At GitHub, we believe the future of work isn't just about building AI tools, it's about empowering people to use them. As we've navigated our own AI transformation, we've learned that the hardest part isn't the technology. It's the change management.
We created this repository to share our journey with you. These aren't abstract frameworks or untested theories—they're the real strategies, lessons, and sometimes hard-won insights from GitHub's AI for Everyone initiative. We're opening up our internal playbook because we believe the best way to accelerate AI adoption across the industry is to learn from each other.
Whether you're just starting your AI enablement journey or looking to scale what's already working, we hope our experiences can help you move faster and avoid the pitfalls we've encountered.
This repository contains the complete framework GitHub uses to drive AI adoption across our global workforce. We've broken it down into actionable components so you can take what's relevant for your organization.
The main playbook: Our end-to-end framework for building AI fluency at scale
These are the foundational components of our AI enablement model. Each pillar includes deep-dives into strategy, implementation details, and lessons learned:
• AI advocates: Building a grassroots network of internal champions
• Clear policies and guardrails: Empowering safe experimentation without slowing teams down
• Communities of practice: Creating spaces for peer-to-peer learning
• Dedicated program leadership: Why you need an owner and what they should focus on
• Executive support: How leadership sets the tone and creates accountability
• Learning and development: Building pathways to proficiency for every role
• Right-fit tooling: Selecting and deploying tools that actually get used
• Data-driven metrics: Measuring adoption, engagement, and business impact (see below)
For recommendations on how to measure adoption, engagement, and business impact of your own AI tooling see GitHub's Engineering System Success Playbook.
Too many companies invest millions in AI licenses only to watch adoption stall at 20%. The tools sit unused. The potential remains untapped. The investment delivers no return.
We've learned that AI adoption is fundamentally a people problem, not a technology problem. Success isn't about buying the right tools—it's about building the human infrastructure that turns hesitant employees into confident power users.
This playbook represents our systematic approach to solving that problem.
We're actively maintaining this repository and would love your input. Whether it's a question, a suggestion, or a lesson from your own AI enablement journey, we want to hear from you.
Pull requests are welcome, and we encourage you to share what's worked (or hasn't worked) at your organization.
Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.
This documentation is released under CC-BY-4.0.