Earlier this year, Neil Smith, VP of Technical Support at Iterable, discussed his company’s underwhelming pilot of a contact center automated quality assurance (Auto-QA) with CX Today.
"The AI gave inaccurate or irrelevant insights," he noted. "Managers still had to manually check tickets, and the feedback wasn’t useful to agents.
"After four weeks, we concluded the tool didn’t provide the expected value," and Iterable shut the pilot down.
Since then, many other contact center leaders have shared similarly disappointing experiences with Auto-QA solutions.
Chris Crosby, Founder of VenturesCX, spotlighted this trend in a recent LinkedIn post. He wrote:
Weekly now, I talk to a company that is “unimpressed” with the AI or Automated QA from (insert their vendor here).
So, what’s causing these unsuccessful deployments, and how can contact centers ensure their deployments deliver the expected results?
The Fundamentals Need to Be in Place First
Justin Robbins, Founder & Principal Analyst at Metric Sherpa, regularly speaks to contact center leaders and quality analysts.
Recently, one leader told him something profound:
If we’re not getting it right with the 0.5 percent of customer contacts we are currently monitoring, why would I ever believe automating it will fix problems?
In this sense, if contact centers don’t have the fundamentals in place first, they won’t drive sustained improvement with an automated solution.
For Robbins, those foundations include establishing a root cause analysis cycle and continuous identification of predictive, proactive actions.
"Whatever we observe in the quality process shouldn’t happen again," he said.
The goal isn’t to keep observing the same issues forever; it’s to drive business improvement. Simple, but easy for people to lose sight of. It’s not about catching someone doing something wrong today.
To drive that business improvement, Robbins, Crosby, and other industry pros advocate for a more connected learning strategy, which sets the stage for Auto-QA success.
Developing a Connected Learning Strategy
A connected learning strategy ensures analysts and coaches work together to define performance standards, identify and close gaps, and run post-training reinforcement.