Enterprise AI Without Guardrails

When Stanford University's Provost charged the AI Advisory Committee in March 2024 to assess the role of artificial intelligence across the institution, the findings revealed a reality that most enterprise leaders already suspected but few wanted to admit: nobody really knows how to do this yet. The committee met seven times between March and June, poring over reports from Cornell, Michigan, Harvard, Yale, and Princeton, searching for a roadmap that didn't exist. What they found instead was a landscape of improvisation, anxiety, and increasingly urgent questions about who owns what, who's liable when things go wrong, and whether locking yourself into a single vendor's ecosystem is a feature or a catastrophic bug. The promise is intoxicating. Large language models can answer customer queries, draft research proposals, analyse massive datasets, and generate code at speeds that make traditional software look glacial. But beneath the surface lies a tangle of governance nightmares that woul