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Resource Page for /enterprise-content-roundup/december

December ‘25 enterprise roundup

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Resource Page for /enterprise-content-roundup/november

November ‘25 enterprise roundup

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October ‘25 enterprise roundup

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’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’s be clear about what this role *isn’t*. It’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’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’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’t just a good idea; it’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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Resource Page for /enterprise/ai-program-dri

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 this series reflecting on [GitHub’s internal playbook for AI adoption](https://resources.github.com/enterprise/ai-powered-workforce-playbook/) we have covered how to establish clear guardrails, provide learning opportunities, and activate a network of advocates. Now we’re digging into how you scale day-to-day learning to ensure valuable knowledge doesn’t vanish into private chats.

The solution lies in Communities of Practice (CoPs). If your AI Advocates are the catalysts, CoPs are the chemical reaction. They are the forums where individual discoveries become collective intelligence, and where your company's AI fluency truly grows.

This guide shows you how to move from scattered, one-off conversations to intentional, high-impact AI communities.

## The Problem: Fragmented and lost learnings

AI experiments are already happening inside DMs, in private team Slack channels, and buried in slide decks. But most of that insight disappears fast. A clever prompt is forgotten. A smart workaround is shared once and never again. Someone solves a problem, but the next team has to rebuild from scratch.

Without a system to surface and spread these moments, you get duplication instead of acceleration. A solid CoP strategy fixes that. It gives your organization a shared, searchable, and social space to learn together.

## A blueprint for your communities

Cultivating a CoP network is an act of intentional community management. It’s about providing the right structure and then actively nurturing the conversations within it.

### Step 1: Start with a hub-and-spoke model 

Don't start with a single, monolithic `#ai` channel. It will quickly become too noisy to be useful. Instead, launch with a "hub-and-spoke" model to serve different needs.

* **The hub:** A general-purpose entry point. Create a single, company-wide channel (e.g., `#how-do-i-ai`) to serve as the front door for your entire AI ecosystem. This is the place for broad announcements, general questions, and for employees who don't know where else to go.  
* **The spokes**: These should be deep-dive, specialist communities. Launch one or two specialized channels for your most critical user groups. A developer-focused community (e.g., `#copilot-power-users`) is almost always a good place to start. As adoption grows, you can spin up new "spokes" for other functions like marketing, sales, or finance, as demand grows.

### Step 2: Assign clear ownership and a simple charter 

Every community needs a designated leader and a clear purpose. This is a perfect role for your AI Advocates.

* **Assign a leader:** Ask an Advocate from the relevant department to be the official owner or facilitator of each community.  
* **Write a charter:** The leader should write a simple, one-paragraph charter pinned to the channel. It should answer three questions: What is the purpose of this community? Who is it for? What are the key topics of discussion? This prevents confusion and keeps the conversation focused.

### Step 3: Actively manage and sustain momentum 

Creating channels is easy, keeping them valuable is hard. The AI Program DRI, in partnership with the community leaders, must actively work to sustain momentum.

* **Showcase and amplify:** Create a rhythm for finding and celebrating the best content from your CoPs. This could be a weekly "best of" post that summarizes the most interesting use cases or a "discussion firehose" that automatically shares tagged posts from various repos into a central channel.  
* **Run community programming:** Use the communities as the primary venue for your AI events. Announce new L\&D opportunities, host tool-specific Q\&As, and run your office hours within the relevant channels to drive engagement.  
* **Facilitate connections:** The DRI and Advocates should actively monitor conversations to act as connectors. When one person asks a question, they can tag another employee who they know has the answer. This active facilitation builds the network and reinforces the value of the community.  
* **Encourage leadership participation:** Momentum is not just a grassroots effort; it needs air cover. Leaders and senior ICs play a critical role in sustaining community health. They should be encouraged not only to direct their teams to these communities but to participate themselves. A senior engineer asking a question in the developer channel or a VP celebrating a use case shared in the general hub provides powerful social proof, signaling that these communities are a valuable and important place to spend time.

