Enterprise AI leadership.
I lead enterprise AI programs end to end: setting direction with your executives, governing the work, and staying close enough to the build to know when the plan is drifting. A track record of leading complex programs means the advice comes from delivery, not from a deck.
The short version
I lead enterprise AI programs: strategy, governance, budgets, and delivery. Most recently as Director of Applied AI at a multi-billion dollar Ontario pension plan, where the work spanned AI governance, applied AI initiatives, and enterprise platform evaluation under a regulator's eye.
I got here the long way. Developer, then infrastructure and architecture, then fifteen years leading projects and programs, and eventually a PMO where I owned governance, metrics, and financials. Thirty years of building is why the governance is practical and why engineers take the direction seriously.
That range is the point. I can talk value and risk with your executives, then sit with your engineers and tell whether the estimate is real.
How I can help
Most AI programs die somewhere between the deck and the shipped system. I work the whole distance: picking the problems worth solving, building the thing, and staying until people use it.
AI Strategy
Find the few AI opportunities that matter, size them honestly, and sequence them into a plan leadership can fund and engineers can build.
Software Engineering
Move your team to AI-assisted workflows, agentic tooling, and the engineering practices that keep them fast and safe.
Embedded Delivery
I embed with your team to make sure the program actually ships, and stay until it runs in production.
How I work
Understand the business before the technology
A short, structured look at your workflows, data, and constraints to find where AI will pay for itself.
Build a proof of concept against real work
A working prototype on your data, with your people. You decide whether to invest based on something real, not a demo.
Work inside your team
I join stand-ups, sit with operators, and build alongside your engineers so the solution fits how you actually work.
Make it dependable
Integrations, security, monitoring, and rollout so the prototype becomes a system the business can rely on.
Leave you self-sufficient
Documentation, training, and a clear operating model. The measure of a good engagement is that you do not need me afterwards.
Building with AI
Why I work this way
FDE job postings grew roughly 729% in a year and the labs committed some $9 billion to deployment ventures. That is not a sign enterprise AI is working — it is a sign 95% of pilots stall at the integration wall, and someone gets paid to close the gap.
Most teams trying to bring an AI system to life start by building the AI. That is the expensive half, the fastest-moving half, and the half where you have no advantage.
Anthropic started watermarking Claude's text this month, and a good part of my feed reacted like it had been caught. The reaction is more revealing than the announcement.
Begin with a conversation
Tell me about the mandate, the people, and what success needs to look like. We can start with a conversation about where I could help.
