Strategic perspectives on enterprise AI, technology architecture, and the decisions that shape how organizations adopt and operate intelligent systems.
Most 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.
Read on SubstackAI governance that lives in quarterly committee reviews can't keep pace with weekly deployments. Governance must be engineered into code, tests, and runtime controls.
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