Quality metrics for conversational analytics include: 1) Semantic correctness—does the system interpret the user’s intent and generate the correct query? 2) Accuracy of results—are the underlying data and calculations correct? 3) Hallucination rate—how often does the model produce answers not grounded in the data? 4) Latency and uptime. 5) User satisfaction scores and engagement.

Bain’s generative AI survey found that more than 80 % of use cases meet or exceed expectations and about 60 % of satisfied firms report business gains.

However, top adoption barriers include security, talent shortages and output quality.

Regular evaluation using test suites and real user feedback helps reduce hallucinations and improve trust.

MageMetrics provides ‘guardrails’ that constrain queries to the semantic layer, minimizing unsafe or irrelevant answers.