April &lsquo;25 enterprise roundup

Resource Page for /enterprise-content-roundup/april

hero-2

Resource Page for /enterprise-content-roundup/august

August &lsquo;25 enterprise roundup

hero-4

Resource Page for /enterprise-content-roundup/december

December &lsquo;25 enterprise roundup

enterprise roundup

Resource Page for /enterprise-content-roundup/july

July &lsquo;25 enterprise roundup

june roundup

Resource Page for /enterprise-content-roundup/June

June &lsquo;25 enterprise roundup

Resource Page for /enterprise-content-roundup/march-2025

March &lsquo;25 enterprise roundup

Background image for Google Cloud Next keynote

Google Cloud Next 

Resource Page for /enterprise-content-roundup/may

May &lsquo;25 enterprise roundup

Hero-3

Resource Page for /enterprise-content-roundup/november

November &lsquo;25 enterprise roundup

Resource Page for /enterprise-content-roundup/october

October &lsquo;25 enterprise roundup

hero-1

Resource Page for /enterprise-content-roundup/september

September &lsquo;25 enterprise roundup

Published via [GitHub Executive Insights](https://github.com/solutions/executive-insights "Executive insights, curated just for you")

The developer tech stack is shifting, which means that the notion of who a developer is and what they do is evolving, too. With powerful AI models rapidly changing the development landscape, the most nimble business and engineering leaders are embracing generative AI to keep pace. 

Our white paper, **[Engineering Leadership in the Age of AI: Insights from GitHub](https://assets.ctfassets.net/wfutmusr1t3h/71Tv1g9g7em6GNdeZZIOQO/5083290f232ba3f2d68864b92f8654ae/GitHub-Engineering-Leadership.pdf)**, distills findings from our [2024 Octoverse report](https://octoverse.github.com/). Published at the end of each year, this report explores public data from GitHub&rsquo;s global base of developers, giving leaders like you the knowledge to navigate these changes and leverage them for strategic advantage.

You&rsquo;ll also find critical insights into the latest trends in AI, the rise of Python, and the growth in global developer activity, enabling you to make strategic investments in the technology and talent that will help you win.

**What&rsquo;s inside the white paper:** 

* How a 98%&#8239;year-over-year surge in public generative AI projects on GitHub underscores how AI experimentation has moved from curiosity to mainstream development  
* The importance of investing in agentic AI to compress workflows with new multi&#8209;agent systems that can plan, generate, and validate code (and security fixes) autonomously  
* Specific guidance for leaders on how to equip teams with the right tools and strategies to stay competitive. 

This asset was designed to be your comprehensive guide to understanding the current landscape, making informed decisions, and giving your teams what they need to excel in the age of AI.

**Key takeaways for business and engineering leaders:**

* AI coding tools are now central to software development  
* AI is driving productivity ([up by 55%](https://github.blog/news-insights/research/research-quantifying-github-copilots-impact-on-developer-productivity-and-happiness/)) and improving code quality  
* Developers are shifting toward more efficient AI models that reduce compute and operational costs  
* AI agents (e.g., GitHub Copilot Autofix and Copilot Workspace) are extending developer capabilities  
* AI is attracting more developers and expanding talent pools  
* AI-powered security tools are helping bridge the cybersecurity talent gap

Generative AI has moved from a curiosity and area of experimentation to a core reality in today&rsquo;s software development lifecycle. With the emergence of more powerful AI models and agentic AI, the scope of what these technologies can help engineering teams achieve is only expanding. 

Read the **[white paper](https://assets.ctfassets.net/wfutmusr1t3h/71Tv1g9g7em6GNdeZZIOQO/5083290f232ba3f2d68864b92f8654ae/GitHub-Engineering-Leadership.pdf)** to learn what this shift means for organizations, along with strategic insights on how to guide your teams in the months, quarters, and years ahead.

---

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.

---

What GitHub data reveals as the top priorities for modern engineering teams.

Octoverse whitepaper

Resource Page for /enterprise-octoverse

Engineering leadership in the age of AI: Insights from GitHub

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 piece, [GitHub&rsquo;s internal playbook for building an AI-powered workforce](https://resources.github.com/enterprise/ai-powered-workforce-playbook/), we argued that AI adoption isn&rsquo;t a tech problem, it&rsquo;s a change management problem. We laid out an operating model built on eight pillars, from executive sponsorship to data-driven metrics. But one pillar consistently makes the biggest difference in turning plans into real, widespread use: a network of internal AI advocates.

