How I work.
How I take AI from "we should try this" to something a regulated shop can actually stand behind: adoption that sticks, governance that isn't theater, and launches that don't slip. Same rule everywhere - make the hard part feel possible, and leave the place better than I found it.
AI work drifts. I keep yours pointed at one outcome, not the newest shiny tool. That's Stay in Orbit: every prompt earns its place, or it gets cut.
Find your Moon.
Most AI projects fail before a prompt gets written: nobody named the outcome first. I do, every time. That's finding your Moon.
Too many people start with ChatGPT. I start with the Moon.
The method isn't a slide, it runs live: this site and the operation behind it ship through it every day. See the architecture →
Four hard parts,
and how I work them.
Most AI problems are one of these four - and they blend: adoption usually needs a little governance, and a stalled rollout usually ends in adoption done right. Here's how I think about each.
AI adoption & enablement
The licenses are bought; usage is low. The way I move "we should try AI" to "we use it every day and love it" is rollout strategy, hands-on prompt training, the change management that makes it stick, and starter automations built with the team, not handed down to it.
AI governance & guardrails
Most "AI adoption" quietly ships tech debt: unverified docs, hallucinations nobody caught, automations no one maintains. I build the opposite - someone owns every output, it's verified, maintained, and retired when it's stale. It's real governance, mapped to the NIST AI RMF (govern, map, measure, manage) that examiners and the NCUA point institutions to - with model risk (the 2026 interagency guidance that replaced SR 11-7), vendor and partner-ecosystem risk, explainability, and fair lending covered in plain English. Lightweight enough that people actually follow it.
AI rescue & dig-out
Tools bought, pilot run, and it stalled - or it's a tangle no one trusts anymore. The way I unstick it is to audit what's actually there, find what's salvageable, and turn a stuck rollout into something that delivers. No rip-and-replace reflex; make the existing investment earn its keep.
Fintech implementation & delivery
Fifteen years leading digital banking and fintech rollouts across Visa, Fiserv, and Lumin Digital - nine launches in five years, every one on time, on budget, and with teams who enjoyed the ride enough to send the recommendation later. That's the delivery discipline I bring to a launch that cannot slip.
AI dos & don'ts,
on the house.
A taste of how I think about adoption. Consider this the napkin version.
- Start with one painful, repetitive task, not a moonshot.
- Name a real internal champion and give them time, not just a title.
- Write the dos and don'ts down where people can find them.
- Measure adoption and outcomes, not vibes.
- Let people watch a skeptic become a believer. It converts the room.
- Buy a pile of seats before you have a use case.
- Ban it in a memo and hope it goes away. It won't.
- Let "AI policy" mean one Slack message no one reread.
- Put sensitive data into tools you haven't vetted.
- Assume the loudest skeptic won't convert. They convert hardest.
Leave It Better.
My framework for moving fast on AI without dumping a mess on whoever comes next. It's the NIST AI RMF, translated out of compliance-speak and into four things a real team can actually do. Governance as manners: own your outputs, and leave the place better than you found it.
Name an owner
Every automation, prompt, and AI-written doc gets a human whose name is on it - because "the AI did it" is not an owner.
Map where it lives
We find every place AI has quietly crept into the work and rank it by how much it'd hurt if it were wrong.
Verify & maintain
We check it for accuracy, bias, and drift on a real cadence - not once at launch and then never again.
Retire the stale
When something's outdated, we decommission it on purpose, so your knowledge base gets cleaner over time instead of muckier.
Working with me,
in their words.
Building an AI or
delivery team? Let's talk.
Standing up AI adoption, governance, or a launch that can't slip is exactly the work I want to be doing next. If that's what you're building, I'd love to be part of it.