The situation
The focus was to support AI adoption across engineering groups with different needs, from first use to specialist workflows. My remit connected individual use with shared practices, clear ownership and a repeatable way to support teams.
My role
I lead the AI-SDLC transformation in this Fortune 100 environment, supporting more than 10,000 engineers. My work connects Copilot adoption and governance, AI Champions, tailored engineering sessions, custom agents and measurement.
What the operating model included
- Adoption and governance. Policy, safe-use standards, and a rollout model that treated activation as the start, not the outcome.
- AI Champions and communities. Peer-led networks so practice spread through engineering rather than a single enablement day.
- Tailored sessions. Specialist training for specific engineering groups, with repeat requests rather than a one-off awareness programme.
- Custom agents and reusable instructions. Shared artefacts that teams could use without inventing a private standard.
- Governance and rollout. A controlled path for distributing agents and instructions, with human review and accountability attached.
What changed
Adoption gained named owners and an AI Champions community. Engineering groups had tailored training, reusable instructions and a controlled distribution path for agents. Repeat requests for specialist sessions provided a signal of demand for further enablement.
The separate platform foundation moved more than 14,000 users from Azure DevOps to GitHub. That is a completed migration outcome. The 10,000+ engineers figure describes the scope supported by the AI-SDLC transformation.
Evidence and validation
The results described here are the capabilities put in place. The engineer count describes programme scope, rather than measured adoption or a productivity improvement.
Further project details or references may be available, subject to client permission and confidentiality obligations. You can also review my background on LinkedIn.