Transforming Cancer Diagnoses and Treatment with Bristol Myers Squibb CDTO Greg Meyers

Speaker A: Hi there and welcome to Enterprise AI Innovators, a, uh, show where top technology executives share how AI is transforming the enterprise. In each episode, guests uncover the real world applications of AI, from improving products and optimizing operations to redefining the customer experience. I'm Evan Reiser, the founder and CEO of Abnormal AI.

Speaker B: And I'm Sam Motamity, a general partner at Greylock Partners.

Speaker A: Today we're talking with Greg Myers, Chief digital and technology officer at Bristol Myers Squibbling. Bristol Myers Squibb is one of the top 10 pharmaceutical companies with about 30,000 employees and roughly $45 billion in annual revenue, making specialty medicines in oncology, immunology and neurology. Greg runs an organization of several thousand people, which gives him a rare view into how AI is reshaping drug development from the lab to the doctor's office. A few things stuck with me from this conversation. First, BMS is the only pharma company with four commercially scalable AI products in the hands of doctors. In a year long lung cancer study across 14 clinical sites, AI red CT scans doubled the number of newly diagnosed patients and caught 116 new cases. When the average lung cancer patient gets about five years after diagnosis, early diagnosis changes lives. Second, BMS runs an AI accelerator. Six to eight engineers and 12 weeks to answer one question, can AI do X or not? Greg deliberately keeps the teams tiny so they're too small to fail in any public or embarrassing way, which means they can easily throw out the work and start over. At a big company, permission to fail quietly is the thing that actually unlocks scale. And finally, Greg's advice for activating AI in a tradition bound industry is to stop asking how a process works and start asking why it works. That way. Most of the constraints people treat as fixed are just old assumptions that AI quietly made obsolete. He calls it the clean sheet. Let go of why there has to be a spreadsheet and ask what the spreadsheet was ever really for

Speaker C: Greg. First of all, thank you so much

Speaker A: for joining us today.

Speaker C: Um, maybe to kick us off, do you mind giving us a little bit of background, um, about kind of your career, maybe how you got to where you are today?

Speaker D: Sure, thanks for having me. Well, I started off in college not really knowing what I wanted to do. I want to be going to business or go into computer science. And I was sort of really lucky that my career has been this intersection of those two things the whole time. And when I came out of college, it wasn't obvious that business and computers would intermix as well, as they have. So we've been really lucky to do that. I've spent about half my career in life sciences, which is where I am now as the CTO at Bristol Myers Squibb, and then spent about half my career in other industries at Motorola. Been at large agricultural industry, so just different industries. And um, yeah, I'm really glad to be back in life sciences. I think there's probably never been a better time to be in technology and in life sciences. So it's been really cool.

Speaker C: Do you mind giving a sense of scale about kind of operations, a little bit about maybe a little more about your role and kind of what you're um, kind of leading at the company?

Speaker D: Yeah, well, Bristol Myers Squibb is one of the top 10 pharmaceutical companies. So we make products that are primarily an oncology, immunology, neurology. So these are, or it's considered a specialty medicines company. So they're people with serious diseases. And yeah, that's sort of what the company is. We're about 30,000 employees, uh, about 45, $46 billion in revenue per year. And you know, I run an organization of, uh, several thousand people, all trying to figure out how to apply technology to maximize the opportunity to help EMS really deliver more medicines to more patients faster.

Speaker B: What are some of the most exciting use cases of AI that are currently being deployed at bms? And if there's several that you think might surprise our listeners, you know, I'd be curious.

Speaker D: Yeah, I think probably the most surprising place, uh, where AI is making a difference is as you get closer to how doctors are treating patients. And I'm sure everybody has heard about the opportunity for AI and diagnostics, but we're actually pretty involved in there. In fact, we're the only pharmaceutical company that has four commercially scalable AI products in the market being used by doctors. And we had one example in oncology. Uh, this is for non small cell lung cancer, which is a pretty common form of cancer that's actually quite hard to detect early because patients often don't produce symptoms. So the goal here is to try to find cancer tumors before they get, before they become a problem. And the way that's typically done today is a CT scan would get taken and a radiologist or radio oncologist would take a look at those scans and try to see if they can find, you know, using their eyes, where those tumors are. And in one study that we've done with one of our partners, using AI to read those CT scans, this uh, is about 14 sites, different clinical sites, over about a year long study that we, we did, it led to a two times increase in the number of newly diagnosed patients in non small cell lung cancer. Now if you figure that that is that flagged about 2,600 patients in that one study alone and, and identified 116 new lung cancer patients. You know why that's important is the average lung cancer patient by the time they're diagnosed are uh, typically given about five years to live. So if you can actually detect this thing early, I mean you're making a big difference for those patients in being able to seek treatment sooner.

