More isn’t always better.
We’ve all heard the old saying that you can have too much of a good thing. And many of us have scoffed at it before learning the lesson the hard way, having overindulged in wine, candy, pizza, or whatever your particular vice is.
And the current epidemic of doom-scrolling through 25 different streaming services for hours on end is a testament to the drawbacks of having too many choices.
However, when it comes to QA, it’s safe to say that more definitely is better.
Across the long history of CX, ever since recording solutions first came on the scene, contact centers have been capturing interaction data ‘for training purposes.’
Yet, for the most part, they’ve done surprisingly little with that goldmine of potential insights.
And while that’s understandable – given tight resources and the time it takes to manually review conversations one by one – it ultimately made it harder than it should have been to improve quality based on real customer engagement.
Enter AI interaction evaluation: a new, data-driven way to measure service quality at scale.
“The big ticket items are every interaction and automatic evaluation,” explains Peter Fedarb, Senior Presales Consultant at Enghouse Interactive.
“You’re not doing that small survey anymore; AI can review every call automatically. That means team leaders spend their time resolving problems instead of finding them.”
From Sampling to Scale
Traditional QA often reviews less than one percent of total conversations. And that one percent is random, even if it’s based on criteria such as “calls longer than x seconds” or “problem calls flagged by an agent.”
AI changes that equation by transcribing, categorizing, and scoring every interaction against consistent criteria. The result is a far more accurate and representative picture of the customer experience.
This isn’t about removing people from the loop; it’s about giving them better information. With AI surfacing trends and outliers, supervisors can prioritize coaching where it matters most.
When it comes to some of the tangible benefits of the tech, accuracy and efficiency are obvious wins, with organizations moving from sampling as few as .05 percent of engagement to 100 percent coverage.
Still, Fedarb points to another crucial outcome: a better relationship between team leaders and agents.
“We’ve had customers who thought they had big problems, only to realize those were just lapses,” he says. “Random sampling was leading them to outliers.”
“Being able to tell the difference between a blip and a real issue builds trust and stops agents feeling like they’re being unfairly targeted.”
That fairness translates into morale and retention. When feedback is based on comprehensive data rather than chance sampling, agents feel recognized for the quality of their work. Meanwhile, leaders gain the visibility to act quickly.
One team leader, Fedarb recalls, discovered that the biggest benefit wasn’t time saved, but time to react.
“She used to do evaluations on a Friday and have the one-to-ones the following week,” he explains.
“Now she can check her dashboard, spot an issue that morning, and give a gentle reminder. By the afternoon, performance is already improving.”
Combating Burnout and Improving Experience
By removing the grind of manual scoring, automated evaluation also lightens the load on managers – reducing fatigue and freeing capacity for coaching.
Agents, too, benefit from more positive interactions with their supervisors.
Fedarb details how the tool helped to make one-to-one meetings “less scary.”
“Because they’d had those little reminders during the week, the meetings became more about encouragement and development.”
In a labor market where agent wellbeing and retention are critical, that shift matters. Real-time feedback loops replace delayed reviews, making performance management more continuous and constructive.
Getting Started: Best Practices
Fedarb’s advice for contact centers considering the move is simple: don’t overthink it.
“Typically, we start with a very generic scorecard,” he says.
“Forget what you’re scoring today, just focus on good practice and good etiquette: empathy, process, politeness. Run the AI against that, prove it works, and then start tailoring.”
Rather than trying to replicate every metric at once, begin with two or three questions, tune them for accuracy, and expand gradually.
This phased approach builds internal confidence while teaching teams how to “talk” to the AI: refining questions, adjusting phrasing, and learning how to interpret results.
Setting Realistic Expectations
Like any AI solution, there can often be inflated expectations from the customer.
Organizations should understand that, like a human working in an unfamiliar environment, AI is still young enough that some industry terminology and accents may need to be learned. The good news is that training is possible, and language libraries are always improving.
“In fact, the amount of specialist training we’ve had to do has been minimal,” Fedarb says.
“The other calibration we recommend is to look at ways of framing evaluation questions, so it’s easier for AI to identify the information needed—and we provide guidance on that.”
To combat unrealistic expectations, Fedarb emphasizes the need for transparency and logic.
“I can tell you that right now, vendors in our space are not using AI for data input scrutiny, i.e., evaluating screen activity still needs to be manual. If the tool can do 80 percent of what you’re doing today, that’s still a massive win.
“It will still review 100% of your engagement, but perhaps you’ll still need human attention to get all the results you need. What it will do is help you identify that 20% that needs more human input.”
He also encourages leaders to explore adjacent use cases – from replacing wrap-up codes to flagging trigger words for compliance or safeguarding.
And for those still hesitant? “Be open-minded,” he says. “It’s not a huge project, maybe six weeks, an hour a week. Try it and see what insights it gives you. Worst case scenario, you can turn it off…. but no one ever has.”
The Future Is Continuous
By combining the reach of automation with the empathy and insight of experienced leaders, contact centers can turn QA from a periodic chore into a continuous, organization-wide feedback loop.
For enterprises chasing consistent service quality and faster improvement cycles, the message is clear: the tools are ready, the process is proven, and the rewards are tangible.
Or, as Fedarb puts it: “It’s not about perfection; it’s about progress. If AI can handle the heavy lifting, you can focus on what really matters: the people.”
You can discover more about Enghouse Interactive’s approach to QA by checking out this article.
Learn more about Enghouse Interactive’s AI Quality Management here, or view the company’s full suite of solutions and services at their website today.
