How to Build an AI Native Team with Mike Cannon-Brookes

**Nathaniel Whittemore** (0:00)
Today on The AI Daily Brief, a conversation with Atlassian's Mike Cannon-Brookes about why context matters, how AI moves outside of the chat window, and what separates the enterprise AI leaders.
The AI Daily Brief is a daily podcast and video about the most important news and discussions in AI.
All right, friends, welcome back to The AI Daily Brief. Today, we are doing a bonus operators conversation in partnership with Atlassian around their Atlassian Team26 event. Now, at this point, most of you are probably familiar with Atlassian, or at least their software tools like Jira, Confluence, Trello, Loom, and Rovo. Atlassian's products are used by more than 300,000 organizations. And for today's conversation, I'm joined by Atlassian's co-founder and CEO, Mike Cannon-Brookes. In this conversation, Mike speaks from both sides of the AI transformation. Atlassian is a company adopting AI internally, and Atlassian is a platform provider building the AI infrastructure and product experiences that other enterprises use. And what I wanted to do in this conversation is get Mike's perspective both as the leader of a company who is trying to adopt AI internally, as well as a builder of tools and platforms who are helping productize and bring AI to the rest of the world. We discuss why context is becoming a core layer of enterprise AI, how agents, MCPs, CLIs and headless tool use are changing the relationship between humans, software and digital teammates, the real factors constraining enterprise AI adoption, and why Mike thinks 2026 is the year AI starts moving beyond chat and into more natural product experiences. Now, since one of the big themes is the difference between teams using AI tools or on the other end of the spectrum actually deeply collaborating with AI, I put together a fun little companion quiz that you can find linked in the show notes, where you can answer a handful of questions and find out what kind of AI team you are. Are you a demo floor team who tries a lot but hasn't actually changed how you work? A team full of closet power users? Or an actual super team that treats agents as participants, not utilities? Like I said, there's a link to go do that in the show notes. But with all that out of the way, let's dive into my conversation with Mike Cannon-Brookes.
All right, Mike, welcome to The AI Daily Brief. How are you doing?

**Mike Cannon-Brookes** (2:17)
I'm doing good, thank you. How are you doing?

**Nathaniel Whittemore** (2:20)
Very, very well. I'm excited to have this conversation. I think one of the things that I find so fascinating, it's been this way for a while, but especially in 2026, is this is a year where AI capabilities have gone up, where the recognition of those capabilities have gone up, where a whole bunch of things have been thrown into a frenzy because of that.
And what's always fascinating to me about people who are building companies in that space is that you're kind of dealing with this on multiple levels. You're dealing with it on the level of what are the implications for our customers that are interacting with us, but you're also thinking about it from the standpoint of how do we run a company that does well and uses these tools and integrates these new processes? And I actually wanted to start on that second part because I think it's such rich territory and it's always fascinating to hear how companies are adapting to how they build, how they work, to these new tools. So what are some of the key changes that you at Atlassian have been trying to implement when it comes to using this new set of tools, using new agent capabilities, just thinking about changing workflows more broadly?

**Mike Cannon-Brookes** (3:29)
That's a very difficult question for all businesses around the world, right? We talked briefly before this, you know, model acceleration is happening.
When you're getting that intelligence to accelerate your business, it seems like when you apply one or the other, you don't get it natively, right? We have a lot of reasons why that is the case. I think one of them is the talent changes you require are quite high. The business process re-engineering you require is difficult, right? That has to be done to get the maximum output from that.
We take that on a lot of different ways. Look, we're looking at a lot of processes internally and where we can make them significantly more efficient or high quality or both. The quality of an output is a measure of efficiency, if you want to think about it that way. Secondly, we have a lot of sharing programs.
This stuff is moving so fast. We talk about AI joy at work. You want people to use AI tools and technologies. First, you have to make them available to your team, which is a non-trivial exercise given enterprises and customer data and all sorts of security issues. Availability of tools, we've seen that with DIA.