Spotify<\/a>.<\/p>\nTranscript<\/h4>\n Allison Ryder:<\/strong> 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\u2019s episode.<\/p>\nBernard Hampton:<\/strong><\/strong> I am Bernard Hampton from Bank of America, and you\u2019re listening to Me, Myself, and AI<\/cite>. <\/p>\nSam Ransbotham:<\/strong> Welcome to Me, Myself, and AI<\/cite>, a podcast from MIT Sloan Management Review<\/cite> exploring the future of artificial intelligence. I\u2019m Sam Ransbotham, professor of analytics at Boston College. I\u2019ve been researching data, analytics, and AI at MIT SMR<\/cite> since 2014, with research articles, annual industry reports, case studies, and now 12 seasons of podcast episodes. In each episode, corporate leaders, cutting-edge researchers, and AI policy makers join us to break down what separates AI hype from AI success.<\/p>\nWelcome back to Me, Myself, and AI<\/cite>. Today we\u2019re 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\u2019s effort to upskill, reskill, and prepare talent for the use of AI. Bernard, welcome to the show.<\/p>\nBernard Hampton:<\/strong> Hey, Sam, thanks so much. [It\u2019s] great to meet you. <\/p>\nSam Ransbotham:<\/strong><\/strong> I\u2019m guessing most listeners are pretty familiar with Bank of America. It\u2019s pretty huge. It\u2019s one of the world\u2019s largest financial institutions. I looked [this] up: 70 million clients, 35 countries. It\u2019s huge, but I\u2019m 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? <\/p>\nBernard Hampton:<\/strong> Yes, certainly. The Academy [has] existed since 2017. It replaced our legacy learning organization, and it\u2019s Bank of America\u2019s award-winning onboarding education and professional development organization that\u2019s really dedicated to the growth and success of teammates across the enterprise. <\/p>\nAt The Academy, we\u2019re laser-focused on workforce agility. By that, I mean it\u2019s 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. That\u2019s really about the mobility, upskilling, and readiness of an AI enabled-workforce. <\/p>\n
Sam Ransbotham:<\/strong> You know, we\u2019re kind of the same. I teach a couple hundred students a year, and you\u2019ve got 200,000. That\u2019s about the same, right? <\/p>\nBernard Hampton:<\/strong> Close. <\/p>\nSam Ransbotham:<\/strong> The scale seems kind of staggering \u2014 the scale combined with the speed of change of everything going on. How do you manage those two things at the same time? <\/p>\nBernard Hampton:<\/strong> Our academy pathways are really central to technical skills, data and AI literacy, client-facing excellence, leadership capabilities that scale. 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 skills 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 became a full-fledged Academy teammate, contributing that real-world intelligence to the organization. <\/p>\nSam Ransbotham:<\/strong> That sounds good, and I like the idea, but it just seems really hard. I think about a year ago, everybody [felt they needed] to learn prompt engineering. And then RAG [retrieval-augmented generation] was the latest thing. Then it just feels like these topics are coming along so quickly. And actually, I could pick the topic of today, but we\u2019re recording about a month before this broadcasts, so it\u2019ll 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? <\/p>\nBernard Hampton:<\/strong> AI certainly has created quite a bit of runway and opportunity for us. It shifted the learning priorities toward faster proficiency in core roles; better critical thinking and decision-making, as you can imagine; stronger communication and relationship skills; and then practical fluency in AI tied to daily work. <\/p>\nWhen we use AI-based learning modules, it\u2019s not about saying, \u201cOh, we\u2019re putting an AI tool in front of someone to help aid learning.\u201d It\u2019s thinking in real, practical ways about ultimately who do we serve, what are we trying to accomplish, and then work backward 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.<\/p>\n
Sam Ransbotham:<\/strong> You mentioned things like communication skills at the same time you also mentioned AI technical skills. If you think about the spectrum from supersoft skills versus the more technical skills, where are your challenges? What are you having more trouble with? Or how do the challenges differ for each of those types of learning experiences? <\/p>\nBernard Hampton:<\/strong> Ultimately, it\u2019s been incredibly important that we keep both of them top of mind. I mean, it is easy today, and AI dominates most news cycles. It dominates what you read online. It\u2019s the fun thing to talk about, when the reality is it is not like a toy \u2014 companies [that] treat that really seriously are going to start top-down in leadership and developing skill in the space so that it flows through the organization. Those that may be not so serious are going to treat it like a toy that you play with for a while, and then you put it away. <\/p>\nAt the same time, what\u2019s 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 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. [They] continue to become incredibly important, while at the same time technical fluency in AI becomes incredibly important. <\/p>\n
The other two things that I\u2019d say are a backdrop 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\u2019re 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 the workforce is in their skills journey.<\/p>\n
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 [to] think about, what could more people bring to clients if they were spending less time administratively or on task-type functions, [to] really go support and understand what the needs, the goals, the objectives of the clients are, wherever they are in the spectrum? <\/p>\n
