Teams | Collaboration | Customer Service | Project Management

AI is accelerating implementation. The engineering teams who benefit most will connect context, orchestration, and accountability across the work around code. AI can now help engineers turn ideas into working code in minutes, but software delivery has always been more than just producing code.

AI is changing what entire organizations can accomplish. As it moves beyond individual productivity to orchestrating entire workflows, the value of software outgrows what seat count alone can capture. Atlassian customers are already realizing tangible business value from AI and automations, from reporting processes that run up to 40x faster with agents to saving 400 hours per month with automations.

AI makes it quick and easy to turn rough notes into a clean, easy-to-read document. Job well done, right? Think again. Atlassian’s Teamwork Lab suspected that this professional veneer—what we call “AI polish”—makes it harder to spot foundational flaws and give feedback, so we tested it. We wanted to know: Does AI polish make it harder to identify problems in early drafts? How can we solve for this blind spot?

For many knowledge workers, reaching for AI tools is now automatic. But the Teamwork Lab wanted to know if using AI actually changes how work feels and how much gets done. So Teamwork Lab tested: What happens when you take away AI for a day?

There’s a concept in software engineering called a software factory – a structured, repeatable pipeline that takes raw inputs (requirements, code, tests) and reliably produces high-quality outputs (working software). The magic isn’t just automation. It’s the combination of standard tools, curated configuration, and encoded expertise that makes every run predictable, consistent, and improvable over time.

Customer work rarely lives in one place. Salesforce holds the account, opportunity, contact, and case details. Jira shows the product work behind a customer commitment. Confluence holds the account plan, enablement materials, and meeting notes. Jira Service Management tracks support requests and escalations. Slack, Microsoft Teams, Google Drive, and other tools hold the conversations and assets that explain what’s really happening.

Without the right context, every day is day one for a coding agent. Agents may have the intelligence needed to write, refactor, and review code, but they still face the same challenge developers do: understanding how complex systems actually work. They need the right context to navigate cross-team dependencies, ownership boundaries, and downstream impacts that are hard to understand from any single workspace. Code only tells part of that story.

The past couple of years have changed how my team operates. We’ve experimented, adjusted quickly, and seen meaningful early results. That momentum got me excited about where marketing is headed in the age of AI, even as we continue learning along the way. Since then, we’ve witnessed what holds companies back from succeeding with AI.

@mention an agent on any page and it creates, edits, and comments alongside your team. Through the Atlassian Rovo MCP, the same agents work from Claude, Cursor, or your IDE. Agents have been working in Confluence since we launched custom agents in May 2024, and teams now run more than 5 million agent invocations a month. In February alone, Agents saved Atlassian customers more than 200,000 hours.

A reflection on building an enterprise product using AI agents, and what the data says about how we worked. The observation For the past few months we have been rapidly building an enterprise-wide, production-grade application that helps with employee compensation planning, management and communication. I compared what we built against a traditionally-built product to draw out insights about our new ways of working.