Experience · Executive case
I have created value inside large enterprises and converted technical capabilities into market revenue. That range shapes how I evaluate AI opportunities and what I expect from execution.
Across enterprise portfolios, products, lending strategy, and platform commercialization, the work has required the same thing: connect strategy to an operating mechanism and a measurable result.
Directed machine-learning strategy across nine businesses and built a portfolio of more than 72 production use cases. Business owners reported, and executives approved, more than $250 million in annual value.
Built the operating framework that helped move a 135-person machine-learning organization from engineering-led execution toward product-led delivery, giving teams clearer ownership, priorities, and accountability for business outcomes.
Co-founded and scaled an advanced-analytics software company, launched three commercial ML products, and built $4.7 million in annual recurring revenue.
Owned the P&L and the commercial system surrounding the technology: product strategy, pricing, partnerships, go-to-market, selling, delivery, and customer results. Established IBM, Nvidia, SAP, Microsoft, and Teradata as independent revenue channels.
Designed and executed the mortgage strategy that helped expand the lending portfolio from $11 billion to $22 billion over four years while the broader market contracted.
The work required strategy, analytics, product judgment, risk awareness, and execution across a complex financial institution.
Led financial-services and insurance strategy for an enterprise AI platform, working with executives at major banks, insurers, asset managers, and regulators.
Turned model-validation modernization into a broader platform-adoption strategy: help regulated institutions move models into production faster while improving governance, auditability, and confidence in the result.
Identify the economic opportunity, build the mechanism that captures it, and stay with the work until the value is real.Operating principle
A large enterprise has pilots across the organization but lacks one accountable portfolio, a shared operating model, and a repeatable path to production.
The work: determine which investments matter, establish ownership and decision rights, integrate governance into delivery, and build credible evidence of value.
A technology company has meaningful AI capabilities but needs sharper product judgment, stronger industry relevance, and a clearer route from platform capability to customer adoption and revenue.
A mid-sized company is ready to establish its first serious AI strategy, portfolio, operating model, and governance system.
The early choices matter: where to invest, what to build or buy, who owns the outcomes, how risk will be managed, and how returns will be measured.
The first job is not to produce a bigger AI roadmap. It is to make the portfolio legible enough that leadership can choose, fund, govern, and measure it.
Determine where AI can materially change revenue, cost, capital, risk, service, customer experience, or organizational capacity — and where it is likely to create activity without sufficient return.
Rank opportunities by economic value, strategic importance, feasibility, readiness, and consequence. Make tradeoffs visible and assign an executive owner to every material investment.
Establish decision rights, funding logic, success measures, production criteria, governance requirements, and the route from investment decision through deployment and monitoring.
When model risk is designed as an operating capability, it can increase delivery speed, reduce rework, strengthen customer and regulatory confidence, and improve the commercial position of an AI product or platform.
Requirements become useful when they are known early enough to influence design. Validation becomes faster when evidence is created as the work happens.
Explore ProvanceThe conversation can start with the first serious AI bets: where to focus, what to build, who owns the result, and how the work will be governed.
It can also start with an existing portfolio that needs sharper choices, clearer ownership, stronger adoption, credible governance, and economics leadership can defend.