An executive committee makes AI decisions without having a framework to evaluate them. This is the problem before the problem — and it makes all other decisions fragile.
An AI decision framework is a structured framework enabling general management to evaluate, validate and govern AI-related decisions according to explicit strategic criteria: value created, dependencies generated, reversibility, compliance.
The majority of boards decide on AI by analogy or reaction, without criteria or framework. The result: fragmented decisions, unanticipated dependencies, and inability to arbitrate between competing initiatives.
What we call "decision architecture": the implicit structure that defines who can decide what, with what criteria, at what level. Without an explicit decision framework, each AI decision creates a precedent. And precedents become implicit norms that nobody has arbitrated.
The board is not responsible for AI. It is responsible for the company's decision structure — and AI transforms it. The fiduciary responsibility of executives is engaged as soon as material decisions are influenced by unmastered AI systems.
For each board-level AI decision: expected value, process criticality, data involved, dependency level, exit cost, supervision capability, regulatory exposure, responsibility, differentiation, reversibility scenario.
The problem is that you do not know where AI is already deciding in your place. You have already lost control of certain decisions. The question is: which ones.
We build your executive AI decision framework. Exclusively general management and boards.
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