AI, Engineering, And Formula One: The Tech Driving the Atlassian Williams F1 Team

[00:00:04] - [Speaker 0]
What can Formula One teach the rest of the business world about AI, teamwork, and high performance decision making? Well, this week, I'm recording at team twenty six in Anaheim. And one of the partnerships that keeps coming up in conversations here on the show floor and in keynote sessions and across the wider audience here is twelve months ago, Atlassian Williams Racing made headlines when Atlassian entered the world of Formula one as both title partner and technology partner. And at the time, many may have seen this as a branding exercise, but over the last year, something much, much bigger has been taking shape behind the scenes. But today's guests are helping lead this transformation because I'm gonna be joined by Andrew Boyagi and Matt Harman for a conversation about how data, AI workflows, and organizational context are all reshaping one of the most demanding environments in global sport.

[00:01:06] - [Speaker 0]
And, honestly, today's discussion will go far beyond racing. Yes. We will talk about how Formula One teams process millions of data point during race weekends and why reducing idea to track timelines matter so much and how AI is helping their engineers and strategists make faster, more informed decisions under extreme pressure. But we will also unpack the human side of transformation. Those cultural shifts required to reduce operational friction.

[00:01:35] - [Speaker 0]
The challenge of moving from endless meetings to outcome focused collaboration, and why AI alone is never the competitive advantage. Organizational context and decision making culture, all these things matter enormously. So I wanna learn about how Atlassian tools like Jira, Confluence, Loom, Rovo, and Teamwork Graph are all helping Williams reduce lead times, improve throughput, and create more agile engineering capable of responding faster both on and off the track. And perhaps most importantly, we'll explore what other industries can realistically learn from Formula one team operating at 300 kilometers an hour with millions of people watching their every move. So if you've ever wondered what enterprise transformation looks like when every millisecond counts, you're gonna love this one.

[00:02:27] - [Speaker 0]
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[00:03:31] - [Speaker 0]
So thank you for joining me here at Team twenty six. Obviously, we met remotely a few months ago, but can you tell everyone listening a little about who you are and what you do?

[00:03:39] - [Speaker 1]
Hi, everyone. I'm Andrew Boyaji. I'm the customer CTO at Atlassian, and I work with many customers, but primarily with Atlassian Williams Formula One on our joint goal to help them get back to the top of the grid.

[00:03:51] - [Speaker 0]
Awesome. And we've got not one but two guests today. Would you mind telling me a little about you too?

[00:03:56] - [Speaker 2]
Hi. I'm Matt Harman, so technical director for the Atlassian Formula One team, working really closely with with Andrew on lots of integration projects with Atlassian, but also looking after our various car programs, the current one you see, the one that we got coming in future years, and also the transformation of the technical department to, again, get us to the top of the grid.

[00:04:16] - [Speaker 0]
Awesome. Well, there's so much I wanna talk with you about. It was almost exactly twelve months ago that Atlassian Williams Racing became one of the biggest news stories in Formula One when Atlassian entered the sport as both title partner and technology partner. But looking back now, what was the original vision behind that partnership and what problems were you both trying to solve right from day one?

[00:04:36] - [Speaker 2]
I think from my perspective, we've got a lot of transformation to do in particularly in the way in which we manage the integration of data and how we apply that data in the team. We very much would like to spend more time using the information that we have rather than manipulating it and trying to get the architecture correct. And it's absolutely important, very important aspect of that working with Andrew and the team and Atlassian generally to try and build that foundation, making sure that we can work integrated and collaboratively and really with some purpose using the insights that it provides either be it through Confluence or through Agencik AI to to actually give us the insights so we can be quicker at the decision making and and make the influence. We've got a very agile team, but we need to still be agile in the decision making which is important.

[00:05:23] - [Speaker 0]
Yeah. And Andrew, from Atlassian's point of view, it felt like a real big bold move last year, but tell me more about the thinking behind it from your side too.

[00:05:30] - [Speaker 1]
Yeah. I mean, like, to go back, both organizations are really focused on, teamwork and technology, and both organizations believe that's how not just Formula One teams win, but how any organization wins. And for us, you know, it's a tremendous opportunity to work with Williams to demonstrate our system of work and the impact it can have. Williams help us to demonstrate that on a global scale. And over the last twelve months, we've made several huge steps forward together, which has been really pleasing for us to see.

