Operations came first.
AI followed.

In supply chain, that meant looking at inventory, purchasing, and the handoffs between teams. One improvement was redesigning a min/max system that reduced lab inventory by 20%. That was an operational result, not an AI consulting engagement.

In technology projects, the work is sorting out requirements, dependencies, and the decisions that keep a project moving. I now manage technology projects in a global logistics environment. Alongside that work, I’ve conducted AI training and working sessions with professionals and senior leaders.

My independent consulting practice is new. The experience I bring comes from my operational career and those training sessions.

A tool can produce a tidy answer while leaving the underlying problem untouched. If a report takes too long because nobody agrees on the numbers, faster writing won’t resolve the disagreement.

My process-improvement background makes me look at those problems before reaching for a tool. Once the task is clear, AI can be useful for organizing source material, exploring an approach, or drafting something we can test.

My degree is in English, with a writing concentration. That matters here too. A large part of management work is making a complicated situation understandable without making it sound simpler than it is.