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Managing AI in Quality Assurance

How to Achieve Predictable, Measurable Results with Modern LLMs AI-generated metrics and scores increasingly inform critical decisions — from workforce upskilling and performance steering to finding selling patterns. The key question is not whether to use AI in QA (as a part of conversation analysis as whole), but how to calibrate and govern it so that outputs remain reliable, comparable, and strategically relevant. One of the core challenges in QA automation is the relative novelty of the management toolkit. We will examine how to structure, tune, and continuously refine AI-driven metrics to ensure controlled and actionable results. Webinar focus We demonstrate how configurable AI evaluation enables operational control in dynamic service environments. Topics we cover: • 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 What you will get: • 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 • Quality metrics that directly support workforce and coaching decisions