I like the part between “this process is broken” and “this is how we work now.”

My career has been a progression toward one kind of work: taking ambiguous operational problems and turning them into systems people can actually use.

I started as a teacher, moved into e-commerce and enterprise technology implementation, led software transformation across a distributed franchise network, and eventually became deeply involved in enterprise AI adoption and workflow automation.

Today, I work across business strategy, implementation, AI, automation, and change. I’m comfortable sitting with an operator to understand why a process is frustrating, translating that conversation into requirements, building or prototyping a better workflow, working with technical teams when necessary, and then helping the organization adopt the change.

That last part matters to me.

A technically impressive solution that never becomes part of someone’s real workflow is not a successful implementation.

I’m particularly interested in roles where I can own the space between customer or stakeholder discovery and measurable deployed outcomes — especially in AI implementation, AI deployment, forward-deployed product, AI transformation, and product-adjacent solutions work.

Start with the problem, not the technology.

The best automation removes work, not just clicks.

Human-in-the-loop is a design choice, not a failure of automation.

Deployment is not done when the system goes live. It is done when the new behavior becomes normal.

If we cannot explain the business outcome, the AI project probably is not ready.