AI + Data Transformation | Engineering Enablement

From AI access to 92% active adoption in four months

A global transportation technology company needed GitHub Copilot to become part of how 500 software engineers worked. Aditi Consulting built the governance, enablement and feedback model that helped drive adoption across 27 engineering teams and demonstrated early productivity gains.

At a glance

500 software engineers
across 27 teams

Scale

GitHub Copilot
Enterprise

Platform

4 months

To reach 92% active adoption

The Challenge

Making AI useful across 27 software engineering teams

The Challenge

Deploying GitHub Copilot was only the first step. The organization needed a consistent way to help developers use the technology confidently, responsibly and effectively, supported by clear governance, practical training and measurable value.

The challenge was to create an adoption model that could

  • Establish clear governance for AI-assisted development
  • Work within existing development environments
  • Equip engineers to apply Copilot in their daily work
  • Create recurring feedback between users and the program team
  • Track adoption, licence utilisation and productivity
  • Sustain usage beyond the initial rollout

The Solution

Building adoption into the rollout

The Solution

Aditi designed a structured adoption program that connected governance, enablement, integration, feedback and measurement.

  • Governance: Established a common framework for AI-assisted development. This provided a consistent foundation for adoption across all 27 teams.
  • Enablement: Conducted biweekly training to help engineers understand how to apply Copilot in their work, rather than leaving each team to determine its own approach.
  • Integration: Enabled Copilot within existing development environments, allowing engineers to use it within familiar tools and workflows.
  • Feedback: Created monthly feedback loops to support continued adoption and improve usage.
  • Measurement: Tracked adoption, license utilization and time savings.

Together, these elements turned the implementation into an ongoing adoption program that could be monitored and improved over time.

Business Outcomes

From rollout to measurable results

92%Active adoption within four months
77%Licence utilization
1,457Hours saved across
January–April

What Changed

AI adoption became visible and measurable.

The program embedded Copilot within existing engineering workflows, while governance, training and feedback helped teams move from access to active use.

Client Perspective

“Copilot serves as a great companion in my daily work—whether I’m coding, testing, documenting, or writing emails. It significantly speeds up my workflow while improving quality.”

— Engineering team member

Technology Environment

The platform, environments and languages in scope.

Technology Environment

GitHub Copilot Enterprise, inside the tools the engineers already used.

Technology Environment

Platform

GitHub Copilot Enterprise

Development environments

  • Visual Studio Code
  • Visual Studio
  • JetBrains IDEs
  • Other existing development environments

Languages and frameworks

  • C++
  • Java
  • Python
  • TypeScript
  • React

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