Speaker C: I mean you guys are already a leader in kind of using AI in all these different kind of ways. I'm kind of curious, like when you look over the next like two or five years, are there kind of other things you think are going to more fundamentally transform the industry?

Speaker D: There's 40 trillion cells in the human body. Uh, every one of those cells has about a trillion molecules in it. So if you add that up, that's seven octillion atoms that make up the human body. And there are cases where one cell or one atom out of place could potentially cause, cure or prevent a disease. And so there's a huge computational space that has just barely been, the uh, surface has been scratched. And so this idea that you can you mine this information without having human beings to do this, very painstaking part of the process, really, you know, what we're excited about is the ability to uncover effectively dark data inside biology. And a lot of times if you look at things like even trying to get something to be successful in what we call the preclinical stage of something. So if you find something that you think might be workable, you know, if you look at the whole industry, the pharmaceutical industry, if you can improve the success even in the early stages of R and D for, for a drug, because very, very few of them actually make it to the end, they typically will fail somewhere in the clinical trial process. Like 80% of drugs fail before they get to even the stage where we would apply for the fda. If you can get just a uh, two and a half percent increase in success rate by just how you design the molecule earlier on in the process that would actually generate probably 30 new medicines or new drug approvals over the next 10 years in the United States

Speaker B: alone, the stakes are also a lot higher. Right. And then also you're operating in a highly regulated environment where mistakes have like real consequences. And so how do you square that with like AI iteration, AI velocity?

Speaker D: I think you have to pay a lot of attention to the quality and quantity of data that goes into these tools. You've got to spend a lot of time doing context engineering so that the AI understands all the different data sources it has and what they all mean and how you have to sort of teach it how to think the same way that you would teach. I, uh, describe this often like if you were to hire a new employee, you'd have to sit down and explain to them how we do our work, how we think through things, what this data means. You have to do the same thing with AI. So there's a lot of work in doing that. So I think people almost underestimate how hard it is to get AI to the level of quality, particularly as you point out, and really high stakes decisions that it's good. But I can say that we are really reaching the inflection point where, where I would say, you know, we started this journey maybe three years ago. You know, three years ago we really couldn't trust a lot of what it was producing. Now it's reaching the end point where it is a, if you do a good job with the things that I mentioned, it can do about as good of a job, if not better job than humans. And a lot of these really intricate analysis that are really important. So I think just as time has gone on and the model's gotten better and then also we have learned the hard way how to engineer these things the right way. I think we, we continue to build a very high confidence that, you know, they can play an important role and that you can manage things like hallucinations.

Speaker C: Are there any kind of lessons from kind of what you guys have built over the last 10 years, 20 years that are kind of informing your kind of usage, some of the kind of frontier models?

Speaker D: Well, you know, it's funny that you said we don't think of our, we have not historically thought of ourselves as a data company. It's totally obvious as you say that why we, you would think we would think that, but we're actually typically a science company. And if you think about what most scientists do is they're trying to run an experiment, right? You're usually trying to prove, uh, you're either trying to fail or reject the null hypothesis. If you remember back from college. And whatever data gets expressed or exhausted off of that experiment is a byproduct. It's not the point historically. So that what we're finding is we have to re, we do have to think of ourselves as a data company because to models Negative results matter as much as positive results. I like to describe it as like a game of connect the dots. The more dots that you have on the page, the better the drawing is going to appear. But historically, if you've been very experimental focused, you know, you're going to focus on the things that succeeded and you're going to ignore the results that failed because you don't need to know them to move an experiment forward. So it really has caused us to have to rethink how we store data, how we annotate it, uh, how we curate it, um, because those models need all the data. And in many ways it has been a, a real change, a cultural change in terms of how we think about what we do. Whereas the data is not simply the exhaust from an experiment, but it becomes a goldmine of data that you can then retrospectively use later to help inform other experiments down the line.