### Step 4: Supercharge your communities with events 

Async channels are the foundation, but live and structured events are what turn a community into a vibrant hub of innovation. These events create focal points for learning and strengthen the connections between members.

* **Host virtual learning events:** Organize company-wide events like a "Day of Learning" or a hackathon focused on solving specific business problems with AI. These events, run through the CoPs, generate a burst of energy, collaboration, and a wealth of new use cases. Crucially, all sessions should be recorded and shared for asynchronous consumption, turning a one-time event into a lasting learning artifact.  
* **Run executive listening sessions:** Create a direct feedback loop by hosting regular, informal listening sessions between community leaders and executive sponsors. This gives leaders an unfiltered view of what's working, what's not, and how they can best support the company's AI and L\&D efforts. It also provides community leaders with a direct line to decision-makers, making them feel valued and empowered.  
* **Organize community leader offsites:** Bring your community leaders (your AI Advocates) together for a dedicated virtual offsite or summit. This provides a valuable opportunity for them to share best practices, align on goals for the next quarter, and build the personal relationships that are essential for a strong, self-sustaining network.

When you shift from a passive approach to actively nurturing community, those scattered conversations start to work together. You build a space where people learn from each other, knowledge flows more freely, and AI fluency spreads across your organization.

## Complementing CoPs with tooling

Your CoPs generate the knowledge, and these tools ensure it’s captured, refined, and shared effectively. Each tool play a distinct role in how knowledge is created, shared, and scaled across your organization.

* **Slack/Teams channels** are your CoP’s real-time pulse, where ideas come alive, problems are solved, and questions find quick answers.  
* **GitHub Discussions** (or similar) provide structure and continuity—a searchable home for insights you don’t want getting buried in chat.  
* **Internal wikis** are where best practices, reusable assets, and canonical guidance live on.

Here’s how to connect them:

* **Capture valuable insights:** When a conversation in chat surfaces a breakthrough, move it to GitHub Discussions. Add a clear title, summary, and tags like `prompt`, `copilot-tip`, or `use-case` which will also make content easy to discover later.  
* **Foster async participation:** GitHub Discussions support thoughtful input across time zones. They’re ideal for deeper problem-solving and design debates.  
* **Automate highlight sharing:** Use lightweight bots to summarize top GitHub Discussions on a regular basis or auto-post starred threads into CoP channels.  
* **Build cross-functional bridges:** Encourage cross-posting between functions, like surfacing a product team’s AI win in an engineering CoP, or vice versa.  
* **Promote graduation to wiki:** Once a discussion gains consensus or maturity, document it in your internal wiki. Include links to the original thread for context.

## **Make learning visible, scalable, and durable**

If you want AI adoption to stick, you need more than tools and training. You need a system that captures what people learn, connects those learnings across teams, and turns them into reusable knowledge.

Communities of Practice give you that system. They make day-to-day learning visible. They prevent repetition by sharing solutions and accelerate progress by connecting people solving similar problems. And when combined with structured tools like GitHub Discussions and internal wikis, they ensure your best ideas don’t get lost.

Don’t let AI learning stay locked in private chats or isolated teams. Build a network where insights are shared, refined, and documented. That’s how you move from scattered experimentation to coordinated transformation.

AI fluency at scale doesn’t happen by accident. It happens when you design for knowledge flow, sustain community energy, and make collective learning part of how your company works.

Your AI knowledge is vanishing in private chats. It's time to build Communities of Practice—the engine that transforms scattered individual insights into collective, scalable organizational intelligence.

Resource Page for /enterprise/communities-of-practice

Playbook series: Communities of practice

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

Our playbook on [building an AI-powered workforce](https://resources.github.com/enterprise/ai-powered-workforce-playbook/) focused on the idea that adoption is a change-management challenge. The foundation of that change is trust, and trust is built on clear, practical policies. 

