Delivering excellent customer service depends on well-trained agents, but quality assurance (QA) teams often face an uphill battle.
Call centres generate huge volumes of call data, which QA teams use to coach agents and instil best practices.
However, traditional QA processes rely on manually reviewing calls, which is extremely time-consuming. In fact, manual QA reviews only capture 1-2% of total calls, and feedback typically comes days after the initial call.
As a result, QA teams spend more time scoring calls than actually coaching agents.
This has many organisations asking: “Should we introduce AI to quality assurance?” And more importantly: “How do we use AI in quality assurance to improve both efficiency and agent performance?”
While some fear that AI will replace human roles, the reality is that AI is an assistive tool. It automates repetitive scoring and analysis, giving QA teams the time and insight to focus on what matters.
In this blog, we’ll explore the role of AI in QA and how solutions like Sense can help call centres streamline processes, strengthen coaching, and drive continuous improvement.
The Limits of Manual QA Reviews
A typical call centre will handle thousands of calls every day, and QA teams are tasked with ensuring those interactions meet company standards and deliver the best customer experience possible.
But not every call can be assessed, and reviewing even a single call is time-intensive. QA teams often measure against multiple metrics, such as customer satisfaction, net promoter score, and first call resolution, making the process even more complex.
This turns QA teams into auditors rather than coaches. As a result, agents miss out on vital training, leading to lower confidence, reduced customer satisfaction, and reputational damage.
How to Use AI in Quality Assurance
AI is transforming workplaces, improving employee productivity by 66%, and 78% of companies now adopt some form of AI technology into their processes.
So, should call centres introduce AI to quality assurance? The benefits are hard to ignore. Let’s delve into how to use AI in quality assurance.
For QA teams, AI relieves the burden of manually scoring calls, freeing up time to focus on what truly matters – coaching agents and improving performance.
One of AI’s greatest strengths is automation. Tools like Sense can evaluate 100% of customer interactions within moments, delivering consistent, unbiased scoring. More than just automation, Sense highlights actionable insights, flagging coaching opportunities, and supporting a culture of continuous improvement.
With these insights at hand, QA teams can provide tailored one-to-one feedback to agents, helping them know exactly how to improve their next call. And when trends point to wider issues, QA can run group workshops to address them, raising performance across the board.
Integrating AI may feel daunting at first, but it’s not about replacing human expertise. Instead, it’s about creating a partnership where AI handles repetitive tasks, and QA teams focus on developing people and driving better customer experiences.
Overview: The Real Benefits for QA Teams and Agents
Identifying Training Needs: With AI tools such as Sense, agents no longer need to wait days or even weeks for vital training. Automated analysis allows QA teams to quickly spot issues at both the individual and team level, enabling bespoke coaching that keeps performance on track.
More Consistent Scoring: Manual scoring is time-consuming and prone to inconsistency. AI, by contrast, applies the same preset metrics across every call, ensuring fair and unbiased results. This consistency builds greater agent trust, as feedback is always accurate and transparent.
Continuous Self-Improvement: Agents can only improve if they know where they’re falling short. AI-powered quality assurance highlights specific areas for development, giving QA teams the insights they need to provide targeted coaching. This results in confident agents who deliver better customer interactions.
Better Customer Experience: When QA teams spend less time auditing and more time coaching, agents feel supported and capable. Confident, well-trained agents deliver smoother, more personalised interactions, and customers notice the difference, strengthening the brand’s reputation.
How Sense Supports QA Teams
Sense is an AI-powered tool specifically designed to support call centres. While traditional QA teams typically review just 1% of daily calls, Sense can analyse 100% of interactions in real time, giving QA teams the insights and time they need to focus on coaching and development.
AI doesn’t replace QA teams; it empowers them. By automating repetitive scoring, Sense elevates the role of QA teams by allowing them to maximise their impact and deliver greater value to agents and customers alike, solidifying their place in call centres.
Ready to see the difference Sense can make?
Book a demo today and discover how SystemsX can help your QA team focus on what really matters: coaching, developing agents, and driving customer excellence.
