3237: The System of Work: Atlassian's Blueprint for Modern Collaboration

[00:00:03] What if the way we work could finally catch up with the way that we want to work? Well, at Team 25, Atlassian's flagship event, that vision isn't just aspirational, it's becoming operational. So in this special episode of Tech Talks Daily, I'm recording live at the event in Anaheim, Los Angeles. And today I'm going to be joined by the president of Atlassian.

[00:00:28] Her name is Anu, and together we're going to unpack what the future of collaborative work really looks like. Because together we're going to explore Atlassian's newly formalized system of work, which is essentially a unifying framework that is promising to move teams beyond productivity hacks and isolated workflows toward a more measurable and connected outcome.

[00:00:53] And from the power of the teamwork graph and the rise of AI assistants like Rovo, there is a lot to talk about. And of course we will also discuss the challenges of information overload, cross-functional silos, and how to reduce friction and work smarter. So whether you are leading digital transformation, modernizing how departments collaborate,

[00:01:14] or just trying to make better use of your tech stack, this conversation will offer a new lens in how AI and human creativity can complement each other, not compete. So how do you unlock performance across every team inside your organization? Let's find out now. So thank you for joining me here at Team 25. Can you tell everyone listening a little about who you are and what you do? Thanks for having me. I'm Anupar Adwaj. I'm the president of Atlassian.

[00:01:44] I build collaborative tools for software building teams. Atlassian's in the business of unleashing the potential of all teams. So that's pretty much what we think about day in and day out. How do we make teamwork work? Love it. And confession time. This is my first Atlassian event. So here I am at Team 25. I'm curious though, tell me, what does Team 25 mean to you when you got on the plane to get here? What does it all mean to you? Team 25 is really one of the events that I look forward to the most all through the year.

[00:02:14] It's our chance to meet with customers, partners, our beloved users that have been with us for so many years and have so many insights to share with us. It's also a great opportunity to really thank our partners and our community for their tremendous support all through the year. I've been at Atlassian for 10 years now, and this is my 10th team event.

[00:02:36] And it's always super fun to come and show our customers what we've been building for them and hear directly from them as to how they feel about the whole thing. Wow, 10 years. You must have seen so many big changes throughout your career. And I'm curious, what inspired Atlassian to formulize its system of work with Jira?

[00:02:55] And how does the philosophy differ from maybe the traditional productivity frameworks that you saw when you joined the company and how workflow management systems operate in the enterprise space? So much has changed. We've been through a global pandemic with the arrival of AI. What's changed here and why are you changing? So Atlassian serves 300,000 customers and through seeing millions of teams for the past 24 years, we have a strong set of intuitive hunches, intuitive opinions around how teamwork should work.

[00:03:25] And especially for tech driven companies where technology plays a big role in your business, we believe that there are strong practices that you can adopt to make your teamwork really shine and to really optimize your organization. So to do that, we help our teams, we help our customers build different kinds of blueprints for whatever works for their teams. This is what we call the system of work.

[00:03:50] Think about it like we're giving each of our customers a set of Lego blocks and showing them how to put this together to make something that makes sense for them. So it's unique and customized to them. But the building blocks are all in the form of collaborative products around Jira, Confluence, Loom, and the teamwork graph that really powers all this on a connected data platform such that companies can make use of their data themselves today. So a typical enterprise uses multiple SaaS apps.

[00:04:18] In fact, on average, they use around 150 plus SaaS apps today. And with the advent of AI, what becomes interesting is how do you put your data into use because your AI applications are going to be as good as the data you feed them. And a lot of companies are sitting on top of data where their data is locked in silos. So Atlassian is offering this connected data platform such that you can take control of your data and really apply them in realistic applications to see how can you become more productive? How can you increase your revenue?

[00:04:46] How can you decrease your costs? And how can you make your teams more productive and happy? I love that. I love the analogy you use there. And I think so many different businesses are looking for a more personalized, unique approach that's useful to them. And before you join me today, I was reading that 25 billion hours are lost annually due to ineffective collaboration. And I suspect we've all seen what that and felt what that feels like.

