Published via [GitHub Executive Insights](https://github.com/solutions/executive-insights "Executive insights, curated just for you") | Authored by [Bronte Van der Hoorn](https://github.com/brontevdh)

At GitHub, accessibility isn&rsquo;t just about compliance. It&rsquo;s about ensuring every developer can do their best work. When the GitHub Licensing team needed to address critical accessibility improvements while maintaining their roadmap commitments, they turned to [GitHub Copilot coding agent](https://github.blog/news-insights/product-news/github-copilot-meet-the-new-coding-agent/) as an accessibility champion.

## Prioritizing accessibility while maintaining momentum

The Licensing team faced a common engineering dilemma: important accessibility improvements from two audits needed addressing before a deadline. The work was substantial enough to potentially impact roadmap delivery, yet essential for creating an inclusive product experience.

Instead of the traditional approach of reallocating engineers, they asked: "What would this look like if agents were already part of the team?"

## From concept to concrete results

The team delegated the outstanding accessibility issues to the Copilot coding agent in batches, with an engineer serving as reviewer and safety net across several short working sessions. The results demonstrated significant potential:

* 70% of issues: Smooth landings where the agent implemented correct solutions with minimal guidance  
* 10% of issues: Needed human co-piloting to reach an acceptable solution  
* 20% of issues: Required the engineer to take a more hands-on approach

Most importantly, the team resolved all accessibility issues without disrupting their roadmap commitments.

"Despite a learning curve and the assistance we needed to provide the agents, we achieved our aim of delivering both our roadmap and our accessibility goals."   
*Josh Schultz, GitHub Engineering Team Leader*

## Where agents excel in accessibility work

The experiment revealed clear strengths where AI agents can provide the most value to teams:

* *Standardized improvements:* Adding server names to links, fixing aria-label mismatches, and making heading structure changes  
* *Bounded modifications:* Small, well-defined changes with clear success criteria  
* *Documentation and labeling:* Ensuring proper annotations for assistive technologies

&ldquo;This isn't about deprioritising accessibility. For us, it's about watching agents actually shoulder important work that makes our product more inclusive for everyone.&rdquo;   
*Josh Schultz, GitHub Engineering Team Leader*

## How to integrate agents into your accessibility workflow

Our Licensing team's experience yielded several practical insights for teams looking to leverage AI agents effectively:

1. *Batch for efficiency:* Plan for some overhead with each agent interaction; batching reviews keeps things streamlined.  
2. *Use AI to prepare for AI:* Copilot Chat can quickly transform raw notes into agent-ready tickets, streamlining the preparation process.  
3. *Fix small nits yourself:* Tiny fixes are often faster to implement directly than through an agent feedback cycle.  
4. *Be precise in feedback:* Crystal-clear pull request comments (e.g., "Remove Y first, then add X") increase the likelihood of agent success on the next attempt.  
5. *Proactive labeling:* As you review your backlog, label agent delegatable items.

## The multiplier effect

The GitHub Licensing team's experience demonstrates that AI agents can function as team multipliers, allowing engineers to maintain focus on high-priority roadmap items while still addressing critical accessibility work. This creates ways of working that aren&rsquo;t simply additive but potentially multiplicative.

For engineering leaders, the implications can be transformative: AI agents aren't replacing accessibility expertise &mdash; they're increasing your ability to attend to this critical work without deprioritising your feature roadmap. By thoughtfully integrating agents into your workflow, particularly for well-defined accessibility tasks that might otherwise need to wait for the next sprint, you can maintain velocity on your roadmap while creating more inclusive products now.

In this new era of development, the teams that thrive won't just be those with the most resources, but those who most creatively harness the complementary strengths of humans and AI to deliver better, more accessible experiences for everyone.

---

Want to learn more about the strategic role of AI and other innovations at GitHub? Explore [Executive Insights](https://github.com/solutions/executive-insights) for more thought leadership on the future of technology and business.

Facing a critical accessibility deadline, a GitHub team decided to experiment with a new approach: delegating the work to AI agents.

