Conversation quality, not just speech recognition accuracy, now defines AI success in the contact centre, according to Dmitry Sityaev, Head of AI at Connect. For years, success was measured largely through technical indicators such as speech recognition accuracy, word error rate (WER), latency and speech-to-text performance. While these measures still matter as a critical foundation for intent detection, routing, summarisation, compliance monitoring and analytics, baseline technical performance is no longer enough to determine business value. As customer expectations and AI capabilities mature, leaders need to understand whether an AI agent resolves the customer’s issue, follows policy, protects trust and reduces effort. Competitive advantage is shifting from model performance alone to the quality of the customer experience, measured across dimensions such as accuracy, relevance, tone, compliance, consistency, and task completion. Delivering this performance often depends on a well-orchestrated approach blending conversation design, human expertise, and multiple AI models. Evolving an agentic AI strategy across three core pillars of modern conversational quality can support operators in crafting human-like engagements. As AI lacks subjective consciousness, its ability to show empathy relies entirely on advanced cognitive processing and pattern matching to detect signals in language, sentiment and context. True humanistic engagement means the AI responds in a calibrated, respectful and helpful way based on the customer’s emotional state, reinforcing the need for clear guardrails, quality evaluation and escalation pathways.