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 — and the organization as a whole — when approached intentionally through the ESSP framework.

## Step 1: Identifying barriers to success

One of the team’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’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’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 — 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’s Trade Compliance team turned AI experimentation into lasting engineering system improvements.

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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")

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’s the catch: Simply purchasing Copilot does not **immediately** unlock its value.

To realize Copilot’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’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.

What’s the key difference between these plans? Some advanced features—like [Copilot coding agent](https://docs.github.com/en/copilot/using-github-copilot/coding-agent), knowledge bases, and fine-tuning with GitHub Models—are only available with Copilot Enterprise. If your organization wants early access to new capabilities or deeper AI customization, Copilot Enterprise provides the broader feature set. 

Pro tips: If you’d like help on deciding which plan is right for your organization, see [this guide to choosing your enterprise’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’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’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’t just technical controls, they’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’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’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’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’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’s time to glean even more value with GitHub’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’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’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’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’s full potential, leaders need to roll out Copilot strategically, thinking holistically about access, governance, enablement, and adoption.

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