I help teams move from AI curiosity to AI-enabled software delivery.
I work in GitHub Offerings and AI enablement, where my focus is turning fast-moving GitHub Copilot and agentic development capabilities into practical material that teams can actually use: workshops, delivery guidance, demos, field notes, governance discussions, and adoption patterns.
The work sits at the intersection of developer productivity, enterprise readiness, AI-assisted engineering, and enablement at scale.
- GitHub Copilot enablement — helping engineers and organizations understand where Copilot fits across planning, coding, testing, review, and modernization.
- AI offerings and delivery content — building and refining workshops, training paths, demos, labs, speaker notes, and reusable delivery assets.
- Agentic development workflows — exploring how coding agents, MCP, custom instructions, repository context, and automation change the way teams build software.
- Enterprise adoption patterns — translating governance, security, policy, measurement, and rollout strategy into guidance that is easier for teams to apply.
- Operational quality — keeping offerings current through review loops, diff tracking, quality checks, release awareness, and structured handoff processes.
I like the middle between new AI capability and real enterprise usage.
That usually means answering questions like:
- Where does this capability fit in the developer workflow?
- What should a team try first?
- What needs guardrails before it scales?
- How do we explain the value without overselling it?
- How do we keep training material current when the platform keeps changing?
- How do we make adoption measurable, repeatable, and useful for real developers?
That is the work I enjoy most: taking something new, technical, and sometimes unclear, then turning it into a path a team can follow.
| Area | What it means to me |
|---|---|
| AI-assisted development | Helping teams use AI as part of their workflows, not just as a chat window next to the code. |
| Copilot adoption | Moving from individual experimentation to team-level practices, governance, and measurable outcomes. |
| Offerings development | Creating delivery assets that are accurate, usable, current, and easy for other engineers to run. |
| Enterprise readiness | Balancing speed with security, policy, trust, and operational consistency. |
| Hands-on enablement | Teaching through examples, demos, exercises, and practical workflow changes. |
I try to keep things practical.
The best AI guidance is not just a list of features. It should help a team understand what changed, why it matters, what to try, what to avoid, and how to know whether it is working.
I care about:
- Clear explanations
- Useful demos
- Repeatable patterns over one-off wins
- Governance that helps teams move safely instead of freezing them
- Training that respects the developer's actual day-to-day work
A few areas I keep coming back to:
- Copilot across the full SDLC
- Agentic workflows and human review loops
- MCP and tool-using agents
- Custom instructions and repository context
- AI governance for enterprise engineering teams
- Measuring developer productivity and adoption signals
- Keeping technical enablement content fresh as the platform evolves
I like learning, experimenting with tools, and building small things that help me understand bigger systems. Some projects are polished. Some are experiments. Most are part of the same habit: learn it, break it, rebuild it, explain it better.
If you are thinking about GitHub Copilot adoption, AI-assisted software delivery, or how teams should prepare for agentic workflows, I am always happy to compare notes.
Opinions here are my own and do not represent my employer.