Buying AI tools without empowering people to use them is a fast track to failure. Even with great tech and clear policies, employees will hesitate if they don&rsquo;t see how AI fits into their day-to-day work. That&rsquo;s where advocates come in. They&rsquo;re the human bridge between strategy and execution.

This post focuses on how to build and scale an AI advocates program that drives bottom-up change.

## The role of the grassroots champion

AI advocates are volunteer champions. They&rsquo;re part coach, part translator, and part feedback loop. Their real influence comes from peer-to-peer trust, not executive mandates.

Advocates drive change by:

* **Making AI real:** They find and share practical examples from their teams that show how AI helps in everyday work.  
* **Helping others get started:** They answer questions, mentor peers, and help others clear early hurdles.  
* **Keeping learning continuous:** They act as an ongoing training engine, regularly surfacing the most useful tools, tips, and use cases to help their teams stay sharp and effective.  
* **Surfacing insights:** They bring real-time feedback about what&rsquo;s working, what&rsquo;s confusing, and where more support is needed.

## A blueprint for building your advocate network

You don&rsquo;t need a big budget to launch this program, but you do need intention and support. The goal isn&rsquo;t to control advocates &mdash; it&rsquo;s to empower them.

### Phase 1: Launch and recruitment (first 30 days)

Find your champions by asking for them. The most motivated advocates will raise their hands.

1. **Write a clear call to action:** Explain why the program matters and why being part of it is a unique opportunity.  
2. **Promote it widely:** Post the announcement on high-visibility internal channels with a deadline to create energy around a founding group.  
3. **Onboard thoughtfully:** Host a kickoff session. Set expectations (e.g., 30&ndash;60 minutes a week) and introduce the support available.

### Phase 2: Enablement and community (first 90 Days)

Once you&rsquo;ve recruited your advocates, help them connect and start contributing.

1. **Create a community hub:** Use a private Slack channel (or similar) so advocates can share wins, troubleshoot, and stay connected.  
2. **Set a regular cadence:** Hold monthly check-ins to keep momentum and gather feedback.  
3. **Encourage quick wins:** Ask advocates to start small, share a use case, answer a question, mentor a teammate.  
4. **Highlight early impact:** Publicly celebrate advocate contributions. Recognition builds credibility and attracts more participants.

### Phase 3: Operationalize and scale (day 90 and beyond)

As the program matures, it&rsquo;s time to scale the impact and make the community self-sustaining.

1. **Nominate community leaders:** Identify standout advocates who are ready to take on a leadership role. These leaders can help run check-ins, mentor new advocates, and guide the community&rsquo;s direction.  
2. **Distribute ownership:** Gradually transition responsibility for the hub, updates, and cadence from the central team to community leaders. This shared ownership boosts engagement and long-term sustainability.  
3. **Build leadership structures:** Create lightweight roles (e.g., regional leads, topic leads) to organize efforts and give leaders clear ways to contribute.  
4. **Support with tooling:** Provide templates, facilitation guides, and spotlight opportunities to make it easy for leaders to run effective meetings and showcase progress.

By Phase 3, the advocate network should be less dependent on the core team and more of a self-running engine that is led by the community.

## Navigating early questions from your advocates

Once you've assembled your founding cohort, they will have questions. This is a sign of healthy engagement. Anticipating and addressing these early concerns is key to maintaining momentum. We found two questions came up consistently:

* *"What is the expected time commitment?"* Volunteers are often passionate but also busy. It's crucial to frame the role as flexible and low-pressure. We positioned it as a "choose your own adventure," with a suggested minimum of 30-60 minutes per week. This assures advocates that the program is meant to fit within their work, not become a second job, empowering them to contribute in a way that feels valuable and sustainable.  
* *"What should we be doing right now?*" New advocates are eager to contribute but may need direction. Rather than providing a rigid checklist, we found it more effective to empower them. We explained that their primary role was to be the program's eyes and ears, and that we couldn't build tailored support without their partnership. We encouraged them to start by simply observing and sharing what was already happening in their teams. This includes the successes, the questions, the roadblocks, and to connect with the program lead for 1:1 consulting to brainstorm org-specific strategies.

