Playbook series: Fostering AI learning opportunities

Published via GitHub Executive Insights | Authored by Matt Nigh, Program Manager Director of AI for Everyone

In our recent playbook, GitHub’s internal playbook for building an AI-powered workforce, we emphasized that giving people access to AI tools isn’t enough. Once your organization has tools in place and clear policies established, the next barrier is scale: How do you help thousands of employees actually start using AI in their day-to-day work?

The blocker isn’t willingness, it’s know-how. Most employees aren't asking if they should use AI. They're asking how. Without the right learning opportunities, even the best tools go underused. You end up with uneven adoption, siloed skill growth, and missed opportunities to turn curiosity into fluency.

A rich ecosystem of learning and development opportunities is the engine that drives AI fluency at scale. It moves beyond one-off training sessions to create a sustainable environment for continuous learning. This post provides a blueprint for fostering learning opportunities that empower your employees to not just use AI, but to master it.

Creating a central hub for AI learning

The internet is flooded with AI tutorials, courses, and experts. The challenge isn't a lack of information; it's a lack of curation. An effective approach to internal L&D cuts through the noise and provides a source of training that is relevant, practical, and tailored to your company's specific context.

Your goal should be to create a centralized, internal hub that serves as the front door to all AI-related learning opportunities. Based on our experience, this hub should be built on five key pillars:

• Curate, don't create
• Structured learning paths
• A library of real-world use cases
• AI building blocks for technical users
• Live learning rhythms

Curate, don't create

Before building the pillars, adopt the right mindset. The pace of AI innovation means that any custom training content you create will be outdated in months, if not weeks. A "curate, don't create" philosophy is a more sustainable and effective approach. Your L&D team's primary role should be to act as expert curators focused on finding and vetting the best external resources.

But curation isn't a top-down job. Empower your employees to crowdsource and recommend the high-quality resources they discover. This creates a dynamic, self-updating library of content that is validated by the people doing the work, saving significant resources and ensuring everyone is learning from the most current and valuable information available.

Structured learning paths

Employees need a clear starting point and a map for their journey. Instead of a random collection of resources, provide structured learning paths as an opportunity for those who want a guided experience.

• Create foundational paths: Develop distinct "zero-to-one" learning journeys for both technical and non-technical employees. The goal is to build initial confidence and a baseline of practical skills. For your non-technical staff, this means learning the fundamentals of prompting to get useful summaries or brainstorm first drafts. For your technical teams, it's about introducing AI into their existing workflow, like using code completion to write boilerplate code or generating basic unit tests.
• Offer advanced tracks: For those ready to go deeper, provide specialized paths that cater to specific outcomes. For technical staff, this means moving from using AI to building with it, with content on leveraging models, agents, or turning code completions into full-fledged automated workflows. For business roles, it means mastering domain-specific prompting for functions like marketing, finance, or legal analysis. The best sources for this content are often the tool vendors themselves (e.g., Microsoft Learn, Google's AI training) and specialized courses on platforms like LinkedIn Learning.

A library of real-world use cases

Abstract concepts are hard to grasp; concrete examples are not. The single most effective way to accelerate adoption is to show employees exactly how their peers are using AI to solve real-world problems.

• Build a central catalog: Create a searchable library of use cases, categorized by role (e.g., Marketing, Sales, Engineering) and task (e.g., Writing, Planning, Data Analysis).
• Source from your advocates: Your AI Advocates are the best source for this content. Encourage and empower them to document and share their wins, creating a virtuous cycle of peer-to-peer learning.

AI building blocks for technical users

For your technical staff, learning goes beyond basic usage. The goal is to accelerate their ability to build custom, AI-powered solutions. An effective way to do this is by providing a library of "AI Building Blocks" which are reusable, pre-built components that handle common AI tasks.

These aren't just code snippets; they are well-documented, cloneable repositories, templated files, and reusable workflows that your engineers can use as a launchpad. By providing these foundational elements, you eliminate redundant work and allow your technical teams to focus on solving unique business problems, rather than reinventing the wheel on common AI patterns.

AI office hours

A self-service portal is essential, but it isn't enough. True learning often requires interactive, real-time problem-solving. Establishing a cadence of live learning events creates opportunities for this and signals an ongoing commitment to employee development.

One of the most effective and low-overhead formats is Office Hours. At GitHub, our "How Do I AI?" office hours are informal, drop-in sessions with no set agenda. The content is driven entirely by the attendees. An employee can bring a specific, real-world problem—"I'm trying to write a prompt for this marketing campaign," or "This AI feature isn't working as I expect"—and get immediate help from program leads and, more importantly, from their peers.

To run your own, simply schedule a recurring 30 or 60-minute meeting, invite the company, and have an expert or an AI Advocate ready to facilitate. The goal is not to present, but to listen and help unblock people. This creates a powerful, just-in-time learning environment that a static portal can't replicate.

Running your own office hours

If you want to start your own office hours, here is how we run ours at GitHub. We have a weekly one-hour meeting with a simple agenda:

1. Demos: Sharing demos from within our community
2. Q&A: An informal conversation where hubbers can bring any questions, issues, and learnings to the group

Meeting practices

• Demo first, Q&A second: Show 1-2 specific tools before opening up
• Set interaction rules: "Take yourself off mute" vs hand-raising
• Name regulars early: Pull known participants in by name
• Do: Name people → Ask for examples → Offer immediate help → Set next steps
• Don't: Vague "thoughts?" → Long answers → Fake expertise → Promise to "look into it"

Question flow

• Opening: "Open it up for questions, comments, AI issues"
• Keep moving: "Other thoughts, questions, shares?" (every 5-7 minutes)
• Pull people in: "Kyle, your take on X?" / "I see Anthony asks..."
• Get specific: "Quick question..." / "Send me the link" / "Can you screen share?"

Day one integration

Your company's culture around AI is set from the moment a new employee joins. To establish AI fluency as a core competency, it must be woven into the new-hire experience. Partner with your HR and onboarding teams to introduce a simple, self-service "AI at Our Company" module into the new-hire experience. This module should introduce them to your AI vision, point them to the approved tools and learning opportunities, and show them how to join the community. This sends a powerful signal that using AI is not an optional, add-on skill, but a fundamental part of how work gets done.

Learning is a community sport

A static portal is a good start, but true learning happens in conversation. Your L&D hub should be a launchpad that drives employees into your internal AI communities. Every learning path and use case should be connected to a relevant community channel where employees can ask questions, share their results, and learn from their peers.

By building a centralized and user-friendly L&D hub, you transform AI learning from a daunting, individual task into an integrated, continuous, and collaborative part of the employee experience. You give your people the tools, the map, and the community they need to confidently navigate the future of work.

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