Atlassian partners with Williams Racing to boost technology and teamwork with Andrew Boyagi

Hey everybody. We're back at Atlassian Europe, and we're talking to my good friend Andrew here, who's the customer, CTO for the Atlassian Williams F1 racing team. Andrew, welcome to show.

Hey, Mike, thanks for having me. How did this whole relationship between Atlassian and Williams come about and, and, and, and why Atlassian and how did you guys like decide that Williams was the team to be? Yeah, so, um, earlier this year we signed on as a title partner and technology partner for, uh, Williams Racing.

Uh, and initially we, we were having a look at many teams and, um, it was clear that Williams had a great culture. They were, um, on the, a similar journey to, uh, to the top. Uh, we felt like we could support them in getting there, uh, and they really felt like teamwork and technology was gonna be the key to help them get back to the top of the grid.

And when you're looking for better teamwork, who do you come to, I suppose? So what were they using before they found you guys, and what have you done as the technology partner, the upgrade their environment? Yeah, So, uh, if you look at the history of Williams racing, they haven't really invested too much in technology, uh, in terms of knowledge worker technology, uh, over the past 10 years.

Uh, and so they were using a variety of, of different tools. Uh, however, if you look at the different software, the different collections from Atlassian, uh, we are leaders in, in every market that we play in. Uh, and so we came in and we had a look at, um, how are they working?

What kind of products are they using? How could we help? Uh, and so we've started rolling out our teamwork collection there.

Uh, and they re uh, Williams are really seeing a massive uplift, uh, as a result of that. Yeah. So are they wor, are the engineers working differently now together?

I mean, you know, gimme an example of what they're doing that they probably weren't doing before or should have been doing before. Yeah, I mean, uh, a good example is what happens at the track. Um, so a lot of people dunno that when a Formula One team turns up to a track, uh, they have a garage.

When you see it on tv, it looks great. It, there's Atlassian everywhere, lots of shiny cupboards, two beautiful cars inside. Um, but the reality is when a team first turns up there, it's an empty concrete box.

They, they start off by painting the floors. So that's the level of work that needs to go into it. And a lot of people don't think about what does it take to get all those things there and set it up.

Uh, and one of the, one of the things that they were doing we're tracking basically in a notes app, all the tasks that had to be done to set up a garage. Uh, and as a part of that, there are incidents and things that go wrong, uh, that they also need to keep track of. So rather than using a a Notes app for that, which, uh, wasn't really giving them a lot of insights about repeat issues and, and ways they can improve, we transitioned them onto Jira.

Uh, where now we have a repeat, uh, a repeatable cycle. Uh, the tasks are assigned to people when something goes wrong, they're able to, uh, log it as an incident, talk about what's happened, uh, and that way after the race, they're able to go back and see how they can improve and how do they avoid those things from happening again. Uh, an extension of that actually is they can ask Rover at the end of the day, how was the setup today in Singapore?

Uh, and Rover will give a report about what's been done, what's outstanding, uh, how many incidents happened, uh, which really helps, uh, in terms of continuous improvement, How big is the team and is it the same team that goes to every one of these cities where the race is at? Yeah, a lot of people don't really think about the size of a Formula One team, and they're surprised when I say there's about 1,100 people who work at Atlassian Williams Racing. Uh, I don't remember the exact number of people who travel to a race, but I think it's like 80 people go to a race.

Uh, and so it's not always the same 80 people who go every week to different races, but, uh, they have different teams. So for the, for example, the Garage team that I spoke about earlier, uh, they have, I believe an A and a B team. Uh, so they're setting up two different tracks at the same time.

Yeah. So how is the team doing from your perspective? Yeah, they're doing great.

I mean, it's been six months since we, uh, started working and we're already seeing massive improvements, uh, in the way that they work. Uh, we've reduced how many meetings they're having. Uh, we've been unlocking knowledge between the different teams, uh, at a, at the, at the race team at predominantly using Confluence and Rvo.

Uh, they're seeing great benefits from using Loom. Um, that was one way of cutting down meetings, but also recording meetings, uh, which gives you a nice summary with nice actions, uh, automated as a result of that, uh, is also paying some big dividends for them. Um, are the engineers discovering anything or you, they, they didn't know before or are they finding duplicate efforts or anything like that?

Yeah, I mean, if you look at any company they, they're problems that happen, uh, in any company where you have people working on the same thing or the same thing in different ways. Uh, I think it comes down to a few things. It comes to prioritization and to, um, visibility across what people are doing in different teams.

If we, if we talk about prioritization for a second, um, their, their top level goal is to improve lab time. Now, the thing about Formula One teams is they have a cost cap. And so nearly all the people they hire are trying to contribute to reducing lab time.

Uh, and so then it becomes hard to prioritize because everything is contributing to the overall goal. Uh, and so what they've been able to do using JPD is define what is value for them, uh, what does it mean to reduce lap time. They put all of their I ideas in one place with all of their data that supports that idea.

They can track how much effort something will take and the impact they think it's going to have. And it becomes a really nice way for them to prioritize what's important for them to work on right now versus something that we can work on a little bit later on. I would imagine that they're also trying to prioritize their own efforts, but a lot of the knowledge you seem to be capturing used to be, you know, what we call wet wear between somebody's ears.

So have they kind of figured out that they can now maybe, uh, you know, if somebody leaves the team, it's not as catastrophic an event because there's a, the tribal knowledge is captured. Yeah, absolutely. I mean, um, as a part of the design of the car and when they're, when they're doing their aerodynamics, uh, a lot of the information was captured in places where it was only accessible to one or two people or, or a very small group of people, uh, which is problematic when, um, that's the very beginning of a process.