Without clear rules for AI adoption and usage, employees face a paralyzing choice: They can either avoid using powerful new AI tools for fear of breaking a rule they don't know exists, or they can forge ahead, potentially exposing the company to security risks and data leaks. The first hinders innovation and the second creates avoidable risk.

This post provides **a practical blueprint for creating an AI policy framework —** effective AI guardrails that will empower your employees to experiment, innovate, and adopt new tools safely in this new AI era.

## Start with a data classification standard

Before you can set rules for tools, you must have clear rules for your data. A policy that says, "don't use sensitive data in public AI tools" is meaningless if your employees don't have a shared understanding of what "sensitive" means.

This is why a simple, clear data classification standard is critical. It gives employees a mental model to assess risk on the fly. You don't need a complex system; a few tiers are usually sufficient. For example:

* **Public:** Information that is already public or approved for public release.  
* **Internal:** Non-public company information that is generally available to all employees but would not be shared externally (e.g., internal announcements, project plans).  
* **Confidential:** Sensitive information accessible only to specific teams or individuals (e.g., unannounced financial results, employee PII, pre-release source code).

With this foundation in place, your AI usage policies become instantly more practical and actionable for every employee.

## A simple framework for a complex landscape

The AI tool ecosystem is evolving at a dizzying pace. To avoid creating a complex web of rules that no one can follow, we recommend a simple, three-tiered framework that categorizes tools based on risk and data sensitivity. Below is an example of how we tier our own tooling.

### Tier 1: Approved and enterprise-ready tools

This is your "green light" category. These are the tools that have been fully vetted by your Security, Legal, and IT teams. They have enterprise-grade security controls and a formal contract in place. For us, this includes tools like GitHub Copilot, Microsoft 365 Copilot, Slack AI, Zoom AI, etc,. These tools should take up the bulk of your use cases, and be the primary AI tools your organization uses.

* **The policy:** Employees can use these tools with **internal** and **confidential** data.  
* **Why it matters:** This provides a powerful, secure, and versatile set of default tools that can meet the vast majority of employee needs, giving them a safe sandbox in which to work and innovate.

### Tier 2: Unvetted public tools 

This category includes the vast world of free, public AI models and services that do not have a formal enterprise agreement with your company. These should be used primarily in edge cases.

* **The policy:** The rule here is simple and absolute: **Public data only.** Employees must not input any proprietary company code, customer data, or internal-only information into these tools.  
* **Why it matters:** This policy allows for low-risk experimentation and learning. Employees can still use these tools to explore new capabilities, learn prompting techniques, and stay current with  state-of-the-art technology, all without exposing sensitive information.

### Tier 3: Local-only AI tools 

A growing category of powerful AI tools can run entirely on an employee's local machine without sending any data to the cloud (e.g., LM Studio, MacWhisper).

* **The policy:** These are generally safe for a wide range of data, but with two key caveats. Employees must ensure that any features that could transmit data (like cloud sync or automatic transcription services) are disabled. For use with highly sensitive data, a quick consultation with your IT/Security team should still be required.  
* **Why it matters:** This empowers advanced users to leverage powerful models for sensitive tasks, while still maintaining a crucial security checkpoint.

## Handling special cases

No framework can perfectly capture every scenario. A mature policy acknowledges and plans for exceptions, as you will inevitably encounter tools that don't fit neatly into the three tiers.

* **Experimental and pilot programs:** Your most innovative teams will build or discover new AI tools that aren't ready for a full enterprise rollout. Create a specific "Experimental" category for these tools. The policy should be strict, and should consider treating them like Tier 2 tools (public data only), but their existence creates a sanctioned space for R\&D. This allows innovation to flourish without waiting for a full security review, with a clear path to graduate to Tier 1 once proven.  
* **Department-specific tools:** A one-size-fits-all toolset is a myth. Your sales team may need a specialized AI-powered CRM tool that is irrelevant to engineering. Your policy should include a clear, lightweight process for individual departments to get approval for their specific tools. This provides flexibility and ensures teams have what they need to succeed.

