Most contact centers are still running QA the old way.
They’re only getting a fraction of the value QA can deliver.
The old QA model looked something like this:
– Manually listen-in on a sample of calls each month
– Score whether agents followed your processes
– Report on agent performance
It told you how agents were doing, but not much else.
Modern AI QA tools have changed that entirely. They now:
– Analyze every single interaction, not just a sample
– Detect customer tone and sentiment in real time
– Identify patterns and trends at scale
At that scale, the questions you can ask change entirely.
Instead of only asking: Did the agent handle the call correctly?
Now you can find answers to:
– Why are these calls happening at all?
– Are our internal processes actually resolving the issue?
– Where in the business is the real problem originating?
Take this example from a healthcare client:
Call volume up. Satisfaction down. Angry patients.
On the surface, it looked like an agent problem.
But the QA data told a different story.
The same pattern kept surfacing: patients calling in angry because they’d shown up to appointments and their physician wasn’t there. No notice. No rescheduling.
It turned out the physician’s team hadn’t been notifying patients when they’d be absent.
The contact center was the first place that failure showed up.
But the problem originated two steps upstream
Not in the contact center.
That’s what modern QA makes possible.
– Fewer unnecessary calls
– Faster root cause identification
– Processes that actually get fixed upstream
Most contact centers are sitting on this data right now.
Few are using it to look beyond agent performance.
I’m Mark Danielson, and I help healthcare leaders reduce support costs while improving patient satisfaction.
Follow me for practical insights on cutting contact center costs, improving service quality, and modernizing operations without the tradeoffs.
