Thousands of companies across the globe use Atlassian’s collaboration tools—which include Jira, Confluence, Trello and Loom—to work together and manage projects. I talked to President Anu Bharadwaj about how Atlassian serves companies’ tech and AI needs, the culture at the 22-year-old company—which is still founder-led, and its growth prospects.
Atlassian President Anu Bharadwaj.
Atlassian
This interview has been edited for length, clarity and continuity. It was excerpted in the Forbes CEO newsletter.
How are things going at Atlassian?
Bharadwaj: Overall, I’d say it’s going great. At Atlassian, we’re a company of builders, so we are really building our way through the next phase of growth for ourselves. We’re right now at about a $4.5 billion run rate. We’ve been fortunate enough to get to this point over the last decade or so of being a public company. The way we think about the future at Atlassian is how can we continue to build products and services that can really unleash the potential of teams. We think a lot of teamwork, people working together in different contexts—whether you’re a team building a Mars rover, or a team building physical things [or] virtual things. We think a lot about collaboration. It’s an exciting time to be in this kind of business to be pursuing this mission, especially given all the technology that is now available to fulfill the mission.
What are you seeing in the way of AI demand from enterprises, and what are you doing to meet it?
AI demand for enterprises has gone through a bit of a crest and a trough over the last year and a half. Atlassian builds a lot of collaboration software—mostly things like project management tools, knowledge management tools like company intranets, wiki or tools like Trello, Jira. We announced over the last year a number of AI-driven offerings in our portfolio. One, Atlassian Intelligence, really helps users in the workflow of project management, of writing content, writing articles or sharing collaborative documents with their teams. We brought a lot of AI features in the middle of that workflow to help summarize, edit your content, figure out how to draw information from multiple sources and answer a question that a new employee might have. We built that into the platform.
In addition, we have an AI-centric offering that we call Rovo, which really helps teams build agents which can automate and take the next step and action in a smart and intelligent way in any use case that they might be operating in.
I’ve worked at Atlassian for 10 years. When we launched it 18 months ago, it was the most positive reception that we’ve ever received across our products. We have millions of users on an active basis. Enterprises, especially, were battling with the problem of: We have a lot of information and a lot of data across the company, but how do we really make use of this? How do we make this accessible and actionable to all of our employees? The two offerings across Atlassian Intelligence and Rovo helped answer that particular question for our enterprises.
We’ve already seen several thousands of customers adopt our AI products. As a technologist, I feel like this is amazing that enterprises are [not just] seeking these sorts of tools, but adopting them at speed. Because, as you know, large companies can be slow at adopting new technology, but I’ve been very positively surprised by how quickly companies are starting to adopt it.
The one thing that I would say is different over the past years is more and more enterprises and now asking specific questions about what are the use cases where this is going to make my team productive? And where, specifically, do you see this being additive? Where specifically do you see this being a complete automation replacement of certain workflows? And how many of these tools do I actually need?
I think there’s also been a bit of a trough of disillusionment. Companies have bought into large packages that seek to AI-ify everything they do.
You said you’ve seen AI enthusiasm peak and also go down into a trough in the last 18 months. What has brought it down, and where are things right now?
Initially when the technology wave broke out, there was a lot of promise around what can AI do. It’s hard to predict timelines. But when people talked about AI, we talked about the full spectrum of what’s possible, so a lot of customers saw the extent to which AI can be deployed. We started talking about AI doing code generation. I heard a lot of questions about: Do you think software developers are no longer going to be required? Are we going to replace all the engineers in a company with AI? Which is honestly not a question anybody can answer, and we should be suspicious if somebody gives a certain answer.
But the timeline over which that happens, if it happens, is very long. When we started out with AI, there was a lot of hype around all of that appearing tomorrow. What is definitely possible is augmenting existing people that do certain jobs. There are really a lot of automated AI workflows that can help support people. A lot of enterprises thought about what’s the extreme possible application of AI? Now they’re seeing what are applications that are realistic and possible?
I would say what is possible and realistic today and in the short term is still a phenomenal uplift. At Atlassian, we’re a 12,000 person company, and we’ve deployed our own virtual service agents powered by AI. Over half of the service requests that we needed to have our people answer are now handled by the AI agent. A customer who uses our virtual service agents has managed to reroute roughly 85% of the service requests they used to ask humans to handle to the agent first. So the productivity benefits are quite dramatic in specific use cases and specific arenas. I think enterprises are going through that calibration of saying, Okay, what is immediately actionable and where can I see evidence of this happening right now?
