Two-thirds of enterprises deploying AI in contact centers lack the automated QA infrastructure to monitor what their systems are actually doing. The TELUS Digital survey of 815 enterprise decision-makers reveals a stark measurement gap: whilst 56% plan to invest in AI copilots for agent assistance, only 32% have already deployed the automated QA tools needed to evaluate performance at scale. This disconnect matters because organizations are scaling AI-assisted agent models—now the dominant CX approach—without visibility into whether their substantial investments (61% of respondents spend over $10 million annually on CX delivery) are delivering measurable improvements or simply automating blind spots. The risk is particularly acute because modern AI agents behave probabilistically rather than deterministically; they adapt to context and language, which means failures are unpredictable and often invisible until customers report them. In regulated sectors like banking, where accuracy is non-negotiable rather than probabilistic, this gap becomes a compliance liability.
The implications for CX teams are immediate and operational. Your current QA processes—whether manual sampling through Zendesk or Freshdesk or spot-checking through Salesforce Service Cloud—are no longer fit for purpose if you're running AI copilots at scale. The survey indicates that 47% of respondents now prioritize CSAT/NPS improvement and 45% prioritize service consistency, yet these outcomes cannot be achieved without real-time visibility into AI decision-making. When an AI agent hallucinates information or drops context during a handoff to a human agent, your team discovers the failure through customer complaints rather than proactive monitoring. This creates a compounding problem: longer handle times, repeated contacts, and eroded trust. The question becomes whether your organization can afford to continue treating AI as a technical deployment separate from your operational QA framework, or whether you need to fundamentally restructure how you monitor, coach, and optimize agent-AI interactions.
The market is beginning to recognize this gap, with enterprise leaders shifting focus from efficiency metrics (average handle time ranked at just 19% of priorities) toward quality and consistency. However, recognition alone does not close the gap. Organizations running multiple AI initiatives simultaneously without consolidated strategy—a pattern TELUS Digital observes frequently—are particularly vulnerable. The competitive advantage in 2026 will not accrue to teams with the most AI, but to those with the most reliable AI, backed by robust automated QA infrastructure that evaluates 100% of interactions rather than sampling. For CX leaders, this means the funding conversation must change: investment in AI copilots and agents is only half the battle. The other half—and arguably the more critical half—is building the observability layer that transforms AI deployment from a box-ticking exercise into measurable performance improvement.
Enterprises are pouring money into contact center AI, with 61% spending over $10 million annually on CX delivery. Yet, a startling two-thirds of these organizations have limited visibility when it comes to quality assurance. According to new research from TELUS Digital, while companies rush to deplo