Dave Meyer of Atlassian, vice president and head of product for Jira, joins theCUBE Research to discuss agentic Jira and artificial intelligence-driven work orchestration at Atlassian Team '26. Meyer outlines Jira's agentic roadmap and long-term platform strategy, and they describe how agents move Jira from a recording surface to an orchestration surface to reduce manual work and enrich issues automatically.
The conversation covers AI for Jira and Jira for AI, proactive issue enrichment, meeting-transcript automation and how integrated context and teamwork graphs improve work fidelity and coordination across teams. Meyer argues that lasting differentiation arises from embedding organizational context and intelligence into workflows rather than relying solely on copilots, and they emphasize impacts on developer productivity and knowledge preservation.
Alison Kosik of theCUBE Research hosts the discussion and Christophe Bertrand of theCUBE Research provides analysis. This session delivers insights on product strategy, platform strategy, work orchestration and knowledge graph applications for software teams.
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Dave Meyer, Atlassian
This conversation focuses on artificial intelligence and its role in the software development lifecycle. Ming Wu of Atlassian, head of engineering for DevAI, joins theCUBE Research hosts Alison Kosik and Christophe Bertrand at Atlassian Team '26 to discuss how Atlassian builds AI-native capabilities. Wu outlines work on coding agents, reviewers and the teamwork graph and explains how graph-based connectors such as Rovo and context engineering help agents navigate enterprise content and repositories to support the full software lifecycle.
Key takeaways include that coding becomes increasingly commoditized while developers spend approximately 16% of their time writing code and 84% on context, reviews and alignment, according to Wu. They emphasize that solving shared context through context engineering and graph-based orchestration is essential to unlock productivity gains, enable AI-native engineers and foster more cross-disciplinary collaboration across product and engineering teams. The discussion highlights practical approaches to improve developer productivity across the SDLC and to scale intelligent coding agents and code review capabilities.
In this interview from Atlassian Team '26, Dave Meyer, vice president and head of product for Jira at Atlassian, joins theCUBE's Alison Kosik and Christophe Bertrand to discuss the transformation of Jira from a work-tracking tool into an AI-powered orchestration layer for enterprise software development. Meyer frames Jira's AI strategy around two reinforcing halves: embedding AI to eliminate "work about work," and positioning Jira as the connective layer for agent-driven software delivery. He highlights how proactive integrations — such as automatically synci...Read more
exploreKeep Exploring
How is the rise of AI affecting Jira’s product strategy, and how much is the Jira product/platform changing as a result?add
How can we break down small, low‑level tasks in software teams so they can be delegated to AI—for example, automating Jira work‑item updates from meeting recordings?add
How should a product organization build lasting differentiation beyond generic copilots, and how should that shape product strategy and platform integration to capture organizational context and drive greater developer productivity?add
How different will Jira feel to the average user one year from now?add
>> Welcome back to Atlassian Team '26. We're streaming live here in Anaheim. I'm Alison Kosik, alongside Christophe Bertrand. And Atlassian Team '26 is alive and well here on the exhibit floor.
Christophe Bertrand
>> Absolutely. Lots of activity and I'm very excited about this segment. We're going to be talking product and we're going to be talking about agentic Jira. So let's talk about that.
Alison Kosik
>> All right, let's dig into it. I want to bring in Dave Meyer. He's the vice president and head of product for Jira here at Atlassian. Welcome to theCUBE.
Dave Meyer
>> Thank you.
Alison Kosik
>> So talk me through what you do at Atlassian. And I'm curious how Jira actually works when AI agents are part of the team.
Dave Meyer
>> Great question. It's something we're thinking a lot about. So just to introduce myself, my name's Dave Meyer. I'm the head of product for Jira. So I lead the product management organization and look after the overall health of the Jira business. So I would say our agentic Jira journey started two to three years ago, but it's really accelerated over the last 12 months and even in the last six months. And that's a direct reflection of how we see our customers adopting AI. And I would be lying if I said the world has not realized, especially over the last six months, that the job of software development and technical teams has fundamentally changed in a way that we're never going to go back to the old ways of building software. On the other hand, the core problems that Atlassian solves and that Jira solves for technical teams also I don't think have fundamentally changed. Jira has existed for 20, 25 years, solving problems kind of at the intersection of the software development, the technical teams, and then the rest of the business. It's not an IDE tool. It's not a source code repository. The same things that you can do in Jira can be done whether you're an engineer or a product manager or a business analyst or a marketer or an event planner or an accountant. And that's a lot of what Jira has always solved for organizations is although there's problems left of code that inform and drive the outcomes that companies are trying to get out of their software development teams and how do we make those more efficient? And so in a world where the way software development is done is changing and being oriented around agents is certainly going to change the kind of product that we need to deliver to our customers. But at the end of the day, the problems we're solving for those customers are the same, which is how do we make it more efficient to go from we want to achieve this business outcome to working software on the other side.
Christophe Bertrand
>> Right. So it seems to me that there are two dimensions, two sides of the same coin, right? There is Jira for AI and there is AI for Jira. That's the reality of the world we live in. So let's start with AI for Jira. What is new is how much is the product, you're the head of product, how much is the product or the platform, I should say, changing at this point?
