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Pilot design: scope, success metrics, sunset criteria
In this lesson
Pilot design: scope, success metrics, sunset criteria
Define clear, measurable success criteria and failure thresholds for an AI pilot before deployment begins, applying the principle that mastery requires explicit performance descriptions known up front rather than moving…
You'll be able to
- Define clear, measurable success criteria and failure thresholds for an AI pilot before deployment begins, applying the principle that mastery requires explicit performance descriptions known up front rather than moving targets [^1].
- Evaluate pilot scope boundaries by classifying which business outcomes are measurable against specific objectives and which fall outside achievable targets, ensuring the pilot provides a clear path to production [^2].
- Apply the NIST AI RMF MEASURE function categories to design formative assessment checkpoints within a pilot timeline, structuring low-stakes diagnostic feedback loops that inform go/no-go decisions [^5].
- Create a pilot sunset decision matrix that maps quantitative performance metrics (accuracy, latency, resource efficiency per unit of work) to predefined continuation, pivot, or termination actions, grounded in material efficiency baselines [^6].
- Justify pilot scale trade-offs by analyzing whether the scope is small enough to contain risk yet large enough to yield statistically informative results that align stakeholders to new business processes from the initiative's start [^2].