Beyond speech recognition accuracy: why conversation quality now defines AI success in the contact centre
Dmitry Sityaev, Head of AI at Connect, discusses why technical measures such as speech recognition accuracy are no longer enough to evaluate AI performance in the contact centre.
For years, success in automated contact centre engagement 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; accurately, fast transcription remains a critical foundation for intent detection, routing, summarisation, compliance monitoring and analytics. Baseline technical performance is no longer enough to determine whether an AI interaction creates business value.
As we enter the next stage of the agentic AI adoption curve, Word Error Rate (WER) remains a useful indicator of transcription quality. But customer satisfaction is not determined solely by recognition accuracy alone. As customer expectations and AI capabilities mature, business leaders increasingly need to understand whether an AI agent resolves the customer’s issue, follows policy, protects trust and reduces effort throughout the interaction, as Dmitry Sityaev, Head of AI at Connect explores further…
The Shift To Conversation Quality
As AI becomes more capable, competitive advantage is shifting from model performance alone to the quality of the customer experience. Contact centre leaders should therefore measure success by conversation quality, not just error rates, across dimensions such as accuracy, relevance, tone, compliance, consistency, and task completion. Delivering this level of performance often depends on a well-orchestrated approach that blends conversation design, human expertise, and multiple AI models, rather than expecting a single model to handle every aspect of an interaction.
Evolving an agentic AI strategy across three core pillars of modern conversational quality can support operators in crafting compelling human-like engagements that meet evolving customer expectations, improve resolution rates, and derive greater benefits from the AI value proposition.
1. It’s no secret that customers quickly recognise rigid, scripted or poorly contextual responses with AI agents. 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 can respond in a way that is calibrated, respectful and helpful based on the customer’s emotional state. That distinction matters for business leaders because it reinforces the need for clear guardrails, quality evaluation and escalation pathways.
To meet evolving customer expectations around AI-led interactions, contact centres need to design AI agents that reason, react, and converse as close to a human as possible. This goes far beyond cloning a realistic voice using billions of parameters. Effective voice AI also depends on latency, turn-taking, interruption handling, intent recognition, domain knowledge and the ability to choose responses that are appropriate to the customer’s situation.
Modern AI agents must also manage non-linear conversations, as customers often change topics, add information late, interrupt, or combine multiple needs in a single interaction. The agent must maintain context, clarify ambiguity and guide the conversation without sounding mechanical. Ultimately, the measure of success is not whether the AI sounds impressive. It is whether the interaction feels efficient, understandable and appropriate for the customer and the brand.
2. Moreover, an AI agent cannot be warm and helpful in one moment and abrupt or inconsistent in the next. In a contact centre environment, persona consistency is not simply a branding exercise; it is part of risk management, customer trust and operational control. This consistency is achieved through a combination of conversation design, system instructions, policy constraints, retrieval of approved knowledge, testing and continuous monitoring.
Prompt engineering is a critical component of building reliable AI agents, but it should not be viewed as the only control mechanism. Developers and operations teams must define the agent’s role, tone, escalation criteria, approved actions, and operational boundaries, then rigorously validate its behaviour against real customer interactions.
Effective prompt engineering goes beyond instructing an LLM to simply generate answers. It provides the model with the context, reasoning framework, and decision boundaries needed to produce consistent, contextually appropriate responses. When combined with retrieval mechanisms, historical knowledge, and structured workflows, well-designed prompts enable AI agents to reason more reliably, remain aligned with business objectives, and deliver consistent customer experiences.
A clearly defined persona at the system and policy layer sets behavioural guardrails for each interaction, helping the AI remain aligned with brand standards, regulatory obligations and customer expectations. Where appropriate, contact centres can use examples from high-performing human agents to shape conversational patterns, escalation cues and service behaviours. These examples should be curated carefully to avoid reinforcing inconsistent, biased or non-compliant practices.
Curation should not stop at deployment. Successful implementations require human-in-the-loop (HITL) oversight, ongoing quality assurance and a clear process for reviewing edge cases, customer complaints, policy changes and model performance. Human review is also essential for interaction analysis and A/B testing can help refine prompts, knowledge sources and response strategies over time. To keep up with the evolving AI landscape, teams should regularly sample AI-handled conversations, assess whether the agent stayed within its intended persona and compare outcomes such as containment, escalation accuracy, compliance and customer effort.
3. While an empathetic voice and a consistent persona shape the quality of the conversation, they mean nothing if the AI cannot help the customer complete their task. Ultimately, an effective conversation must lead to a successful resolution or a well-managed handover. As customer journeys become more complex, AI agents need the capability to navigate multi-step scenarios, policy constraints and exceptions.
Customers rarely follow a straight line. They may switch topics, add an unrelated request, or return to a previous issue mid-conversation. An AI agent must be able to handle these pivots without losing the original context or forcing the customer to start again. When a request is ambiguous, the AI agent should ask targeted clarification questions before proceeding. If a query falls outside its permissions, confidence threshold or knowledge base, it should seamlessly escalate the interaction to a human agent with the relevant context attached.
The agent should also recover gracefully from errors by correcting courses where possible or escalating when necessary thus supporting governance, testing and human oversight to ensure these behaviours support both customer experience and risk management.
Conversation quality that delivers business outcomes
Low word error rates and accurate speech-to-text conversion remain foundational for any effective voice AI solution. But they are only the starting point. Competitive advantage comes from AI interactions that resolve issues, reduce customer effort, support compliance and improve operational performance. By evolving the business agentic AI strategy to focus on humanistic reasoning, persona consistency, and seamless task completion, all orchestrated by a robust AI ensemble, contact centre operators can create automated experiences that don’t just deflect calls but genuinely satisfy customers.
Dmitry Sityaev is Head of AI at Connect
Connect is a global customer experience specialist, systems integrator and digital transformation partner with industry-leading, technology-enabled capabilities. We orchestrate modernised and operationally sustainable CX that is ready to scale and mature.
Founded in 1990, we’ve evolved alongside every major industry shift; from on-premise to cloud, voice to omni-channel, and now AI-enabled experience. Built on this extensive market experience, our approach is focused on delivering outcomes-based solutions that accelerate value, informed by what it takes to operate, scale, and continuously improve CX in live environments.
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