I have built a career out of noticing the gap between how work gets done and how it could be done, and then being the person who steps in to close it.
Lately that has mostly meant teaching non-technical teams to find, build, and fund their own AI use cases. Before that it meant running programs, running operations, and making the case for change to the people holding the budget.
These look like four different careers on paper, and in practice they have all been the same move: find the thing nobody has claimed yet, understand it properly, and then do it. Every role below started as work that was not in anyone's job description.
Reading a business to find where value actually gets created and where it quietly leaks out, then making the case for change to the people who control the budget. Ideas do not move without a number attached.
Built an AI enablement program from scratch inside a global electronics manufacturer, serving six operational functions. Non-technical teams went from curious about AI to pitching their own funded use cases.
Supported an international engineering team as they stood up a state-of-the-art AI and data runtime and harness platform inside a global applied-AI office, coordinating across time zones, languages, and very different levels of technical maturity. Also led training delivery.
Operational readiness and service delivery at real scale, with real throughput and turnaround targets. This is where I learned that the hard part of any change is never the tool.
These are the numbers I can stand behind. The teams did the work. My job was to give them a method, get the room safe enough to use it, and stay long enough to see it land.
Specific AI use cases surfaced across departments and business functions.
Person offshore operations workforce trained, driving a 200%+ turnaround-time improvement.
Operational functions enabled, from HR and finance to legal, supply chain, IT, and marketing.
Years designing and delivering training in complex service-delivery environments.
The hard part is never the tool. It is the distance between finishing a course and shipping something that actually delivers value, and everything I do is built around closing that distance. The shape is deliberately repeatable, so it works for one team or for twenty.
Before any workshop I learn where the team actually is. Surveys and light pre-work surface the real pain points, and the material gets rebuilt around what I find. Nothing generic survives this step.
Hands-on rather than lecture. Teams work their own use cases the whole way through, and at the end they pitch them to their own sponsors, the way you would pitch an investor.
This is where most programs stop and mine keeps going. I track each use case toward development, run office hours to keep people unblocked, and recruit champions inside the team to carry it.
Turn a vague frustration into a concrete problem worth solving.
Design how AI fits the real workflow, with a human in the loop where it counts.
Decide what to build, what to buy, and how fast you can get to something working.
Put numbers on it. This is the step that turns a good idea into a funded one.
Put it in front of real users and learn what the slides could not tell you.
Instrument the result so the impact is provable and the next move is obvious.
Adoption happens when people feel safe enough to admit what they do not know. These are from participants in recent enablement workshops.
"Kevin created an atmosphere of psychological safety, enabling many valuable cross-functional conversations during our recent workshop."
"The facilitator explained concepts well. I appreciated when he slowed down at times to make sure nobody in the room was left behind."
"This workshop was time well-spent. It opened my eyes to new ways of using AI."
I came to this work through adult learning rather than engineering, which turns out to be the useful direction to come from. I hold a BA in Communications, I have worked in Japan and the Philippines, and I live in Vancouver, Washington.
The other thing worth knowing is that I build constantly. It is how I learn a tool properly, and it is where most of what I teach came from originally.
The goal has never been to automate people out of a job. It is to automate the boring parts so that capable people can spend their time on the work that actually needs them.
A running list of projects that exist because I wanted to know whether I could. Several of them turned into things I now teach professionally, which was not the plan.
A personal agent orchestrator with a persistent context brain, subagent workflows, a tracked portfolio with value and complexity scoring, and a dashboard to see the whole thing at once. My favorite part is the least glamorous: a budget guard that stops a run before it overspends, and a verifier agent that grades the work before anything reaches me.
It is the most honest answer I can give to "do you actually use this stuff." I have been living inside it for a year.
Several small games, some finished and published, some still only a good title and a bad idea. They live together under a studio name so they stop cluttering my desktop.
A fantasy home run derby that runs on a Google Sheet and the MLB stats feed, and a fantasy football assistant that writes my league a personalized newsletter every week. Sometimes the right tool is the one already open.
Tracing my family from a farm in Pudasjärvi parish to a dairy farm in Eden Valley, Washington, working from communion book scans and a lot of guessing at handwriting. The goal is a written narrative my family can actually read.
From writing blogs and novels to building mental health awareness communities to producing original music, art, games, and other products and content, Meatbag Made is where my wife and I keep our creative portfolio.
You do not need a project, a budget, or a reason. If something about the way your team works is slow, expensive, repetitive, or just strange, tell me about it and I will give you a straight answer about where I would look first.
No form, no sequence, no funnel. The message goes to my inbox and I answer it myself, usually within a day or two.