I’m an enterprise AI leader i Canada focused on orchestrating value through institutionalized governance and adoption. I standardize how AI is evaluated, deployed, and measured so organizations realize material cost improvements, revenue impact, and reliability gains-with responsible guardrails. I lead portfolio-level adoption (AIOps/MLOps, LLMOps, observability, evaluation gates, human-in-the-loop) and embed repeatable frameworks. My background spans telecom/media and enterprise technology across multi-shore delivery, with an emphasis on scaling value beyond one-off pilots.
Vera Rubin: The Infrastructure Question Worth Asking
TL;DR NVIDIA’s Vera Rubin architecture offers up to 10x inference cost reduction vs. Blackwell for large-scale AI workloads (Source: NVIDIA CES 2026 keynote) This changes the build vs. cloud calculus for agentic AI systems Q1 2026 action required: Budget conversations, vendor evaluations, governance alignment Headwinds: Power constraints (120kW+ for leading-edge racks), 18-24 month procurement cycles, […]
Shadow AI: The Governance Signal You’re Ignoring
When 44% of workers admit to unauthorized AI use, the message isn’t sabotage > it’s demand. Something curious is happening in enterprises across North America and Europe. While IT governance committees debate AI policies and legal teams craft acceptable-use frameworks, employees are quietly solving their own problems. They’re paying $20 a month out of pocket. […]
AI Governance Theater: When Policies Exist But Nothing Actually Changes
There’s a pattern I see repeatedly. A company shows me their AI governance framework. Glossy presentation. Ethical principles. Risk assessment templates. A committee that meets quarterly. Then I ask three questions: When was the last time this framework stopped an AI deployment? Who has the authority to kill a model in production? What happened the […]