AI Adoption - Get AI into your development process.
Most AI rollouts stall in the same place. Licences are bought, two developers get enthusiastic, the rest quietly go back to how they worked before, and six months later somebody asks what the return was.
The tools aren’t the hard part. The hard part is consistency — getting the same quality of output regardless of which developer, which application, which day.
Where we’ve done this
I’ve led teams through the full evolution of AI code generation in my own organisations: GitHub Copilot, then Cursor, then Claude Code. Different tools, same lesson each time — the value shows up when the process changes, not when the tool is installed.
The engagement
- Baseline. How your team actually works now. Not the wiki version.
- Pilot. One team, one real project, one tool. Measured.
- Rollout. Team by team, with us in the room, until it’s second nature rather than an initiative.
- Widen the surface. Code generation is the obvious use. The bigger wins are usually elsewhere: unit, end-to-end and integration test generation, ticket creation and grooming, code review, production release.
- Write it down. The deliverable.
The deliverable
A framework: documented rules, skills and processes that automatically guide the AI tooling in a consistent manner.
Concretely, that’s a rules and instructions layer committed to your repositories; a skills library for the tasks your team repeats; testing and review standards the tooling enforces rather than suggests; and onboarding docs that get a new developer productive on any of your applications.
The point is redundancy. When any developer can pick up any application and deliver, you’ve removed the single-person dependency that small high-performing teams otherwise create.
Let’s have a chat.
Thirty minutes, no pitch deck. Tell me what you’re building or what’s stuck, and I’ll tell you what I’d do.
Ireland. Remote-first.