{"id":6623,"date":"2026-06-09T10:00:00","date_gmt":"2026-06-09T03:00:00","guid":{"rendered":"https:\/\/kpsg.com\/?p=6623"},"modified":"2026-06-04T10:14:13","modified_gmt":"2026-06-04T03:14:13","slug":"improving-quality-assurance-with-ai-monitoring-in-contact-centers","status":"publish","type":"post","link":"https:\/\/kpsg.com\/en\/insight\/improving-quality-assurance-with-ai-monitoring-in-contact-centers\/","title":{"rendered":"Improving Quality Assurance with AI Monitoring in Contact Centers"},"content":{"rendered":"

In many contact centers, operational efficiency often appears to improve after technology adoption. Handling time decreases, interaction volume increases, and dashboards look \u201cgreener.\u201d However, if repeat contacts remain high and service quality does not truly improve, the issue may not be the technology itself\u2014but how performance is measured.<\/em><\/p>\n\n\n\n

This is where quality assurance with AI monitoring becomes important. It is not only about monitoring conversations faster, but also about helping organizations assess service quality more accurately, consistently, and in a way that remains relevant to today\u2019s customer needs.<\/p>\n\n\n\n

Why Is Traditional Quality Assurance Becoming Limited?<\/strong><\/h2>\n\n\n\n

Conventional QA approaches often rely on manual sampling. This means only a small portion of interactions is evaluated. As a result, many important insights may be missed, such as escalation patterns, the root causes of repeat contacts, response inconsistencies, and friction points that force customers to repeat their explanations.<\/p>\n\n\n\n

As contact centers begin adopting AI, the challenge becomes more complex. Interactions no longer only happen between customers and agents, but also involve chatbots, voicebots, or hybrid flows between AI and humans. If the KPIs being used are still outdated, the real impact of AI becomes difficult to measure.<\/p>\n\n\n\n

What Is the Role of AI Monitoring in Quality Assurance?<\/strong><\/h2>\n\n\n\n

AI monitoring helps contact centers improve quality assurance by monitoring interactions at scale and identifying patterns that are not easily captured through manual evaluation. This allows QA to move beyond a reactive approach and become an active, continuous monitoring system.<\/p>\n\n\n\n

To ensure implementation delivers real impact, there are three key indicators that need to be prioritized:<\/p>\n\n\n\n