I help teams move from using AI in isolation to designing intentional workflows: understanding the opportunity, providing the right context, connecting the necessary tools and keeping people accountable for the outcome.
The question isn’t "how do we add AI?". It’s "where does AI actually create leverage?".
A model alone is not enough. A useful AI system needs a lot around it: context, tools, data, permissions, feedback and someone accountable for the outcome.
The model is just one layer in the middle. The value —and the risk— is in how everything around it is designed.
A simple map to locate how your team works with AI today, and decide the next step with judgment.
A few people use AI tools individually and occasionally.
AI consistently assists individual work.
Useful AI workflows become repeatable and documented.
AI can access approved information and tools.
Agents perform bounded multi-step workflows with controls and review.
Products or processes intentionally designed around human + AI collaboration.
Higher maturity is not automatically better.
Not every company needs stage 6, and not every workflow should become agentic. The right level depends on the problem, risk, economics, data and people.
Moving software teams from casual usage to intentional, reviewed AI-assisted engineering.
Connecting scattered company knowledge and automating repetitive work, with human approval.
Using AI as leverage to build real products, from discovery to ship.
The more capability we delegate to our tools, the more it matters to know what to ask, what to review and what decision to make.
And those remain human decisions.
Tell me the workflow or the problem and we’ll assess it together — without assuming the answer is "more AI".