Steve Holden, SVP of Analytics at Fannie Mae, describes how to build a gen-AI program in a regulated bank using three principles: balance (moving neither too fast nor too slow), transparency (weekly blogs and cross-division councils), and humility (test-and-learn, avoiding irreversible early choices).
What do you think are the key components of building a successful generative AI program within an enterprise like Fanny Mae today?
You know, I was asked to take this role on back in November of last year. So it actually hasn't been quite a year yet. And I spent a couple weeks really thinking carefully about the approach I wanted to take. And I largely concluded that we needed a set of guiding principles. And so I established three that I'll take you through. But what I'll say is, throughout this whole program that we've stood up, those three guiding principles will keep showing up again and again. And they've proven to be really important in our approach. And so they are as follows. The first is balance. And by balance, what I mean is I think there's a risk here of going too fast and of going too slow, especially for a highly regulated financial institution such as Fannie Mae. We've got to get that balance right. We want to make sure we're making progress, but we want to make sure that we're doing so in a responsible way. I think there are examples out there in the press that you can read about companies that went really fast out of the gate and ended up finding out that there are some real legitimate challenges with this tech. And so striking the right balance is important. And again, this is on both sides of the equation. It's not going too fast, but it's not going too slow. The second is around transparency. And you mentioned that I write a blog every week. That's one example. We also have a bi weekly, the knowledge share that we have. Our engineers come in and share their work so that as people are figuring things out, that information is finding its way across the organization. We're sharing any decisions that we're making in very public ways. And then we set up advisory groups and councils so that knowledge flows through divisions in a very organized fashion. So transparency is important for us. And then finally I'll say, and this is probably the most important thing is humility. This is such a dynamic area. Literally every week something's changing. You have to understand that what you thought was true yesterday may not be true tomorrow. And so we have a very much a test and learn approach. We're trying to figure things out. I was at the AWS conference back in November, and there was a talk given by Jonathan Allen, one of their executives, who talked about one way versus two way doors. And I found that a really compelling kind of way to think about this, which is make sure that at these early stages that you've got this ability to kind of back out of decisions. Don't make these sort of choices that are irreversible too early because things are going to change and you're going to need that nimbleness, that flexibility as you figure things out as you move along. And so humility, I think, is a way to remind everybody to stay in that learning mode, to keep testing and figuring things out and not declaring really long term sort of final decisions about how things are going to turn out, because you will inevitably be wrong.
Okay, that's really great. I want to unpack a lot of these. Right. So you mentioned balance, transparency and humility. And I love humility. And I'm going to talk, maybe discuss that in a bit more depth. But first, maybe let's chat about balance because I think moving too fast or too slow indeed is a major consideration for many organizations today. And it really depends on where you sit in the risk spectrum, the type of industry that you're in. So maybe Steve, outline for us kind of the key considerations or factors that determine speed to market. Right. With these use cases, maybe what are the different considerations that you have when thinking about the speed of execution for these use cases?
So what I would say is this tech is new and we manage a lot of different types of risk at Fannie Mae. And so probably one of the things that really is the determinant of the pace that we can move at is getting the governance right. There are a lot of individuals within the company responsible for managing different aspects of our risk. And everybody's trying to figure out what generative AI means to our risk surface, how our risk surface is being impacted and changed. And so, you know, we're trying to engage our risk teams in an efficient way. And one of the things that we, one of the ways that we accomplish that is through really establishing a prioritization for what we send through governance. And the idea here is that if every idea went through governance all at the same time, all of our governance teams would just grind to a halt because we couldn't possibly move, especially at this very early stage where there's just a lot of learning going on. And so we've been, I would say, fairly ruthlessly prioritizing the places that we want to engage our governance teams. And we have four criteria that we think about here. One is, well, we first start with, is this a low versus high risk use case? Because we really want to be in the realm of low risk to get started. I believe that a lot of what we're going to do out of the gate here is very much internally focused and likely productivity focused, although there are some things that go beyond that. Second thing is uniqueness. What we don't want to do is have the same ideas being pursued in different parts of the company without that awareness of one another. And so what we're really looking for is, is this idea that we're going to Eng our governance teams around something that we are already trying somewhere else so that we can be really efficient on that. And the third thing is I call it tech Stack aligned, which is if you're going to investigate some new sort of technology, let's make sure that it's nicely aligned with what we're already doing, or if it's going outside of our existing sort of tech footprint or tech Stack, that we're very purposeful, that the juice is worth the squeeze, that the opportunity there is really pretty high because we're going to have to engage a lot more of our technology partners if we're going to be able to pursue those ideas. So making sure that it's low risk, it's a unique idea and it's tech Stack aligned. And then finally we ask that the teams are really clear on the business use cases so that we understand what the business outcome is that they're expecting from pursuing these concepts.
