Quality assurance (QA) has always been central to maintaining service standards in the contact centre.
But traditional QA methods, which are often based on manual monitoring and random sampling, can leave teams with a limited view of performance.
The rise of automation and AI is transforming QA from a time-consuming, reactive process into a continuous, data-driven strategy that delivers clearer insights and faster improvements.
To find our more, we asked Jonathan Kenu Escobedo, Customer Success Manager at MiaRec, to explain how AI and automation is redefining contact centre quality assurance.
Video: Staying Ahead With QA & Call Recording
Watch the video below to hear Jonathan explain how to stay current with Quality Assurance and Call Recording, and how it allows contact centres to move beyond random sampling for a complete view of agent performance:
With thanks to Jonathan Kenu Escobedo, Customer Success Manager at MiaRec, for contributing to this video.
This video was originally published in our article ‘How to Stay Ahead in QA and Call Recording’
Four Ways Quality Assurance Is Evolving
Modern QA is no longer just about monitoring, it’s about empowering agents and improving service through data and automation by replacing outdated methods with smarter, more efficient tools, as Jonathan explains:
“Quality assurance in contact centres has evolved from manual call monitoring into an automated data-driven strategy for improving compliance, agent performance, and customer experience.”
Here’s four ways the landscape is evolving and what it means for contact centres:
1. Moving From Random Sampling to Full Visibility
In the past, QA teams could only review a small fraction of calls, often missing critical trends.
“Modern QA platforms move beyond random sampling, enabling leaders to have every interaction automatically evaluated for consistency and adherence to standards.
This shift provides a complete view of agent performance while saving QA managers countless hours otherwise spent reviewing only a fraction of calls.”
Automated QA tools now evaluate every single interaction, providing complete coverage and removing the guesswork, giving leaders a reliable, data-backed view of performance and saves QA teams hours of manual work.
2. Tailored Scorecards for Different Call Types
Today’s platforms allow multiple, customizable scorecards to be applied to different interaction types, which ensures evaluations are relevant and consistent and reflect the context of each customer conversation.
“To stay up to date, leaders should leverage QA tools that allow multiple customizable scorecards tailored to different call types, apply advanced analytics to uncover performance trends, and implement automated coaching that delivers actionable feedback.”
3. Using Analytics to Spot Trends
Advanced analytics uncover patterns in agent behaviour and performance that traditional sampling would overlook.
These insights make it easier to target training, improve coaching, and address issues before they affect the customer experience.
4. Real-Time, Automated Coaching
AI is now generating feedback automatically after every call, so agents receive clear, actionable insights in real time, helping them improve their performance on future interactions without waiting for scheduled QA reviews.
“In the past year, automated QA tools have advanced further, offering AI-generated feedback on every call so agents can improve performance on future interactions.”
This creates a continuous improvement loop that benefits both staff and customers.
Author: Robyn Coppell
Reviewed by: Jo Robinson
Published On: 2nd Jan 2026
Read more about - Video, Analytics, Artificial Intelligence (AI), Automation, Jonathan Kenu Escobedo, MiaRec, Quality, Scorecards, Training and Coaching, Videos