By framing the program as a partnership, you transform advocates from passive participants into active co-creators of your company's AI journey.

## Measuring the impact of your advocate network

To justify continued investment and steer the program effectively, it's crucial to measure its impact. While the ROI of a grassroots community can seem intangible, you can track its influence through a combination of qualitative and quantitative measures.

* **Build a narrative with qualitative data:** The most powerful evidence of success comes from the stories of impact. Systematically collect and showcase advocate-driven success stories. Did an advocate help a team automate a tedious process? Did they unblock a colleague who was struggling with a new AI tool? These narratives provide concrete proof of value that resonates with leadership and inspires other employees.  
* **Track engagement with quantitative data:** Complement the stories with leading indicators of the program's health and reach.  
  * Community growth: Monitor the membership and activity levels in the advocate community's dedicated communication channels.  
  * Advocate-led activities: Track the number and attendees of workshops, office hours, or learning sessions hosted by advocates.  
  * Program mentions: Include questions about the advocate program in broader employee engagement or productivity surveys to gauge its visibility and perceived helpfulness.

As the program matures, you can begin to correlate these activities with broader business KPIs, such as improvements in developer satisfaction, reduced cycle times, or increased product innovation, demonstrating a clear link between this human infrastructure and business outcomes.

## Your real ROI is your people

The difference between a company that just *has* AI tools and one that is truly *powered by* AI isn't the number of licenses you buy. It's what happens after the purchase. It's about solving the "how do I actually use this for my job?" problem that every employee faces.

This is where your AI advocates program delivers its real value. It's not just a "nice to have" community; it's a strategic investment that pays off in concrete ways:

* You get faster, more authentic adoption. When an engineer sees a teammate, an advocate they trust, share a specific prompt that saved them an hour of debugging, that's more powerful than any top-down mandate. Advocates create this critical social proof.  
* You discover what actually works. Your central program team can't possibly know the unique workflows of every team. Your advocates are on the front lines, discovering novel, practical use cases that solve real problems. They build the internal library of what good looks like.  
* You become faster at adapting. The next big AI feature or tool is always just around the corner. With an established advocate network, you have a built-in team of early adopters ready to learn, test, and then teach the rest of the organization. Your company's ability to absorb change accelerates dramatically.

Ultimately, empowering your most passionate employees to teach their peers is how you scale AI fluency. You're not just building a program; you're building a resilient, self-sustaining learning engine that drives the value of your technology investment far beyond the initial purchase order.

---

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.

To turn AI strategy into sustained impact, you need more than tools, you need internal champions who can bring it to life across every team.

GHAS-unbundling-hero

Resource Page for /enterprise/activating-internal-ai-champions

Playbook series: Activating your internal AI champions

How GitHub scaled AI fluency across its workforce by focusing on people not just technology.

copilot

Resource Page for /enterprise/ai-powered-workforce-playbook

GitHub&rsquo;s internal playbook for building an AI-powered workforce

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.

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&rsquo;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&rsquo;re digging into how you scale day-to-day learning to ensure valuable knowledge doesn&rsquo;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&rsquo;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&rsquo;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&rsquo;s real-time pulse, where ideas come alive, problems are solved, and questions find quick answers.  
* **GitHub Discussions** (or similar) provide structure and continuity&mdash;a searchable home for insights you don&rsquo;t want getting buried in chat.  
* **Internal wikis** are where best practices, reusable assets, and canonical guidance live on.

Here&rsquo;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&rsquo;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&rsquo;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&rsquo;t get lost.

Don&rsquo;t let AI learning stay locked in private chats or isolated teams. Build a network where insights are shared, refined, and documented. That&rsquo;s how you move from scattered experimentation to coordinated transformation.

AI fluency at scale doesn&rsquo;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&mdash;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")

GitHub Copilot is an enterprise-grade productivity tool, [enabling teams code up to 55% faster](https://resources.github.com/learn/pathways/copilot/essentials/measuring-the-impact-of-github-copilot/). Developers using Copilot find that it helps them ship more efficiently, reduce time spent producing boilerplate code, and stay in the flow longer. But here&rsquo;s the catch: Simply purchasing Copilot does not **immediately** unlock its value.

