Teams | Collaboration | Customer Service | Project Management

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.

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.

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.