'cause that that information, uh, results in the design, which ends up in the wind tunnel, which eventually ends up as a part of a car. So there are many people who need to contribute to, um, that process. And so having the very beginning of that process locked away, uh, doesn't enable the, a nice collaboration flow, uh, throughout that value stream.

So now that it's being captured in Confluence, it's indexed by vo uh, and people are able, are able to surface the right information at the right time throughout the, uh, the end-to-end process. And the thing that's different about all this with the AI is that, at least as I understand it, it used to be somebody would stand around with like a clipboard and then capture data and then type it in somewhere that nobody else could access it. Um, now it seems like the AI agent is actually capturing all that data and then sharing it with the team.

So I'm not sitting there doing a lot of data entry. Yeah, I mean, so with, with the Atlassian platform, the, the most powerful thing is actually the teamwork graph. So people don't really have to go too far out of their way to put information somewhere.

We try and connect all the different elements into the teamwork graph so teams can put the information where it works for them, uh, and, and it'll still be accessible through the platform and through vo. Yeah, because I think half the battle we've seen with any application is nobody actually wants to spend time putting the data in there, which kind of defeats the purpose. So, um, are we gonna get the value out of the software investments that we've been making all these years in ways that we never could before?

I think there's a different way to think about things now. Uh, I've been speaking to a lot of customers at this conference, uh, about standardization and why they wanna standardize the way teams work. If you think about what a why people wanna standardize is so that information is stored in a consistent way in a, in a place that people can find it and digest it easily.

But teams don't want that. Teams wanna work in a way that works for them in a way that supports them to go faster and get to their outcomes faster. The beauty of teamwork graph and rvo means teams are able to work in a way that goes faster for them, maybe with minimal standardization, uh, but other people in the company can still find that information without knowing exactly where to look and in an expected format.

Uh, and so now we, we kind of solve that problem around standardization and, uh, limiting the way that teams work through the use of robo and the team of graph. Are they consolidating the number of tools they have? At least I know in my job, my issue isn't necessarily that I don't have a tool.

It's more like I got too many of them and I can't quite figure out how to make them all work together. Yeah, I mean, obviously, uh, they're huge advocates of the Atlassian platform. Uh, and so it's more around enterprise architecture and what products should we use for different things.

Uh, and so they are centralizing on Atlassian. Alright. Um, are they playing around with AI agents or are they gonna build their own, or what are you guys thinking?

Yeah, They've built, uh, they've built many different Rover agents. Uh, we had a session this morning where, uh, Richard Slaughter from a WR spoke about, uh, a wind tunnel tapping agent that he built. So he had a lot of knowledge in his head, actually.

He used to be an aerodynamicist. And, uh, he's captured all that information in the platform and built his own rover agent so that he can reduce the number of questions he's being asked, uh, about this particular thing. And people are going to the agent now, he's actually been tracking how many people are hitting the agent and he counts that as a benefit to him.

'cause all those people would come to him in the past. Yeah, You hit on an interesting point, right? Because there are people who are specialists and they have a lot of knowledge, but I might argue they spend half their time just answering questions from the rest of the organization rather than doing what their real day job is.

So will that change the way we think about general purpose workers versus specialists and, um, the way we might even organize our companies? I, I think about it in, um, this concept of low value collaboration and high value collaboration. So low value collaboration is something similar to what you described where you have someone with deep expertise, people contact that person to extract information that's low value for the person who has the information, even if it's high value for the other person.

So overall, that collaboration is low value 'cause only one party gets, uh, gets benefit from it. High value collaboration is, uh, what I think is being enabled through ai, which is, um, they can go to an agent, they can get the information they need, then if they need to work together on something, both parties will get information be but get value, uh, because the person who was originally asking the the question has enough information to have an informed conversation rather than asking basic questions. Yeah.

So at the end of the day, um, am I gonna get more work done or am I just gonna have less stress in my work life per se? Uh, I think about it in terms of value, right? Like if we think about the person who is answering questions, that's low value for them.

If they're an expert in something, you want them spending as much time as they can working on whatever's, whatever's their area of expertise. Uh, so I think by uh, introducing ai, we're able to free up that person's time to spend more time probably on the thing they like doing rather than answering questions. If that reduces stress or not, I don't know, depends on the individual.

Is Williams trying to figure out, you know, how much of a productivity boost they're seeing or are they just kind of accepting the fact that everybody seems to be working more cohesively and with less toil and that's the benefit in its own rate? Yeah, Productivity, productivity is an interesting one. Uh, academics have been trying to measure productivity for decades unsuccessfully.

Um, but absolutely we're looking at how can we be more efficient? Uh, so where is their time wastage, uh, in the things that they're trying to do, and how do we reduce that? And that shows up in different ways.

Like it shows up in number of meetings or effective meetings, uh, you know, how many emails are being sent, things like that, uh, where we often see efficiency traps, um, showing up. Yeah. So you've been working with them for a while.

What's that one thing that you know, kind of surprised you to discover? Actually, their culture is very similar to the Atlassian culture, where, uh, people are very supportive. Everybody who works there really loves their job.

Uh, they're all happy to be there and, uh, they're teams who want to collaborate. Uh, and so for me, it's an absolute pleasure to be able to enable them to do what they already want to do. All right, folks.

You heard in here Atlassian Williams is a winning team, and when they actually win the next trophy, we'll see. Hey, thanks for coming By. Thanks a lot.

All right.