To realize Copilot&rsquo;s full potential, leaders need to roll out Copilot strategically, thinking holistically about access, governance, enablement, and adoption. The effort is worth it. Done well, Copilot adoption at scale accelerates delivery, improves developer satisfaction, and creates lasting ROI for your business.

This document will help you empower your developers to fully embrace the product and make the most of AI-powered development. 

### **1\. Subscribe your organization**

Before you begin your rollout, you&rsquo;ll need to ensure your organization has an active [Copilot subscription](https://docs.github.com/en/copilot/managing-copilot/managing-github-copilot-in-your-organization/managing-the-copilot-plan-for-your-organization/subscribing-to-copilot-for-your-organization) in place. There are two ways to subscribe:

* **Copilot Business**: An individual organization can subscribe directly to Copilot Business. This plan includes core Copilot capabilities like code completion, chat, and code review features.

* **Copilot Enterprise**: For organizations that are part of an enterprise account, Copilot is enabled and managed at the enterprise level. Enterprise owners control subscription and access across multiple orgs within the enterprise. 

Pro tips: If you&rsquo;d like help on deciding which plan is right for your organization, see [this guide to choosing your enterprise&rsquo;s plan for GitHub Copilot](https://docs.github.com/en/copilot/rolling-out-github-copilot-at-scale/planning-your-rollout/choosing-your-enterprises-plan-for-github-copilot). And If you&rsquo;re unsure which plan you're currently on, or need enterprise-level access, visit your [Copilot settings](https://github.com/settings/copilot) to request access or talk with your enterprise owner.

**2\. Set policies that fit your needs**

Next, you&rsquo;ll need to [set up policies](https://docs.github.com/en/copilot/managing-copilot/managing-github-copilot-in-your-organization/managing-policies-for-copilot-in-your-organization) to control how Copilot behaves in your org. This includes deciding whether features like [Copilot Chat](https://docs.github.com/en/copilot/using-github-copilot/copilot-chat) or [Copilot coding agent](https://docs.github.com/en/copilot/managing-copilot/managing-github-copilot-in-your-organization/adding-copilot-coding-agent-to-organization) are enabled and setting filters for content or code matching.

But policies aren&rsquo;t just technical controls, they&rsquo;re also a key part of your rollout strategy. Use role-based permissions to pace adoption, manage demand, and build momentum across your teams.

Here&rsquo;s how a phased rollout can look:

* **Start small:** Many organizations begin by enabling Copilot for a select group of early adopters. This helps surface blockers, uncover edge cases, and create internal champions who can help others adopt the tool.

* **Expand deliberately:** As confidence grows, you can scale access team-by-team or business unit-by-business unit.

* **Leverage self-service:** Many successful organizations enable self-service licensing, allowing developers to claim seats without lengthy approvals, accelerating adoption while keeping license management flexible.

Why take this approach? A phased rollout gives you better visibility into adoption patterns, developer satisfaction, and technical blockers, leading to a smoother, more durable rollout at full scale. Learn more with these [tips for a successful rollout of GitHub Copilot](https://resources.github.com/learn/pathways/copilot/essentials/tips-for-a-successful-rollout-of-github-copilot/).

**3\. Set up networking (if needed)**

While most teams can start using Copilot immediately, certain enterprise environments may require additional networking configuration to ensure seamless access. If your developers connect through a [proxy server or firewall, you'll need to configure a few things for Copilot](https://docs.github.com/en/copilot/managing-copilot/managing-github-copilot-in-your-organization/configuring-your-proxy-server-or-firewall-for-copilot):

* Add key Copilot-related URLs to your allowlist  
* Ask your developers to configure proxy settings in their IDE and install custom SSL certificates [if required](https://docs.github.com/en/copilot/managing-copilot/configure-personal-settings/configuring-network-settings-for-github-copilot).

Why this matters: Addressing network prerequisites early avoids common deployment blockers, reduces support tickets, and ensures your developers can begin using Copilot without delays. This is especially important if you're running a staged rollout where early adopters serve as your first test group. Identifying network issues at this stage helps clear the path for wider adoption.

More detailed guidance is available in [GitHub&rsquo;s networking configuration documentation](https://docs.github.com/en/rest/code-security/configurations?apiVersion=2022-11-28).