## Making your policies usable

The best policies are useless if no one knows they exist or if they are too difficult to understand. To make your guardrails effective, you must make them accessible.

1. **Create a central, simple list:** Don't bury your policies in a 50-page document. Create a simple, easy-to-scan page on your internal wiki that lists the approved tools for each tier, including any special cases.  
2. **Provide clear points of contact:** Designate a specific place where employees can ask questions and get fast answers. For us, a dedicated Slack channel (`#security-help`) where employees can ping the security team directly has been invaluable.  
3. **Socialize and educate:** Regularly communicate your policies in company all-hands meetings, newsletters, and through your AI Advocate network. The goal is to make the "rules of the road" a part of the company's shared knowledge.

By implementing clear guardrails, you are not slowing down your AI adoption. You are building the foundation of trust and safety that gives your employees the freedom and confidence to move fast, experiment boldly, and unlock the full potential of AI.

## Putting it all together

Creating effective AI guardrails isn't about locking things down — it's about unlocking safe, scalable adoption. Clear policies give employees the confidence to move fast without second-guessing what’s allowed. They turn ambiguity into alignment.

With a strong data classification model, a practical tool-tiering framework, and a lightweight evaluation process, you equip your organization to keep pace with AI’s rapid evolution — without sacrificing trust, security, or momentum.

---

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.

By creating clear, practical policies and a framework for tiered tool usage, companies can build the trust necessary to empower employees and safely scale AI adoption.

Resource Page for /enterprise/creating-clear-ai-policies-guardrails

Playbook series: Creating clear AI policies and guardrails

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 [journey to build an AI-powered workforce](https://resources.github.com/enterprise/ai-powered-workforce-playbook/), we’re discussing the foundation of the entire structure: **active, visible, and unwavering executive support.**

An AI adoption program without leaders may have a budget, but it will never have the strategic alignment, organizational authority, and grit to navigate the inevitable challenges of a massive technological shift. It will become a series of tactical projects, not a true transformation.

This post is a blueprint for leaders who want to be more than just sponsors. It’s for those who want to be the engine of their company's AI transformation.

## Beyond sponsorship: Setting a new standard

Effective executive support isn’t about passively signing checks; it’s about actively setting a new standard. It’s about fundamentally reframing AI from a "nice-to-have" tool into a core competency for every single employee. Communicating that this isn't a suggestion, it's the new reality of work.

This requires two bold actions:

* **Make AI fluency a foundational expectation:** The most powerful message a leader can send is that learning to use AI is not optional. It’s as fundamental as email or using a spreadsheet. You have to be direct: in the age of AI, standing still means falling behind. The expectation must be crystal clear that everyone, in every role, is responsible for building their AI skills.  
* **Operationalize the expectation:** A declaration is just words without a system to back it up. To make it real, leaders must weave this new expectation into the company’s core operating rhythms.

You can operationalize the expectation by:

* **Connecting it to performance:** Explicitly include the effective use of AI as a factor in performance reviews, promotion decisions, and reward cycles. This sends an unmistakable signal that AI proficiency is a valued and required competency.  
* **Challenging resource allocation:** Before approving requests for more headcount or budget, challenge your teams. Ask them, "How have you tried to solve this with AI first?" This forces a crucial mindset shift, from "we need more people" to "how can our team, augmented by AI, achieve this goal?"

## A cascading strategy

So you've set the new standard. How do you make sure it sticks? A single all-hands announcement from the CEO is a powerful catalyst, but it's just the first domino. A truly successful communications plan ensures the executive message is not just heard, but understood, translated, and acted upon at every level. It’s a deliberate cascade that turns a single moment into a sustained rhythm.

