The Finding
New research from TELUS Digital’s Enterprise CX AI: 2026 Global Survey, polling 815 enterprise decision-makers across 12 countries, finds that 61% of organizations now spend over $10 million annually on CX delivery. Yet only 32% have the automated QA infrastructure needed to actually measure how that AI is performing. As Peter Ryan, President and Principal Analyst at Ryan Strategic Advisory, put it, adoption of AI-powered CX solutions has moved fast, but enterprises have not caught up to optimizing it yet.
Why This Matters for BFSI and Collections
The most common CX model today is human agents assisted by AI, and 56% of organizations plan to invest further in AI copilots for real-time assistance. But only 46% plan to invest in the automated QA and coaching infrastructure needed to monitor those copilots, and just 32% have it in place now. For collections and BFSI operations, this gap is not a minor operational inconvenience. Tony Shen, Senior Product Manager at Amazon Connect, made the point plainly: when an AI agent sits in the middle of a transaction, the humans overseeing it need visibility into what decisions it made, or they risk duplicating work or confusing the customer. In a regulated collections conversation involving payment terms or account details, that lack of visibility is an immediate compliance exposure.
What the Numbers Do Not Say Out Loud
The investment figures suggest enterprises are prioritizing the visible half of AI deployment (agent-facing copilots) while under-investing in the invisible half that makes those copilots trustworthy. Traditional deterministic automation fails in obvious, easily-spotted ways. AI copilots adapt to language and context, which means they can fail unpredictably, hallucinating incorrect information or dropping context mid-handoff, without anyone noticing until a customer complains. What the adoption numbers do not capture is that this is already the operating reality for the two-thirds of enterprises currently running AI without the QA infrastructure to catch these failures in real time.
The Practical Read
Encouragingly, the survey also shows priorities are shifting: 47% of respondents now cite CSAT and NPS improvement as a top priority, and 45% cite consistency in service quality, compared to just 19% for handle-time reduction alone. That is a meaningful move away from efficiency-only thinking toward outcomes. But as TELUS Digital’s Jamie Timm noted, most enterprises are running a dozen AI initiatives at once without a consolidated strategy to maximize results. For collections operations specifically, this means treating automated QA and observability as a launch requirement, not a phase-two upgrade. A voicebot handling payment negotiations needs the same real-time monitoring discipline as the conversation itself.