[00:05:59] - [Speaker 0]
Incredibly cool. And for people listening, well, I think when many of them think about Formula One, they only think about speed on the track. But the real battle often happens behind the scenes through data, collaboration, and decision making. So how do you identify where the friction existed in the organization before that transformation began?

[00:06:18] - [Speaker 2]
Yes. So one of the big initiatives that we have in the team is something we call Idea to Track. It's about how you can take that initial concept and actually get that performance to the circuit itself. And the difference between the performance that you have and the performance you've deployed can be at the moment or has been too long. And I think it was a big part of that.

[00:06:37] - [Speaker 2]
And I wouldn't say it's friction particularly, it's just the manner in which that we organized ourselves, manner in which we collaborated with each other on a daily basis. But what we've really understood is that we can significantly reduce that period of time. We can significantly reduce the amount of time we spend waiting for an update which allows us to deliver that performance directly to the circuit, which is what everybody sees on television. And as you said earlier, there's a really amazing team trackside, but there's also echoing a huge team back at the factory both with Atlassian and the Williams team at Grove where there's an awful lot of work that goes in there to try and make sure those updates happen. And I think to work collaboratively like that with those tools is just great to see.

[00:07:21] - [Speaker 0]
Yeah. And I recently spoke with Ruth at Bushcombe about F engineers processing millions of data points every single second during the race. It blew me away. But how do you translate that enormous volume of information, that data, into decisions that engineers, strategists and operational teams can actually act on in real time?

[00:07:40] - [Speaker 2]
It's a fantastic point actually and I know Ruth very well. So yeah, so and she's absolutely right, we're processing a lot of data both at the circuit and at the factory every single day. And it's all about understanding what you're trying to achieve out of it and actually condensing it into a format where we can be interpreted either via an agent or by ourselves or both. So that we can then actually use that data to be informed on the results that we're trying, the outcomes that we're trying to achieve. In the end of the day, the data is really, really important but we need outcomes.

[00:08:11] - [Speaker 2]
It's all about that. It's those marginal gains outcomes that we're trying to drive all the time. It's not the big always the big ticket decisions, it's those little decisions that happen every day by the people who are operating a team and I think that's what we've tried to focus on. How can we get all that information into one place? How can we ensure it's condensed in a way that people can consume it and then make those decisions quickly?

[00:08:30] - [Speaker 0]
And one of the things that we discussed earlier this year, Andrew, was how the industry conversation has shifted from AI experimentation to execution. So in Formula One, where every millisecond matters, how do you evaluate whether AI is genuinely improving performance rather than maybe dangerously adding more complexity? How do you avoid falling into that trap?

[00:08:49] - [Speaker 1]
It's a great question. And in some ways, things have not changed with AI Yeah. Because the the fundamentals are the same. There's four things that, any team needs to be successful, Formula One or any other type of company that we're talking about. One is, purpose flow.

[00:09:05] - [Speaker 1]
So people need to know what's important to the organization, why is it important, because then they can make really good decisions. There's no use speeding up teams with AI if they're going really fast in the wrong direction. So purpose is really important. Workflows where people generally spend a lot of time with AI, speeding up tasks, but it'll it's a double edged sword because, Matt mentioned something being an idea to execution to having an impact. Everybody wants the the impact in the end.

[00:09:34] - [Speaker 1]
AI helps you generate more work or more output, but if it still has to go through those same bottlenecks, the outcome comes later. Mhmm. So it's important to look at that end to end flow. Knowledge, so we talked about data points, but, you know, knowledge is a foundation of productivity. Can you find what you need when you need it?

[00:09:52] - [Speaker 1]
You want insights, not information. So that's another element. And then AI, you know, when all those things are helpful, the real benefit of AI is not more to generate more output. It's to allow humans to move up the value chain and work on more valuable tasks. You wanna delegate the repetitive stuff to AI.

[00:10:12] - [Speaker 1]
So in my view, they're the four things that teams need to be successful, and that's what we're supporting Williams with.

