At two healthtech companies, I built the AI systems and the adoption behind them that move marketing and revenue teams from experimenting to operating.
Real workflows. Real compliance. Real people who now reach for these tools instead of avoiding them.
All with no transformation mandate and no authority, inside a marketing role.
The difference between a novelty and an operation is architecture. Anyone can prompt a chatbot. The harder work, the work that actually holds, is building systems a non-technical team will trust, adopt, and reach for.
On a skeptical team. In a regulated industry. Without breaking brand or compliance.
A marketer doesn't think "is this a GPT or an agent?" They think "I need to QA this asset." I built the library around jobs to be done, so people find what they need without learning my taxonomy first.
In healthtech, one unapproved claim is a real risk. I embedded claims-safe and regulatory guardrails directly into the tools, so the compliant path is the default path, not a manual check at the end.
A brilliant tool nobody opens saves zero hours. I'm a certified professional coach, and I put that behavior-change training to work here, building a belief-based framework that moves skeptical teams from resistance to building. It's how I've helped leaders who were openly resistant to AI begin building their own tools.
AI should elevate the human side of this work, not flatten it. Every system is tuned to protect warmth, specificity, and real point of view, the things that make anything worth a person's attention.
Adoption is the most meaningful work to measure.
Across two healthtech companies, I've moved 30+ people from AI skeptics to active users. And I've taken a core group of 15, including leaders who were openly resistant, all the way to building their own AI tools.
Selected as one of a small company-wide cohort to lead AI adoption across the organization, validating what I'd already been doing without a mandate.
Deploying tools is the easy part. Changing whether a skeptical team trusts them, and reaches for them without being told, is the actual work. That's the part I do.
Then the systems earn their keep. The agents below run real workflows for real teams. The figures are time-saved estimates, reported conservatively.
GPT = custom GPT · Project = Claude Project · Skill = Claude + ChatGPT Skill
Plus research, briefing, and long-form content tools built the same way, organized by job, tuned for compliance, and shipped when a real workflow needed one.
Twelve for account executives and twelve for the BDR team, each engineered for a distinct buyer, employer, health plan, government, labor, education, tribal, and veterans health, with prevention-first positioning and procurement-safe language baked in.
The same architecture, tailored to each audience's objections and pain points.
If you're bringing AI into your organization, and you care about doing it without losing quality, trust, or compliance, I'd like to talk.
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