How accurately and reliably AI models perform against defined quality benchmarks in development and production
How quickly and safely AI models move from experiment to production, and how efficiently the MLOps pipeline operates
How fit-for-purpose the data underpinning AI systems is for training, validation, and inference
How consistently AI systems operate fairly, transparently, and with appropriate human oversight
How measurably AI systems contribute to strategic outcomes, operational improvement, and user value
How sustainable, effective, and well-governed AI teams and operations are across the delivery lifecycle