From IVR to AI: The future of voice testing in contact centres with Satish Barot, Klearcom

Agentic AI is moving from helping employees to serving customers. But getting the technology to work is only part of the job. At CCW Europe in Amsterdam, I spoke with Michiel Kouijzer, SAFe Product Manager at Essent, about the practical foundations behind customer-facing agentic AI: knowledge management, fast APIs, the experience customers receive, and bringing the organisation along with the change. We also explore what happens when customers send their own AI agents to businesses. Could personal assistants become a new sales channel? Will they call contact centres, use self-service, or connect directly to business systems? Michiel shares Essent’s experience with a small home-battery pilot and his perspective on how these customer channels may develop. His comments on future agent behaviour are predictions, rather than established outcomes.

Automation Anywhere has agreed to acquire boost.ai. What could that mean for customer experience, personal AI agents and the autonomous enterprise?In this exclusive interview, Kane Simms speaks with Jerry Haywood, CEO of boost.ai, and Ankur Kothari, co-founder and COO of Automation Anywhere, about the proposed combination. They discuss how conversational and voice AI could connect to work across enterprise systems, where people remain involved and what customers need to prepare for.The conversation covers the rationale behind the deal, customer and employee operations, the changing relationship between CX and IT, personal agents, agent-to-agent interactions and the foundations of successful AI adoption. Kane closes with his view on why the combination matters.Acquisition status: the companies announced a definitive agreement on 7 October 2026. Completion is expected in Q4 2026, subject to regulatory approvals and customary closing conditions. Show notes Follow Kane on LinkedIn: https://linkedin.com/in/kanesimms Find out more about VUX: https://vux.ai Subscribe to The AI Ultimatum Substack: https://open.substack.com/pub/kanesimms

Reliable voice AI comes from rigorous testing and evals, and Rohan Vasishth from Bluejay sees trust as the fundamental bottleneck holding back enterprise deployment. Rohan is Co-founder and CEO of Bluejay, which tests and monitors voice and chat AI agents for companies including Google and DoorDash. We work through what to test, when to test it and what each approach costs. We discuss why testing voice AI introduces a whole new layer of complexity, from accents and background noise to interruptions and speech recognition errors. Rohan also shares how customers can stress-test agents, why starting with text-based evaluations can save time and money, and how businesses can identify and reduce the latency customers actually experience. Rohan closes by exploring the widening gap between rapidly advancing AI capabilities and slower enterprise deployment, and why evaluation is essential to building the trust needed to close it.

Stacey Kyler and Guy Tonye of Zammo return to the show for the first time since 2021. Back then they were building for Google Assistant, Alexa and Cortana. ChatGPT arrived soon after and reshaped what customers expected. They cover the move from NLU and diagram flows to agentic systems, why customers now want both the freedom of Gen AI and firm control, how Zammo enforces determinism at the code level while Gen AI handles the conversation, and why evals are the thing Guy would add to any conversation design course today.

The contact centre holds a record of almost every customer interaction. Every conversation carries a signal about why customers are calling and what is behind it. Processing those conversations at scale took the power the contact centre never had before. Ping Wu, CEO of Cresta, joins us to work through what that shift means for enterprise contact centres, from cutting the calls that should never happen to automating the ones that can be. We get into how AI changed what a contact centre can see. Every call, every screen, every exchange can now be processed rather than sampled. Human effort never scaled the way silicon does, so for years most of that record went unread. Large language models bring processing power and language understanding to work through millions of conversations, see what is driving them and turn that into insight, coaching and automation.