AI Quality Monitoring in Contact Centers: How to Turn QA from Cost Center to Strategic Intelligence

By Kathleen McNair and Tonya Webber

AI-powered quality monitoring tools have moved from pilot to production in most large contact centers around the globe and are being used to listen to every interaction, score every call, and flag every potential issue in real time. Yet at the executive level, returns remain unclear. Core outcomes like issue resolution, customer satisfaction, and cost-to-serve often look much the same as they did under traditional sampling.

In our most recent research at COPC, we found that 79% percent of organizations currently use AI in customer care, and an additional 15.9% plan to implement within 18 months. Only 5% have no plans whatsoever. But the critical nuance is that among current users, nearly four in five (61.7% of all respondents) are actively planning to refresh, change, or upgrade their AI solutions. This could signal a gap in expectations vs. reality.

This is exactly the gap we see in our work at COPC Inc. For almost thirty years, we have helped organizations improve customer experience (CX) operations, designed and refined the COPC Customer Experience (CX) Standard, and advised leaders on how to get real value from their technology choices. We’ve learned you only see returns when technology is aligned with how work actually gets done and how customers experience your brand.

Right now, many quality programs still carry a small-sample mindset into a population-level world. Some do attempt to measure issue resolution, but usually from the company’s perspective (i.e., did the agent follow the script, enforce the policy, and provide the approved answer?). In that setup, the customer’s outcome is assumed rather than measured, and automation produces scorecards that measure how well agents adhere to policies. What’s still missing is the diagnostic detail needed to understand which policies, processes, or tools are actually driving resolution failure from the customer perspective. 

When quality is reorganized around customer outcomes, a different picture emerges. A large share of unresolved issues is often driven by policies, processes, or tools rather than agent behavior.

Ahead, we’ll break down the three things required to turn that visibility into meaningful ROI: data organized around customer outcomes, quality teams equipped to act on what they see, and AI and human expertise working in partnership rather than in isolation.

How AI Quality Monitoring Turns QA Data Into Actionable Insight

Traditional contact center quality assurance was built around scarcity. Most teams could review only a small sample of interactions, often  two to five percent, and use that sample to coach agents, track compliance, and estimate performance at the program level. The challenge was visibility. Organizations lacked the technology to analyze interactions at the depth and scale needed to understand why issues were or were not being resolved. They could identify that a problem existed, but not easily pinpoint the policy, process, or tool failures behind it.

AI quality monitoring changes that. It can score, tag, and classify every call, chat, and digital interaction. That is a major step forward over traditional programs that might manually review only a handful of contacts per agent each month. But more data does not automatically create better decisions. In many organizations, it simply creates more dashboards, more alerts, and more transcripts for teams to sort through.

The real question is not whether technology can monitor every interaction. It can. The question is how to gather and organize the right data so leaders can see where value is being lost and what to change first.

Start with the customer, not the checklist.

In our work at COPC, the first shift is simple. We stop asking only whether the agent followed the correct procedures and start asking whether the customer’s issue was actually resolved.

Those are not the same thing.

If a customer calls to request a refund and the agent follows the policy perfectly, denies the request, and documents the interaction correctly, many traditional quality programs mark that contact as resolved. From the company’s perspective, the process worked. From the customer’s perspective, it did not. The customer still wanted a refund and left without one.

A simple way to show the difference is this:

If quality is scored only on whether the agent followed the rules, organizations will overestimate performance and miss the policies, processes, and tools that are blocking true resolution.

That distinction matters because it changes what AI quality monitoring is measuring. If the system is only checking compliance, it becomes a faster version of the old QA model. If it’s measuring customer-perceived resolution, it can start showing where policies, processes, and tools are blocking better outcomes.

AI quality tools tend to measure rule-based behaviors more reliably than customer outcomes. Script compliance, courteous greetings, and proper hold procedures are easier to score because they follow clear patterns. Resolution, effort, trust, and customer satisfaction are harder to assess with the same confidence. That can leave organizations with quality scores that appear precise while missing the experience signals that matter most. 

Structured analysis reveals what is driving resolution.

In our work with contact centers, we use a resolution tree visualization as one way to organize AI quality monitoring data around that customer perspective. Another is a drilldown Pareto dashboard that ranks the biggest contributors to unresolved contacts and lets teams click through to root causes. The format can vary. What matters is giving quality teams a practical way to move from broad outcomes to specific causes and then to action.

