The Modern CTO of 2025 is Rob Duffy, CTO at HealthEdge!

(Intro Narrator at 00:00:00) Today, we're bringing you our Modern CTO of 2025, Rob Duffy, CTO at HealthEdge. This is the advice that we thought was the most impactful from all of last year. You're listening to Joel Beasley, Modern CTO.

(Joel Beasley at 00:00:20) So what's happening? Give me the insights too.

(Rob Duffy at 00:00:22) What's happening? I'll give you the insights too. I went to a conference a couple of months back, and it was a health care technology conference. And there was a lot of presentations. And as you can imagine, a lot of presentations on AI.

(Rob Duffy at 00:00:34) And the remarkable thing about that conference is I could have created a little time machine and teleported you to that conference last year and teleported you to the conference this year and challenged you to tell the difference between the demos and the presentations and the talk tracks and the narratives between those two years. And I think that even though the pace of AI development is incredible and moving very, very quickly, the adoption and the things that the use cases that people are talking about and the demos that people are giving isn't keeping up with the same pace. And I think that's largely because everyone now has demo fatigue, and everyone has seen all the demos, and they've seen all the flashy use cases. But people are struggling to take that flashy demo and flashy use case and actually put it into production environments and scale it and get it into a system that's delivering real ROI. And that is what everyone is talking about right now is, like, we see the potential. We've seen the demos. But what are the actual real world hard ROI use cases that we're able to bring to production? And a lot of us are thinking about that, and a lot of us are talking about that. And I think one of the most interesting things that I've started having a conversation with my leadership team about and my board and our investors is that the migration of work from human cognition to AI and agentic systems is probably about as large as a cloud migration would be in terms of moving things from legacy on-premise infrastructure to cloud infrastructure. But if you think about those two projects inside any organization, the cloud migration has a project management office.

(Rob Duffy at 00:02:24) It has a cloud center of excellence. You create a cloud factory. You put things in these waves. You construct a wave plan. There's the books and blogs and, you know, AWS has guidance on its well-architected framework, and it has this method of assessing what you're going to do with all your on-premise infrastructure. You know, they have these seven R's, and it's like rehost, refactor, replatform, et cetera. And there's this whole big motion around it. And everyone knows how to do that by now because most of us have participated in one, if not more than one cloud migration at this point in our careers. And we sort of know how to do that. But then you look at how people are driving the migration of work to AI and agentic systems, and there's none of that.

(Rob Duffy at 00:03:03) There is none of the process and the systems and the tools to look at work the way that you have tools to look at on-premise and catalog all the infrastructure. And I think that's why we're seeing lots of demos, but not much in the way of hard migrations and hard ROIs to these hard migrations to AI and agentic systems. And what we're thinking about and we're putting in place now is that kind of motion and that kind of structure around the project, because it really is as large. When you think about the impact it can have to your organization, it is as large an ROI as a cloud migration might be. It is the hardest thing in engineering leadership is changing your team and changing your culture and changing the way work is structured, you know, and moving stuff away from humans and into AI and agentic systems is probably one of the largest scale changes that we're going to have to levy on the team in the next century.

(Rob Duffy at 00:04:05) And we need to focus on that and have tools and systems and processes on how to do that. And I think, you know, you ask what the conversation behind closed doors is. Like, that is the behind-the-scenes conversation. Like, how do we move this organization forward? And in this case, there is a really strong headwind, which is people are terrified. They're worried that if they do move some of their work over to an AI and agentic system, that means they don't have a job anymore. So you have—

(Joel Beasley at 00:04:36) Well, for some of them, that's true.

(Rob Duffy at 00:04:38) Yeah, it is. But it was that way with SaaS software. You would go into—we would go into teams that had a hundred people in the team, and we would leave after implementation. They have ten people that learned how to use the new tool that did the work of a hundred people. I mean, this isn't new. This has been going on for a long time.

(Rob Duffy at 00:04:54) I know. It's just that tech has always been on the other side of that equation, right? Like, if you think about software and software developers, it's always been the software developers who are installing software as a service or the software developers who are creating a new tool that's going to automate things and, like, you know, and then the engineering teams are the ones that are building and creating and not the ones that are always seeing the benefits from automation and optimization. And then now they're in this position of, like, well, hold on. If ten, 30% of my work goes away to an AI and agentic system, what does that mean for me? What does that mean for my career? What does that mean for software development? And, you know, we're strongly of the opinion that what it means is that a lot of the work that software engineers are doing is going to be supervising AI and agentic systems. We still need software engineers, and we still need people to review the code that comes out of these tools.

(Rob Duffy at 00:05:51) And we need people to think about architecture and design, and we need people to get really, really great at prompt engineering and creating tools and creating MCP tools. And that's the work. The work is going to shift. We're going to see the emergence of these AI-enabled developers or agentically enabled developers. And I think those are the teams that are going to win are the teams that can make that shift quickly.

