AI readiness is mostly data readiness plus process clarity — the model is the easy part, and a one-week assessment beats a six-month science
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Use-case value (volume × time × error cost), data condition (does the knowledge exist digitally, with permissions?), process clarity (is 'correct' definable?), and organizational ownership (who approves, who operates).
Rank by ROI and feasibility, not novelty. High-volume, moderate-variation, reversible-action workflows score best; judgment-free work routes to cheaper automation instead.
Most 'AI projects' stall on scattered, stale, or permission-tangled documents. The audit names what's answerable now and what needs curation first — a cheap, honest finding.
One pilot, measured targets, human gates, and a defined graduation path — readiness assessment done right ends in a decision, not a deck.
Skipping the discipline this article describes until an incident, audit, or stalled project forces it — every practice above is cheaper adopted early than retrofitted under pressure.
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