Speaker B: I think any company can become AI native. It's just, uh, I'm curious, what's your philosophy around this? How have you transformed or are in the process of transforming BMS to be AI native and the core of the company versus just something you do or

Speaker D: an add on what becomes really challenging? And part of when you think about how you make AI foundational is you have to be able to ladder up and look across the processes of the company and ask yourself, okay, which problems require fundamental rethinking? And then being, being clear is what stuff should continue to go the continuous improvement route and what do you really need to start completely over again? I think the other thing is that when you're taking on something completely new, it's very intimidating because there's, you know, as you pointed out, we're in a highly, uh, regulated industry. There's a lot of risk, cost of failure is very high. So you can't sort of do that in a big bang way. You sort of have to. You have an hypothesis and almost every conversation I run into, you know, starts off something like, hey, you know, can AI do X? My answer is, I don't know. We have to go see. And the ability that you can take this concept and get it down to a really clear, testable hypothesis and actually get a very small group of people to prove or disprove whether AI can do X or not, at least in a controlled setting, gives you then the ability to, in a short period of time, get to the level of conviction that you build internally. Also a level of self confidence that you can take on a really big, completely new way of approaching a problem. I Think that is sort of the secret to scale is you create environments where you can, you know, fail quickly and quietly or you succeed quickly and quietly. And that, that basically is a path to proving that uh, your hypothesis was right. And that is where you start building enrollment, uh, and self confidence that you actually can do something, you know, fundamentally different.

Speaker C: I'd love to hear your thoughts on like what are some like the cultural leadership required to kind of create that environment where people are experimenting? Because it's kind of some of the nature business experimentation, but also um, making sure you're kind of being done in the right way. Like how do you balance kind of innovation and rigor and cultural rituals or kind of guidelines you kind of share the team to kind of help activate that mindset.

Speaker D: Yeah, we, we created something called the AI accelerator which is an environment that is, it's agile based. But you know what it does is it gives people a lot of room. So these are like six to eight people really trying to pursue the answer to the question of can AI or can it not do X? And you give them a common set of tools, you give them the best engineers we have in the company and then you give them a uh, time compressed period of time. We typically give them, let's say in two week sprint, sprint increments, we give them six sprints, so 12 weeks. And over those 12 weeks every two weeks they come in and present what they've achieved. So a couple things happen. One is culturally, because it's a small team, you give the crew, the group a lot of latitude to fail. And a lot of times at big companies, when you create these big projects that are very highly visible and expensive, you create an incentive system where it's just not okay to admit that you failed in some way. But when you have a handful of people working for six or seven weeks, like you can totally throw away the work and start over again. So I think that give, making them too small to fail in a uh, in a public or embarrassing way was really important. And I also think this exogenous pressure on every two weeks you have to demonstrate that you are starting to triangulate in on the core hypothesis because groups will tend to get really distracted by wanting to build UIs and what are we going to call it and what does it integrate to and you get really sort of lost in the sauce of stuff that's not really important. If you really get them to focus on the kernel of can AI do this or not, you then create room where you're not getting distracted by you know, sort of the window dressing or the theatrics about something and you really focus on the core hypothesis. So those are, that accelerator has really helped us to keep the teams really tight, really crisp, uh, allowing them to show progress quickly. And if they don't show progress, that there's no embarrassment. And the answer is no, AI can't do this well. And we move on to the next thing.

Speaker B: If one of your teams comes to you with an idea and they haven't built it yet, uh, they haven't entered the accelerator, do you have any interesting mental models on how to evaluate if an AI idea makes sense? So if it's worth, you know, investing in?

Speaker D: We learned our own lessons. I think the first year, maybe this is like going back maybe two and a half, three years ago, we didn't really know, so we kind of let a thousand flowers bloom. And that didn't really work well. And the reason why it didn't work well is remember how I said earlier, at a big company, someone has got responsibility for some subset of the process. Everyone was trying to build a tool that, that helps them do their part of the process and nothing actually was working together. So what we decided to do is to take more of a top down approach. And so, you know, I sat down with my peers who are the people that run the company, and we just sit down and said, okay, in research and development and commercial and HR and finance, you know, what are the big bets that you want to make? Like, what does your gut tell you are the places that AI can make the biggest difference for the company? And then that actually creates a natural filter by which all the ideas come through. And then we let them go to work with their own teams. So the research team did this, the development team, they sat down internally and said, okay, if I'm in charge of drug development, what matters most to me? Well, what matters most to me is can I accelerate my clinical trials? Can I lower the cost of a trial? Can I actually increase the probability of success that, uh, this trial will be successful? So if you have those three anchoring points, you kind of have this. What becomes emergent are the obvious things that you need to go do. So we kind of blended this sort of top down and bottoms up approach that I think created a natural filter of, you know, what the surface area of opportunities were for us with AI. And then we had this method that those groups can plug into to actually demonstrate that their concepts, uh, worked.

