Strategic perspectives on enterprise AI, technology architecture, and the decisions that shape how organizations adopt and operate intelligent systems.
AI has collapsed the cost of producing content while the cost of verifying it has not moved. The next enterprise data moat is adjudicated data: authority, provenance, correction history, and outcome evidence. A 90 day trust audit checklist is provided.
ReadMany companies built their first enterprise AI systems to retrieve internal information and generate plausible answers. That was a reasonable first move. It gets fragile once those same systems start influencing pricing, underwriting, compliance, and other decisions they were never architected to support.
Read on LinkedInMost responsible AI programs start too late. By the time monitoring dashboards flag a bad answer or bias tools detect an uneven outcome, the model has already been shaped by training data, features, retrieval logic, and prompt context. The real controls have to move upstream.
Read on SubstackThe responsible AI controls most companies built — output review, prompt testing, human signoff — were designed for bounded systems that summarized and drafted. Once agents start taking action across live workflows, reviewing the output isn't enough. The control surface moves to action.
AI governance that lives in quarterly committee reviews can't keep pace with weekly deployments. Governance must be engineered into code, tests, and runtime controls.