[00:10:18] - [Speaker 0]
And at Atlassian, you've framed this transformation around people, tools, and AI, always starting with people. But what cultural or operational changes had to happen inside your team before the tech itself could start delivering those results? Because very often, it's not the tech, it's the culture and mindset shift which is an even bigger challenge.

[00:10:36] - [Speaker 2]
So it's interestingly, actually, maybe not before, but one of the big impacts that Andrew has actually had on me personally actually is to try and challenge the way in which I turn up every day as a senior leader in the team. And I very much enjoyed that in the fact that I think the notion of continuously meeting and continuously discussing and continuously debating everything contributes to that lack of performance in the idea to track domain. And I think by introducing the concept of looms and the confluence pages and how they interact with each other to allow us to not focus on the debate but to focus on the outcome, focus on the decision that we want to make every day, give people the information to hand very quickly and concisely on how to make those decisions and then we talk about and we use our time together like this to actually agree what we're going to do moving forward and set that time scale. And I think that has really moved us forward. It's very different but I've very much enjoyed it.

[00:11:33] - [Speaker 2]
And actually I've seen the enjoyment spread across the whole team in that decision focus.

[00:11:39] - [Speaker 0]
And Formula One teams are traditionally filled with highly specialized departments all working under intense pressure. But how did bringing together Atlassian tools like Jira, Confluence, and AI driven workflows, how did all these things change the collaboration between engineers, strategy operate operations, and indeed your factory teams? How bringing it all together there.

[00:11:58] - [Speaker 2]
I think it's really interesting because as as the teams have have moved on and and with certain regulations that have changed particularly around the cost cap, actually people being less specialized and more generalist is becoming more and more important. We need to be more modular, more agile. The regulations change in a cycle every three or so, three to five years and that means that we where the performance is going to be extracted can move around across the car. So we're trying to build a more generic workforce, a group of people that can be more adaptable, more agile. And what the tools from Atlassian allow us to do is to provide that backbone, that understanding of some of the tacit knowledge that we have in the team that can be spread more evenly across the team, allowing the specialisms to be less sharp but actually allow more and more people to input when when required.

[00:12:41] - [Speaker 2]
So actually for me it's it's just made us more agile. We we have very very clever individuals in the team. Everybody can be a specialist but they can also be a very effective generalist. And and that is again allowing us to put deploy more people at the right time on the thing to bring the performance to the circuit. And the tangible part about this, that's very measurable.

[00:12:59] - [Speaker 2]
You can see every two weeks, sometimes three times in a week. You can see very clearly how well we're doing. And I think it's going to be important to watch that over the next year and year or two as we we really start to kick up a gear and the transformation we put in place starts taking hold.

[00:13:15] - [Speaker 0]
And again, for many people listening, they see race day as the main event, but development cycles happen continuously back at the factory. Can you give any listeners a real world example of how improving workflows or reducing operational friction actually translate into those measurable gains on the grid, that that 1%, those marginal gains? There anything you can share? I I appreciate you probably can't share too but is there anything you can?

[00:13:37] - [Speaker 2]
No. I think I think to start off with, it's it's it's really important to say that in every given moment, we're working on the current car that we see at the race, the car for next year and the car for the year after that. So any moment in time, there's a lot of technology development ongoing, there's a lot of systems transformation ongoing but there's also a lot of car development ongoing. So there are lots of examples where what we do at the factory, you can see very, very quickly at the circuit. I think one great example is something very recently where we wanted to improve our idea to track for our front wing.

[00:14:08] - [Speaker 2]
The front wing lead time was greater, quite considerably greater than I've ever experienced before. But through non sequential workflow, through through the Atlassian tool to allow people to collaborate in parallel, to actually allow tasks to flow in parallel, sometimes at risk, but you have to accept risk with performance, We were able to actually divide the lead time of the front wing by three and that performance was then able to arrive at the circuit some three events earlier which I think is a huge when you look at the actual performance and the area under the curve of that, that definitely converts into constructors points. And that was an example that actually happened on the FW forty seven, which was last year's cup.