### 1\. The executive mandate: Setting and reinforcing the "why"

The cascade begins with a clear, high-level mandate from the top. This is the initial, non-negotiable standard: *AI is now a core part of how we work, and proficiency is expected.* But the most effective leaders know this isn't a one-and-done memo. They treat the announcement as the first beat in a continuous drumbeat, reinforcing the message in meetings, Q\&As, and internal posts. More importantly, they make choices in strategy, in organization, and others that make the commitment to AI tangible and impactful..

### 2\. Leadership translation: Defining the "how"

Next, VPs and directors must translate this "why" into a "how" for their specific functions. This is more than just forwarding an email. It’s about actively creating opportunities for their teams to apply the new mandate. At GitHub, we’ve seen our VPs push teams to use AI heavily for maintenance tasks, internal apps, triaging help requests, writing tests, and building proofs-of-concept. Our leaders have even organized department-specific AI learning weeks and hackathons. This step makes the high-level vision relevant, actionable, and a genuine learning opportunity.

### 3\. Front-line rhythms: Executing the "what"

Finally, the message cascade transforms into the daily, practical work led by managers and senior individual contributors. This is where the strategy becomes reality. The communication becomes a continuous, peer-to-peer rhythm in team meetings, 1:1s, and async channels. The focus shifts to:

* **Sharing specific wins:** "Check out this prompt that saved me an hour."  
* **Troubleshooting challenges together.**  
* **Celebrating peers** who are finding clever ways to use AI.

This is the grassroots momentum that turns a top-down directive into a living, breathing part of the culture.

## Enabling your executives

The relationship between the AI program and its executive sponsors is a two-way street. While leaders provide top-down support, the AI program must, in turn, provide bottom-up enablement to the executive team. A key function of the program's DRI is to make it easy for leaders to be effective and consistent champions.

This "Executive Enablement Loop" involves proactively equipping leaders with the content and context they need, including:

* **Providing curated talking points:** Supplying executives with a steady stream of the most compelling internal use cases, success stories, and data points. This arms them with fresh, relevant material for board meetings, investor calls, and public appearances.  
* **Preparing high-impact demos:** Assisting leaders in preparing for all-hands meetings or customer events by building powerful, tailored demos that showcase the company's AI capabilities in action.  
* **Drafting internal communications:** Partnering with the executive team and internal comms to ghostwrite or consult on company-wide memos and posts. This ensures the AI narrative is authentic, consistent, and lands with maximum impact.

This reciprocal support ensures that the executive message is always grounded in the reality of how the company is using AI, making their sponsorship more credible and effective.

### The executive's public learning journey

Beyond the memos and the mandates, the most powerful way for a leader to signal commitment is to model the behavior they expect. This means learning in public. When executives share their own journey: the wins, the struggles, the "aha\!" moments, they demystify the process and make the call to action feel human and achievable.

This doesn't need to be polished. In fact, unscripted moments are often the most powerful:

* **Sharing a win in a public channel:** A leader posting a prompt they used to summarize a long report.  
* **Recording a quick, unpolished demo:** A short screen recording of an executive brainstorming presentation ideas with an AI tool.  
* **Admitting a fail:** Openly talking about a time they struggled to get the right result. This normalizes the learning curve and shows that it’s okay not to be an expert overnight.  
* **Celebrating team wins:** Publicly amplifying a specific team or individual's success, highlighting not just the outcome but the cleverness of the solution.

This vulnerability and transparency is incredibly powerful. It transforms the AI mandate from a top-down directive into a shared, company-wide endeavor.

Ultimately, executive support is not a line item on a project plan. It is a continuous commitment: the steady, visible force that gives the entire organization the confidence and motivation to build the future of work, together.

## Communications plan checklist

A project or communications manager can use this checklist to operationalize the cascading strategy:

**Executive mandate (The "why")**

* \[ \] Draft the CEO's memo or talking points in partnership with the AI program DRI and communications team.  
* \[ \] Secure final review and approval from key stakeholders (e.g., legal, HR).  
* \[ \] Schedule the delivery of the message (e.g., all-hands meeting, internal blog post publication).  
* \[ \] Plan 2-3 follow-up communication beats for the executive to reinforce the message in the following month.

