By Kathleen McNair and Tonya Webber
Reviewing every customer interaction sounds impressive, especially for quality teams that spent years working from small samples. Traditional contact center quality assurance was built around scarcity. Most teams could review only 2% to 5% of interactions and use that sample to coach agents, track compliance, and estimate program performance.
AI-supported quality monitoring changes the scale of what teams can see. The problem is that more monitored interactions do not automatically tell leaders where value is being lost or what to change first. Teams still need to know what they are measuring, why it matters, and how the information will be used.
A customer interaction can look clean on paper and still fail the customer. The agent followed the process. The answer was accurate. The policy was enforced. The documentation was complete.
Traditional contact center QA often stops there.
In our work at COPC Inc., we look at the same interaction through a different lens. We want to know whether the customer’s issue was actually resolved, what prevented resolution, and whether the barrier was something the agent could control.
That question changes the role of quality. It moves the discussion beyond agent compliance and into the policies and processes that shape what customers actually experience.
Why Contact Center Quality Assurance Has to Question the Policy, Not Just the Agent
Agents need clear standards, and organizations need to know whether those standards support the customer’s expected outcome. A policy can be applied correctly and still result in an outcome that the customer experiences as unresolved.
At COPC, we think the even harder question is, “How much issue resolution is that policy costing you, and is it worth it?”
This is where AI tools for quality assurance can help by showing when the same policy, process, or tool issue keeps appearing across interactions. The value comes from using those patterns to understand their impact on resolution, then deciding whether the business should keep the rule as written, change the workflow, create a digital option, or give agents a better path to resolution.
Where Contact Center QA Can Reveal Policy Friction
Take a warranty issue, for example. A customer contacts support because they want a repair or refund. The agent checks the policy, sees that the item is outside the warranty period, explains the rule correctly, denies the request, and documents the interaction.
From a traditional quality view, the agent did the right thing.
From the customer’s view, the issue was not “resolved.”
The next question is what prevented resolution. Did the agent miss a step? Did they give the wrong answer? Did they need more coaching? Sometimes the answer is yes. In many cases, the quality data points somewhere else. The agent may have followed the process correctly, while the policy, workflow, tool, or business rule created an outcome the customer experienced as unresolved.
When we do this analysis for clients, we group unresolved contacts by what the agent could control versus what they could not control because of policy, process, tools, finance rules, compliance requirements, or other business constraints.
For one of our North American B2B e-commerce clients, the numbers changed the story. Sixty percent of issues were resolved from the customer’s perspective, and 40% were not. Of that unresolved 40%, 12 percentage points were tied to agent-controllable factors and 28 points were driven by policies, processes, tools, or other business constraints.
How AI Tools for Quality Assurance Turn Patterns into Business Decisions
COPC uses AI tools for quality assurance to make the patterns visible to our clients. They organize that data so leaders can see at a glance where resolution is being lost and what is driving it, whether that is a policy, a process, a tool gap, or agent behavior.
The point is to move from thousands of monitored interactions to a short list of business decisions.
- Which policy needs review?
- Which process is creating repeat demand?
- Which tool issue is adding friction for customers and agents?
- Which change would improve resolution fastest?
Where Quality Teams Take It From Here
For customer experience operations, the value is in understanding what the business should do next. AI-supported quality monitoring can create scale. Quality teams create the value. They define what “resolved” means from the customer’s perspective, validate the results, interpret root causes, and bring the business a fact-based case for action.
That’s how contact center QA moves from scorekeeping to strategic intelligence. It shows where the agent did the right thing, the customer still left unresolved, and the policy, process, or tool deserves a closer look.
To go deeper, read the full article, AI Quality Monitoring in Contact Centers: How to Turn QA from Cost Center to Strategic Intelligence, and learn how quality teams can use AI-supported monitoring to turn customer outcome data into root-cause findings and business decisions.
About the Authors
Kathleen McNair, CEO, Americas Region
Kathleen leads COPC Customer Experience Consulting, Certification, and Training Americas practices. She is responsible for all service delivery and P&L. With deep expertise in vendor management, contracting, and performance improvement, she has led transformational projects across operations management, BPO sourcing, and customer journey design for large-scale global operations. Kathleen has a proven record of building multichannel programs spanning sales, customer service, and technical support, helping clients scale both assisted and digital customer experiences.
Tonya Webber, Director of Consulting
Tonya brings over sixteen years of CX leadership experience, specializing in operational transformation, process optimization, and performance improvement. She partners with COPC clients to strengthen customer engagement operations through process gap analysis, knowledge management, governance frameworks, and data-driven improvement initiatives. As former Director of Operations at SaaS provider RealPage, Tonya led teams of analysts to drive efficiency, support product launches, and close critical process gaps. Skilled in project management, quality, and strategic vendor relationship management, she is recognized for applying root cause analysis and continuous improvement practices that deliver measurable business impact