Turning AI hype into outcomes.
Most enterprise AI doesn't fail on the technology; it's never been stronger. It stalls between a flashy prototype and a production system people actually adopt. Closing that gap takes someone who can own the whole problem: what to build, how to ship it, and how to make it stick.
I guide AI programs in healthcare from concept through production. I figure out what's worth building and translate between the people who buy it, build it, and use it.
That looks like scoping use cases and navigating technical trade-offs one day, designing evals and driving adoption the next.
Selected cases
A single AI solution can transform a process. The engine that produces the next ten transforms an organization.
A large healthcare organization recognized early on that AI would change how they operate, and started building right away. But proving a technology "works" is the easy part. The harder question is which ideas are actually worth the investment. Time and resources aren't limitless, and picking wrong looks bad on everyone.
We built the discipline around that question: how to evaluate ideas honestly, prioritize the right ones, measure what's working, and govern it all as scope grew. That foundation turned a single proof of concept into a real vision for what AI could do across the organization, and ultimately into funded delivery where it mattered most.
Point solutions impress. The discipline to evaluate, prioritize, and repeat is what compounds.
Enterprise agentic AI is a coordination problem disguised as a technology problem.
A healthcare contact center wanted agentic AI at scale. We pulled together a large team across multiple tech partners in under two weeks. The technology worked fine. The hard part was keeping that many people, priorities, and decisions moving in the same direction on an aggressive timeline.
Scope kept growing as launch got closer. We negotiated trade-offs directly with client execs to protect the date. When timelines tightened, I got in the code alongside engineering to close the gap. Senior leadership stayed close the whole time.
The system went live supporting frontline agents with AI-driven workflows and human oversight built in.
Agentic AI at enterprise scale ships on coordination and clear ownership, not technical capability alone. Get that right and you create the conditions to deliver at the leading edge.
A technically impressive solution that doesn't fit the workflow is just an expensive demo.
A clinical AI program had lost momentum. Not for lack of talent or budget. The scope kept shifting and the dynamics between teams had gotten complicated. We were brought in to get it moving again.
We reset priorities, restored the sprint cadence, and got onshore and offshore teams pulling together. But as we dug in, the real issue surfaced: the technical solution and the clinical reality weren't connected. What looked right on paper didn't match how the work actually happened.
We found the value where nobody expected it, built a simpler solution to meet the actual need, and improved the interface so users could genuinely benefit.
Production AI needs someone who understands the business and the technology. Without that bridge, solutions end up solving problems that don't exist.
Most people talk about AI transformation. Few actually live it.
I stay close to what's new, try it fast, and keep what holds up in real use. I use it to learn faster, make better decisions, and build small tools that cut friction out of everyday life. Not because it's novel, but because it consistently works.
That personal loop is what sharpens the professional work. The ideas I back most confidently tend to start as something I've already pressure-tested myself. And my teams move faster because we use the same tools to accelerate our own delivery, not just the client's. The benefit shows up twice: better work from us, better outcomes for the organizations we support.
You can't credibly guide AI adoption if you haven't adopted it yourself. Fluency is the credential.
Say hi on LinkedIn. Side projects on GitHub.
These are representative examples. Details including timelines, team sizes, and implementation specifics have been modified to protect confidentiality. · llms.txt