Case Study: Driving AI Adoption and DevOps Maturity at Scale — Nike Global Tech
Client Overview
Nike Global Technology comprises approximately 12,000 professionals powering the digital backbone of one of the world’s most recognized brands. Of these, roughly 6,000 are hands-on-keyboard developers responsible for building and maintaining a broad portfolio of digital products and platforms. With accelerating interest in generative AI, Nike’s technology leadership sought to drive meaningful adoption of GitHub Copilot to enhance productivity, reduce cycle times, and support innovation at scale.
The Challenge
While GitHub Copilot offered the promise of faster coding and developer efficiency, large-scale adoption within Nike Global Tech required more than simply provisioning licenses. Leadership faced two critical gaps: first, a lack of visibility into how the tool was actually being used across thousands of developers; and second, no consistent way to measure whether Copilot was driving real improvements in software delivery performance.
In short, there was no single definition of “success” — and no data infrastructure to support the AI adoption journey.
Strategy & Goals
To address these challenges, Nike’s technology team designed a unified strategy centered around three core objectives:
Establish clear success criteria for engineering health and performance by adopting the four DORA metrics (Deployment Frequency, Lead Time to Change, Change Failure Rate, and Mean Time to Recovery).
Develop a centralized dashboard to visualize DORA metrics across all developer repositories and surface actionable insights.
Integrate GitHub Copilot usage data with engineering performance metrics to correlate AI tool adoption with business outcomes.
Critically, this effort was paired with a broader change management strategy. The team began collecting use cases and best practices from high-adoption teams, while also gathering feedback from groups where adoption lagged. This dual approach ensured that the rollout strategy was informed by real-world experiences rather than assumptions.
Execution
The first step was operationalizing the DORA metrics across Nike’s diverse and distributed codebase. A custom dashboard was developed to track software delivery performance across all development repositories. Simultaneously, GitHub’s billing API was used to collect license and usage data for Copilot, enabling the team to understand adoption patterns across business units and geographies.
While technical integration was relatively straightforward, the real complexity lay in organizational alignment. As is often the case in large enterprises, a mandate alone wasn’t enough to create consistent behavior. True leadership required equipping stakeholders with data to focus their energy where it would have the greatest impact.
By combining usage data with performance metrics, Nike created a feedback loop that allowed leadership to support under-adopting teams, surface emerging success stories, and allocate resources with greater precision.
Insights & Learnings
One of the key insights from this effort was that visibility enables leadership. With the right data in hand, Nike’s leaders could prioritize support, coaching, and communication to teams who needed it most — rather than relying on assumptions or fragmented reporting.
Another critical lesson was the importance of selecting the right metrics. In any large organization, "you get what you measure" — and misaligned KPIs can lead to unintended behavior. By anchoring on well-established DORA metrics and avoiding overly simplistic success criteria (e.g., just license activation), the team ensured that their measurement efforts encouraged sustainable, high-quality development practices rather than vanity metrics.
Results
While the program is still evolving, the initiative has already delivered strategic wins:
Nike now has real-time visibility into engineering health across thousands of developers using standardized metrics.
Leadership can identify high-performing teams and replicate successful AI adoption practices across the organization.
Teams have begun aligning around shared goals and shared data, strengthening cross-functional collaboration and platform thinking.
This initiative has positioned Nike Global Tech to be a data-informed leader in enterprise-scale AI adoption—maximizing its investment in generative tooling while reinforcing a strong engineering culture.
Deliverables
Centralized DORA metrics dashboard across all developer repositories
Integrated GitHub Copilot usage and license visibility
Strategy for aligning AI adoption with performance insights
Change management framework based on user feedback and team benchmarking