**4\. Grant access to the right people**

Deciding who gets access and when is one of the most important levers you have to drive a successful rollout. [Common access models](https://docs.github.com/en/copilot/managing-copilot/managing-github-copilot-in-your-organization/managing-access-to-github-copilot-in-your-organization/granting-access-to-copilot-for-members-of-your-organization) include:

* **Targeted pilots:** Enable Copilot for select teams or early adopters who are excited to test and provide feedback.

* **Org-wide deployment:** Grant access to all eligible developers at once, if you&rsquo;re confident in your environment and the demand.

* [**Self-service model**](https://docs.github.com/en/copilot/rolling-out-github-copilot-at-scale/assigning-licenses/setting-up-a-self-serve-process-for-github-copilot-licenses)**:** Allow developers to claim a license without requiring manual approval (a highly effective approach we see across many customers).

Why start with early adopters? Smaller pilot groups help surface technical blockers, build internal champions, and generate early wins. These teams often become internal advocates who help promote Copilot adoption across the organization.

You can find more rollout strategies in [GitHub&rsquo;s rollout best practices](https://resources.github.com/learn/pathways/copilot/essentials/tips-for-a-successful-rollout-of-github-copilot/).

**5\. Drive adoption across the org**

The most successful deployments [build support and energy around Copilot](https://docs.github.com/en/copilot/rolling-out-github-copilot-at-scale/enabling-developers/driving-copilot-adoption-in-your-company). They educate teams, share use cases, and track success.

Strategies that drive lasting adoption include:

* **Internal champions:** Empower enthusiastic developers to serve as advocates and peer coaches.

* **Training sessions:** Host lunch-and-learns, office hours, or Q\&A sessions to share best practices and answer common questions.

* **Leadership alignment:** Ensure executives are visibly supportive of Copilot adoption to signal org-wide commitment.

* **Success tracking:** Monitor usage patterns and gather feedback to refine your rollout as adoption grows.

The goal, of course, is to not just help employees access Copilot, but to actually integrate it into their daily work and start unlocking real productivity gains and ROI. Implementing a strong [enablement plan](https://resources.github.com/engineering-system-success-playbook/) ensures your AI adoption efforts actually stick.

**6\. Enhance the Copilot experience**

Once your teams are comfortable with Copilot, it&rsquo;s time to glean even more value with GitHub&rsquo;s latest AI features, including:

* [**Copilot coding agent**](https://docs.github.com/en/copilot/rolling-out-github-copilot-at-scale/enabling-developers/using-copilot-coding-agent-in-org): Automate tasks asynchronously and reduce manual toil.

* [**Copilot code review**](https://github.blog/changelog/2025-04-04-copilot-code-review-now-generally-available/): Get intelligent, customizable suggestions during pull requests.

* [**Copilot Spaces**](https://www.youtube.com/watch?v=c5iFS0nlAxM): Share context across teams to improve Copilot&rsquo;s relevance and recommendations.

* [**GitHub Models**](https://github.blog/news-insights/product-news/introducing-github-models/): Build custom developer tools that call AI models within your enterprise environment.

* [**Copilot Autofix**](https://docs.github.com/en/code-security/code-scanning/managing-code-scanning-alerts/responsible-use-autofix-code-scanning): Automatically remediate security vulnerabilities flagged in code scanning.

As adoption matures, these capabilities allow you to optimize security, automation, and code quality. Want to see how it all comes together? Check out [**this example**](https://github.blog/news-insights/product-news/from-pair-to-peer-programmer-our-vision-for-agentic-workflows-in-github-copilot/) of how GitHub&rsquo;s AI features work in tandem across the software development lifecycle.

## The bottom line

Rolling out Copilot across your org is a chance to boost velocity, creativity, and collaboration among your dev teams. But getting it right means thinking beyond activation and focusing on the experience, security, and strategy behind it.

Copilot is ready to work hard for your teams, helping deliver increased productivity and profit. Are you ready to let it fly?

[Build what&rsquo;s next with Copilot today \>](https://github.com/features/copilot/copilot-business)

---

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.

To realize Copilot&rsquo;s full potential, leaders need to roll out Copilot strategically, thinking holistically about access, governance, enablement, and adoption.

