By Kathleen McNair and Tonya Webber
AI quality monitoring can score, tag, and classify more interactions than a manual quality assurance (QA) team could ever review. However, more data can become more dashboards, more alerts, and more transcripts unless the data is organized so leaders can see where value is being lost and what to change first.
In our work at COPC Inc., we see this as QA’s opportunity to move closer to the decisions that shape the business. AI can create more visibility, but someone still has to know what the data means.
Does this score reflect what happened for the customer? Is this really a coaching issue, or is the process getting in the agent’s way? Is the pattern tied to a policy, a tool, a knowledge gap, or a training need? Which issue is worth bringing to leadership first?
That is the work AI cannot do on its own, and it is exactly where experienced QA professionals are most valuable.
Quality Strategists Connect Patterns to Business Decisions
A quality strategist helps the business understand what QA data means and what should happen next, sitting at the intersection of quality, operations, training, technology, and leadership.
That person is looking at the bigger pattern behind the score.
- Why are customers calling back?
- Is the agent missing a step, or is the workflow confusing?
- Is the knowledge article incomplete?
- Is AI scoring the interaction fairly?
- Is the issue tied to coaching, policy, process, tools, compliance, or training?
Experienced quality leaders and QA teams are well-positioned for this work because they know how to interpret large volumes of interaction data, identify trends, investigate underlying causes, and connect those findings to business decisions. They can see where a quality issue reflects an individual coaching need and where it points to a larger problem in the process, policy, knowledge base, technology, or customer journey.
How Leaders Build a Contact Center QA Career Path that Matches the Work
For QA teams to grow into this strategic role, leaders have to build a path around the work.
That means giving QA teams the training, authority, and access to move findings beyond the scorecard. The operating model needs a clear way for those findings to reach the teams that can act on them.
The next version of contact center quality assurance needs clearer ownership around calibration, AI output review, root-cause reporting, quality form design, action planning, and follow-up measurement.
As AI quality monitoring expands, those skills become more important. QA professionals and trainers still need to understand whether the scoring is fair, whether classifications are accurate, and whether the data points to a coaching issue or a larger operational problem. They also need to know when an AI-generated score or summary misses context that an experienced quality professional would catch.
Some organizations may use titles like AI QA specialist or quality strategist. The titles may vary, but the direction is the same. QA is moving closer to interpretation, calibration, and operational influence.
Building this career path also requires deliberate investment in career development. The skills that matter most in this environment are data interpretation, business case development, and the ability to communicate findings to leaders who can act on them.
At COPC, we help contact center leaders build the operating model, roles, and quality programs needed to turn AI monitoring into sustained performance improvement.
Read the full article, AI Quality Monitoring in Contact Centers: How to Turn QA from Cost Center to Strategic Intelligence, to see how COPC puts this into practice.
About the Authors
Kathleen McNair, CEO, Americas Region
Kathleen leads the COPC Customer Experience Consulting, Certification, and Training practices in the Americas. 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.