Speaker C: How do you kind of activate that? Right. I think a lot of the leaders we Talk to struggle where the obvious application is, like, doing a little bit better, right? A little bit faster. But it's very hard to kind of step back, especially across a business process that spans multiple teams or multiple functions. Like, any. Any advice about how to. How you get more of this, um, transformation versus kind of like digitization of the thing?

Speaker D: I think that part of what I'm seeing an expectation being said is that we're going to be able to. To reach over into the way something works and ask questions. Uh, not. Not how. Like, rather than us asking someone in the business, how should something work, us asking them, why does it work this way? And really trying to understand why is the process what it is today? And what you'll find when you really ask that question, you start to find that there are certain reasons for it that are baked in constraints that when you really look at it from an AI perspective, are no longer constraints where you can revisit whether they're actually constraints. So you almost have to completely explode the process. And just. That's why we use the word clean sheet. It's like, okay, let go of all of our biases about why there needs to be a spreadsheet to do this and just ask, what is the spreadsheet doing and why does it exist in the first place? And then, then you're really starting to get to this space of what might be possible. And I, And I think what's really hard about this, I describe this like a Venn diagram. On one side of the Venn diagram is, what does technology make possible? And that's a constantly changing picture. You can't expect people who are accountants or people in HR or people that do clinical trials for a living to know that, right? They don't know the difference between what Opus 4. 6 and Opus 4. 8 does. I mean, that's sort of our jobs, right? On the other hand, as an IT professional, you can't just get away with, I don't really know how the process works. So it's like where these two circles meet, like in almost a Venn diagram, that becomes the sweet spot where you're really looking at what technology makes possible. And you start questioning the foundation of why something is done the way that it is. This is where you start finding these sort of discontinuous, uh, intuitions that you really want to start testing about whether, um, certain things are possible now that maybe weren't before. And the other thing that's hard about this is you have to be humble about what you think is true, because something that maybe you tried with an LLM six months ago and it didn't work well, can now work well because you're like five versions ahead of where you were six months ago. So that's also hard is that you can't be too certain about what will or won't work. It's a constantly changing picture.

Speaker C: So like how do you, you know, especially maybe for some of your peers that are in older industries for like, they have a great way to do this work great for 100 years, like, and now it's faster. How do you kind of activate that maybe imagination about what's now possible with AI?

Speaker D: Einstein said this that if he had an hour to solve a problem, he'd spend 55 minutes thinking about the problems and 5 minutes solving. People will consume their entire time thinking about solutions and business people spend their entire time thinking about problems. And I, and I think we've got to. You can't lead with AI is a hammer. Now let's go look for some nails. You really, to influence, you know, other business executives or other parts of the organization, you've really got to be paying attention to what their problems are and what the opportunities are. Not looking for an excuse to deploy AI or some other technology that's just a recipe for a solution, looking for a problem. And that's, you know, to some extent a reputation that we as a profession have built over the years. So people really want you to meet them where you are. When I'm sitting down talking, for example, to our head of research, you know, he, uh, doesn't really want me talking about all the startups that are doing drug discovery that he should be considering. You know, he wants me to meet him where he is with the challenges that he has, like increasing pressure to deliver more molecules and a growing and growing, uh, you know, library of compounds to choose from and how you use computers to sort through that. So I really spend most of my time thinking carefully about the same problems that my peers have. But I have a unique way to look at them because I can kind of, you know, connect the dots because I'm lucky enough to see across the organization and also have experience in other industries where I can see, you know, this is maybe something that's worth trying or we should consider. But it's not coming at it from a solution mindset, it's coming at it from a problem or opportunity mindset.

Speaker C: Right at the end of the show, we like to use a quick like five minute lightning round where we give you basically like five questions or so they're like impossible answer in, uh, the one tweet format. But that's what we're looking for. So maybe, uh, so, I mean, kick it off for us.

Speaker B: So maybe to start in this AI era, like. And we've talked a lot about a

Speaker D: lot of the change.

Speaker B: Like, how do you think companies should change the way they measure the success of a CDO or cto?