(Joel Beasley at 00:06:20) These frameworks for understanding and implementing this, are you creating internal versions of it as you go and sharing it with your peers? Or is McKinsey creating stuff or Deloitte?

(Rob Duffy at 00:06:31) No. We have a largely internal, grassroots LLM adoption process. And, you know, it started with this pilot, 53 users. We got on to Claude by Anthropic and used their Claude for Business offering. And, you know, we put all those people in their channel. We had weekly leaderboards. We had show-backs on data. You know, we created some gamification. One of the things that I did was offer a jar of spicy pickles to a person who produced the best tools. Right? So it was like it was a fun, kind of convivial atmosphere, but we put in a huge amount of energy to it. And, you know, we got people sharing and showing what they had completed and showing what they'd done using the tool. And then that provides this sort of collective intelligence. It sparks other people's imagination. Claude has a great project facility where you can sort of create an almost like a mini-RAG with the project knowledge and then a mini-system prompt with the project instructions.

(Rob Duffy at 00:07:32) So that then provided a path to production. Right? Like, you have a user who can go in somewhat motivated by the chance to win a jar of spicy pickles, and they create a project. They put in their information, then they share it and show other users that. And, you know, we have little videos and recording sessions. So, like, you have to put in that much energy and that much momentum to get people really thinking about, like, how can this be injected in my everyday? And, like, how can I personally use it? And how can I share it? And, you know, now we've got AI champions on every team. We give them some swag, and they are the people who are sort of going into those teams, listening for all the updates from all the other teams, and sharing that knowledge.

(Rob Duffy at 00:08:15) And it's a real grassroots movement for us. And for me, you know, I want us to be the most AI-native and AI-literate company in all of health care technology. And I don't think we can do that by relying on external partners to lead that transformation. We have to be the change, you know.

(Joel Beasley at 00:08:35) It has to be baked into the culture because all the small details matter in the time of execution. Like, in the runtime environment of your day-to-day job, you need people that understand what is possible, what the limitations are, all of that. Congratulations on that. So would you recommend this process of getting a bunch of different—were they—let's talk about these 53 users. Different people from different teams? Like, what was it? How did you pick these people?

(Rob Duffy at 00:08:59) We picked five teams. So cross-functional teams. Right? Product managers, subject matter experts, QA folks, developers, managers. We created the team captains. We had a kickoff. So basically the premise of the pilot was if we go all in on AI, how far can we get? We mapped the whole software development life cycle and the whole process. And again, this goes back to my earlier comment around, we really need to understand work before we can understand how work can be migrated to AI and agentic systems. We mapped the whole software development life cycle, identified every step, every handoff, like, how many times teams were doing that, how long it took, you know, and then could that be offloaded to an AI agentic system, and then identified a proof of concept, AI prompt in just the Claude web UI, and then thought about how can we extend that.

(Rob Duffy at 00:09:55) So, like, the teams were picked because we wanted end-to-end cross-functional reimagination of the software development life cycle. And we ended up with 53 people in there. It was—the energy around it was incredible. It was, you know, as soon as you give people permission to just go end-to-end and tear up everything that they thought they were doing and just point an LLM at it instead, you get huge results out of it. Yeah. And I've come to the realization that actually what you have to do is just drive usage of tools. Like, you have to really get people to use tools aggressively. And it's not just about making them available. It's actually about thinking every day about how you can get more and more people using the tools. And I think, for me, the best way to do that is to use tools yourself.

(Rob Duffy at 00:10:46) Right? So if you're using tools and you know how to do things, when someone's showing you a problem or giving you a status update on a project or, like, you know, explaining how they're going to approach something, you can say, "Well, hey, look. Let's just try this in this tool together. Like, let's try and pull up Claude Code or Amazon Q, now Kira from AWS, and let's try it. Let's do a quick proof of concept." And you have to be—I think you have to be able to do that. I'm coming to the realization that to lead through this change, you have to figure out how to drive these tools yourselves. And they're different. Right? They're just different and difficult. And I don't think enough people in leadership positions are actually learning how to use tools and writing code for side projects or, like, you know, just creating things or, you know, sitting in meetings and trying out different things, and I don't think you need to. Another thing that I think is kind of interesting about AI—a lot of people are, like, going through this traditional product cycle and project intake process where they've got an AI council and, you know, you go to—you present AI council with, here's my project. Here's what I anticipate the ROI to be, here is the investment, here's how we're going to measure success, you know, and then AI council sort of says, "Okay, this is an approved project, this is not an approved project." I don't think we know enough to really estimate the ROI accurately.