Resource Page for /enterprise/copilot-adoption-rollout-impact

How to lead a GitHub Copilot rollout that removes blockers and delivers real impact

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 &mdash;** 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 &mdash; it's about unlocking safe, scalable adoption. Clear policies give employees the confidence to move fast without second-guessing what&rsquo;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&rsquo;s rapid evolution &mdash; 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 [Bronte van der Hoorn](https://github.com/brontevdh)

When considering AI in software development, many organizations focus narrowly on code generation. But this view misses the transformative potential of AI agents across the engineering system. Our [Engineering System Success Playbook (ESSP)](https://resources.github.com/engineering-system-success-playbook/) provides the framework for understanding how AI agents can elevate quality, velocity, and developer happiness across the organization.

At GitHub, our Trade Compliance team's experience illustrates how AI agents can transform engineering systems &mdash; and the organization as a whole &mdash; when approached intentionally through the ESSP framework.

## Step 1: Identifying barriers to success

One of the team&rsquo;s main challenges was finding time to document complex trade controls embedded in Ruby code. This is a common engineering issue that creates knowledge silos, where non-engineering stakeholders are unable to access information that they need. The team was curious if GitHub&rsquo;s [Copilot coding agent](https://github.blog/news-insights/product-news/github-copilot-meet-the-new-coding-agent/) could help them document the controls more efficiently, thus breaking down these knowledge silos.

*"Good documentation scales our small team by making key logic accessible across GitHub. However, it's an ongoing challenge to prioritize this work. We saw experimentation with AI as a means to address this challenge."* Shruti Corbett, Engineering Director

## Step 2: Evaluating the path forward with a pilot

Rather than viewing AI as a one-off automation tool, the team initiated a pilot to test how effectively the agent could translate complex code into clear documentation as a potential long-term solution.

During this pilot phase, they invested time in prompt engineering to increase the quality of the agent's documentation and [Mermaid diagrams](https://github.com/mermaid-js/mermaid) from complex code structures. The team recognized that while the initial investment in refining prompts was substantial, the greatest benefit would come from scaling the agent for long-term use across the team. 

*"We'll benefit from this experimentation when we reuse the prompt to keep our docs current."* Shruti Corbett, Engineering Director

## Step 3: Implementation, monitoring, and adjustment

Our Trade Compliance engineering lead observed during the pilot that the team could make the most of the agent by sharing optimized prompts more broadly across the organization. By democratizing access to these carefully crafted prompts, they could multiply the return on their initial investment and create a compounding effect throughout the engineering system.

This engineering insight improved what might have been a localized productivity increase into an opportunity for organization-wide knowledge-sharing transformation. The team is now committed to exploring how they can better share, version, and continuously improve these prompts as organizational assets.

*"By treating prompts as reusable assets, a one-time investment becomes long-term leverage, first for our team and potentially across the company."* Shruti Corbett, Engineering Director

## The systemic impact of AI agents in engineering systems

As they continue to use their refined pilot prompt at scale, the team anticipates that documentation will become more comprehensive and accessible. When knowledge flows more freely between teams, collaboration barriers diminish, potentially contributing to faster time to market for new features and compliance updates. 

This effect may compound over time as more teams adopt similar documentation practices leveraging agents, creating an interconnected knowledge ecosystem that accelerates innovation.

## Three principles for engineering leaders in the age of agents

For enterprise leaders applying GitHub&rsquo;s ESSP today, our Trade Compliance case study highlights key insights:

1. **Investment vs. cost mindset:** Approach prompt engineering and agent experimentation as investments in knowledge assets that compound in value over time, just like well-designed code libraries.  
2. **Complementary partnerships:** Use the ESSP framework to identify where human and AI strengths can be optimally deployed with humans providing judgment and strategy, while agents can deliver consistency and scale.  
3. **Systems transformation:** Measure success not just in the initial experience, but in how AI agents can change *how* you work across your engineering system &mdash; reducing friction points that previously hindered innovation.

## Engineering success through human-AI collaboration

AI's potential isn't in replacing human creativity, but in removing the toil that constrains it. For our Trade Compliance team, the creation of documentation no longer competes with development for engineering attention. This means knowledge can flow more easily between teams and may unlock even more engineering success.

It's crucial to recognize that in this case, the transformative value of AI agents emerges through scale and reuse, when refined prompts become organizational assets that teams can leverage repeatedly. This is where the engineering system truly benefits from the compounding returns on that initial effort.