You are not only a company that creates these solutions, but you’re also an enterprise yourself. What kind of use of AI systems do you have internally at Atlassian? How did you develop them and how does that inform what you do as a company?
It’s one of our company priorities. We think about it in two lenses: What can we do to serve our customers and what can we do to use it ourselves as a public company? We’ve tried a few things. Some things have worked, some things have not worked.
Because we are a company that spends a lot on R&D, we have a 6,000-person-strong engineering workforce. There, code generation has been particularly helpful, and we’ve used multiple tools for code generation. We think about what does a software engineer really need help with on a day-to-day basis, and code gen has been particularly helpful for us in speeding up the coding part of what a software engineer does. But nearly 80% of [where] a software engineer or developer spends their time on is not coding. It’s coordination with the designer who’s building the visuals for the application, coordinating with the product manager who’s talking to customers, talking to the support person who’s logging a lot of these bugs, fixing those bugs, deploying to production. The approach we have taken as a company is to say, code generation is awesome, but that’s only step one, and that only solves 10% of the problem. How do we help deploy AI to all of the other surrounding areas such that a software engineer’s life gets better?
We’ve built several internal agents, like Rovo, which is an agent framework. We’ve built agents that can help translate user experiences from designers directly into deployable code. We’ve built agents that can help prepare deployment environments that software developers can automatically deploy into CI/CD workflows. Because we are also a software development provider, it helps us in two ways: using it ourselves and also making it available for customers.
A second category is around customer support or service requests. This can take the form of external customers calling up service centers or filing tickets, or even internal help desk customers. We are now a 12,000-person-strong company. As new hires come online, they tend to submit requests like, I need a new laptop in this particular geo, or I have this question about payroll. A lot of HR, IT, those sorts of service workflows, that is an area where we’ve seen dramatic productivity benefits.
Then there’s this third category around collaboration and knowledge workers, which is largely around the blank page problem: How do you start creating content given a few cues, and then how do you iterate on top of it? I want to change the tone of it. It should sound as though Anu said this; it shouldn’t sound like it’s some kind of corporate comms coming in based on attempts.
A lot of that category of use cases around communication, editing, summarizing, drawing content from multiple sources—both content creation and knowledge discovery—has been the third category where AI has been particularly helpful for us. The thing that we’ve discovered [is] the number of SaaS tools, the number of products that enterprises use has been exploding over the last few years. Companies used to consolidate on an ERP platform, but then the era of SaaS began. A typical enterprise now uses 150 plus of these tools. Information gets fragmented everywhere. When I come into a meeting, I think, [this company is] actually a Jira and Confluence customer. I wonder what they actually use of our products. How much do they use it? What do they use it for? How happy are they? What was the last support ticket that they filed? When was the last time I actually spoke with [them]? What would [they] likely be interested in?
With Rovo, we built a centralized search and a knowledge discovery engine, where I can ask the question: I’m going to meet [a company representative] now, so tell me what’s the most interesting set of things I need to know before entering this meeting? The customer agent works in the background to bring all of the Salesforce information behind [the company’s] entry with Jira and Confluence. Then the Confluence agent goes back and looks for meeting notes that I’ve had with [the representative] earlier. Then people can write custom agents that can then say, okay, here’s [a journalist’s] authored articles, so these are her likely interests, and bring it all together in one place.
How do you figure out which problems Atlassian should be solving next with AI, and how do you go forward doing it?
We are a company of product builders. Our philosophy in the world is: Create the things that you want to use yourself. A lot of our product heritage has been we build products for ourselves and then figure out [if] this is interesting for other people, and how can we make it more generalized and applicable in different industries and contexts?
Or we go off and acquire a product. Loom is a great example of a company we acquired last year that we used heavily inside Atlassian ourselves. We are a 12,000-person company distributed across 13 countries. [At] the beginning of the pandemic, we made the decision to go fully distributed. We said to employees, we will not ask you to come into an office for any number of days a week, but we will set some guidelines around how many working hours overlap you have with your team. When we compose a team of people on the West Coast and the East Coast, that’s okay. But if you take people on the East Coast, West Coast, Australia and Singapore, it becomes a problem because there’s not enough working hours overlap. A lot of the kind of tools that we use are really our own tools for collaboration and for teamwork. And one of the tools we heavily relied on was Loom, which really helped, creating video snippets that would help disseminate information such that people can then consume it at their own pace and will. We decided to buy the tool ourselves and integrate it with our product suite because it really helped with asynchronous communication and distributed teamwork.