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Published via [GitHub Executive Insights](https://github.com/solutions/executive-insights "Executive insights, curated just for you") | Authored by [Matt Nigh](https://github.com/mattnigh), Program Manager Director of AI for Everyone

Unlocking the full potential of your company's AI usage comes down to one crucial element: leadership. Whether you're just starting to build your AI program or looking to accelerate its impact, having a clear, dedicated owner, a **Directly Responsible Individual (DRI)**, is the key to transforming scattered efforts into a unified, powerful movement.

A strong DRI program can turn pockets of innovation into company-wide best practices and ensure your grand strategy translates into tangible results. It&rsquo;s the difference between declaring a strategy and truly executing it with excellence.

This post offers a blueprint for defining, empowering, and maximizing the impact of this critical role.

## Enabler, not gatekeeper

Before we go further, let&rsquo;s be clear about what this role *isn&rsquo;t*. It&rsquo;s not another project manager chasing status updates or a compliance officer policing tool usage. Viewing the AI DRI through that lens is a fundamental misunderstanding of their purpose.

Think of this person as an **enabler and an accelerator**. They are the connective tissue between the executive vision and the on-the-ground reality, a coach whose mission is to help everyone else succeed with AI. Their job is to clear roadblocks, not create them.

This can't be a side-of-the-desk job or a rotating hot potato. If you're serious about building AI fluency, it demands a dedicated leader who is accountable for the program's success. At GitHub, for example, this function is formally resourced with a Program Director and a Program Manager who partner to drive their "AI for Everyone" initiative.

## The Core Responsibilities of the AI Program Lead

While the mission is strategic, the responsibilities are tangible. The DRI is a unique blend of strategist, change manager, consultant, and community builder. Here&rsquo;s what they own:

* **Chart the course:** The DRI translates the high-level vision for AI into a concrete, actionable roadmap. They define clear goals, manage the planning process, and ensure every part of the program aligns with what the company and its senior sponsors want to achieve.  
* **Be the Storyteller-in-Chief:** The DRI is the company's lead change agent for AI. They build and execute a comprehensive change management plan, ensuring that new AI tools and skills are introduced in a way that feels helpful, not disruptive, driving excitement and adoption.  
* **Serve as the go-to AI consultant:** When teams hit a wall, the DRI is their first call. They serve as the go-to expert for the organization, providing 1:1 support and hosting office hours. This hands-on help builds immense credibility and unblocks the most valuable use cases.  
* **Amplify what works:** The DRI is constantly on the lookout for clever uses of AI across the company. They find these bright spots, celebrate them in workshops and communities, and turn individual wins into company-wide best practices. This creates a virtuous cycle of inspiration and adoption.  
* **Quarterback the AI toolkit:** The DRI acts as the central intake point for new AI tool requests. They partner with IT, Security, and Legal to create a streamlined process for vetting and deploying tools, ensuring a consistent and safe approach for everyone.  
* **Own the scoreboard:** The DRI is accountable for the health of the program. They track key metrics on adoption and fluency, using data to show what&rsquo;s working, justify the program's value, and make informed decisions about where to invest next.

### What the DRI Actually Does

That all sounds great, but what does the DRI actually *do* all day? The day-to-day work is tactical, hands-on, and deeply collaborative. A typical week might look like this:

* **Hosting office hours:** Running the weekly drop-in session where anyone can get real-time help with their specific AI challenges. It's not a presentation; it's an open forum for unblocking real work and hearing unfiltered feedback.  
* **Consulting with champions:** Taking 1:1 calls with internal AI advocates to brainstorm how to drive adoption within their departments, perhaps by co-developing a short training session for their team.  
* **Curating the learning hub:** Finding and vetting a new external course or an insightful internal use case and adding it to the central learning portal. This keeps the content fresh and prevents it from becoming a stale, unused resource.  
* **Telling stories with data:** Pulling the latest adoption metrics and weaving them into a concise, compelling update for executive sponsors. This data-driven storytelling is crucial for securing continued investment.  
* **Connecting the dots:** Identifying two people from different departments who are unknowingly working on a similar problem and making an introduction. This active community management prevents siloed work and sparks cross-functional innovation.

