Andrew Boyagi of Atlassian, customer chief technology officer CTO, joins theCUBE Research hosts Alison Kosik and principal analyst Christophe Bertrand to discuss developer experience DevEx and enterprise productivity at Atlassian Team '26. Boyagi explains four productivity flows—purpose, workflow, knowledge and intelligence—and describes how tools such as the Teamwork Graph, Rovo and Confluence, together with behavioral change and coaching, drive faster iteration and alignment across teams.
Boyagi highlights the centrality of purpose flow for aligning priorities and enabling outcomes driven by artificial intelligence AI. They recommend documenting decisions in Confluence to surface knowledge and reduce low-value questions. They and Bertrand observe that DevEx principles scale beyond engineering to accelerate decisions and improve quality. The Williams Formula One use case demonstrates speed, shared intelligence and human AI collaboration as practical levers for enterprise productivity.
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Andrew Boyagi, Atlassian
Andrew Boyagi of Atlassian, customer chief technology officer CTO, joins theCUBE Research hosts Alison Kosik and principal analyst Christophe Bertrand to discuss developer experience DevEx and enterprise productivity at Atlassian Team '26. Boyagi explains four productivity flows—purpose, workflow, knowledge and intelligence—and describes how tools such as the Teamwork Graph, Rovo and Confluence, together with behavioral change and coaching, drive faster iteration and alignment across teams.
Boyagi highlights the centrality of purpose flow for aligning priorities and enabling outcomes driven by artificial intelligence AI. They recommend documenting decisions in Confluence to surface knowledge and reduce low-value questions. They and Bertrand observe that DevEx principles scale beyond engineering to accelerate decisions and improve quality. The Williams Formula One use case demonstrates speed, shared intelligence and human AI collaboration as practical levers for enterprise productivity.
play_circle_outlineBeyond Tools: DevEx and Four Enterprise Productivity Flows — Aligning Purpose, Workflow, Knowledge, Intelligence with Coaching
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play_circle_outlineDevelopers as the Productivity Blueprint: Eliminating Workflow Friction with DevEx and DevOps to Boost Speed, Quality, and Satisfaction
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play_circle_outlineKnowledge flow crucial: document decisions in Confluence for searchable, trusted information
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play_circle_outlineIntelligence flow: shared knowledge enables higher-value interactions and self-service
In this interview from Atlassian Team 2026, Andrew Boyagi, customer chief technology officer of Atlassian, joins theCUBE Research's Christophe Bertrand and Alison Kosik to discuss how developer experience is becoming the blueprint for enterprise-wide productivity in the AI era. Boyagi outlines four flows of enterprise productivity — purpose, workflow, knowledge and intelligence — explaining why purpose flow is the critical foundation that ensures teams are aligned on strategy before AI accelerates their execution. Drawing on his transformation work with Atlas...Read more
exploreKeep Exploring
What is DevEx, what is "purpose flow" among the four flows of enterprise productivity, and how does this system of work differ from merely using a set of integrated tools?add
Why focus on developer experience, and why target developers specifically?add
How can organizations make AI effective when important knowledge is locked in emails, spreadsheets, and people’s heads?add
How can an organization enable intelligence to flow (encourage shared knowledge and self‑service information) so that shared intelligence becomes an advantage and reduces low‑value interactions?add
>> Welcome back to Atlassian Team '26. We're streaming live here in Anaheim. I'm Alison Kosik alongside Christophe Bertrand, and we're about to delve into a use case. It's always good to bring customer examples forward.
Christophe Bertrand
>> Absolutely. And we're going to hear about DevEx. So let's get started.
Alison Kosik
>> Let's get started. I want to bring in our guest, Andrew Boyagi. He's customer CTO with Atlassian. Welcome to theCUBE.
Andrew Boyagi
>> Thanks for having me.
Alison Kosik
>> So talk us through what your role is and maybe one of these examples of who you work with, who you want to highlight today.
Andrew Boyagi
>> Yeah. So I'm Andrew Boyagi. I'm the customer CTO at Atlassian. I work with Atlassian Williams Formula One. Late last year, I relocated from Sydney to the UK to work closely with Atlassian Williams Formula One and lead their transformation.
Christophe Bertrand
>> So let's talk about DevEx. What is DevEx? And tell us about purpose flow, which I think is too many one of the most interesting phases of four phases that you've highlighted in a couple of articles that were on CIO.com, I believe.