[00:14:49] - [Speaker 0]
And Andrew, from your viewpoint, you've entered a a brand new industry here, challenges that you probably didn't even know existed, and probably on a great journey and learning curve along the way. Tell me a little bit more about your experience over the last twelve months as well.

[00:15:02] - [Speaker 1]
Yeah. It's been fantastic to work closely with the Williams team and, understand how they work and, some of the uniqueness about Formula One. As you mentioned, speed of decisions and precision engineering, every, tech person's dream, really. But essentially, what I've what I've learned is the foundations are the same for most companies. You know, every company Matt Matt mentioned earlier, the cost cap.

[00:15:25] - [Speaker 1]
Every team has a cost cap. Every company has one. No one has unlimited money. And so what most companies are looking for is efficiency. How do you get to the outcome as fast as possible with as little friction as possible?

[00:15:37] - [Speaker 1]
And so there's so many things like that. I have, something that I shared with someone, yesterday, which was the more time I spend with Williams, the more that I can see that, Formula One is an enterprise performance problem. The main difference is that it's traveling at 300 kilometers an hour, and millions of people are watching you do it every weekend.

[00:15:58] - [Speaker 0]
Wow. How do you you cope with that kind of pressure? Because there's no margin for error there at all, is there? Is it do do you feel the pressure at Atlassian on something like this when the whole world is watching?

[00:16:07] - [Speaker 1]
I can tell you that, I very much feel like a part of the team. And when I'm watching a race on the weekend, my my, my hoop on my wrist, gives me gives me stress alerts that are that I'm watching, something that I'm enjoying. So

[00:16:21] - [Speaker 0]
I'm incredibly grateful to the team at Denodo for backing the Tech Talks network and helping us produce over 60 interviews a month. And if you are looking for better ROI from your lake house, this message is going to be worth hearing because Denodo helps reduce complexity, control costs, and accelerate time to insight. And it does that by connecting all of your data sources in real time. So make your lakehouse work harder with Denodo, and you can do that by simply visiting denodo.com. And AI is becoming deeply embedded across engineering and software development, but f one also involves a lot of huge human instinct and experience under pressure.

[00:17:06] - [Speaker 0]
Where do you think AI genuinely augments decision making and where do the human driver and the human team still outperform the machine? Because it isn't one or the other, it is a collaboration there, but where do you see each role?

[00:17:17] - [Speaker 2]
Yeah, I think one great example of that at the moment is strategic decisions that we take at a race weekend. There are many, many years of back catalogue of strategic outcomes that you could take in any given race weekend. And what the AR tool does is allow, it presents that in a way that provides you with more accurate statistics on what you might do in the context of absolutely everything that's ever happened at a race event from the beginning of Formula One. And what that allows the people that are actually interpreting that information and making those decisions in the moment on the pit wall is it just makes those decisions statistically more precise and the outcomes more likely. And I think that's a really really great example of the fact that straight AI would not provide that but it does provide you with that breadth of understanding immediately on the pit wall at the moment in which you need to deploy it.

[00:18:06] - [Speaker 2]
And I think that is where when you think about that use case and you apply that across the whole team, there are hundreds of use cases in the Formula One team where we could apply the same techniques.

[00:18:16] - [Speaker 0]
And one of the things that fascinates me about Formula One and listening to you both here is is how effectively a live innovation lab is operating under extreme constraints. And as you said, Andrew, every business, every organization, every industry, they all think they're uniquely different. But when you're working with so many different industries here, you you notice that there are a lot of parallels. Everyone's got the same problems and chasing the same things. So have there been any lessons from this transformation that maybe other industries outside motorsport, they're listening to today's conversation, but maybe they could realistically apply in their own organizations?

[00:18:49] - [Speaker 1]
Yeah. I mean, like, the lesson maybe that's been confirmed is this is a human change, not a technology one. Yep. Tools are excellent. AI is phenomenal.

[00:19:00] - [Speaker 1]
But nothing actually changes unless humans change what they're doing. And so, realistically, everyone's on a learning journey with AI and working out how best we can use it and where it's gonna get us in the future. And so it's really supporting teams, supporting people, experimenting, understanding how you can match this technology with the organizational outcomes that you're looking to achieve. That's gonna it's already delivering some great successes for Williams, and I think the same approach would apply to any organization.