### The DRI's Essential Partnerships

The AI Program Lead does not work in a silo. Their success depends on building strong, collaborative partnerships across the company. Key relationships include:

* **IT and Security:** This is the DRI's most critical operational partnership. They work together to vet new tools, manage licenses, and ensure the company's security posture keeps pace with AI innovation.  
* **Legal and Compliance:** To navigate the complex and rapidly evolving landscape of AI risk, data privacy, and intellectual property.  
* **HR and L\&D:** To ensure AI enablement is woven into core employee processes like new-hire onboarding, performance management, and formal training programs.  
* **Communications:** To amplify success stories, manage company-wide announcements, and build a consistent and compelling narrative around the company's AI journey.  
* **Executive Sponsors:** For budget, strategic alignment, removing organizational roadblocks, and providing visible, top-down support for the program.

## A Strategic Investment, Not Overhead

An AI enablement program without a DRI is a ship without a captain. It may have a destination in mind, but it lacks the leadership to navigate the currents of a major organizational transformation.

The DRI is not overhead. **They are the strategic investment that ensures all other investments you make in AI, from tooling to training, deliver their full value.** For any leader who is serious about building a truly AI-powered workforce, appointing this individual isn&rsquo;t just a good idea; it&rsquo;s the essential next step.

---

Want to learn more about the strategic role of AI and other innovations at GitHub? Explore [Executive Insights](https://github.com/solutions/executive-insights) for more thought leadership on the future of technology and business.

Unlocking the full potential of your company's AI usage comes down to a clear, dedicated owner who can transform scattered efforts into a unified, powerful movement.

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Playbook series: Why you need a DRI for your AI program

Published via [GitHub Executive Insights](https://github.com/solutions/executive-insights "Executive insights, curated just for you") | Authored by [Matt Nigh](https://github.com/mattnigh), Program Manager Director of AI for Everyone

In our recent playbook, [GitHub&rsquo;s internal playbook for building an AI-powered workforce](https://resources.github.com/enterprise/ai-powered-workforce-playbook/), we emphasized that giving people access to AI tools isn&rsquo;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&rsquo;t willingness, it&rsquo;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&mdash;"I'm trying to write a prompt for this marketing campaign," or "This AI feature isn't working as I expect"&mdash;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 &rarr; Ask for examples &rarr; Offer immediate help &rarr; Set next steps    
* **Don't:** Vague "thoughts?" &rarr; Long answers &rarr; Fake expertise &rarr; 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.

---

Want to learn more about the strategic role of AI and other innovations at GitHub? Explore [Executive Insights](https://github.com/solutions/executive-insights) for more thought leadership on the future of technology and business.

Building a fluent AI workforce requires more than just providing tools; it requires a strategic approach to continuous learning and development.

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Playbook series: Fostering AI learning opportunities

Catch up on the latest information on GitHub products released daily. In this webinar, we summarized the updates announced in the Changelog by product, such as GitHub Copilot, Actions, and security.

Please find [presentation slides here](https://assets.ctfassets.net/wfutmusr1t3h/7b3GfkZMtCkCPjBhpTk4TP/cb7b30997d2d67d8369fe7175f2dddff/GitHubUpdateWebinarSep29JP_SlidesPDF.pdf).

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This webinar is in Japanese only: GitHub Latest Updates Webinar on September 29th

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Join us for a walkthrough of the latest product updates and the priorities shaping GitHub&rsquo;s roadmap as we navigate one of the fastest moving markets in software development.

We&rsquo;ll cover our latest advancements in agent-powered developer experiences, dive into MCP Server enhancements, and give you a closer look at some of our newest features with hands-on demos.

Whether you're deep in the code or leading a team, this is your opportunity to see what&rsquo;s coming, ask questions live, and get inspired by what&rsquo;s ahead.

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