I have spent over a decade sitting at the intersection of data, AI, and organizational decision-making. Not as a researcher. Not as a theorist. As someone responsible for making it work inside real organizations, with real constraints, and real consequences when it does not.
That experience has shaped how I think about this work and what I believe it actually takes to get it right.
I studied physics and mathematics at Queen's University, which gave me an early appreciation for something most people learn much later: the difference between a model and reality. A model is always a simplification. The question is whether the simplification is good enough to be useful, and whether the people using it understand its limits.
That question has followed me through every role I have held since.
Before data, I worked in finance and accounting. That background shaped something I have never lost: the instinct to ask what a number is actually supposed to do for the person looking at it. Not what it says. What it is supposed to do. That distinction turns out to matter enormously in data and AI work, where the gap between a technically correct output and a genuinely useful one is where most initiatives quietly fail.
That instinct is what drew me toward data in the first place. I wanted to be on the side of the table where the numbers were built, not just received.
My career in data started at Microsoft, where I worked inside partner incentive programs, untangling data problems that were costing partners time and the business credibility. I learned there how to translate complex technical findings into language that moved decisions at the executive level.
From Microsoft I moved into the public sector, joining the Early Years and Child Care unit at the Ontario Ministry of Education. The mandate was specific: help government leaders allocate early childhood funding more effectively. That meant building machine learning models to identify optimization opportunities in how funding was distributed, and automating the manual reporting processes that were consuming the time of people who should have been focused on program outcomes, not spreadsheets. Fifty-plus BI products deployed to deputy ministers and executive leadership. Budget and policy decisions informed by evidence rather than instinct. And a clear lesson I carried forward: the most valuable thing data work can do for a leader is give them back their attention.
KPMG brought a different scale of challenge. I built and led the analytics function inside the Marketing and Communications division, taking over 100 static reports and consolidating them into governed, dynamic BI platforms that saved the organization $500K annually. Then I moved into the Audit Centre of Excellence, where the work shifted to data governance frameworks, AI-enabled audit tools, and advising C-suite stakeholders on analytics transformation in one of the most regulated environments in the country.
That work reinforced something I had started to believe earlier: data and AI initiatives fail far more often because of organizational and governance gaps than because of technical ones.
That belief is now central to everything I do.
Data and AI are not technology problems. They are leadership problems.
Most organizations that struggle with AI are not struggling because they lack the right tools or the right talent. They are struggling because nobody has done the harder work of defining what good AI looks like inside their specific context, what guardrails are necessary, who is accountable when something goes wrong, and how the organization will know whether it is working.
That harder work is governance. And governance, done well, is not a constraint on AI ambition. It is what makes AI ambition achievable.
I have seen what happens when organizations skip it. Models deployed without explainability frameworks. Dashboards built before anyone agrees on what the metrics mean. AI features shipped into consequential domains without bias testing. The outcomes are predictable and they are expensive; financially, reputationally, and sometimes for the people the systems were supposed to help.
I have also seen what happens when governance is treated as a foundation rather than an afterthought. Executive teams that can finally trust the numbers they are looking at. AI systems that survive regulatory scrutiny because they were built to. Organizations that move faster, not slower, because everyone is working from the same playbook.
That second version of events is what I work toward.
My work sits at the intersection of three capabilities. Most AI leaders bring one or two of them. The combination of all three is where I operate.
I work with executive teams to cut through the noise around AI and build a plan that survives contact with budget cycles, board meetings, and the day-to-day reality of the organization.
Data governance, responsible AI frameworks, model risk policies, and the organizational structures that keep them alive after the initial build is done.
I have led complex, multi-workstream programs across consulting, government, and technology environments. I know how to move organizations from intention to execution.
I care about this field because the decisions AI systems influence are rarely trivial. They affect how organizations allocate resources, how people are assessed, how risk is managed, and increasingly, how individuals access services and opportunities.
That weight deserves to be taken seriously. The organizations that take it seriously tend to build better AI. They also tend to build more trust with the people they serve.
That is the work I find meaningful. And it is the standard I hold myself to.