**Leadership translation (The "how")**

* \[ \] Prepare a briefing packet for VPs and directors with key messages, talking points, and an FAQ document.  
* \[ \] Schedule department-level meetings for the week immediately following the CEO's announcement.  
* \[ \] Provide a simple slide deck template that leaders can adapt for their team discussions.

**Front-line rhythms (The "what")**

* \[ \] Create a resource guide for managers on how to discuss AI proficiency in 1:1s and team meetings.  
* \[ \] Partner with AI advocates to seed internal communication channels with conversation starters and examples of early wins.  
* \[ \] Establish a simple process for employees to share their AI success stories for potential amplification.

---

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.

For AI to successfully transform an organization, leaders must be actively involved, not just approving budgets. This playbook is a blueprint for executives who want to drive, and not merely sponsor, their company's shift into an AI era.

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Resource Page for /enterprise/executive-support

Playbook series: Executive support

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’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’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.

---

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.

Resource Page for /enterprise/fostering-ai-learning-opportunities

Playbook series: Fostering AI learning opportunities

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

Choosing the right AI tools is one of the biggest challenges for leaders today. The market is overwhelming, and common pitfalls can paralyze your organization.

In our [Playbook for building an AI-powered workforce](https://resources.github.com/enterprise/ai-powered-workforce-playbook/), we introduced the core principles for building an AI-native organization. This guide dives deeper into one of the most critical components: building a "right-fit" AI tooling strategy. We'll show you how to move beyond not knowing where to begin and create a curated portfolio of vetted, high-impact tools that empower your teams and accelerate your company's AI vision.

## A portfolio approach to AI tools

Your tooling strategy should be guided by a simple principle: make it easy for employees to do the right thing. This means providing a core set of powerful, vetted, and well-supported tools that can help them get their work done, whether they’re engineers, designers, operations, HR, etc. This curated portfolio becomes the default, safe choice for everyone.

At GitHub, we think about our toolset in three key categories:

* **Core enterprise tools:** These are the "green light" tools that are fully vetted, have enterprise-grade security, and are approved for a wide range of data types. They are the foundation of your AI-powered workforce. Think of tools like GitHub Copilot, Microsoft 365 Copilot, or Google's Gemini for Workspace. They provide a secure, default starting point for everyone.  
* **First-party tools:** These are the AI-powered products and features you build yourself. They create a powerful feedback loop between your employees and your product teams, accelerating development and ensuring your products meet real-world needs. For us these tools are primarily the Github Copilot portfolio of products and VS Code.  
* **Department-specific tools:** Not every need will be met by your core tools. A sales team might need a specialized AI-powered CRM feature, while a marketing team might benefit from an advanced analytics tool. Empowering departments to select these tools is key, but it requires a lightweight yet rigorous approval process to manage risk.

This model doesn’t require a full security review for every single tool, but it should build confidence that you are creating a solid foundation —and that you can enable smart, decentralized decisions to scale without compromising safety.

## Evaluating new tools

When a new AI tool shows up on your radar, you need a fast, structured way to decide whether it’s worth exploring, adopting, or investing in. Here’s a simple framework you can use.

### Tips for evaluating AI tools

When a new tool is submitted, evaluate it against a consistent set of criteria. Frame them as questions to guide the review:

1. **What problem does it solve? (Functionality):** Is it a niche tool or a broad platform? Does its core function address a high-value business need?  
2. **Is it a standalone solution or part of a platform? (Ecosystem):** Does the tool integrate with your existing systems? A tool that fits into your current tech stack is often more valuable than a standalone product that creates another data silo.  
3. **Where does our data go? (Data sensitivity & flow):** This is the most critical question. What data does the tool access? Where is it stored? Is it used to train public models? You must have a clear understanding of the data lifecycle.  
4. **Does it meet our security standards? (Security & compliance):** Does the tool support single sign-on (SSO)? What are its data retention policies? Does it comply with relevant regulations like GDPR or CCPA?  
5. **What is the expected benefit and/or return on investment? (Value & ROI):** How will this tool impact the business? While direct revenue attribution can be difficult, consider proxies for value. Will it significantly speed up a common workflow? Does it reduce a specific type of risk? A tool with a high price tag may be well worth it if it delivers substantial time savings for a large team.