[00:05:11] But what are some of the unseen or underestimated barriers to teamwork that Atlassian's system of work is maybe helping to uncover and ultimately resolve too? Yeah. So the Atlassian system of work sets out to solve four fundamental problems. One is that lots of our customers tell us we've spent millions of dollars and a lot of man hours on creating a transformation strategy.

[00:05:36] But we don't really know whether it's working because it's really difficult, especially when your organization scales to align work with goals. So several times people set their company goals, but it's hard to track whether every person in your company is contributing work to those goals or not. So the system of work introduces goals at a platform level such that every person in the entire ecosystem is able to say, here is my goal and here is what I'm doing in order to hit that goal.

[00:06:03] And goals cascade. So you're able to say, okay, if the company's goal this year is to grow revenue by 15%, let's say, here is what Neil's doing in order to help further that goal. Here's what Anu's doing in order to help further that goal. And you can be in marketing and you can be in engineering, but both of us have a clear sense of what goals we're moving forward. So that's one. The second one is really, how can you plan and track work at scale, which is traditionally the problem we've solved with Jira.

[00:06:31] But with Confluence and Loom, we really help teams bring distributed work to the fore. So Atlassian is a distributed company ourselves. We are 13,000 people across 13 different countries and we are fully distributed. So we don't mandate people come into a physical office any days of the week. But orchestrating work and planning and tracking work across remote teams requires certain remote practices. That's what the system of work helps you implement. And third, it really helps unleash shared knowledge.

[00:07:01] Like I spoke about earlier, data today is captured in silos, but the system of work really helps you connect all of that on one unified connected data platform. And last but not the least, it makes AI part of your team. So what does that mean? Today, a lot of people think about AI as, oh, it's a productivity enhancer or it's a tool or something that can help with a specific job. But really, the way we think about AI at Atlassian is that you can have AI powered teammates.

[00:07:27] So teamwork of the future is basically going to look like human AI collaboration where you have an AI powered teammate that has a certain character. We're an Australian company, so we have a lot of AI powered teammates. We've built ourselves with Australian accents and Australian humor and AI powered teammates that understand specifically the personal context that you have. Neil likes to work on Slack and he talks to Christine a whole lot.

[00:07:55] But Neil typically works on podcasts and getting transcripts from recordings is important to Neil. So here are the tools he works with. And here's typically the knowledge that is useful for Neil. And something happened out there in the world that's going to be interesting to Neil based on a conversation he's had with Anu in the past. So AI powered teammates can really help you bring all of that context with character and personality and really enhance collaboration across a team. So that's one of the things we're trying to do with this.

[00:08:25] And I think that character and the personalization and the human aspect, that's what people crave more than anything, isn't it right now? And I must admit from the outside looking in the teamwork graph sounds like a major innovation in contextualizing work. So for anyone listening, hearing about this stuff for the first time, can you walk me through how it is helping bridge the gap between those business objectives and equally that technical execution across large organizations?

[00:08:51] Can you walk me through that, how it would work just to help people listening, no matter what industry then, how it would work? Yeah, definitely. So teamwork graph is one of the unique things about the Atlassian system of work, which requires a lot of technical investment to build. Why is that? What do we mean by the teamwork graph? So any particular person in their daily work interacts with multiple people and with multiple tools or applications.

[00:09:18] And then they also do multiple jobs because each of our jobs involves doing different tasks. And sometimes it varies by context, et cetera. Now, today, the way to relate the people you work with, with the work that you do and where you do that work, all that sits in your head. The teamwork graph really helps articulate that in a graph. For example, Neil's working on a podcast about AI with Atlassian and Neil's talking to Anu and Christine.

[00:09:48] So now the graph has the three of us in context. Now, Neil is typically interested in the following topics and AI and productivity and business teams. So the graph is able to capture your interests and my interests and who's interested in AI and organizational dynamics and applied strategic planning and analysis. Christine's interested in communications and media and PR and a bunch of other topics. So we're going to be talking about the three of us.

