Twelve years orchestrating complex, global programs — for Fortune 500 enterprises and AI-first companies operating at the edge of what's possible. PMI-trained discipline, applied where AI ambition meets enterprise accountability.
I sit at the intersection of frontier AI and enterprise rigor — translating model capability into measurable, repeatable business outcomes for the executives who depend on them.
The hardest problem in applied AI isn't the model. It's the operating system around it — the workflows, the quality bar, the SLAs, the people, and the executive narrative that holds it all together as the program scales.
For twelve years I've built those systems — anchored in PMI standards and PMBOK fluency, sharpened across PMP and PMI-ACP, and applied to every program I've owned. I've led 45+ specialists across geographies, defined human-in-the-loop frameworks for AI products serving Fortune 500 customers, and given executives the kind of clarity that turns ambiguous initiatives into confident go-decisions.
My work is quiet by design. Programs that ship on time, teams that scale without breaking, stakeholders who get answers before they ask. That's the bar.
I don't manage projects. I build the conditions under which complex AI programs become inevitable.
A snapshot of what disciplined program leadership produces when applied to AI delivery.
A selective view — the programs that shaped how I lead today.
Three disciplines, deeply integrated. They are the difference between an AI demo and an AI program your customers can rely on.
Designing the human-in-the-loop architecture, review workflows, and quality bars that make probabilistic systems behave like dependable products.
Translating ambiguous AI ambition into roadmaps, OKRs, and stakeholder narratives that earn — and keep — executive confidence.
Twelve years of PMBOK-grounded program rigor — across PMP, PMI-ACP, SAFe, Agile, and Waterfall — applied so AI delivery scales without losing the quality bar.
The credentials are table stakes. The discipline they signal is the point.
Operating principles I've earned the hard way — across twelve years, dozens of programs, and one constant: senior executives who needed certainty.
"Velocity without quality is just expensive activity. I design programs where rigor and speed reinforce each other instead of trading off."
"Executives don't want status. They want certainty. My job is to convert ambiguity into the smallest possible set of decisions they have to make."
"AI quality is not a model property. It's an operating property. The team, the workflow, and the feedback loop are the system."
"Forty-five people in five time zones is not a logistics problem. It's a clarity problem. Get that right and the work runs itself."
If you're a founder, hiring leader, or executive scaling an AI program — I'd like to hear what you're building, what's in the way, and where I can help.