The resolution tree takes population-level data and structures it hierarchically:

• Outcome: issue resolved or not resolved
• Controllability: agent controllable or non-agent controllable

Root cause: policy, process, tool or other drivers

When we shifted quality monitoring from an agent perspective to a customer perspective for one North American B2B e-commerce client, the numbers told a very different story.

On the left of the tree, 40 percent of issues were resolved from the customer’s perspective, and 60 percent were not. When we broke down that 60 percent, only 7 percentage points were truly agent-controllable. The remaining percentage points, or percent of failures, were driven by policies, processes, or tools rather than by individual agents. We see a similar 70/30 pattern in many environments when we apply this approach. The exact percentages vary by client and journey, but the dominant story is consistent: most quality problems are systemic, not individual.

The tree lets us go one level deeper. Of those 28 systemic points, 24 came from CX policy and process issues:

• 14 points from a requirement that the customer submit a written request regarding their issue
• 6 points from escalations or transfers
• 4 points from customer confirmation requirements

Finance rules, tools, and client-specific constraints made up the remaining 4 points.

Why this Structure Changes the Conversation

A drilldown dashboard can apply the same logic in a different format, helping teams see the biggest contributors to unresolved contacts first and then drill further into the policies, processes, tools, or agent behaviors behind them.

Once the data is organized this way, three things change immediately.

1. Realistic targeting. Most organizations set resolution targets by looking at historical performance and then adding a stretch goal. In this example, the organization had been pushing for a 75 percent resolution rate. Month after month, the contact center missed the target despite more coaching and more pressure on frontline teams.
   
   The structured analysis showed why. Even if every agent performed perfectly, the contact center could only move from 60 percent to about 72 percent resolution. Without changing policies, processes, or tools, 75 percent was mathematically out of reach. No amount of coaching will get agents past a ceiling the system itself has created. Instead of asking, “Why are agents not hitting 75 percent?” leaders can ask, “What has to change in our policies or processes to make 75 percent achievable?”
   
2. Quantified business cases. When one policy, such as “written request required,” is responsible for 14 percentage points of unresolved issues, leaders can put real numbers behind it. With a few additional inputs, such as cost per contact, refund policies, and potential digital alternatives, quality teams can estimate the annual financial impact and compare it to the cost of keeping that rule versus replacing it with a technology or process solution that eliminates the customer friction.
   
   Instead of saying, “We think this policy is causing friction,” the quality team can say, “This specific requirement is costing an estimated amount in extra contacts and lost resolution.” In one modeled scenario, 100,000 initial contacts grew to nearly 136,972 total contacts over time as customers circled back, adding roughly $147,888 in annual avoidable costs. Seen that way, quality becomes a way to expose hidden demand and cost, not just score behavior. That is the shift from quality as a cost center to quality as a provider of contact center business intelligence.
   
3. Faster deep dives. The technology makes deep-dive analysis much faster, while a structured view helps teams target the right branch or category. If “requires escalation or transfer” is costing 7.1 percentage points of resolution, teams can pull every interaction that requires escalation or transfer in seconds.
   
   Quality and operations leaders can review a focused sample and quickly see what is driving the pattern:
   • Are escalations required by policy or simply by habit?
   • Are there knowledge gaps or tool limitations that prevent first-contact resolution?
   • Are certain products, processes, or customer segments overrepresented in that branch?

Without structure, those questions get buried under thousands of undifferentiated scores. With structure, leaders move from “we have a resolution problem” to “we know which levers will move resolution fastest and can prove the impact before asking the organization to change.”

Why Human Oversight Still Matters 

The next challenge is not scale, it’s judgment. McKinsey has argued that even as AI takes on a growing share of service interactions, human-powered contact centers remain crucial both for validating AI and for handling the kinds of complex, emotionally nuanced issues that technology still struggles to manage well. CX Dive has made a similar point from a different angle: in CX, the bigger barrier is often not the technology itself but change management and adoption. 

This is the point where AI quality monitoring programs either deliver ROI or fall short. The technology can analyze interactions at scale, detect patterns quickly, surface issues across channels and journeys, and take much of the manual scoring work off QA teams. That is a major advance over small-sample QA. But broader coverage does not automatically improve outcomes.

