Quality management has always been a cornerstone of the contact centre, combining people, processes, and data to monitor performance and meet regulatory standards.
But the way it’s done is changing fast, and what used to be a manual, sample-based process is now becoming more efficient and consistent thanks to AI.
To find out more, we spoke to Martin Taylor, Co-Founder and Deputy CEO at Content Guru, about how contact centres can use technology to improve how their quality assurance teams work.
Video: Staying Ahead With QA & Call Recording: Auditor’s Role Has Shifted to Reviewing Calls Flagged by AI
Watch the video below to hear Martin explain how to stay current with quality assurance and call recording, and how the auditor’s role has shifted to reviewing calls flagged by AI:
With thanks to Martin Taylor, Co-Founder and Deputy CEO at Content Guru, for contributing to this video.
This video was originally published in our article ‘How to Stay Ahead in QA and Call Recording’
How AI Is Reshaping Quality Management
Originally, QM and call recording focused on monitoring agent performance and meeting regulatory requirements, as Martin explains:
“The contact centre has always been one of the most closely measured workplaces, bringing together people, processes, and information.
The origins of quality assurance and call recording lie in agent performance management, though regulatory requirements also drove the need for audits.
Today, almost all calls are recorded, not only for agent training and monitoring, but increasingly to support AI training.”
But with the rise of AI, the way quality is managed is changing rapidly. Let’s take a look at two ways this technology is affecting contact centre QA:
From Manual Sampling to Intelligent Analysis
Traditionally, QM relied on reviewing a small sample of calls per agent, as Martin continues:
“Traditionally, quality management relied on sampling a small proportion of calls per agent with more for trainees or for certain call types.
For example, a contact centre might review 3% of medical calls that resulted in an ambulance dispatch. In practice, this meant that much of an auditor’s time was spent locating the right recordings, rather than actually analysing and scoring them.”
Trainees for certain call types, such as medical dispatch calls, would receive extra attention, often just 3% of interactions.
In practice, auditors spent much of their time searching for relevant recordings rather than analysing and scoring them.
Different sectors developed their own auditing methods and toolkits to meet regulatory standards. While effective, these approaches were limited by scale and speed.
“Different sectors develop their own auditing methods and toolkits aligned to their specific regulations. Now AI can transform this process. Instead of sampling 3% of calls, AI can analyse 100% of interactions, applying consistent scoring across both calls and digital communications.
This shifts the auditor’s role from searching for calls to reviewing those flagged by AI. For example, high-scoring examples, poorly handled cases, or calls that are unusually long or short.”
AI now allows organizations to take a different approach, and instead of analysing a small sample, AI can evaluate 100% of interactions (across both voice and digital channels) and apply consistent scoring automatically.
This means auditors can focus on reviewing the calls flagged by AI, such as high-scoring examples, poor interactions, or unusually short or long calls. It shifts their role from searching to interpreting and improving.
Extending Quality Management to Customer Feedback
Customer satisfaction (CSAT) and Voice of the Customer (VoC) programmes have traditionally relied on sampling too.
Post-call surveys and interviews typically receive low response rates, often below 5%, and usually from the most motivated customers.
“CSAT and voice of the customer have historically relied on sampling too, through interviews or post-call surveys. These usually garner response rates below 5%, typically from the most motivated customers.
By applying QM-style AI methods, organizations can generate auto CSAT for every interaction, and then calibrate those results against returns from traditional sampling. This ensures scores remain accurate and consistent.”
By applying the same AI methods used in QM, organizations can now generate auto CSAT scores for every interaction, then calibrate these results against traditional survey responses.
This ensures scoring remains accurate while providing a complete, real-time view of customer sentiment.
A Smarter Future for Quality Management
AI doesn’t replace human judgement – it enhances it.
By automating analysis, applying consistent scoring, and surfacing meaningful insights, AI enables quality teams to work more efficiently and focus their expertise where it matters most.
For contact centres, this means faster feedback, fairer evaluations, and a more complete understanding of both performance and customer experience.
Author: Robyn Coppell
Reviewed by: Jo Robinson
Published On: 4th Dec 2025
Read more about - Video, Artificial Intelligence (AI), Content Guru, Martin Taylor, Quality, Videos
