AI semantic metrics: what to track beyond accuracy, latency, and cost

AI semantic metrics assess whether AI-generated analytics and decision-support outputs are business-aligned, grounded in governed data, traceable to evidence, contextually relevant, and trusted by users, complementing conventional measures such as accuracy, latency, cost, BLEU, and ROUGE. The guide highlights how technically correct systems can still produce conflicting results when they apply different definitions, such as gross versus net revenue, creating risks that traditional performance metrics do not detect. It organizes semantic evaluation into governance and consistency, contextual relevance, explainability and faithfulness, and user trust and adoption, and recommends a production scorecard centered on exactness, faithfulness, coverage, governance conformance, P95 latency, and cost efficiency. Implementation involves defining and versioning critical business entities, building knowledge graphs connected to catalogs and lineage systems, and linking metric changes to operational or financial outcomes. It also advises teams to control graph traversal, refresh embeddings, maintain a single source of truth for definitions, enforce row-level access controls, establish baselines, and alert quickly when governance conformance or faithfulness declines.

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