On a rainy Tuesday in London, a consumer goods company’s leadership team tackled two key decisions: where to open the next five stores, and whether to pivot the brand towards wellness. Generative AI supported both discussions, yet the results exposed a critical issue — not all AI fits every decision equally. Narrow decisions like store locations have clear objectives and measurable outcomes, making them suited for analytical AI. Wide decisions like brand pivots involve ambiguity, contested goals, and require alignment; generative AI can help synthesize perspectives but cannot replace judgment or commitment. The article explains how organizations often misapply AI by treating all decisions the same, leading to weak results or poor buy-in. Leaders are encouraged to diagnose decisions as narrow or wide using criteria like objective clarity and data readiness, then align AI tools accordingly — analytical engines for narrow decisions and generative helpers for wide ones. Practical steps include inventorying decisions, tailoring AI playbooks, and ensuring decision processes incorporate review and alignment. Ultimately, AI’s value depends on matching its strengths to the nature of the decision to drive impact and organizational commitment.
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