[00:10:18] But the three of us are speaking about one common topic, which is AI and Atlassian. And so we are today doing a recording using a camera and a mic, and it produces a video file, an audio file, a transcript, which is a text file. So those three objects are also shared between the three of us. And then where you do that work, you have a post record video editing tool, and then you're going to share that with Slack, with other people.

[00:10:45] And then you're probably going to create a confluence page or a Jira ticket with it. All of those objects get stored in the data graph. And now what has happened is the entire event, the entire conversation has been converted into a series of interesting interconnected nodes. So the next time Christine goes out and talks to somebody else about podcasts and best practice of podcasts, the graph is able to say, you know what? You did a podcast with Neil and it met with a great reception.

[00:11:13] Here's what happened in those cases. And it knows which objects, who Neil is, where was the transcript store and what was interesting about that for Christine. I go out and I talk about organizational dynamics and the graph is able to surface that information to me to say, you know, remember Neil asked you that question about business teams and technical teams. Why is this interesting across all industries? That's an interesting thing to talk about in this context. So then it surfaces your question, which is like one part of the object.

[00:11:42] And then Neil does another podcast with Mike Cannon Brooks and the graph is able to say you've had an interaction with Atlassian a few years ago with Anu. So here's what happened then. This is what she said. This is a potential question you can ask Mike and Brooks based on that conversation. So it really is able to predict and contextualize a lot of information and store that in a personalized fashion for all three of us. Doing that requires a lot of resources and a lot of interconnections.

[00:12:10] And for example, we have 10 billion data objects in the teamwork graph today. And what's fascinating about AI is it's able to surface unexpected connections. So it's unexpected moments of delight where when you're using it, you think, oh, my God, that was really cool. I hadn't thought about that. Neil really asked me this question in a different way 10 days ago. And now I'm meeting with a customer who's facing exactly that problem. So I'm able to cross reference that conversation. That's what's magical about Tim. Wow.

[00:12:40] And what I love about everything that you just said there is I think technology and hybrid working and working from home, a lot of people say that it removes serendipity. Those accidental moments where you bump in somebody at the water cooler from a completely different department. You share ideas and technology has been accused of removing that serendipity. But for what you just said, it actually encourages it and brings it back in almost a full circle. Exactly. Yeah, well said. That's very much what we rely on as a distributed company, too.

[00:13:06] It brings really those unexpected moments of delight, which are good substitutions for the water cooler conversation that you talked about. Love it. And another thing everyone's talking about here is Rovo. So again, for people hearing about it for the first time, what role do AI tools like Rovo play in elevating again that human potential within a system? And how are you, Lesi, in ensuring these tools become teammates, not just task bots?

[00:13:32] They're more like a teammate in a modern workforce where you're working and collaborating alongside the technology. Great question. So we think about Rovo very much from the perspective of human AI collaboration. Like I said, our goal is really to build an AI powered teammate that can be a companion to you in any given context.

[00:13:50] So if my day job is to be a developer, for example, the teammate I have could potentially be another developer or could be my cons person who's telling me here's the kind of things that you want to write in this blog or say in this interview. So the AI teammate could be performing any given role, just like you can hire into your team, a teammate that can perform any given role. But what's most important is how do you collaborate?

[00:14:19] How do the AI teammates and human teammates collaborate? And how do we help ensure that the AI teammates are taking those tasks that are rote and repetitive and boring for the humans to do? So we really unlock the creativity and potential of humans to do better. Right. And so what we do with robo agents is that we create we let people create their own teammates. So with their own character, with their own context.

[00:14:49] And for example, different companies have different roles. Some companies like to have the front end developers and designers collapse into a single role. So you can create a robo teammate that basically does both front end design and prototyping. You can also be in a company that has hyper specialization where there is a cons person for certain outlets. There's a cons person for written communication. There's a separate cons person for different formats like podcasts, etc.

