Teams | Collaboration | Customer Service | Project Management

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.

TLDR: Key updates: Rovo Search now provides AI-generated answers with citations, routes intent-aware queries in Jira, searches across 50+ connected tools, delivers richer visual results, and is embedded directly in Atlassian workflows. This blog summarizes improvements that have shipped over the past year to Rovo Search. Rovo Search keeps getting better over time! It is now approximately 60% faster than six months ago!

Your transformation strategy is only as strong as its alignment with team execution and financial investment. Now you can see portfolio health in real time, intervene before initiatives drift, and tie every request and dollar to the priorities that matter. The latest Strategy Collection innovations bring financial investment, delivery risk, and cross-team demand into one view. The best part? Every capability covered has shipped since our previous Innovation Spotlight.

Bring document knowledge you already have access to from Google Drive, SharePoint, and OneDrive into Rovo Search, Chat, and Agents so teams can move from questions to useful context faster. The project plan is in Google Drive. The financial model is in SharePoint. The work is tracked in Jira, and the decisions are documented in Confluence. Your team has the information it needs.

Connect any coding agent to your system of record to automate engineering loops AI coding agents have made individual developers faster. But faster individuals working in their local environments do not automatically create faster engineering organizations. The bottlenecks that slow software delivery are usually systemic: work stalls in human queues, triage waits for idle cycles, and routine handoffs depend on manual intervention.

AI is changing expectations for service. Employees want help that feels seamless. Customers want answers without long waits or repeated explanations. Operations teams want to get ahead of incidents and prevent disruptions rather than just respond. Those expectations rest on a critical dependency: context. AI is only useful when it can see the full picture – from people and knowledge to services, assets, and code – and all connections in between.