Sam Ransbotham:<\/strong> I\u2019m 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 [the] goal to use more internal or more external, or how are you thinking about that mix? <\/p>\nBernard Hampton:<\/strong> You [are] really close. We hired 20,000 people last year, and that includes 2,000 of our student campus hires \u2014 45% of open roles last year almost were filled internally, which further leans into why skilling and upskilling are so important across the organization. <\/p>\nSam Ransbotham:<\/strong> I was reading somewhere you\u2019re talking about the desire to redeploy talent versus reduce head count. I think there\u2019s certainly a headline out there right now. It seems like every time I look at the news, there\u2019s \u201cCompany X has reduced head count by thousands of people, all because of AI.\u201d One, I\u2019m suspicious in the first place that that\u2019s actually due to AI. But I think you\u2019re on the record of trying a different approach versus that reduction. What\u2019s your thinking there?<\/p>\nBernard Hampton:<\/strong> Our CEO\u2019s been quite clear, and really this is about our clients, it\u2019s about our teammates, and it\u2019s 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. For our teammates, yes, we\u2019ve said that over time you may need less people to do certain functions in the organization, but at the same time, there [are] opportunities for reinvestment. <\/p>\nSo our opportunity is the recognition that our employees bring a lot of value to the organization. They\u2019ve had a commitment and a level of loyalty, and those [who] want to continue to learn or [who] are curious, [who] 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. At the same time, they carry with them a level of acumen and experience that\u2019s beneficial for our clients and the organization, whether it\u2019s working in risk or if it\u2019s 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. <\/p>\n
Sam Ransbotham:<\/strong> You\u2019ve got a massive variety of people within your organization, from super technical to super nontechnical. How do you figure out who needs to know what? That stymies me. <\/p>\nBernard Hampton:<\/strong> In short, everyone needs to know something. I think in its simplest form, we think about AI in three different levels. At level one, that is about every role and function. That\u2019s about your personal use of tools. It\u2019s about personal productivity. 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 [they] need to write or analyze information, maybe prepare something, the task-oriented or administrative-type functions. We want them to feel confident and capable, to be able to use AI in creative ways to make their workload simpler. <\/p>\nNow, on one side of that you can certainly say, \u201cOh, I save a bunch of time. I can take a deep breath and kick back,\u201d but the reality is the best measure 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 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?<\/p>\n
And then there\u2019s a secondary level of AI that is really about functions. We\u2019ll take unique systems. Maybe there\u2019s one group that needs to use one system most often, and we\u2019ll curate using agents to be able to decipher, pull together, aggregate information that simplifies this one function across a particular group. <\/p>\n
And then there\u2019s level three, where we think about large workflows, multiple data sources, multiple agents involved, and that\u2019s usually [at a] large scale and horizontal across the organization. <\/p>\n
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, decide where are opportunities for redeployment or where are opportunities that maybe you don\u2019t replace a role, but it doesn\u2019t mean you need to go into a situation as in some companies that generate large-scale layoffs at the end of the day.<\/p>\n\n<\/aside>\n\n<\/aside>\nSam Ransbotham:<\/strong> Those levels are interesting, and I\u2019m glad you mentioned measurement, particularly in the first one. I\u2019m sure Bank of America, like everywhere else, has a whole bunch of KPIs that measure what\u2019s 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 is going to lead us toward really focusing on what you call that level one, which is much easier to measure. It\u2019s going to fit well with the existing KPIs, versus that level three, which seems more cross-cutting. It has the chance of changing balance within organizations. <\/p>\nHow do you keep from just making everything a level one type \u2014 \u201cHey, let\u2019s get more efficient and more productive?\u201d<\/p>\n
Bernard Hampton:<\/strong> From our perspective, it means that (1) you do them all simultaneously, and (2) 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 fluency across the organization and readiness to be able to use those tools, but also our skills library helps us continually provide opportunities for teammates to invest in themselves. So there\u2019s a balance of what you measure. Sometimes that measurement 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. <\/p>\nThe other part of that is measuring what\u2019s the [amount of] time that we spend on high-value work? And that\u2019s not necessarily a function of only measuring productivity. You\u2019ve got a couple of different triggers. You\u2019ve 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 what\u2019s appropriate for the volume of what\u2019s left over. <\/p>\n
