Speaking
I speak about the organizational and architectural decisions that determine whether AI, data, and cloud programs succeed or stall at the enterprise level.
Talk Topics
Why Most Enterprise AI Programs Fail Before They Start
The failure mode isn't technical — it's organizational. This talk covers the operating model gap: why enterprises skip governance, what an AI operating model looks like, and how to build one that enables rather than constrains.
Data Architecture for Regulatory Defensibility
How to design data platforms that satisfy BCBS 239, DORA, and emerging AI regulations by default — not through bolt-on compliance. Covers Data Vault 2.0, lineage patterns, point-in-time reconstruction, and why insert-only modeling matters in banking.
The AI Operating Model: From CoE to Enterprise Capability
Centers of Excellence centralize capability. Operating models distribute it. This talk covers the structural shift from centralized AI teams to distributed AI capability with shared standards — platform teams, domain ownership, and tiered governance.
AI Governance as Enablement
Governance doesn't have to be a bottleneck. This talk presents a tiered governance framework that scales requirements with risk — lighter governance for low-impact models, rigorous governance for consequential ones. Includes practical patterns for risk classification, deployment gates, and monitoring standards.
Platform Thinking for Enterprise Data
Why treating data infrastructure as a product — with platform teams, developer experience, and self-service capabilities — produces better outcomes than project-based data engineering. Covers platform architecture, dbt patterns, and the organizational design that makes data platforms work.
Inquiries
Available for conferences, executive offsites, and panels.
If you're organizing an event on enterprise AI, data architecture, or technology leadership and think there's a fit, I'd welcome the conversation.