What human quality teams still need to do

In COPC’s model, human oversight still has to do the work that creates quality ROI:

• Define the outcome standard by deciding what “resolved” means from the customer’s perspective
• Calibrate the system against real outcomes such as repeat contacts, issue resolution, and customer satisfaction
• Interpret root causes by separating coaching issues from policy, process, or tool failures
• Prioritize the findings so leadership can focus on the changes that will move performance fastest
• Build the case for change in terms of operations and finance that can be acted on

This is the human-AI partnership that actually delivers value. AI provides the scale; humans provide the structure, judgment, and follow-through that turn it into intelligence.

The calibration loop that turns visibility into ROI

The strongest programs use a clear calibration loop:

1. The system scores and classifies interactions
2. Quality teams validate those results against resolution, repeat contacts, and satisfaction
3. Criteria get refined
4. The system becomes better aligned to what actually matters to customers and to the business

That makes the system more useful to leadership because it ties AI outputs to real outcomes, not just automated scores. It also makes the process fairer to agents. Instead of judging performance from a tiny sample, leaders can look at a much fuller body of evidence and see more clearly when the real issue sits in the system around the agent rather than with the agent alone.

From scorecards and reports to strategic insights

In a traditional QA model, much of the team’s time is spent reviewing transactions, completing scorecards, and documenting missed steps. In an AI-enabled model, teams are no longer just publishing reports produced by the technology. They’re analyzing and synthesizing the data, organizing it into usable formats, and presenting it in ways leaders can act on. That puts them in a much better position to answer bigger questions:

• Which policy is driving repeat contacts?
• Which transfer rule is suppressing resolution?
• Which tool issue is adding avoidable friction for agents and customers?
• Which change would move the metrics fastest?

That shift can create measurable value. In one COPC engagement with a large U.S. telecommunications provider, the company aligned standards, management practices, and vendor oversight across internal and outsourced centers. The result was a rise in issue resolution from 66 percent to the low 80s, a 17.5-point improvement in top-two-box CSAT, and more than $50 million in savings.

The lesson is not that AI alone delivered those results. It is that once teams have the right structure, standards, and management discipline, technology becomes much more effective at scaling the right behaviors.

The opportunity now is not just to score more calls. It is to give quality teams the visibility and authority to identify what is blocking resolution, quantify the impact, and help the business decide what to fix next.

What Separates AI Quality Monitoring Programs That Deliver ROI

The difference is rarely the tool itself. In our experience, the bigger difference is the operating model around it.

Some organizations treat automated monitoring as a faster version of the old QA program. They score more interactions, generate more dashboards, and coach agents more, but they do not change how quality is defined or how findings are acted on. In those environments, the technology usually creates more visibility without creating much more value.

The stronger programs take a different path. They start with the customer perspective, organize the data in a way that supports drilldown to root cause, and equip the quality team to turn patterns into business decisions. In that model, monitoring is not just a way to review more interactions. It becomes a way to identify which policies, processes, and tools are suppressing resolution and driving avoidable costs.

That distinction matters because the biggest gains often sit outside the scorecard. If unresolved contacts are being driven by transfer rules, outdated requirements, or tool limitations, more coaching alone will not move the business case. What moves the business case is using quality intelligence to decide what the organization should fix first.

The practical takeaway is straightforward. Meaningful ROI depends on three things:

1. A customer-perspective definition of quality
2. A structured way to separate agent issues from systemic issues and understand the root causes
3. A quality team that can translate insight into action
   

Without those elements, organizations may get more data, but they rarely get the full return.

If Your Quality Program Is Not a Profit Center Yet

AI quality monitoring creates abundance. The return comes from what your organization does with it. That means defining quality from the customer’s perspective, structuring the data so it points to real causes, and equipping the quality team to turn insight into action.

If your quality program is not giving you that kind of visibility, and if it is not seen internally as a profit center, we should talk. COPC helps organizations turn quality into a more strategic function through assessment, structure, and a model built for measurable business impact.

If QA only tells you who missed a step, it stays a cost center. If it shows you what is costing the business money, it becomes a profit center.

About the Authors

Kathleen McNair, CEO, Americas Region
Kathleen leads COPC America’s Customer Experience Consulting, Certification, and Training practices and 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 16+ 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.