Andrew Boyagi
>> Yeah. So there are four flows of enterprise productivity, purpose flow, workflow, knowledge flow, and intelligence flow. Purpose flow is around one of the most important flows, which is how does information about priorities, about strategy, the context behind those things make its way from the boardroom to teams across the organization. It's really important because it's how teams can effectively prioritize and self-prioritize. It's about how teams know that they're working on the right things and the things that they're working on are important. And it's a key enabler for AI, because without strong purpose flow, teams could be moving really fast, but they could be moving in the wrong direction or working on something that's not important.
Alison Kosik
>> How is this system of work different from just having a set of tools that just kind of integrate with each other?
Andrew Boyagi
>> That's a great question because tools are just tools. You can implement really great tools, but they don't change much unless the humans using them also change. So we take approach, for example, with the transformation that we're running at Williams, yes, we've implemented tools. We've got really strong adoption with no top-down push across all the products, but we're also helping them with things like executive coaching, with flows practice workshops where we teach teams modern practices for collaboration across the organization. Product training, of course. So human changing their behavior in combination with good tools gets you the outcome.
Christophe Bertrand
>> So let's talk about Williams Formula One. Of course, it's sort of the dream customer or the dream team in many ways. Well, I think people may not understand actually when it comes to Formula One, it's not just a car and a driver. We still have a driver driving thankfully, it's no AI, but everything is data-driven. Everything is improved through data. And I imagine there's a significant amount of change that happens on a daily basis to keep optimizing that performance. Tell us more about that. Tell us about how the technology kicks in, and frankly, maybe what you see it doing in the next few years.
Andrew Boyagi
>> Yeah. So Formula One, I like to think of it as an enterprise performance scenario. The difference is that it's going at 300 kilometers an hour and millions of people see the outcome of your teamwork every weekend. So they have the same challenges and the same benefits as most organizations. And certainly what we see is the four flows that we spoke about earlier, they're really helping Williams to move forward as we saw last year. So yes, like you say, every weekend, we need to make progress, which requires rapid iteration. Continuous improvement is a strong flavor within a Formula One team. If you take the car that was the slowest at the end of last year and you put it in the first race of last year, it would've won that race by a long way, which goes to show you how quickly and how rapidly all Formula One teams are evolving and improving every week.
Alison Kosik
>> In F1, everything is connected from the car to the pit to the pit crew to the race strategy. Where do you see that level of coordination maybe missing in enterprises?
Andrew Boyagi
>> Yeah, I mean, definitely there's a rapid aspect of it. So every week, they make a lot of learnings from the car on track. We have the wind tunnel, we have simulators, we have all of these different data points which need to come together to get a positive outcome every week. Actually, I think it's very similar to most organizations. You have all of these different teams who are all working towards the same outcome. It's really important that they're connected. With Williams, we're doing that through the Teamwork Graph. So they're using our Teamwork collection, all of our products. And just by doing their work every day, it's feeding the Teamwork Graph with more and more organizational context, which then gets surfaced in different ways. So using Rovo, one of our products, they can ask a question and get all of that context around the car parts, the incidents that have happened, what races are coming up, who's working on what. They get all of that instantly, which helps teams become more productive really quickly.
Christophe Bertrand
>> So I have a question that's really about, okay, you mentioned teams and you mentioned individuals. Obviously AI and agentic AI is a reality now. So should we consider AI as a teammate, as another part of the team, or is it just an extension of the individual? Or is it potentially a foe, an enemy, someone who's going to take my job at some point? What are your thoughts on that?
Andrew Boyagi
>> Firstly, on taking the job, what we've seen with all advances in technology is they always create more jobs and we're seeing that. So we'll definitely see a change in people's roles and it's already been changing over the last few years and it's all positive themes, but AI is definitely a teammate when used correctly. So when I think about AI and why organizations should adopt it, it's really so that we can elevate humans to be working on value add and more important things and then giving AI all the repetitive and all the grudge work that really humans don't want to do.
Christophe Bertrand
>> Let me just follow up on that. So is it changing the profile of the individuals on the team now that they are in a sense more capable? Should they be thinking differently? Should it be trained differently? Should they interact differently with other teams? Because we're talking about managing now all sorts of agents and doing more with less in many ways.