Dave Meyer
>> Yeah, that's actually a fantastic framing. We frame the Jira strategy internally the exact same way. Our vision for the future of Jira has two self-reinforcing halves. And the first half is the core job that whether it's Jira or any other issue tracker, project management, software, the job to be done for our customers is, all right, you want to plan and track all of the work that the knowledge workers in your organization are doing and that creates a lot of organizational value to know what everybody's working on and what depends on what, and be able to report on it and search it and discover insights from it. The fundamental problem is that individual humans don't actually like writing down all their tasks, keeping them all up to date. It's work about work and it's a tax. And on the flip side, companies generally aren't paying their employees to write down their tasks, they're paying them to do the outcome for the task. And so all players are incentivized here to offload that work to AI as much as possible. And so that's really the AI for Jira story is, okay, how can we take those key aspects of, all right, I need to create Jira issues, Jira work items to track all the different tasks and then move them through a workflow and keep them up to date. And ideally, the system itself is more valuable if those are enriched as much as possible. So like a simple title and description isn't nearly as good as you have a detailed rich description of the outcome you want to achieve, and the fields are all filled out and like the workflow actually matches the work. And it's really a matter of, okay, how can we take each one of ... So what we're thinking about is how do we break down all of those kind of small, low level tasks that people do to produce that system and then make it as easy as possible to delegate that to AI. So a great example that I'm super excited about is proactive updates to Jira work items from Loom meeting recordings. So I think a lot of software development teams have been in this position where you have a planning meeting and you say, "All right, we're going to get these 10 tasks done, and you're going to work on these ones and I'm going to work on these ones." And you all sit around a table and talk about, "Oh, we need to do this and this system needs to depend on that other system." And then somebody gets tasked ... Either everybody sits there and one person updates everything live in real time and everybody watches them type or nothing happens. Or somebody sits there after the meeting and does all the updating after the fact. And what if instead, you go back to your Jira board and there's just a message waiting for you saying, "Hey, we actually detected that your team, because we know who your team is, had a meeting and we recorded that meeting. Even if that meeting was in Zoom or Google Meet, we can record that meeting in Loom. And we took the transcript, matched it up to the Jira work items on your board that you talked about and here's all the updates, just one click to accept all of those updates." And I think that's going to be really powerful. And the example of the kind of efficient enrichment of the work data that organizations track in Jira.
Alison Kosik
>> This is all about orchestration. It's about context. These are kind of the themes that really run through it.
Dave Meyer
>> Well, so I think if we can ... That's where, like I said, there's two halves to the vision and if we can make the Jira, the more accurate, the higher fidelity, that record ... Ultimately, what are you trying to do? People are doing stuff in the organization and you want that record of the stuff that exists in Jira to reflect, as much as possible, the reality. And a lot of that context is captured in people's heads. And so the better job we can do at capturing that context and saying, "All right, you have ..." Instead of just saying, "Do the thing with the fix the security problem," it's like, "Okay, here's a detailed description of exactly what the security problem is and what we think the root causes are and the acceptance criteria and the customer feedback on it." And instead of that being a 30 minute, 60 minute task for somebody to enrich that Jira issue so that you can assign it to an agent and produce the right outcome, if that takes 30 to 60 minutes for somebody to assemble all that context manually, nobody's ever going to do it. If we can give you a button that says, "Improve this story and we can actually search our graph and search Confluence and search your Google Docs and search your Slack conversations and your customer support system," and actually bring all that context directly into the issue, then one, when you're actually going to orchestrate your agents, you're going to get higher quality output in fewer attempts worth the AI. And hopefully it's more efficient and economically efficient, token efficient as well because you don't have to go back and forth to collect all of that data in an LLM.
Christophe Bertrand
>> So let me follow up on this with a question that really is probably an important question for you as the head of product, which is is that how you're going to build lasting differentiation? Everybody's using a copilot these days, that cannot be where you build your moat. There has to be more, right? So how do you see that driving your strategy from a product perspective and a product strategy standpoint? Because to me, there is a lot going on. There's a lot of friction between all of the components potentially of the platform and so much opportunity at the same time. So yeah, how do you build that lasting differentiation in the market?
Dave Meyer
>> Yeah, that's a great question, something we're actively thinking about a lot. Very realistic that, as a developer, I can sit down with Claude Code today and use the Atlassian and MCP server to pull in all of my Jira data, all of my Confluence data. I'll say a couple of things. One, we can do a lot of this proactively in a more efficient and more autonomous way because we already have all of the systems connected together. So instead of, as a developer, I'm going to a coding agent and saying like, "All right, let me go find the system architecture diagram and find some example pull requests and point it to the right parts of the code base that have relevant changes and find the original product requirements document and construct all of this context in order to prompt the agent and get a quality response," it's like, "All right, well, if we can do that all for you on your behalf, then we actually saved the developer quite a bit of time."