[00:15:15] So you can hyper specialize your AI teammate and say this is the sources of knowledge that I want my AI teammate to be aware of. And these are the hyper specializations that I wanted to know, whether it's modality like text or video or audio. And so what you can do is you can train the teammates to be optimally useful for you as a person.

[00:15:38] And ultimately, in a team, we believe that every future team is going to have a power teammates as well as human teammates. So how do you make that collaboration easy? How do you not create a situation where there's the same problem with managing AI teammates as you have with human teammates? How do we handle the kind of orchestration and rollout of AI teammates? All of those are problems we think about with robots. Love it.

[00:16:03] And for any executives listening and they're looking to accelerate time to results, get back to ROI, tangible differences. Where do you recommend they begin with adopting the system of work and how can they avoid some of the common pitfalls when scaling it across departments? Because very often it's the culture change and adapting that causes the problems rather than the technology itself. So where should they begin? Yeah, it's a great question.

[00:16:26] I think the groundwork to prepare for any AI deployment is to make sure that you have a strong underlying data graph. Yeah. Because your AI applications are as good as the data that they work on. And this is why we stress so much on the teamwork graph. Our last thing philosophy is that of an open ecosystem. So we integrate with every tool out there. So we have 50 plus connectors today and we continually build more and more connectors.

[00:16:51] So any company using pretty much any set of applications and products to get their work done. The first thing they want to do is to roll out the teamwork graph along with the connectors. We've done the hard work so they don't have to. So we've built the connectors. Once you have the data graph, I think the second step, which is an important psychological step, is really to think about what are places that AI can augment your existing teams?

[00:17:15] Where can AI really give your team superpowers rather than try to take away the parts of the job that they enjoy? Right. So really our goal is to increase joy in teams, increase developer productivity, developer joy. That's a lot of what we think about in our dev tools.

[00:17:32] Well, and for business teams, what can we do that really helps you grow your top line, that really helps you control costs, focus on things that matter rather than some of the taxes that you have to pay along the way or some of the side jobs that you have to do? So there I think it's important for leaders to think about what are those key places where if I give my team superpowers, they can really shine. And deploying AI teammates in that context is really helpful.

[00:17:57] For instance, HarperCollins, one of our customers deployed AI and reduced manual work across their entire publishing organization by Forex. And we have another customer, Procore, who's really gone and created their own AI teammates with Robo. And they've created several of those AI teammates and have been able to save several hundreds of hours in terms of time spent on the road tasks and time saved so that the humans on the team can really focus on creative tasks.

[00:18:27] And we have so many of them. We have about a million users of Atlassian AI now. We released Robo six months ago. And now with Robo for everyone, that number is only going to go up. So I'm very excited to see what companies and our customers do with Robo and how they can unleash the potential of their own teams.

[00:18:47] And what I love about what you just said there is I think there's a lot of hype around AI, AI for the sake of AI, AI because it's cool, but you're using it to focus on what matters, helping teams focus on what matters too. And you've also unveiled the teamwork collection, which is a curated combination of Jira, Confluence and Loom, all supercharged with powerful agents that we've talked about today.

[00:19:11] Many people listening will hear about all these tools, get excited, but I'd love to bring it to life with maybe a real world example or use case. Just the kind of difference and measurable difference that it can bring to a business. Is there anything you can share around that on how it could work? Yes, definitely. So we've several of our 300,000 customers adopting our system of work and seeing productivity gains in terms of total hours saved or in terms of reduction of manual tasks.

[00:19:38] For example, HarperCollins saw a reduction of Forex in terms of their overall manual tasks. Doodle.com is an online e-commerce engine that adopted Jira product discovery and the system of work. And we're able to see a 93% reduction in the time spent in manual planning thanks to automatic curation of product ideas and insights through JPD.

[00:19:59] Thumbtack, another one of our Jira service management customers, were able to get to 15% ticket resolution automatically without human having to get involved. And we ourselves have been using a lot of our products before we roll it off to customers. And one of the latest products I'm really excited about is RoboDevAgent's deployed within the system of work, which has helped us cut down our PR cycle time by 45%. And that's been phenomenal. And I'm really excited to see what customers do with it.