Andrew Boyagi
>> Yeah. We're definitely in a period of learning. So I don't think anybody has nailed yet this is the way AI can help and this is the way agents can help. We're all experimenting and learning together as an industry, as many industries. And so I do think things need to change, but it's more about giving teams space to work with agents, to work with AI, to identify how it can help them, not just at the individual level, but at the team level.
Alison Kosik
>> Talking about developer experience, and you've said developer experience is a blueprint for enterprise productivity. Why developers specifically?
Andrew Boyagi
>> Developers, and this is probably not known to a lot of people, but in most organizations, developers are the most productive group of people in any organization. And to understand that, you need to go through the history of little bit of developers, their job primarily in the past was to write code. That was all they had to do. If you look over the last 20 years, now they write code, they test it, they run applications, they need to know about cloud, about AI, about infrastructure. They manage all of these things that were previously different people's roles. And so to be able to do multiple people's roles in one role means that over time, you've become more productive and more capable of doing different things. And the way we've done that is through agile ways of working, through developer experience where we focus on removing friction. We focus on not just talking about what work has to happen, but how the work happens. And so what we've seen and what we're doing with Williams is applying those same principles to the entire organization. So if you think about even a marketing team, they're actually very similar to developers in a lot of ways. They have a tech stack called MarTech, which is very similar to a CRCD pipeline for a developer. So they have the same challenges and applying those same principles gets you a very similar outcome in most cases.
Alison Kosik
>> So I'm assuming that continuity and speed are the positive ramifications of this.
Andrew Boyagi
>> The speed, but it's also happier people. So if you think about your own work, you want to get from idea to impact with as little friction as possible. And DevEx and DevOps, that's what they do. They remove friction from people's end-to-end workflow, help you to get through the work faster, which is a byproduct, but also at a higher quality point.
Alison Kosik
>> Which I'm assuming is super productive for F1 because things are happening in seconds, milliseconds.
Andrew Boyagi
>> Yeah. I mean, if you look at a race, you need to be able to make a decision really quickly. So if something goes wrong on the track, you need almost instant knowledge on what are the faults what we've had, what parts are we using, what changes have we made? And then scenario planning, do we bring the car in? Do we leave it out? What's the risk that we're taking by doing either one of those things? And so fast feedback loops and a lot of testing and iteration helps with that.
Christophe Bertrand
>> Right. So what's interesting here is that it must be very challenging when you think about all of this, right? With the cognitive load that people have to deal with, with the fact that knowledge itself is probably in many, many places, people's heads, emails, texts, you name it. How do you reconcile that? AI can just fix all of that, right?
Andrew Boyagi
>> Well, AI can't help in those scenarios. So when information is locked in emails, in spreadsheets, people's heads, AI is not going to help you there. It'll give you a generic answer without any of the context. The journey that we've been on with Atlassian Williams Formula One is we've been coaching them on documenting their knowledge, documenting decisions in a single knowledge repository, which is Confluence. And what we've seen as a result of that is that information is now being surfaced. It's not something that a lot of companies, they go to a lot of links to protect their information, as they should, but they don't go through the same amount of links to exposing that information internally. So if you think about knowledge and knowledge flow, it's a foundation of productivity. It means you can find what you need when you need it without asking someone and you trust the information that you find. So that's something that we've had a strong focus on with the Williams team.
Christophe Bertrand
>> So you have to have this unity of purpose, so at least alignment of purpose, that makes perfect sense, and of course, alignment of knowledge. Let's talk about now, you mentioned the developer's role having fundamentally changed, that job description is changing as we go, but isn't everybody becoming a developer at this rate?
Andrew Boyagi
>> Loaded question. I'm going to say no. There's a lot of different opinions out there. I'll share with you mine. The job was never about coding.
Christophe Bertrand
>> Right.
Andrew Boyagi
>> Okay? So we ran a survey last year. I can't remember the exact stat, but it was something like developers are spending 20% of their time coding, just one day a week. And let's say, it's not there yet, but let's say that AI can do all the coding. Yes. So what were developers doing the other four days a week? Can AI replace that as well? And so then if the job's not about coding, what's it about? It's about deeply understanding a problem, having a logical mind and applying technology to solving those problems. AI can still help with those things, but that's the skill. Coding is just the way that you implement that.
Alison Kosik
>> So how do you deal with legacy systems that aren't necessarily built for this kind of coordination that we're talking about?
