Contact center AI evaluation has shifted from feature comparison to operational proof. ISG's 2026 Buyers Guide weighted product experience at 80% but explicitly included AI governance and model operations alongside capability—signalling that vendors are now judged on whether they can integrate with enterprise workflows, govern AI behaviour, and demonstrate lifecycle value after deployment. Telstra's Agentforce rollout across 1,000+ representatives illustrates what this scrutiny means in practice: AI agents handling refund calculations, eligibility validation, compliance monitoring, and record updates. These are not conversational enhancements but controlled actions affecting money, regulatory obligations, and customer records. The competitive test has moved from faster answers to safe, auditable action within live workflows. For teams already running Agentforce or evaluating similar agentic platforms, the question is no longer whether the tool can automate—it is whether your governance infrastructure, role design, and accountability structures can support AI making decisions at scale.
Cresta's workforce data exposes a critical gap between adoption and design. Only 9% of conversations are fully autonomous; 76% involve human-AI collaboration. Yet 93% of leaders report that agent-handled calls are becoming more complex, and 97% say AI has enabled redeployment to higher-value work. This is not labour replacement—it is workforce redistribution that demands new operating models. The bottleneck is architectural: 81% cite integration complexity as a barrier, and only 7% have easily accessible conversation data across the business. Without integration and data access, contact centers cannot redesign workflows, monitor AI performance, or coach agents to work effectively with AI. This explains why ISG's framework now weights platform adaptability and manageability so heavily. The workforce impact cannot be separated from the systems underneath it.
ScorebuddyCX's QA findings reveal where adoption breaks down into impact. Nine in ten contact centers use AI for QA scoring, yet only 14% always review those scores, and 15% generate no coaching from AI insights at all. Trust is uneven: 76% of managers trust AI scores versus 57% of agents. The gap between measurement and action is the real risk—automated insight that does not change decisions creates the illusion of progress without operational improvement. For support team leads and CX consultants, this means QA is no longer a performance-management function but part of the operating system that governs whether human-AI interactions meet standards. The proof point for any platform is not the score itself but whether it triggers review, coaching, and measurable behaviour change. Without that feedback loop, AI governance remains incomplete.
Contact Center and Omnichannel teams are moving into a tougher phase of AI evaluation, as new research and deployment signals show buyers need proof across platform selection, workflow execution, workforce design, QA governance, and measurable value. TL;DR ISG’s 2026 Contact Centers Buyers Guide sho
ISG, Telstra, Cresta, and ScorebuddyCX Expose AI’s Contact Center Test cxtoday.com