I started out as an engineer, moved into enterprise delivery, and then into AI and automation consulting. I still write code. Most of what I do sits between those two worlds.
Over the last five years, I’ve worked on AI and automation strategy, use-case prioritisation, ROI modelling and maturity assessment, usually with leadership teams trying to work out where to start. Alongside that, I design and build the systems themselves, which keeps the advice honest about what is actually possible.
Recent work includes RxField, a field-force app I designed and built for a pharmaceutical client in React Native, TypeScript and Supabase, replacing manual sales tracking across a three-tier manager hierarchy. Also an automation programme for a hospitality client covering 50 properties.
I can sit in a conversation about payback periods and a conversation about data models and integration on the same day, and I’ve been accountable in both. It usually means I can tell early whether an idea is buildable, rather than six months in.
Most AI projects fail for reasons that have nothing to do with the model. What actually matters:
- Understanding the real problem
- Working out whether AI is the right answer, or whether something simpler would do
- Checking the data and the integrations before anyone commits budget
- Involving the people whose work actually changes
- Building it properly, then measuring whether it changed anything
I’m open to roles in AI adoption, business transformation and process improvement, and to consulting work.