Sam Ransbotham:<\/strong> Are there things that you\u2019ve looked at on paper that might be a good place to use AI, but you\u2019ve decided that the risk didn\u2019t pay out? Or how are you instilling some of those guidelines, and where should we be using tools rather than where could we be using tools? <\/p>\nBernard Hampton:<\/strong> Think about what AI is really good at. AI is really good at research. It\u2019s really good at writing, administrative functions. It\u2019s good at tasks. AI is not good at judgment that requires a human in the loop. <\/p>\nWhen we think about the what and the how in our training process, we deploy AI to make learning (1) more practical, (2) more relevant, and (3) scalable. So that includes AI-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. [It\u2019s] AI guided by the way. And then it\u2019s designed to accelerate readiness for teammates across various roles, support career mobility, and ensure human oversight remains central to the learning experience. <\/p>\n
In doing so, we begin to somewhat be able to say, \u201cHey, here\u2019s what AI is really capable of doing very well.\u201d We want to put people in a situation where they experience and improve their skill. The one I\u2019m talking about in particular is an interactive platform that enables teammates to practice real-world scenarios. That\u2019s 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. <\/p>\n
Sam Ransbotham:<\/strong> 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\u2019re doing, you\u2019re more likely to use these tools, even though you might think that they may be tools that \u201creplace\u201d us as humans, and that\u2019s not at all the perspective. And I love that simulation aspect. <\/p>\nDo you watch The Good Place<\/cite>, the TV show? I recommend it. I think it\u2019s hilarious. But one of the scenarios in The Good Place<\/cite> is they have someone go through a simulation of breaking up with his girlfriend. You just don\u2019t get that many chances to break up with your girlfriend, and you want to do it right. For the lovers out there, I\u2019ll say that he finds there\u2019s no good way to do it; it\u2019s going to be painful no matter what. <\/p>\nBut what you\u2019re talking about there is having people practice things that are hard in safe places, things we don\u2019t get to practice very much. What kinds of things are you putting through this interactive simulation environment? I\u2019m curious about the actual things that people can practice. <\/p>\n
Bernard Hampton:<\/strong> These are mostly used in our high-volume, high-paced environments. Think about our contact centers. Think about the 3,500 financial centers around the country and the peaks that happen at different time periods. At every one of them, whether it\u2019s a slow-paced time or a fast-paced time, we need people to exercise great judgment, have a client feel that they\u2019re listened to by somebody who\u2019s 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. <\/p>\nSo it could be anything from cashing a check to performing a complex transaction. We put people in real-life situations that allow them to respond to an avatar that looks like a live walking, talking, breathing client. You get to engage in various scenarios and get feedback [in] real time on your handling of it, on your use of tools and resources in the process, and how you exercise judgment. So that\u2019s one. <\/p>\n
Two, when I think about human in the lead, one of the key questions behind developing, training, and using technology correctly is in situations where, let\u2019s say, you process a high-risk transaction five times in a row. If you were using [an] AI of sorts and it said, \u201cyes, yes, yes.\u201d The key question has to be, \u201cWell, what happens that sixth time?\u201d Does Sam look at that and say, \u201cWell, it\u2019s said yes five times in a row. This is probably the same\u201d? The ability to recognize human in the lead is to use scenarios that prepare people to say, \u201cWhat is it that I should be using?\u201d as the human to validate the accuracy of the recommended action that\u2019s taking place in that moment. 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. <\/p>\n
Sam Ransbotham:<\/strong> You\u2019ve got a huge organization. You\u2019ve got a lot of resources and scale that you can put into that. What\u2019s the advice to people [who] may not have those resources for developing that level of infrastructure? Do any of these things work well in small chunks, or do they need a big scale to work? <\/p>\nBernard Hampton:<\/strong> You know what? They absolutely do. In fact, there [are] a few other routines that we have. Yes, there [are] 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 [an] individual group around the organization? Some of those things most would have access to depending on the AI tools that they\u2019ve chosen and what their infrastructure looks like. <\/p>\nBut 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. [We] 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 their thoughts about AI. What are they finding helpful? What are they still curious about? Where do they need help? All those ideas and feedback generate everything from feedback for my group, as we\u2019re building and developing training; feedback for our technology group and council as we think about what\u2019s 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? <\/p>\n
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\u2019s a value regardless of the size of the organization. <\/p>\n
