Automated Quality Management: The Smarter Way Forward

November 2025

Traditional quality management (QM) has had a long and valuable contact center run, but it’s time to upgrade to automated quality management (AQM). AQM applications leverage artificial intelligence (AI)-based conversation (speech and text) analytics (e.g., natural language processing (NLP)/natural language understanding (NLU), sentiment detection, emotion analysis), business rules, and intelligent automation to identify, classify, and score up to 100% of voice and digital interactions based on defined quality criteria. AQM identifies agent training gaps and recognition opportunities and can assign appropriate courses and generate congratulatory notifications. These solutions provide actionable insights that enhance the customer and employee experience (CX and EX), increase first contact resolution (FCR) rates, reduce customer holds and transfers, increase productivity, and reduce operating costs. 

AQM Explained

AQM is Better Than Traditional QM

Although the objectives of AQM and QM are the same, the former automatically delivers statistically valid omnichannel findings and insights in near real-time, while manual QM programs typically evaluate less than 1% of calls. And while traditional QM is people-intensive and time-consuming since a supervisor or QM specialist must manually review and score each interaction, AQM automates the entire evaluation and coaching process, providing agents with timely and continuous feedback. 

Transitioning from QM to AQM

Contact center leaders migrating from QM to AQM can get a head start on the process by applying the evaluation criteria already created to manually assess agent conversations. This also helps put employees at ease during the transition, because they are familiar with the existing expectations. To increase comfort levels with the new automated process, DMG recommends conducting an AQM pilot while continuing to manually evaluate interactions. Allow four to six weeks to learn to use the AQM application, train the QM team/supervisors and agents, and to enhance the automated evaluation criteria. Once satisfied with the output and findings from the AQM solution, do a clean cutover and remove the manual QM process entirely. Make the transition even more beneficial by leveraging AQM to assess conversational AI (CAI) self-service solution performance to determine whether these systems are executing properly and meeting customer expectations.

Final Thoughts

Traditional QM has served contact centers well for close to 50 years, but it’s time for these applications to be retired and replaced by AQM. Today’s AI-enabled systems and processes provide timely insights and coaching recommendations that enhance quality and productivity while improving the CX and EX. AQM solutions may not yet be perfect, but their findings are already more accurate and actionable and their suggestions are better received than evaluations from very outdated and limited manual QM programs.