How Contact Centre QA Is Changing

Customers now expect to move seamlessly between voice, email, chat, messaging, and social media. That means contact centre quality assurance can no longer treat each channel in isolation.

To deliver a consistent customer experience, QA is evolving beyond manual call sampling and channel-specific scorecards. The future is omnichannel, automated, and increasingly powered by AI.

To find out more, we asked Martin Taylor, Co-Founder and Deputy CEO at Content Guru, to explain how the future of contact centre QA is omnichannel, automated, and increasingly powered by AI.

Video: QA Across Channels: Turn Omnichannel Chaos Into QA Consistency

Watch the video below to hear Martin explore how contact centres can manage QA well across different channels, focusing on how to turn omnichannel chaos into QA consistency:

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 Manage QA Across Different Channels’

4 Ways AI Is Transforming Contact Centre Quality Assurance

Modern quality assurance is about much more than reviewing a handful of calls, and by combining omnichannel measurement, automated evaluation, and AI-driven insight, contact centres can improve consistency, efficiency, and customer outcomes.

Here are four key ways quality assurance is changing:

1. Creating Consistent QA Across Every Channel

One of the biggest challenges in omnichannel quality assurance is that each communication channel has different performance characteristics.

For example, written channels such as email and webchat may measure how many responses are required before an interaction is resolved, while voice conversations do not follow the same structure.

The goal is not to use identical metrics across every channel but to create equivalent scoring standards so performance can be compared fairly regardless of whether the customer contacted the business by phone, email, chat, or social media.

Building Equivalence Between Channel Metrics

Some metrics naturally carry across multiple channels, like average handle time, for example, which can be measured for both voice and digital interactions, even though the way that time is accumulated may differ.

Creating this equivalence allows contact centres to maintain consistent quality standards while recognizing the unique characteristics of each channel.

2. Moving From Sampling to Measuring Every Interaction

Traditional quality assurance relied on supervisors reviewing a small sample of interactions for each advisor.

That approach often meant important conversations were missed simply because they were never selected for review.

Automated QA tools are changing this model by evaluating every interaction, giving contact centres a much more complete picture of customer experience and agent performance.

Finding the Conversations That Matter Most

Instead of spending hours searching through recordings and transcripts, supervisors can be automatically directed to interactions that deserve attention, including:

• Excellent examples of customer service
• Interactions that show compliance risks
• Conversations where customers experienced problems
• Cases that require coaching or follow-up

As a result, quality teams spend less time searching and more time improving performance.

3. Using AI to Focus Human Expertise

Automation is not replacing quality analysts, it is allowing them to focus on higher-value work, as Martin explained:

When AI evaluates every interaction, supervisors no longer need to manually identify the few conversations worth reviewing.

Instead, the system enables human reviewers to investigate root causes, provide coaching, and improve processes.

This makes quality assurance more proactive, more targeted, and far more efficient.

4. Preparing for the Era of Agentic AI

The next stage of quality assurance will extend beyond measuring human performance, Martin continued:

As Agentic AI becomes more capable across voice and digital channels, contact centres will increasingly evaluate AI containment, the proportion of customer interactions that AI can successfully resolve without requiring human intervention.

A New Evolution of First Contact Resolution

Containment is effectively an evolution of first contact resolution, but rather than simply asking whether the customer’s issue was resolved, contact centres will measure:

• Which interactions were handled entirely by AI
• Which required escalation to a human advisor
• Where AI performed well
• Where AI needs improvement

This will become an important indicator of how effectively human and AI resources work together across the customer journey.

The Future of Quality Assurance

Quality assurance is no longer about reviewing a handful of calls each month. Instead, the future lies in omnichannel consistency, automated evaluation, and AI-driven insight.

By measuring every interaction, creating equivalent scoring across channels, and preparing for AI containment metrics, contact centres can improve customer experience while making quality teams significantly more effective.

Ultimately, contact centres that modernize quality assurance will be better equipped to manage increasingly complex customer journeys and deliver consistently high service standards across every channel.

Author: Robyn Coppell
Reviewed by: Xander Freeman