Sam Ransbotham:<\/strong> I think that\u2019s a great way to think about that. \u2026 Things that don\u2019t require a lot of resources to implement, it seems entirely within the realm of most organizations. <\/p>\nBernard Hampton:<\/strong> I talk to companies of all sizes. On this journey, being curious has been important to me. It\u2019s been important to my leadership team as well. We meet with some of the largest companies around the world, but we also meet with several midsize and small organizations because out of that I\u2019m thinking about how our clients are potentially thinking about it and what 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? That general curiosity and learning from missteps and learning from successes of other companies is just an important place to engage in dialogue and being thoughtful about what do you do first and next so that you\u2019re not just experimenting, but you\u2019re being really intentional about what do you adopt? What\u2019s the reason why that gives people confidence on the other end of why am I experiencing X, Y, or Z next? <\/p>\nSam Ransbotham:<\/strong> I like [that] you mentioned missteps. I think we\u2019re always too hesitant to admit that we\u2019ve ever done anything wrong. You know, 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. <\/p>\nI find students don\u2019t actually mind tests as much as you think they do because they see the things that they don\u2019t know well and where they can improve. We very much like to improve. I think that\u2019s been a theme that\u2019s come through what you\u2019re talking about; let people know what areas to improve and how to improve, and people generally like that. <\/p>\n
I mentioned my students. You mentioned 40%, 45% of your people are internal. If I\u2019m 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? <\/p>\n
Bernard Hampton:<\/strong> One, I think you said the right operative word when you mentioned skills, because one thing that\u2019s 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. Continuing to keep those things at the forefront [is an] incredibly important attribute. <\/p>\nWe talk about them not only quite a bit here at Bank of America, but, specifically, curiosity keeps you relevant, and that\u2019s 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\u2019s and tomorrow\u2019s environment just as important as using the telephone or email to be able to do business. <\/p>\n
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 an important trait in this work environment. 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\u2019d say just continuing to develop human skills and recognize that strength of ethics and judgment and decision-making continue to be ultra-important. <\/p>\n
Sam Ransbotham:<\/strong> I like [that] you\u2019ve thought about a lot of these things, maybe in a lot more depth than I have. One of the things we sometimes do in the show, and I think it would be fun for you, is to just ask you a bunch of rapid-fire questions. <\/p>\nWhat do you think people are getting wrong about artificial intelligence right now? You see a lot of people learning about this technology \u2014 what are they getting wrong? <\/p>\n
Bernard Hampton:<\/strong> Probably two things come to mind. One is it will go away, that it\u2019s a fad, and then number two, to think that it within itself means everybody\u2019s job\u2019s going to go away. <\/p>\nSam Ransbotham:<\/strong> What\u2019s moving faster or slower about AI than you thought? <\/p>\nBernard Hampton:<\/strong> Probably what\u2019s moving faster is adoption, and I think some of that may deal more with the approach that we\u2019ve taken as an organization. I think what\u2019s moving slower \u2014 I say slower, but it\u2019s also at an appropriate pace \u2014 we\u2019re a highly regulated industry, so we\u2019re always going to be thoughtful as we move. <\/p>\nIt\u2019s hard to believe that, just over a decade ago, we didn\u2019t 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 some things are moving slower, but it\u2019s appropriately measured with the right risk mindset. <\/p>\n
Sam Ransbotham:<\/strong> 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? <\/p>\nBernard Hampton:<\/strong> I certainly use it to write. I use it to analyze information, and use AI to curate information. I\u2019m always thinking about how we are incorporating and evolving our training solution in a scalable way that fits what they need. Sometimes it\u2019s about individual productivity tools, and sometimes it\u2019s about a vendor or a tool that we might build that is scalable, that will align to how we build the level of proficiency faster than we might be by other means, or maybe differently than what we currently do today. <\/p>\nSam Ransbotham:<\/strong> Building proficiency faster \u2014 that seems like a good way to wrap this up. I think that\u2019s the core of what you\u2019re trying to do. I think it\u2019s a staggering challenge at the scale that you\u2019re trying to do it in. Thanks for sharing your thoughts on it today. Thanks for joining us. <\/p>\nBernard Hampton:<\/strong> My pleasure. Great to be with you. <\/p>\nSam Ransbotham:<\/strong> Thanks for joining us for another season of Me, Myself, and AI<\/cite>. We\u2019ve had some really interesting conversations about learning and AI development, and our discussions on the implications of AI on the workforce feel particularly important. We\u2019ve talked with Taylor Stockton at the U.S. Department of Labor, Andrew Palmer at The Economist<\/cite>, and today\u2019s discussion with Bernard. It is hard to pick a favorite! We\u2019ll 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\u2019d like us to cover.\u00a0Thanks for helping us make Me, Myself, and AI<\/cite> so successful.<\/p>\n\n<\/aside>\n\n<\/aside>\n\n ME, MYSELF, AND AI\u00ae<\/sup> is a federally registered trademark of Massachusetts Institute of Technology. All rights reserved.\n <\/p>\n