(Rob Duffy at 00:12:21) And I think you have to open the cork on the AI project bottle and let a few projects happen first. And then worry about how you're going to measure and which ones are successful. Because we've had things where we're like, "This is the perfect use case for AI. Watch. I'm going to put this into this LLM. It's going to crunch it. And then, like, it's going to do this in two minutes," and the output has been garbage. Like, we're like, "This is never going to work." And then we've had other things where it's like, "There's no way an LLM is ever going to do this. This is, like, so complex and requires so much human reasoning, but let's just try it anyway." And then we try it and we're like, "Wow. This is amazing." So, like, I think we're just—I think we don't have enough pattern recognition yet as leaders and as managers of projects to understand what's going to work and what's not. And I think the only antidote to that is to just get enough buy-in from the business that you're going to try lots of things, not forever, but for a period of time, you're going to try lots of things, and then you're going to get some pattern recognition about what's working and what's not.

(Joel Beasley at 00:13:33) One thing I like to get when I have these conversations with brilliant people like yourself is I like to ask them one piece of leadership advice, and here's the constraints. So one piece of leadership advice that you've heard, you implemented, and it stuck with you through today.

(Rob Duffy at 00:13:52) I think the biggest piece of leadership advice I was ever given. I was running a program at Amazon, and it required many engineering teams to change something and do something in a different way. And I had a letter—we presented it to Jeff. We presented it to Jeff Bezos. We presented it to all the VPs or the S-team. And, you know, we got everyone's buy-in. And I had this letter, you know, in my hand that said, "Everyone has to do this." And, you know, how I used that letter is I went and sort of, like, beat teams over the head with it and said, like, "You can't say no. Here's this letter. You're going to do things the new way."

(Rob Duffy at 00:14:33) And as you can imagine, like, that was pretty disastrous. And my boss pulled me aside at one point, and he said, "Look, you can have a mandate. You can have a letter from Jeff Bezos. You can have whoever it is is your CEO. But as soon as you pull it out and try and use it, it loses all of its power. And you have to go to teams, and you have to tell them why this is important and convince them and bring them along for the journey. And, like, any of these large-scale changes, you have to go to the team, meet them where they are, understand what problems they're facing, how this stacks up in their priority, and, like, do the hard work even though you might have a mandate." And I think that taught me about, you know, building movements and not using mandates and building communities and helping people along on the journey rather than just sort of, like, yelling at them and telling them, like, "You have to do things a new way." And that stuck with me. It's advice I give to all my team all the time when we think about change and we think about getting humans to change.

(Rob Duffy at 00:15:35) Because, really, the thing that humans desire least in the world is change. And you have to help them through it and help lead them through a process rather than just trying to force them to do stuff. And that was a very empowering moment for me. I remember it very, very clearly.

(Joel Beasley at 00:15:49) As we start to wrap up on this topic, rapidly bootstrapping trust, that's very catchy. That's clippable. I like it. I want the one most important thing to rapidly bootstrap trust in an organization.

(Rob Duffy at 00:16:03) The one most important thing, I think, is listening. Just hearing people. And I will—so if I can get a 1.5 on that as well. I think listening and doing it, but doing it with genuine curiosity and the genuine belief that your opinions might be changed by what this person is about to say. Like, that is the single most important thing.

(Rob Duffy at 00:16:36) I'm walking into conversations and just saying, like, you know, show me all the systems that you own and operate and, like, show me the tools that you have. And if you can do that in a way that is deeply genuine about learning about that and, like, you don't have any preconceived ideas about what good or bad is or, you know, whether things are better or not better, and spending time to understand people and their motivations and how they built things and what decisions went into it. And it's an incredible builder of trust with those teams if you can do that. And the sort of opposite of that is like coming in with, you know, a very judgmental approach and, like, saying, no. This is crazy.

(Rob Duffy at 00:17:22) This is, you know, you shouldn't have done it this way. You should have done that way. Like, there's always a better way to do it. I think that's important in any organization with a legacy. You know?

(Rob Duffy at 00:17:34) Like, they've got maybe five or ten years of building software or whatever. Because all of those organizations are gonna have some piece of that software that just looks crazy to the outside. Right? Like, it was—like, why would you ever do it that way? And the answer to that is because at some point, the constraints were different.

(Rob Duffy at 00:17:51) An engineer had to do something and make a decision quickly, and that was the best available thing that they could have done at the time. And they did it, and they made that choice. And now, like, it's a piece of technical debt that, of course, like, everyone wants to eradicate. But, like, you shouldn't—like, it's—you can't judge the teams for creating that. You know?

(Rob Duffy at 00:18:08) Like, you have to be like, okay. Like, everyone has skeletons in their closet, their architectural closet. Closets of closets.

(Joel Beasley at 00:18:16) That's true.

(Rob Duffy at 00:18:17) And, you know, none of these software systems that we build are perfect.

(Joel Beasley at 00:18:24) Thank you so much for listening. And if you found this episode useful, please share it with a friend or colleague who you think would get value from it. And if you have topics that you would like to hear discussed on the podcast, either add me on LinkedIn or send me an email [email protected]. Every time I get an email or LinkedIn message, it absolutely makes my day and inspires me to keep going.