Explore evidence-led notes on where AI should run, what agents need after the demonstration, and how technical mechanisms change workflow economics. Claims are bounded to their workload and evidence.
Build an enterprise AI strategy around accepted workflows instead of one model family. These ten rules separate replaceable supplier facts from compounding workload, authority, evidence, and recovery assets, then turn reversibility, ownership, and change triggers into a practical funding screen for CTOs and boards.
Read MoreDecide whether an AI pilot should advance, remain limited, or stop by using a failure-control-evidence ledger. The gate connects evaluations, permissions, observability, injection and leakage tests, human checkpoints, recovery, agency, and cost controls to named owners, dated evidence, and hard failure conditions.
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