Teams | Collaboration | Customer Service | Project Management

How to securely scale AI agents from individual to org-wide use without introducing unnecessary complexity.

AI makes it possible to produce work quickly, but it can also result in a lot of workslop—that is, work that looks polished, but does little to move the project forward. Sorting through workslop is time-consuming and undercuts the ROI on AI. It may look like productivity, but it just moves the actual work farther downstream. The Teamwork Lab realized that workslop isn’t an AI problem, or even a people problem.

The Atlassian Marketplace has surpassed $7B in lifetime sales, and while that alone is a milestone worth celebrating, the pace behind it may be even more exciting. The most recent $1 billion was transacted in just nine months, the fastest billion in Marketplace history. Nearly half of all lifetime Marketplace sales happened in the last three years. Those numbers reflect something bigger than just a storefront.

AI is making teams faster, but not organizations. The gap is a missing layer: governed work context that turns a change in strategy into a change in what happens next. AI is making teams faster but that doesn’t necessarily mean it is making organizations faster. That distinction matters. In a recent 451 Research survey, 88% of organizations using AI in workplace tools report productivity gains.

Maya runs IT for Beaconstone Financial, a fintech company with 2,000 employees. Her engineering teams work in Jira and GitHub. Sales lives in Salesforce. Most decisions happen in Slack threads, Google Docs, and SharePoint folders that nobody can find six months later. Leadership wants Rovo to answer questions across all of it. Her head of sales has asked for account briefs that pull in the latest Salesforce opportunity data and last week’s customer call.

Atlassian’s 2026 State of Developer Experience report reveals a disconnect between AI adoption, developer productivity and organisational performance. An engineering leader recently asked me how AI could help their team work more effectively, to keep up with a rapidly growing workload. It turned out the problem was upstream teams creating significantly more output with the help of AI.

In 2025, companies spent a collective $581 billion on AI. The results were underwhelming: according to a study by MIT, 95% of Enterprise AI pilots showed zero impact on organization’s bottom lines. In short, the vast majority of AI pilots are unsuccessful. The reflex is to blame the models. The evidence points somewhere easier to control: context. Every answer AI produces is assembled from a context packet—the material used to respond to the presented prompt.

This is a practical guide for anyone using AI to move an idea towards something real. It gives you five ways to keep the people, context, decisions, and quality connected as the work moves.

The Browser Company of New York, the startup behind the Dia browser and its earlier iteration, Arc, moves at a pace that seems impossible from the outside. It’s easy to assume its team of 65 is fast because it’s small and AI-native, but that’s only a part of it. The bigger factor is their unique way of collaborating that they deliberately designed to keep work from getting stuck.

Over the last two decades, millions of teams have run their work on Atlassian, writing code, shipping products, and supporting customers across billions of workflows. Our platform is also where they make the hard calls, prioritizing which features to build, deciding which customer problems to chase, and choosing where to invest their resources.

When we delivered Atlassian MCP a little under a year ago, the idea was simple: let the AI tools your teams already use – Claude, ChatGPT, Cursor – reach the work that lives in Atlassian. The response was immediate. Now, we’re seeing more than 15M calls a day as it’s become the first thing teams reach for when they want to connect AI to their work. But teams have made it clear they want more. More products, more context, and more of the whole job done.

Teams across the industry are sprinting to reinvent their software development processes with agentic AI. Yet, navigating the fast-changing landscape of tools and opinions can be daunting for even the most AI-forward organizations. This guide shares Atlassian’s approach and best practices, informed by our own internal AI SDLC transformation and collaborative learnings from top engineering organizations.