In Practice: AI in the Enterprise | Day 35: Data Provenance Isn’t Optional: Why Most Enterprises Can’t Prove Where Their AI Training Data Came From May 13, 2026
In Practice: AI in the Enterprise | Day 34: The Escrow Problem: What Happens When Your Decision Authority Can’t Decide Fast Enough? May 12, 2026
In Practice: AI in the Enterprise | Day 33: The Model Risk Conversation That Separates Serious Enterprises from Pretenders May 11, 2026
In Practice: AI in the Enterprise | Day 32: Regulators Are Coming: Here’s What You Actually Need to Prepare For May 8, 2026
In Practice: AI in the Enterprise | Day 31: The Blame Framework That Actually Works (Because It’s Not About Blame) May 7, 2026
In Practice: AI in the Enterprise | Day 30: 30 Days In: What We’ve Learned About the Gap Between AI Capability and AI Governance May 6, 2026
In Practice: AI in the Enterprise | Day 29: The Adoption Metric That Actually Predicts Success (It’s Not Usage) May 5, 2026
In Practice: AI in the Enterprise | Day 28: The Sunk Cost Fallacy in AI (Why Teams Keep Funding Programs That Should Die) May 4, 2026
In Practice: AI in the Enterprise | Day 27: The Org Structure That Actually Enables AI Governance (Hint: It’s Boring) May 1, 2026
In Practice: AI in the Enterprise | Day 26: What “Responsible AI” Actually Means in Production (Spoiler: It’s Boring and Unglamorous) April 30, 2026
