QUALITY ASSURANCE
BY MICHAEL HUTCHISON, ECLERX
ILLUSTRATION PROVIDED BY ADOBE STOCK
AI: THE NEW QA STANDARD
WHY AND HOW AI IMPROVES INTERACTION MONITORING.
Poor customer experiences( CXs) [ may have ] cost businesses worldwide as much as $ 3.7 trillion last year, even( Qualtrics): even though solutions to many of their costly CX challenges may already exist in their contact centers. They just need a way to make those solutions visible.
Right now, many contact centers, especially at medium-sized organizations, rely on legacy manual QA processes that analyze just 1 % to 2 % of the interactions the center handles. That’ s not enough data to provide reliable insights at scale for CX improvements.
Meanwhile, the other 98 % of their customer interaction data goes unused and fails to deliver value. This results in the contact center becoming a cost center: requiring storage, security, and risk management.
44 CONTACT CENTER PIPELINE
AI-automated QA and speech analytics now offer companies a way to leverage 100 % of their customer interaction data for insights that can enhance CX and drive customer retention. The same tools and data insights can also help to ensure compliance with increasing regulatory demands.
Together, these capabilities transform customer data from a cost center to a revenue center and increase the value the contact center delivers to the whole organization.
EXTRACTING INSIGHTS AT SCALE
AI-powered QA systems can be configured to automatically ingest customer interactions across channels, including chat transcripts and voice call recordings.
The AI model analyzes each interaction in real time against key metrics. For example:
• Did the agent deploy the appropriate script, compliance language, tone, and empathy?
• Were any escalation triggers present during the interaction?
• What customer sentiment indicators were present during the interaction?
Rather than providing a QA snapshot, as manual spot checks do, this approach delivers comprehensive, continuous visibility into customer interactions.