[00:19:32] - [Speaker 0]
And you're both, appearing on stage, talking about all this. For people listening that can't attend, what are you gonna be covering? What are you gonna be talking about? What are they missing out on today? Because I I had a quick look on LinkedIn.

[00:19:42] - [Speaker 0]
There's a lot of excitement about your session. I'm I'm curious, what you're gonna be covering?

[00:19:46] - [Speaker 1]
Yeah. We're gonna we're gonna, reveal all the secrets. Yeah. We're gonna go through, the framework that we're using to drive the transformation. We're gonna share the metrics that we're tracking to track our success, and we're gonna go through a couple of examples of some innovation that we've deployed within Williams, covering some fault management or issue management for the car, things like trackside, what are we doing with the trackside team, and, of course, how we're generally improving productivity across the board.

[00:20:20] - [Speaker 0]
I know some big announcements this week, and we've seen so many great demos, demos that work flawlessly in front of thousands of people as as well. When you when you're looking at all the things announced and everything you're working on here, anything excite you there where you're setting off those light bulb moments of, oh, actually, this would work brilliantly with the the racing, for example?

[00:20:38] - [Speaker 1]
Yeah. Absolutely. I mean, one one thing we're already doing with Williams was focusing on their teamwork graph. So using all for people who don't know, that's our knowledge graph that underpins the Atlassian platform. Just by using our products Jira, Confluence, Jira Service Management, Rovo, we're populating the Williams Teamwork Graph.

[00:20:58] - [Speaker 1]
Essentially, what that means is it's building a picture of what are the company goals, who are the teams that contribute to those goals, who are the team members in those teams, who do they work with, what's all the knowledge artifact, all of that builds organizational context. And Williams has forty nine years of organizational context. So Matt mentioned earlier, you know, you can get the data from every decision from every race that's ever happened. But the power of the Team O Graph is it has that for your entire organization. And so that unlocks many different things, especially when you're thinking about strategy.

[00:21:33] - [Speaker 1]
And we we shared an example in that keynote where it was on the Williams environment. We asked, should we upgrade the front wing before Monaco? Now, typically, if Matt was going to ask his team that, that would take many people many days to get to a a good answer with options. And as you shared, Mike, our CEO, did that on stage. We had an answer in, like, forty five seconds.

[00:21:55] - [Speaker 1]
And it was it was a very precise answer, had options, pros and cons, which is great because then people like Matt can make a decision really quickly.

[00:22:04] - [Speaker 0]
And from your point of view here, when all teams are gonna be using AI vary in various forms, it's kind of AI versus AI almost. How do you how do you compete with that pressure and and ensure you you you stand out and you're different from what everybody else is doing?

[00:22:18] - [Speaker 2]
Yes. I think the first part of that is about having the right partners, which we believe we have. The other part of that is really looking beyond the norm of the AI deployment, actually looking at where not only we can support the not only can we work on the AI itself but we can work on some of the new software tools and techniques that we're working that will actually amplify the AI on top. So for me, we are looking to make a big step. We're looking for it to become much more embedded in our organization in a manner that maybe others would the risk would be too high for them.

[00:22:56] - [Speaker 2]
For us the risk is low with our partner content, with our performance outlook, with our plan over the next three to five years, we're very much looking forward to sort of pushing the boundary on it and really starting to rely upon it, which will allow us then to, if you like, build our foundations, our infrastructure, infrastructure of the team around that and I think that's a key part for us. We are in a position in our team where it's time to build the foundation at the right level, build the foundation based on the AI techniques, not put the AI techniques on top of an existing foundation and I think that for me is where we're going to extract the most benefit, and that's where we may or we'd like to think that we can steal a march on our competition.

[00:23:36] - [Speaker 0]
And finally, as we look to the future, possibly moving towards a world where AI becomes deeply embedded into every layer of race operations and engineering. How do you see this partnership evolving? And especially, I would imagine, getting that balance between data, technology, human judgment. Tell me more about the partnership and where you see all this heading from your point of view.