We look at what we ourselves require as an enterprise company, but more importantly, pretty much everything we’ve built is informed by customers. Our business model is unique because our customer base is about 300,000 companies. We serve companies all the way from two-person mom-and-pop shops to a 300,000-person financial services or telecom company with the same product portfolio. We rely heavily on what customers are telling us, what are they asking us? What we should be thinking of building next.
Scott Farquhar, one of the founders of Atlassian, recently stepped down from the co-CEO spot. While this may not signify a huge transition for the company, since cofounder Mike Cannon-Brookes is assuming the sole CEO role, what does it mean symbolically for Atlassian?
I have been at Atlassian for 10 years, and the big reason for me staying this long has been Scott. Scott’s a personal friend and has been a great supporter and champion through the time that I have had at Atlassian. Personally for me, I feel bittersweet about it. I feel very happy for him that he is now going on to the next phase of life. He has the energy and the bandwidth to focus on some of the things that he has started, like Pledge1%. He has a number of well-defined philosophical philanthropy projects that he’s rallied the company around. I’m happy for him that he can focus on that.
Operationally day-to-day, it’s not that much of a change for us because he’s been very thoughtful about this transition. He has set up the requisite systems, the requisite leaders in place. I personally have been doing a lot more on the go-to-market, which Scott used to run.
Mike, our CEO, [and Farquhar] have been co-founders and co-CEOs for 20-plus years, which is very unusual in software. Mike staying on as the founder CEO means that even symbolically for Atlassian, it’s really not that much of a step change, because we continue to be founder-led. We continue to have the 20-year context continue.
One of the staggering things about Atlassian is we have a lot of tenured people in the company. We have a Slack channel called Old Timers, which seems to keep growing each day. It’s mostly people who have 10-plus years, and I recently joined. Because we have a lot of tenured people, the culture, the ethos of the company, beliefs, values, they all tend to be strong. We are lucky that we have that cohort of people where this continues to be a defining way of how we run the company.
In Atlassian’s most recent earnings report, some investors reacted negatively to slower growth prospects than they were hoping. Where do you see Atlassian’s growth going? What is your vision for the next six months?
It’s interesting because I’ve been here since we were [about] $100 million in revenue. We are about $4.5 billion dollars in revenue, and we’ve been a growth company throughout. We are committed to a 20% CAGR over multiple years. [A] 20% CAGR on a $4.5 billion base is substantial, and it’s quite rare for a company of our scale. I continue to be excited and grateful that we have a business where the opportunities are so big that we can confidently talk about 20% CAGR over multiple years.
Why do we think that that is the case? Because we operate in three fundamental markets where I think the opportunity is going to grow, because more and more people are poised to be creators. Thanks to gen AI and redefining of the software development lifecycle, more people can do what only technical people used to be able to do before. That’s a good thing for Atlassian because we serve the full cycle, not just the coding part of a developer’s life. That means more software gets created and more opportunity to grow in that business.
Our second business is the IT service market, which is fundamentally service request handling. This is a market that stands to greatly benefit from the AI wave. Even outside of the AI wave, this is a market that continues to grow at a very healthy clip. We just put out the numbers for each of our business lines for the first time back in April. Service management has been our fastest growing business, close to $500 million. We are priced super competitively, and we are just getting started. That’s really at the early phases of the market capture, and that’s several billion dollars that is open opportunity for us to go after.
The last market is for knowledge workers. We can democratize that with an open philosophy of saying companies can use any number of tools for knowledge management applications they require, but we want to be the backbone that connects all of those things in one single place through a connected data platform, and offers customers the choice to use multiple tools, but have a single knowledge backbone across their organization such that they can make the maximum use of their own data and deliver the most insights based on what their own employees are doing. That is definitely the largest market we are addressing, and has the most promise, and we are also the earliest in that market.
Across all these three markets, there is ample opportunity. Really, the big question for us is around how much can we optimise our own execution and how much more efficient can we get at realising and translating that strategy and that vision into reality.