### The evaluation workflow

1. **Submission form:** Create a simple intake form where employees can submit new tools, answering the five questions above.  
2. **Triage workflow:** Designate a small review committee (e.g., from IT, Security, and a business unit lead) to triage submissions weekly or bi-weekly.  
3. **Security and compliance review:** For tools that pass triage, conduct a lightweight review using predefined risk checklists to speed up this step.  
4. **Pilot and feedback:** For promising tools, set up a short pilot with clear success criteria. Collect feedback and security signals before a full rollout.  
5. **Decision and documentation:** Publish all decisions, rationales, and tier placements in a central tool registry to maintain visibility and accountability.

## Running a pilot

A successful pilot is more than just giving people access to a new tool; it's a structured experiment designed to answer key questions about a tool's value, usability, and risks before making a long-term commitment.

### 1\. Assemble your pilot group

The right testers provide the right feedback.

* **For broad, enterprise-wide tools:** Lean on your established group of "AI advocates" or internal champions. These enthusiastic early adopters are often more resilient to the rough edges of new technology and can provide insightful, high-signal feedback.  
* **For department-specific tools:** The pilot group should be the team that will actually use the tool daily. A new AI feature for a CRM should be tested by the sales team, not IT. This ensures the feedback is grounded in real-world workflows.

### 2\. Structure for success

A well-defined structure is key to getting clear results.

* **Define success metrics:** What does success look like? Is it saving three hours per week per user, increasing lead conversion by 5%, or achieving an 80% user satisfaction score? Set clear, measurable goals from the start.  
* **Time-box the pilot:** A typical pilot should run for 2-4 weeks. This is long enough for users to form habits and provide meaningful feedback, but short enough to maintain momentum.  
* **Provide lightweight training:** Host a quick kickoff session to demonstrate the tool's core functionality and explain the pilot's goals. Record the session to serve as an initial training resource. Don't assume users will figure it out on their own.

### 3\. Gather actionable feedback

Good feedback is specific and contextual.

* **Use a mix of channels:** Combine a dedicated chat channel for real-time questions, short weekly surveys to track sentiment, and a final wrap-up session or detailed survey to capture comprehensive thoughts.  
* **Ask specific questions:** Go beyond whether or not someone liked using the tool. Ask questions like, "Describe a specific task where this tool saved you time," "Where did the tool get in your way or slow you down?" and "What's the one thing you wish this tool could do that it can't?"  
* **Look for qualitative and quantitative signals:** A satisfaction score of 7/10 is useful, but a direct quote about how the tool streamlined a frustrating, manual process is often more powerful when making a final decision.

## From reactive to empowerment

Implementing a structured evaluation and piloting process isn't just about managing risk; it's about building a culture of intentional innovation. By empowering your employees with the *right* tools, not just the *most* tools, you create a resilient AI strategy that can adapt as quickly as the market itself.

This process keeps evaluation fast and scalable while maintaining the rigor needed to protect the business. The goal is to move from reactive decision-making to a proactive state of empowerment, accelerating AI adoption responsibly and unlocking your team's full potential.

---

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.

The pace of AI innovation demands a strategy—not a shopping spree.

Resource Page for /enterprise/right-fit-tooling

Playbook series: Right-fit tooling

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’t just about compliance. It’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

“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.”   
*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’t simply additive but potentially multiplicative.

For engineering leaders, the implications can be transformative: AI agents aren't replacing accessibility expertise — 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.

