Operations analyst by training. Self-taught AI builder by obsession. The rare combination behind every line of my resume — and the reason a rollout actually sticks.
The problem everyone has
Buying the licenses was the easy part. The hard part is the people — getting a busy, skeptical, non-technical team to change how they actually work. That gap, between AI bought and AI used, is where the money quietly disappears.
What I actually do
Installation was never the hard part - the licenses, the logins, the kickoff deck. The hard part is people changing how they work. So I run adoption the way change management actually works: meet people at the work they already do, right-size each tool to a single job, and make the first win fast enough that trust arrives before fatigue does. Adoption is the deliverable - and because I build these systems myself, I run it as someone who has shipped, not someone reading from a script.
The unlikely part
For nearly a decade I've worked inside a government agency, turning dense technical systems into the documentation and training thousands of staff actually use. Somewhere along the way I started building the tools too — teaching myself RAG systems, autonomous agents, and automation on Claude and the Anthropic API.
That path is the point. I've spent my whole career standing between complicated technology and the people who have to use it — which is exactly the job when the technology is AI.
Proof, at real scale
Sole owner of enablement for the agency’s internal GenAI platform — driven from zero to 32% daily-active across 110 staff in 8 units, with 10 road-show workshops, 5 workflow guides, and a self-sustaining community of practice. Adoption was the deliverable, not a side effect.
And I build the tools
A self-healing error agent that ran unattended for 7+ months, reading its own code and fixing itself inside hard guardrails. A multi-source RAG over 11,000+ regulatory sections that answers plain-language questions with citations. A fleet of scheduled agents that share a workspace and accumulate knowledge over time. Real pilots, real freelance clients, real code.
I build on Claude and the Anthropic API - and I've written adoption plans for Google's stack end to end. The method doesn't care whose logo is on the tool.
See the work in detail →Why it's rare
Runs the adoption — training, change management, the patient human work of getting people to actually use a new tool. Usually can't build.
Ships the AI systems — RAG, agents, automation. Usually can't (or won't) do the unglamorous work of driving adoption.
One person who runs the rollout and is a credible technical partner. Not a talker. Not just a coder.
In practice, the role I play is champion of champions - finding the power users already quietly getting the most from these tools, then equipping them to carry adoption further than any trainer could alone.
How adoption actually happens
Governance that gives adoption an owner. Use cases attached to work people already do. A champions network, because people trust a colleague over a mandate. Learning paths that climb from literacy to adoption to transformation. And telemetry honest enough to prove what worked. I've written the playbook - including exactly how I'd run the first 90 days.
Read the playbook →Full disclosure
This site, like most of my work, was made with Claude as my reasoning partner and editor. The thinking, the positioning, and the judgment are mine - the AI helped me pressure-test ideas and tighten the writing. That's not a disclaimer; it's a demonstration. Using AI openly, with a human owning the judgment, is exactly the practice I help teams adopt.
Boston, MA · in-office or hybrid preferred, open to remote · government & regulated-industry native