Measurement

AI value should be measured, not assumed.

A strong transformation programme distinguishes activity from adoption, adoption from performance, and performance from realised business value.

Baseline
Current cost / time / quality

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Hypothesis
Expected measurable change

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Experiment
Controlled proof / comparison

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Deploy
Production measurement

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Attribute
Realised value

Revenue, cost, cycle time, throughput, quality, retention or risk appropriate to the use case.

Accuracy/relevance, reliability, failure modes, escalation and human review.

Usage alone is not success; measure workflow change and actual employee/customer outcomes.

Implementation, model/API, integration, oversight and operational costs against realised value.

Incidents, exceptions, control coverage and unresolved governance actions.

Compare use cases so capital and leadership attention move toward evidence, not novelty.