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Measuring time, cost, and quality before + after AI
In this lesson
Measuring time, cost, and quality before + after AI
Apply the NIST AI RMF MEASURE function categories to design a before-and-after measurement plan that tracks time, cost, and quality metrics for an AI system deployment in your organization [^1][^3].
You'll be able to
- Apply the NIST AI RMF MEASURE function categories to design a before-and-after measurement plan that tracks time, cost, and quality metrics for an AI system deployment in your organization [^1][^3].
- Evaluate AI system validity and reliability by comparing documented baseline error rates, rework rates, and processing times against post-deployment measurements, identifying limitations in generalizability beyond development conditions [^3].
- Create a risk-tracking approach for AI implementations where standard metrics are not yet available, documenting incidents, errors, and quality deviations communicated to relevant stakeholders [^6][^2].
- Perform a tradeoff analysis between custom and pre-trained models by measuring time saved, cost differences, and performance benchmarks using centralized tools and cost calculators [^4].
- Classify changes in employee satisfaction and well-being following AI adoption by measuring daily fluctuations in autonomy, competence, and role authenticity against baseline assessments [^8].