Our Trade Compliance team's journey shows that when AI agents are integrated intentionally into engineering systems, they become more than tools. They can become partners in transforming how we build software together, driving better business outcomes.

---

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.

See how GitHub&rsquo;s Trade Compliance team turned AI experimentation into lasting engineering system improvements.

Resource Page for /enterprise/engineering-knowledge-scalable-advantage-ai-agents

Turn engineering knowledge into a scalable advantage with AI agents

Published via [GitHub Executive Insights](https://github.com/solutions/executive-insights "Executive insights, curated just for you")

We've gathered everything enterprise teams need to hit the ground running with GitHub Copilot. From initial setup to advanced features, this guide will walk you through the essential resources to make your Copilot implementation successful.

Curious what GitHub Copilot is and how it can benefit your business? Check out our [essentials of GitHub Copilot learning pathway](https://resources.github.com/learn/pathways/copilot/essentials/essentials-of-github-copilot/). 

## Foundation & setup
   - [Comprehensive quickstart guide](https://docs.github.com/en/copilot/quickstart)
   - [Enterprise setup instructions](https://docs.github.com/en/enterprise-cloud@latest/copilot/setting-up-github-copilot/setting-up-github-copilot-for-your-enterprise)
   - [Scaling and adoption best practices](https://docs.github.com/en/copilot/rolling-out-github-copilot-at-scale/driving-copilot-adoption-in-your-company)

## Team resources
   - [Step-by-step tutorial series](https://github.com/features/copilot/tutorials)
   - [Real-world examples and use cases](https://github.blog/developer-skills/github/what-can-github-copilot-do-examples/)
   - [Practical workflow integration tips](https://resources.github.com/learn/pathways/copilot/essentials/tips-for-a-successful-rollout-of-github-copilot/)

## Get the most out of Copilot
   - [Copilot Chat Cookbook for enhanced productivity](https://docs.github.com/en/copilot/copilot-chat-cookbook) (find examples of prompts to use with GitHub Copilot Chat)
   - [Usage tracking with metrics API](https://docs.github.com/en/copilot/rolling-out-github-copilot-at-scale/analyzing-usage-over-time-with-the-copilot-metrics-api)
   - [Latest capabilities across programming languages](https://docs.github.com/en/enterprise-cloud@latest/get-started/learning-about-github/github-language-support)

##  Tackling technical challenges
   - [Strategies for documenting legacy code and maintaining clean, efficient codebases](https://github.blog/ai-and-ml/github-copilot/documenting-and-explaining-legacy-code-with-github-copilot-tips-and-examples/)

## Looking forward
   - [New model options including Google Gemini 2.0 Flash](https://github.blog/changelog/2025-02-05-google-gemini-2-0-flash-is-now-available-to-all-copilot-users-in-public-preview/)
   - [OpenAI o3-mini availability](https://github.blog/changelog/2025-02-06-openai-o3-mini-is-now-available-in-github-copilot-free/)
   - [Upcoming features and improvements: GitHub Copilot, The Agent Awakens](https://www.youtube.com/watch?v=C95drFKy4ss)

Your development teams are about to experience a significant boost in productivity with GitHub Copilot. These resources will help ensure a smooth rollout and optimal usage across your organization. As Copilot continues to evolve with new features and capabilities, bookmark our "[What's Next](https://github.com/features/copilot/whats-new)" page to stay current with the latest updates and improvements.

---

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.

Getting started with GitHub Copilot doesn't have to be complicated.

Resource Page for /enterprise/essential-copilot-resources

Essential GitHub Copilot resources for enterprise teams

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&rsquo;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&rsquo;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&rsquo;t about passively signing checks; it&rsquo;s about actively setting a new standard. It&rsquo;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&rsquo;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&rsquo;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&rsquo;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&rsquo;s about actively creating opportunities for their teams to apply the new mandate. At GitHub, we&rsquo;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&rsquo;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.

/enterprise/

April ‘25 enterprise roundup

August ‘25 enterprise roundup

December ‘25 enterprise roundup

July ‘25 enterprise roundup

June ‘25 enterprise roundup

March ‘25 enterprise roundup

May ‘25 enterprise roundup

November ‘25 enterprise roundup

October ‘25 enterprise roundup

September ‘25 enterprise roundup