Resource Page for /how-github-licensing-used-ai-agents-champion-accessibility

How GitHub's Licensing team used AI agents to champion accessibility

# MCP Universe – A Micro Summit for Agentic Builders

Model Context Protocol (MCP) is shaping the future of agentic AI—enabling open protocols, powering innovation, and accelerating how developers build intelligent systems. This event is designed for founders, developers, and engineering leaders exploring the next wave of open source and agentic technologies.

GitHub x Microsoft for Startups: MCP Universe

🚀 Hosted alongside GitHub Universe, MCP Universe brings together a vibrant community of open source contributors, startup founders, and enterprise builders to dive deep into the foundations of MCP and the pillars of the agentic web.

Get inside understanding of where Microsoft and investors are thinking about MCP with [Amanda Silver](https://www.linkedin.com/in/amandaksilver/) and [Ursheet Parikh](https://www.linkedin.com/in/uparikh), General Partner at Mayfield

Hear from the leaders driving MCP forward:

- ​[David Soria Parra](https://www.linkedin.com/in/david-soria-parra-4a78b3a/overlay/about-this-profile/) - Anthropic, Member of Technical Staff, co-creator Core Maintainer, MCP Steering Committee
- [Den Delimarsky](https://www.linkedin.com/in/dendeli/) – Microsoft, Principal Product Engineer Core Maintainer, MCP Steering Committee
- ​[Nick Cooper](https://www.linkedin.com/in/nicknotfun/) - OpenAI, Member of Technical Staff, Core Maintainer, MCP Steering Committee
- [Jeremiah Lowin](https://www.linkedin.com/in/jlowin/) – Author of FastMCP, Founder of Prefect.io

And learn from real-world builders and innovators:

- ​[Shannon Williams](https://www.linkedin.com/in/smw355/) - Cofounder and President of [Obot](https://obot.ai/)
- ​[Toby Padilla](https://www.linkedin.com/in/tobypadilla) - Principal, cofounder and builder of GitHub MCP Registry and GitHub MCP Server
- ​[Vitaly Gordon](https://www.linkedin.com/in/vitalygordon/) - cofounder and CEO of Faros AI
- ​[Angie Jones](https://www.linkedin.com/in/angiejones) - VP of Engineer, AI Tools and Enablement at Block

See live demos from [Matvei Popov](https://www.linkedin.com/in/matvezy/?) ML Engineer at [Roboflow](https://roboflow.com/) and [Cathy Di](https://www.linkedin.com/in/cathydi/), cofounder and CEO of ​[Dedalus](https://www.dedaluslabs.ai/)

Moderated by:
[Rob Ferguson](https://www.linkedin.com/in/rwfiii/) (Microsoft for Startups), [Kevin Crosby](https://www.linkedin.com/in/kevinanthonycrosby/) (GitHub), [Simina Pasat](https://www.linkedin.com/in/simina-pasat-5707a633/), VP Product (GitHub)

Key Takeaways You'll Explore: 

🤖 MCP Foundations – Why open protocols matter for agentic AI

🚀 Agentic Future – How MCP accelerates innovation across startups and enterprises

🔒 Trust & Security – Building confidence in open agentic systems

🧩  Real-World Deployments – See MCP in action with apps, agents, and production use cases


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Model Context Protocol (MCP) is shaping the future of agentic AI—enabling open protocols, powering innovation, and accelerating how developers build intelligent systems. This event is designed for founders, developers, and engineering leaders exploring the next wave of open source and agentic technologies.

🚀 Hosted alongside GitHub Universe, MCP Universe brings together a vibrant community of open source contributors, startup founders, and enterprise builders to dive deep into the foundations of MCP and the pillars of the agentic web.

Resource Page for /mcp-universe-2025

CO-GHAC-VDEO-FY26-11Nov-17-MCP-Universe-Gated-Watch-On-Demand-9832

MCP Universe – A Micro Summit for Agentic Builders

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