We all know the checklist. It usually starts like this:
- Greeting and Introduction
– Did the agent greet the customer warmly and professionally?
– Did the agent introduce themselves and the company clearly? - Active Listening and Engagement
– Did the agent actively listen to the customer’s needs?
– Did the agent ask clarifying questions when necessary?
– Did the agent avoid interrupting the customer?
You know the rest; 10 generic questions, repeated across every contact center quality assurance (QA) form since the early 2000s.
And we all know why; because reviewing calls manually takes time. A lot of it. 30-40 minutes per call, maybe 3 calls per agent per month if you’re lucky. Less than 1% of all interactions analyzed. And that typically just voice calls. There is often no evaluation of other channels. Anything beyond that? It just hasn’t been scalable.
Why are we still doing QA this way in 2025?
McKinsey estimates “that a largely automated QA process could achieve more than 90 percent accuracy—compared to 70 to 80 percent accuracy through manual scoring—and savings of more than 50 percent in QA costs.”
Analyse 100% of all interactions, all channels, within seconds.
With AI, we can now automate quality assurance across 100% of interactions; not just voice, but chat, email, social, any channel. No more random sampling. No more hoping that the handful of calls you listen to each month represent the agent’s true performance.
AI can redesign your entire QA framework within minutes.
You’re no longer stuck with 10 questions that “kind of” fit your business. And no longer just 10 questions, because that’s all the time your QA team had to evaluate a call. You probably have 50-100 questions that you want to ask of your customer conversations. It’s now possible. AI can analyze your historical conversations, identify key compliance risks, customer experience factors, or business-specific processes, and automatically generate custom QA questions tailored to your organization.
That means instead of asking, “Did the agent introduce themselves?” you can ask things like:
- Did the agent confirm the client’s loan repayment terms clearly and without error?
- Was the patient privacy disclosure communicated according to regulation XYZ?
- Did the agent follow escalation procedures specifically for tier-2 billing disputes?
Those aren’t off-the-shelf questions. They’re unique to your company, your industry, line-of-business and your customers. And they’re questions help you provide insight and recommendations relevant to your business.
Furthermore, AI can then automatically create training plans for agents based on the QA findings. In minutes. Saving Team Leaders hours each week to focus on mentoring.
From Process Workers to Process Consultants.
For QA teams, this means less mundane, repetitive work.
Instead of spending hours listening to a handful of calls, QA professionals can focus on higher-value tasks: analyzing trends, providing coaching insights, identifying systemic issues, fixing upstream issues that are causing customers to contact, and implementing the AI generated training plans.
Turning the QA team from process workers into genuine process improvement consultants.
Where to Start?
If you’re still reviewing 3 calls per agent per month using a static form from 10 years ago, it’s time to rethink things.
Automation isn’t just about efficiency anymore. It’s about relevance. Consistency. Scalability.
The tools are here. The question is: are we ready to use them?
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