Configuring AI-based evaluation criteria in natural language
Calibrating strictness and management priorities in real time
Detecting subtle behavioural and reputational risk signals
Translating quality insights into steering decisions
A clear framework for managing AI in Quality Assurance
Practical examples of configurable AI metrics (zero-shot/few-shot)
Understanding of how AI evaluates meaning and context
A demonstration of live metric adjustment and its impact
Greater operational control over service quality
Faster adaptation to strategic or regulatory changes
Reduced dependency on rigid scorecards and manual QA
