Run every new AI capability through a repeatable four-step cycle rather than ad hoc experimentation: test it on a small, representative segment with success metrics and a duration set in advance; analyze the result against pre-established baseline KPIs and check for statistical significance; document the configuration, what worked, and what didn’t as institutional knowledge; then iterate — scale incrementally if it worked, retire or redesign if it didn’t. Three things keep the cycle honest: budget dedicated R&D time as an operational investment, document a clear baseline before implementing (uplift can’t be measured without one), and route applications touching sensitive data, generative content, or automated decisions through ethics review before they scale.
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