Speaker B: Probably what's moving faster is adoption. And I think some of that may deal more with the approach that we've taken as an organization. I think what's moving, uh, slower is, and I say slower, but it's also at an appropriate pace. We're a highly regulated industry, so we're always going to be thoughtful as we move forward. You know, it's hard to believe that just over a decade ago we didn't have a client AI solution, but today we have one that clients use more than 169 million times a quarter and growing quarter after quarter. So I say, uh, some things are moving slower, but it's appropriately measured with the right risk mindset.

Speaker C: How do you personally get the most value out of an AI tool? Just in your daily life? What are you getting the most out of?

Speaker B: I certainly use it to write. I, uh, use it to analyze information. I also use AI to curate information. I'm always thinking about how are we incorporating and evolving our training solution in a scalable way that fits what they need? And sometimes it's about individual productivity tools, and sometimes it's about a vendor or a tool that we might build that is scalable that will align to how do we build the level of proficiency faster than we might be by other means, or maybe differently than what we currently do today.

Speaker C: Building proficiency faster, that seems like a good way to wrap this up. I think that's the core of what you're trying to do. I think it's a staggering challenge at the scale that you're trying to do it in. Thanks for sharing your thoughts on it today. Thanks for joining us.

Speaker B: My pleasure. Great to be with you.

Speaker C: Thanks for joining us for another season of me, myself and AI. We've had some interesting conversations about learning and AI development in general, and our discussions on the implications of AI for the workforce feel particularly important. We've talked with Taylor Stockton at the U.S. department of Labor, Andrew Palmer at the Economist, and today's discussion with Bernard. It's hard to pick a favorite. We'll be back this summer with bonus episodes with a more academic research angle. We encourage you to continue to review our podcast and send us any comments or requests for topics you'd like us to cover. Thanks for helping us make me, myself and AI so successful.

Speaker A: Thanks for listening to me, myself and AI. Our show is able to continue in large part due to listener support. Your streams and downloads make a big difference. If you have a moment, please consider leaving us an Apple Podcast review or a rating on Spotify and share our show with others you think might find it interesting and helpful.