Stop measuring AI adoption. Start measuring AI impact. | LinearB’s APEX framework

**Ben Lloyd Pearson** (0:05)
Today, we've got a very special guest, in my opinion. I am joined by my fellow host and LinearB COO, Dan Lines. Dan, it's really great to have you back on the show again. It's been a little while.

**Dan Lines** (0:18)
What's up, BLP? Awesome to be here. Super excited to catch up. We got an exciting topic today.

**Ben Lloyd Pearson** (0:24)
Yeah, yeah. And of course, I'm joking about it's been a little while, because I think you've been on the episode just a couple of weeks ago. So it's actually really nice to get you back for multiple episodes in quick succession.

**Dan Lines** (0:36)
Love being here.

**Ben Lloyd Pearson** (0:38)
Yeah. So all right. So the topic that we want to cover today. So we spent many years on this show, both at Dev Interrupted, but also at LinearB, talking about things like DORA, like space, all these frameworks that really try to measure how effectively engineering teams are operating. The idea being that part of our core mission at both LinearB and Dev Interrupted is really to help engineering teams move from this more gut field driven decision making to data driven engineering. And it's been working well, I feel like, but the world has really sort of changed in the last year or so, as AI coding tools like Copilot, Cursor, Claude, Codex, all these new tools are hitting the mainstream, and executives are seeing the bills for these tools, they're seeing all of these viral things about how teams are getting crazy productivity and writing tons of code with AI and all of these things that claims and hypes that is out there that people are making about AI. And meanwhile, we encounter people, I feel like, on a weekly basis that are asking us, like, does this actually make us better? Like, are we actually more productive? Are we delivering more value to our customers?
And that's the topic that we're going to talk about today, because we've got all these dashboards out there that show you things like AI adoption. You can get your DORA metrics, your cycle time, your CFR, see your predictability. But we started developing this new operating model at LinearB that we're calling APEX. And that's really one I want to talk about today, Dan, because this really gets into why these playbooks that we have, they're still great, but they may not be up to snuff for the AI era. And this is the framework that we hope is going to prove that value. So before we get into the background behind APEX and how to implement it and all the details of what's in it, I want to just maybe start with a high level overview. So from your perspective, Dan, like what is APEX and why should our audience care about it?

**Dan Lines** (2:44)
Yeah. Well, I mean, first of all, coolest name ever, APEX. Got to have a cool name.
And why I love APEX and we're going to talk about each aspect of it, but APEX is a framework that was made by the people for the people. And what I mean by that is it was really organically made through our customer base. Yeah. It wasn't something like, okay, you know, DORA was more like, hey, let's do like a research assignment. Let's go research. And let's do a very, like I would say, research focus, you know, really specific on getting code through the pipeline, you know, also change failure rate balance with quality. But I think what's great about APEX is it came from actual users, actual usage and from our customers. Yeah.

**Ben Lloyd Pearson** (3:36)
And I actually want to point out that, you know, that research focus, you know, because this is something we've seen time and time again, like that, you know, what works in the lab and what you can observe through research experiments and all of that stuff, that doesn't always play out in real life. You know, real life, it can be quite a bit more messy than that. It's, it's, you're dealing with all these different constraints. So I really do think it's important to point out how we're trying to take a much more practical and pragmatic approach that's based on the things we see from, from customers and from, from our community of experts, like every single day, right?

**Dan Lines** (4:08)
Yeah. And no, not to DORA or Space. Awesome frameworks. But revolutions and evolutions has happened since then. You mentioned one of them, obviously AI, taking over the world, eating the world. So we know that, you know, some of the other frameworks have been around now for years and years, probably outdated. But there's another thing that I think happened in between, let's say, like DORA, Space and then where we came to with APEX is engineering organizations over the last, I don't know, five years, are also no longer just responsible to ship code. They are also responsible to the business for value. And so when you look at APEX, right? A and APEX, AI leverage, the P, predictable delivery, predictability, the E, efficiency, still got to be efficient with shipping code. And then the X, DevEx. So what I like about APEX and what our customer base has told us is, yeah, it's up to date because it's AI forward. Okay, it starts with A, it's AI, but it's more about the balance. It takes into consideration, of course we need to ship code, it needs to be efficient and high quality, but there also has to be the value, the predictability side. Are we actually delivering value, stories in a predictable way every sprint? And then it's got the A in there, right? Okay, the AI has to be there, gotta be AI forward, gotta take it into consideration. And then it rounds everything out with developer experience. So when I think APEX, really what I've been thinking about is, wow, this is a really balanced framework for the times that we're living in today.