We see in our data today that even as model capability kind of increases exponentially, the actual delivery velocity of engineering teams, the productivity impacts taper off eventually. And the fundamental problem there is that developers don't spend all their time coding. And so if you only ever solve the problem of how to make the code get generated faster, then you're only going to be able to solve like, at best, a 20 to 30% efficiency.
Christophe Bertrand
>> Okay. So how do we get to the other 70% then?
Dave Meyer
>> Yeah. And so I think a lot of what Atlassian can offer to an organization is solving those problems to the left and to the right of the coding agent itself. And so how do you go ... A lot of folks talking about AI software development kind of talk about this like workflow of, "Okay, first I have intent as a developer, and then I take that intent and pass it to the agent and the agent generates code, and then I review the output from the agent." Well, where does that intent come from?
Christophe Bertrand
>> That's right.
Dave Meyer
>> It's very easy to create a demo and it's like, "I got a cool idea. Here's a proof of concept, here's the demo." Or even I'm a startup and like we're starting from a greenfield space. I think in most mature organizations, that intent from a developer comes from a business outcome that you want to achieve. That business outcome is usually defined by a team of people, not by a single individual, and it's informed by the organizational context that you have. So one of the areas that we're most focused is how do we take all of that organizational context and that business outcome as the starting point, and then break that down into a set of technical tasks that can each then be assigned out to an agent by a developer and then reviewed in that loop. And then capture the context between the developer and the agent and bring it back into the organization's knowledge graph so that instead of it getting all lost between an individual developer and the agent on their local machine just back and forth in the terminal, it's actually preserved and informs the next feature that the developer .
Christophe Bertrand
>> So intelligence is the differentiation in the end, right? In many ways.
Dave Meyer
>> Yeah, exactly. And I think we're going to show examples of it the next couple of days of how we put the teamwork graph into action.
Alison Kosik
>> A year from now, how different will Jira feel to the average user?
Dave Meyer
>> I expect that this is going to be the most fundamentally transformative year that Jira has gone through in its entire 25 year history. We are incredibly focused on ... I think I look at this year as kind of a capstone for things that we've been putting in motion over the last two, three, four, five years in terms of rethinking our application, our user interface, and then building the AI foundation so that we can go really, really fast in terms of transforming the app from just a recording surface for work into a orchestration surface. And so you'll see agents front and center throughout the product, not because we're just trying to like smash agents anywhere we can into the system because we think it's the zeitgeist, but because we're reacting directly to not just how we see our customers working, but how Atlassian is working internally.
Christophe Bertrand
>> And, to me, that's very important what you're describing because there's always a risk in product at some point, as technology evolves to well become a legacy system. And you don't want that.
Dave Meyer
>> And I think it's really easy for some of the companies' products that consider themselves like systems or records to say like, "All right, well, I've got a defensible position as like a data moat. And it doesn't matter if anybody uses my user interface anymore because I've still got like this stranglehold on the core business data in this one particular dimension." And maybe one alternative history for Jira is that we accept that as our fate and carry on our merry way, but I'd like to think that Atlassian and Jira is capable of building something really compelling that solves new customer problems as well.
Christophe Bertrand
>> Excellent. What do you expect from this show? We have lots of people here, lots of customers, lots of partners, more importantly. What do you think will happen in this crucial year for Jira?
Dave Meyer
>> This is my favorite week of the year. This is my 16th-
Christophe Bertrand
>> Oh, wow....
Dave Meyer
>> Atlassian Team. I think only Mike Cannon-Brookes himself has attended more than I have.
Alison Kosik
>> That's impressive.
Dave Meyer
>> So first and foremost, as an Atlassian employee, it's like a fulfilling exercise. It's as a distributed company that works all over the world and a company that really tries to be efficient in how we serve our customers, it's an opportunity, I think, for a lot of our customers to talk to not just their customer success manager, their sales rep, but somebody from Atlassian R&D for maybe the first, certainly the first time this year, if not the first time in their experience as a customer. And then also it's an opportunity, I think, to ... I think Atlassian's fundamental differentiation is that we try to think holistically about the entire business. You see it across our teamwork collection, service collection, strategy collection, product collection. Each of those collections is meant to solve a problem for a business function. And the trade off to building a big horizontal business that's trying to serve all areas of the organization is that it becomes ... You're going very wide and what Team really forces us to do is tell a collective, coherent story about how all of these things come together to solve big problems for our customers. And so as painful as developing the keynotes that we put out across each of our solutions and the keynote that Mike will share tomorrow are from lots of late nights for a lot of folks from the Atlassian inside perspective, it's really about helping everyone understand that each of these pieces in the puzzle fits together in a really coherent way that helps us unlock really big outcomes for a lot of our customers. Bring customers up on stage to help validate that, it's super exciting and very fulfilling.
Alison Kosik
>> Dave, we've enjoyed this conversation tremendously.
Dave Meyer
>> Thank you.
Christophe Bertrand
>> Thank you.
Alison Kosik
>> Thanks so much for stopping by theCUBE.
Dave Meyer
>> Appreciate it.
Alison Kosik
>> And you're watching theCUBE, the leader in live technology coverage and enterprise tech analysis. Thanks for watching.
Alison Kosik
Christophe Bertrand