[00:23:57] - [Speaker 2]
From my point of view, I just like to continue grow it. In fact, an hour ago before this discussion we were talking about just that case. We would like to be more creative. Once we have the foundational elements in and we have some of the fundamental infrastructure in place then we'd like to start talking about the next steps and how we can leverage the tools and techniques that Andrew's and the team provide so that we can start thinking more differently about it. At the minute we're getting ourselves to the correct level and then once we're at that level then we want to start ramping up and accelerating the outcomes.

[00:24:29] - [Speaker 0]
And, Andrew, from your point of view here, incredibly exciting time. I know you're a big fan as well. Tell me a little bit about how you see this partnership evolving.

[00:24:36] - [Speaker 1]
Yeah. I mean, just reflecting on some of the wins that we've had already, something that we're sharing later today. Since we rolled out our our Teamwork collection, teams have had an increased throughput of 83%. So teams have almost doubled their speed, which caused something interesting where in March, see teams backlogs flipped from growing to shrinking. So they're actually getting through more work than they're creating, which is a extremely positive situation because then you can spend more time innovating and making the car go faster.

[00:25:05] - [Speaker 1]
We've also saved them eight hundred and sixty three hours of low value meetings in one month across 200 people. And so and when I share that, people are blown away. Yeah. But to me, I I think this is this is foundational. This is we're just getting started.

[00:25:20] - [Speaker 1]
There's so much more for us to do, so much value to unlock. We talked about the teamwork graph. That's a really strong focus for us. And I think that's the competitive differentiator. It's your organizational context.

[00:25:32] - [Speaker 1]
Every team, every company is gonna leverage AI. You you're not gonna win by using AI alone. It's your company's context, the history of decisions. It's your goals. It's where you wanna go.

[00:25:43] - [Speaker 1]
It's about helping humans make better decisions faster. That's a competitive difference, and that's where we wanna help Williams get to.

[00:25:52] - [Speaker 0]
Awesome. I think it's a powerful moment to end on. I'll include links to everything you mentioned, all the releases, and links to your LinkedIn. I appreciate how busy you are. You are on stage, and you're here talking to me.

[00:26:03] - [Speaker 0]
You should be prepping somewhere. But more than just thank you for sitting down with me. Appreciate it. Appreciate it.

[00:26:07] - [Speaker 1]
Thank

[00:26:07] - [Speaker 2]
you. Yeah. Thanks for your time. Thank you very much.

[00:26:10] - [Speaker 0]
What I really enjoyed about our conversation today was just how clearly it showed that Formula one is ultimately a people and workflow problem just as much as it is an engineering challenge. Yes. The cars are extraordinary and the technology is incredible. And the AI capabilities are moving quickly. But underneath all of this, it's the same challenge facing almost every organization that you are working in right now.

[00:26:37] - [Speaker 0]
How do I reduce friction? How can I help my teams make decisions faster? How do I turn huge amounts of information into meaningful outcomes? So whether you are building race cars, enterprise software, or running a global business, the ability to move from concept to execution efficiently might become one of the one of the defining competitive advantages of this AI era. I'd always thought and I also thought there was something incredibly important in the discussion around organizational context because everyone will have access to similar intelligence, and the advantage comes from your workflows, your institutional knowledge, your history of decisions, and how effectively your teams collaborate around all of that context.

[00:27:23] - [Speaker 0]
In short, context is everything. And honestly, hearing that teams have already doubled throughput in some areas while reducing low value meetings by hundreds of hours each month, all show that this is no longer theoretical. These transformations are happening in real operational environments under intense pressure. This is one of the many reasons why I record this podcast every day to share these stories and hopefully inspire you to follow in the footsteps. But I'd love to hear your thoughts on today's interview.

[00:27:54] - [Speaker 0]
What lessons do you think other industries might be able to realistically learn from Formula One? And how prepared is your organization for a world where AI becomes deeply embedded into every workflow, engineering, and decision making process? Let me know your thoughts. Techtalksnetwork.com. But I see the checkered flag in sight, so it's it's time for me to get out of here.

[00:28:17] - [Speaker 0]
I will return same time, same place tomorrow. Hopefully, I'll speak with you then. Bye for now.