Speaker D: I think a CDO or cto. I mean, at the end of the day, we're enablers of the core business strategy. I've always been a bit allergic to an IT strategy because I don't think. I mean, it is not a thing in and of itself. I mean, there's certain, you know, ways that you've got to run the organization, but the IT strategy should be the business strategy. And so if the business is trying to achieve whatever sales growth or cost reduction or whatever it is that it's trying to accomplish, I think it's the CIO CDOs job to basically create the flywheel that allows those results to come better and faster than they would have otherwise by using technology as an access point. So I try not to overthink that. I think I look at what my boss cares about trying to accomplish and I try to set, you know, my priorities to match. How can I help achieve the outcomes that are, you know, that the company is trying to achieve, but just using IT as sort of a leverage point to achieve those things.

Speaker C: Um, a lot of people, some I talk to, um, try to get input on like, hey, what are, like the kind of quick wins, right? If you're just trying to like, kick off, like, maybe if you're a little behind the curve on kind of AI transformation, try to put some points in the board. What would you kind of like, you know, if you get the, like the three projects that would be kind of like the, uh, must do, right? If you haven't done already.

Speaker D: Well, I'll tell you how we got started because I think that was the quick win. The first thing we did was we gave all the frontier models to all the employees. So we created a tool, we allowed, um, the end users to pick a model, and we didn't spend a lot of time worrying about cost and restricting them. Because the first thing is you just got to get employees using the tools. You're not going to get anywhere if the employees, your company don't know how to use AI other than maybe to summarize an email or to, to draft a performance review. So you've got to expose those tools to People, uh, and it's not hard to do that. Um, there's many that are out there and then you have to invest in the training. It is just not. I think there are people in their personal life that have figured out that these tools are great to do travel planning or things like that, but you'd be surprised that that doesn't. When they look at the blank screen and have to ask machine a question about their jobs, they're not, it doesn't always obvious what they should be doing. So you do have to invest in training people and how to use the tools. And then it becomes kind of this self reinforcing process. Whereas people start to experiment and use the tools, they start to find more and more uses for them. And I think that really becomes the flywheel that starts to expand the uh, types of ideas that you can, you can choose from to be able to apply into the company. So I think the quick win is just making sure that your employees have access to the tools, even if they're just the out of the box LLMs, and that you're training them on how to use them. That becomes the playground by which a lot of the really great ideas come next.

Speaker C: What do you think will be true about AI's future impact in the world that maybe many of your peers would

Speaker D: consider to be science fiction technology? Particularly things that seem really big, they always have a bigger impact in the long run than you expect, but they take a whole lot longer than you think they will to actually achieve that. So my base case is AI is going to have the biggest impact of our lifetime, but it's going to take a lot longer than people think think it's going to take.

Speaker B: Okay, so maybe one final question. Uh, what is your primary advice for a new or aspiring IT leader? Stepping into their first major leadership role?

Speaker D: Yeah, I mean, if you're a technologist stepping into an executive role, I guess the first thing I would say is that psychology and sociology trump technology every time. Like you have to become a master of psychology, you really need to understand the incentive systems of your company, the incentive systems of the other executives you work with. And I think Charlie Munger said, if you show me the incentives, I'll show you the outcome that's 100% true. Um, I mean, even back to your last point around timing, the reason why I'm more bearish than, um, Sam Altman or Dario about how fast AI is going to change the world isn't because the technology. It's because people's willingness and ability to adopt IT is the thing that I think always takes longer than expected. So a lot of what a technology executive's job is is having the right conversations for possibility and then really building the enrollment. That change is really there because at the end of the day, part of what you have to do is to convince people that they can do things that really have never seemed possible before. That's really hard. And you have to really do that by understanding the incentive systems in which they operate, but also some of the psychological biases they have, the concerns, the fears, the worries that they have. And I think that's really important.

Speaker C: Thank you so much for joining us today. Really excited to see you and, uh, looking forward to chatting again soon.

Speaker D: It's my pleasure. Thank you very much.

Speaker A: That was Greg Myers, Chief Digital and Technology Officer at Bristol Myers Squibb.

Speaker B: Thanks for listening to Enterprise AI Innovators. I'm Sam Motamity, a general partner at Greylock Partners.

Speaker A: And I'm Heaven Reiser, the founder and CEO of Abnormal AI. Please be be sure to subscribe so you never miss an episode. Learn more about Enterprise AI transformation at enterprisesoftware blog. This show is produced by Abnormal Studios. See you next time.