[00:20:27] And you mentioned some big stats there, positive stats, but a negative stat that I read before coming in here is I think it's 25% of workers are spending their time looking for information. And I think anyone that's lost a SharePoint document, a file, an email, we all know what that feels like. So is there anything else you can share on how executives can accelerate that time to value through Atlassian system of work? Because we've all been part of that 25% at some point. Yeah, absolutely.

[00:20:57] That's one of the fundamental problems we set out to solve with RoboSearch. And with RoboSearch, as we benchmark against other products, one of the things we see is that the leading open source enterprise search app, which helps you find SharePoint documents or wherever you store your last piece of information, RoboSearch is about 60% faster than the closest open source enterprise search out there.

[00:21:21] Why does this matter? Because no matter what role you perform in knowledge work, as complexity grows, just the amount of information you process grows. And there's no way that any company can control their workforce and say, you will store all your information only in SharePoint and nothing else. Because today we're doing a podcast and the transcript is going to get stored somewhere else before it goes to SharePoint. And you would like to recall all those pieces. This is why the teamwork graph and the 50 plus connectors we've built in RoboSearch is super important.

[00:21:51] So one of the interesting things that we have seen with search is that it's helped a lot of our customers reduce the amount of time spent, not only searching for the object, but like I said, those delightful moments where you're thinking about, oh, it's at the tip of my tongue. I can remember what that was. And Robo is able to surface that automatically in context when a certain situation arises.

[00:22:16] So we've had RoboSearch adoption grow pretty drastically over the last six months across our customers. And you said earlier, this is your 10th team event here. And you've probably spent weeks, if not months, preparing for this very event. But when reflecting on every conversation, everything you've seen and heard here at Team 25, anything you're going to be reflecting on during that plane ride home? Anything you're going to be thinking about and taking away?

[00:22:43] Yeah, it's the unmistakable sense of energy and getting charged and really the infectious enthusiasm from all of our customers and users and partners and community partners from the event. For me, the biggest highlight always is just that instant uplift you get by being amongst your community, amongst your people and seeing how the work that we do day in and day out, all the hard work that we put in really matters.

[00:23:11] And you see the impact of it with your own eyes. That's really what energizes me about this. Love that. And I think that's a perfect moment to end on. But for anyone listening, just wanting to find out more information about anything we talked about, especially if they're unable to attend, where can they find out more information? We will be live streaming all of our talks on the Atlassian website. And we will be storing the content for future viewing as well. So Atlassian.com is the place to go.

[00:23:36] So a big thank you to Anu for sharing a behind the scenes look at Atlassian's vision for the future of teamwork. From the system of work to innovations like teamwork graph and AI-powered tools like Rovo, I think it's clear that Atlassian isn't just iterating on project management. It's actually reimagining what coordinated, effective, and human-first work looks like in an AI world.

[00:24:02] But what stood out to me most was the way that Atlassian is tackling the real blockers, inefficiencies, disconnected systems, and time spent searching for answers. And by simply embedding intelligence, transparency, and purpose into the flow of work itself, real measurable impact can successfully be implemented. So if this conversation, though, has sparked ideas for how your organisation could work smarter, not harder,

[00:24:31] please check out the Atlassian website to explore the teamwork collection, dive deeper into the system of work, or learn more about what has been unveiled here at Team 25 this week. And if you have streamed some of the keynotes online or attended the event, I'd love to hear your takeaways. What new ideas or new tools are you going to be taking back to your team? That is what is important to me. And that is the reason that I come to conferences, to learn from other people and how they're implementing technology.

[00:25:00] So please, email me, techblogwriteroutlook.com, LinkedIn, X, Instagram, just at Neil C. Hughes. But remember, when you return to the workplace, keep building, keep learning, and for this podcast, keep listening. I'll be back again tomorrow live from the show floor with another guest from the event. But more than anything, thank you for listening today, and I will speak with you all then. Bye for now. Bye.