Bank of America's Hari Gopalkrishnan says the bank and its customers are seeing returns from AI investments. He discusses the bank's efforts and an MIT study showing that generative AI investments aren't paying off for most enterprises.

The release of Open AI's ChatGPT has accelerated the development of artificial intelligence and warnings about its impact on society.

Rise of AI

Bank of America's chief technology officer has outlined the bank's extensive use of artificial intelligence (AI) to enhance both customer and employee experiences. According to the executive, 99% of customer interactions in the consumer business are now fully digital and self-serviced, with 65% of account openings conducted digitally—a significant increase from a decade ago. The bank's AI-powered virtual assistant, Erica, has facilitated three billion transactions, reflecting strong client adoption.

Internally, AI tools have streamlined complex tasks for employees, such as preparing client dossiers and summarizing policies, resulting in measurable productivity gains. The bank employs a model-agnostic approach, integrating both industry-standard and proprietary AI models to optimize business processes. Productivity metrics and return on investment are closely monitored, with AI freeing staff to focus on more innovative work. The technology chief emphasized that AI adoption has led to higher client satisfaction and operational efficiency.

BofA's Tech Chief says AI delivers returns for the bank

There is more nuance to that MIT report than the market is giving credence to. Basically it's saying look if you just try to make it up and perhaps build some chachapiti like products yourself it didn't always work. But what have you been doing internally to make things work for Bank of America. Yeah great. Thanks for having me. And look we get to serve 70 million clients every single day. And AI is not new to us. It's been part of our journey. So if I could offer you some sound bites of what we've seen in the last five years. Ninety nine percent of our customers and clients today interact with us digitally fully self-serviced. Ninety nine percent. Ninety nine percent of our interactions in the consumer business are self-service digital. We have 65 percent of our account opening is digitally done. Ten years ago was 0 percent. Our client satisfaction number which we care a lot about has gone from the high 70s to eighty nine point one. And yes we're counting. And so you think about it better client satisfaction shift to more self-service channels and more automation always yields great results. And AI has been part of that journey for us. And I think of as the client the customer perspective is Erica. Yeah. But your client and your other stakeholders like you can your own employee base how they've been adopting generative AI had them in using assistance. Yeah. And maybe if you just rewind the clock a bit right. Our first journey into AI started with things like fraud models and fraud algorithms. We have fourteen hundred plus patents and 250 models in production with things like AI models. And what we realized seven eight years ago is humans wanted to interact with machines in a more humanistic fashion. And Laura long before the large language models came along we thought about things like Erica which allows a customer to simply say a few words and for us to figure out what they want rather than them trying to figure out what our systems actually did. And that's where we've now seen significant adoption. So this morning we had a press release that we now had three billion transactions that have been conducted on Erica. And that continues to grow at an exponential pace. So that's our clients and customers saying we actually like to engage with you in this way. Yeah. When you then pivot to our employee base they're like well why can't we use the same capability. So we've taken this platform and broadened it use. So today a high net worth adviser can actually look to Erica to say look I need to hear about some complex trust policy in Delaware. Can you summarize it for me and tell me how I should talk to the client about it. And one of the more recent implementation has been is I'm about to meet a client. How do I prepare for that meeting. Before a junior banker would take days to prepare a dossier for that. Now we can actually use generative AI models to create the first version of a draft that saves hours and minutes if not time which then drops to the bottom line. I know in many ways this is your secret sauce but I'm reading that you've got what 7000 800 patents of your own. How much are you having to depend on purpose built generative AI. How much are you looking to an open AI for the enterprise offering or are you going to build it yourself. Well I think we have a multi-level approach to this. Right. We're not. You know we have enough reality to know that we're not going to build foundation models. We want to build upon all the investments that are happening in the industry. But the reason they're called foundation models is they let you do a lot of things on top of them. So we have a level of the stack where we absolutely will embrace commodity. Which ones. Well I think a little bit of everything. Right. We are looking at and we intentionally want to be model agnostic. Right. We intentionally do not want to be weighted to any one single solution but then built upon it when it comes to our complex workflows. When you think about our business processes it was just as simple as buy something off the shelf. Every one of us would be done. The reality is you have to take these models in and back to your productivity question. The way you unleash productivity is applying these to your business process. Understanding how your current process work. Don't just try to replicate it. Figure out where you can eliminate parts of it. Simplify it and then use AI to go after it. And what we've seen is it's yielded tremendous results for us. I mean you said and we're counting. How are you measuring that productivity gain apart from the sheer scale of interactions and numbers that you're seeing. I'll give you an example of it. We now have 17000 developers in my own team that use coding agents to help their day to day activities. Within that we actually measure things like cost per story point defect density. There's a set of metrics productivity metrics that we look at and we look at it before and after. We expect to do similar things on a business process by business process basis to actually understand the metrics. When we went into the problem how we deploy these capabilities because they're not cheap. So we need to make sure there's an ROI component to it. But coming out of it be able to see what comes out and fail fast. If it seems like a given process doesn't lend itself appropriately. So I go to the sensitive question of those employees that are embracing coding agents. Yeah. Do you have fewer of them. How are they feeling about being augmented or replaced. Well I would say it actually allows us to get to a backlog. We could never get to. There's always more work than you can get to. There's always things that you could modernize. There's always more things to do. And I actually tell you that our first reaction from our teammates is being this frees them to do more innovative work rather than the mundane work that was consuming their time and resources. And I think we're going to see that pattern play itself out over time.

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BofA's Tech Chief says AI delivers returns for the bank

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Rise of AI