Andrew Boyagi
>> Again, Williams is a great example. They've got a long history. Same as every company, they've got some legacy systems there. And what we've done, for example, with them is parts. Parts is core and fundamental to their business. Previously, it was stored in manufacturing systems. All that data was stored in manufacturing systems. We've now integrated those systems with our assets solution so that they're all in one place. Teams are talking the same language. That knowledge is now serviceable through search, through AI, through different contexts, and that information is now available across the organization. So yes, it's a problem when things are in legacy systems and they can't be accessed, that has to be solved. Otherwise, you can't use AI. It's very hard to be productive across an organization without it.
Christophe Bertrand
>> So you mentioned the fact that obviously in time you want to reduce the low value type of functions across the organization. Let's talk about intelligence flow, because now we're sort of raising to that next level of nirvana, right?
Andrew Boyagi
>> Yeah.
Christophe Bertrand
>> Intelligence. Can you really make that flow? Can you really, truly make it an advantage? Because it used to be that having knowledge and intelligence was something you want to keep for yourselves to be the smart guy in the room and maybe get a nudge over the other guys or look good in front of your boss. It seems that we're totally changing that logic in many ways.
Andrew Boyagi
>> In some ways, it depends how you think about things. Personally, for me and the way I think about it is, if I keep that knowledge for myself, it's harder for me to do my role because I get a lot of questions about what is this, how does it work, why? It's a lot of low value interactions for me. When I document that, if I do get those questions, I send them a link and say, "Here is everything you need, or ask Rovo or whatever you're using." And that helps me to be more productive because when that person does come to me, it becomes a high value interaction because they already have the base knowledge and now we're going to work together on solving a problem.
Christophe Bertrand
>> So would it be fair to say that this sort of shared intelligence is power versus just intelligence is power? That's really where you want to go?
Andrew Boyagi
>> Absolutely it is. I mean, who wants to answer questions all day about things that you've already written down? So I mean, that's fundamental to not just intelligence flow, but when you're looking for a cultural self-service information and information on demand, to me, that's a cultural aspect that most organizations should be looking to implement.
Alison Kosik
>> What do you think is one of the biggest misconceptions that executives have right now about productivity and AI?
Andrew Boyagi
>> Yeah. There's a new trend about tokenmaxxing. I don't know if you've heard this.
Alison Kosik
>> Tell me about that.
Andrew Boyagi
>> Essentially, people are measuring how much tokens each one of their staff are using. I'll start with a positive. We're finding worse and worse ways to measure productivity, which is a good thing because now we know not what to do. Productivity actually cannot be measured. So academics for decades have been trying to find a metric that they can use to measure productivity and it doesn't exist. So let's assume that-
Alison Kosik
>> Don't tell that to the government because they certainly do their best. The US Census Bureau tries to measure productivity, but I digress. Go ahead. Sorry.
Andrew Boyagi
>> I'm talking about knowledge work productivity.
Alison Kosik
>> .
Andrew Boyagi
>> So let's assume all the academics are right, and through all their research, they haven't found something. So really, what do you want to get out of AI? What does it mean to be productive? And to me, it's like I said, we want to measure the value that people are delivering. When an enterprise talks about productivity, the traditional definition is output over time. Output is not helpful. It equates to lines of code, to tokens that you've used. That's output. What you want is value delivered faster, and you want to measure that value chain, not just output.
Christophe Bertrand
>> And in closing, would it be fair to say that really it's about outcomes? I mean, in your case, I would submit to, yes, you can measure productivity. It's going to be that extra half a second winning the race when before you didn't. Maybe that's the real value or measure of productivity.
Andrew Boyagi
>> Yeah. I mean, there's so many different ways that you can measure it. There are obviously outcomes at the organizational level, but it could be progress against your goals, against the company priorities. It's not always the obvious thing. There's many things that companies do behind the scenes that deliver value.
Alison Kosik
>> Okay. What's one lesson from Formula One that every enterprise should take seriously?
Andrew Boyagi
>> It's about speed. You have to make decisions fast. You have to enable your teams to make decisions fast. And the way to do that is through having strong purpose, excellent workflow, knowledge on demand, and leveraging AI.
Alison Kosik
>> Wonderful.
Christophe Bertrand
>> Well, thank you so much.
Alison Kosik
>> We really enjoyed it. Thanks for your time.
Andrew Boyagi
>> Thank you.
Alison Kosik
>> And you're watching theCUBE, the leader in live technology coverage and enterprise tech analysis. We'll be right back.
Alison Kosik
Christophe Bertrand