GitHub is the world’s leading software development platform with millions of developers collaborating on code every day. Alexandra Yanes, Senior Product Manager for People Systems, and Andrew Bauer, Group Product Manager for Internal Tools, were tasked with ensuring their workforce could effectively adopt and leverage AI tools — not just for coding, but across all business functions.
At Canvas 25, Alexandra and Andrew shared how GitHub’s product team transformed AI adoption across their organization — moving from unstructured experimentation to strategic implementation that delivered measurable results for thousands of employees.
Challenge
GitHub understood they needed to deliver a solution with real user value. While engineers were already using GitHub Copilot, the broader organization struggled with different employee attitudes toward AI: from power users who were inclined to work without guardrails, to those who were confused about where to start, and team members who worried about job security and data privacy. Indeed, Miro research shows that these personas are familiar to many enterprises.
Without a structured approach, GitHub risked either deploying AI solutions disconnected from actual user needs or failing to articulate value clearly enough to drive adoption. As many organizations find, there is the risk of “leading with AI instead of solving problems” — putting technology before the people who need to use it, as Andrew explained in their session. They knew they didn’t want to go down this path.
Solution
GitHub’s internal product team launched their “AI for Everyone” program, building a framework that empowered employees to identify, evaluate, and implement AI solutions for real business problems. Critical to the program’s success was their intentional decision to equip teams with the right collaboration tools — including Miro — that would accelerate their path from ideation to implementation.
As part of their continuous product development lifecycle, GitHub integrated Miro alongside their own tools like GitHub Spark and GitHub Copilot to:
- Synthesize workshop insights into actionable strategies, using Miro AI to automatically theme feedback from cross-functional sessions. Teams can now complete a task that previously required hours of manual work in a matter of minutes.
- Accelerate idea-to-execution by taking workshop outputs from Miro and converting them into GitHub issues with proper acceptance criteria. Teams can now move from ideation to backlog in under a day instead of weeks.
- Create a seamless workflow where Miro serves as the collaborative space for discovery and definition, integrating with GitHub for delivery.
The visual canvas became the bridge between abstract concepts and concrete development work, enabling teams to align on solutions before writing a single line of code.
Results
GitHub’s “AI for Everyone” program transformed AI adoption from scattered experimentation into strategic implementation. By equipping teams with the right collaboration tools — including Miro for rapid synthesis and team alignment — they created a framework that delivered measurable results, including:
- 90% of employees now engage with AI tools like Copilot for their tasks, with 77% returning consistently
- 20% monthly increase in active users beyond just engineering teams
- 140% growth in AI tool usage events — from 423,000 to over 1 million monthly interactions
The ability to move quickly proved critical to the program’s success — the faster teams saw valuable output from AI tools, the more readily they adopted them. For example, by using Miro AI to automatically cluster workshop feedback, teams could put the hours they saved toward immediately converting insights into GitHub backlogs for execution.
“This is something that we typically do after a workshop, either a summary or turn something into a PDR, it could take a few hours actually to create and kind of synthesize all of these different things.”
Alexandra Yanes, Senior Product Manager for People Systems
At GitHub, the value of AI adoption was no longer abstract hype. With the right tool set, teams achieved clear results and built confidence that they were solving the right problems.
The bigger picture
GitHub’s success demonstrates that AI adoption isn’t just about deploying tools — it’s about cultural transformation. Their framework of evaluation criteria, proof-of-concept trials, and transparent communication became the foundation for sustainable AI implementation across the organization. Empowering the team with tools like Miro that are built for collaboration quickly delivered results and made the difference.
In short, instead of just building with AI, GitHub built the right AI solutions for their people.
