AI-driven quality assurance tools have created a false economy for support leaders: they promise to eliminate manual QA work by automating interaction analysis, yet they consistently push the interpretive burden downstream. The core problem is that AI excels at pattern matching and flagging conversations for review, but it cannot reliably distinguish between a process failure and a legitimate customer complaint about company policy—nor can it identify the coaching moment that matters most to an individual agent's development. This means support leaders still perform the heavy lifting: they review AI-flagged interactions, contextualise findings, and decide what action to take. For teams already stretched across people, process, and technology decisions, this creates another administrative layer rather than relief. The Quality as a Service model inverts this dynamic by positioning human expertise as the primary analytical engine, with AI handling the volume. Rather than asking teams to implement and maintain yet another platform, this approach delivers curated QA insights monthly, complete with calibration sessions and coaching notes ready for deployment. The question for support leaders is whether the marginal cost of outsourced QA expertise justifies the operational simplification—particularly for mid-sized teams between five and 20 agents that lack dedicated QA capacity but cannot afford to ignore quality entirely.
The tension between comprehensive AI coverage and actionable insight reveals a deeper strategic choice facing CX organisations. Vendors marketing "100 percent interaction analysis" implicitly assume that volume of data correlates with quality of decision-making; in practice, support leaders need signal, not noise. When CSAT scores remain flat despite agents following process correctly, the problem often lies outside the agent's control—a policy customers dislike, a product limitation, or a systemic handoff failure. Traditional QA platforms struggle to surface these systemic issues because they focus on individual agent performance. By anchoring quality scores to tickets and building monthly calibration into the workflow, Quality as a Service creates feedback loops that help leaders distinguish between coaching opportunities and operational constraints. This matters because it reframes QA from a compliance function into a diagnostic tool. For teams considering whether to invest in agentic AI for customer-facing work, the parallel question becomes urgent: if you cannot reliably measure what your agents are doing and why customers respond as they do, how will you govern autonomous systems at scale?
Support leaders are under pressure to improve customer experience, coach agents, manage technology, and keep operations moving. Yet quality assurance often becomes another task on an already overloaded list. In this CX Today interview, Rhys Fisher speaks with Ty Given, Founder and CEO at CX Collecti