Consequently, monitoring quality on only one part of that conversation, between the customer and a human rep, leaves you with an incomplete picture of what happened.
Perhaps not surprisingly, the majority( 65 %) surveyed said that although their QA technology was effective at evaluating their agents’ performance and supporting better training, those insights rarely translated into broader CX improvements.
Moreover, and tellingly, only 12 % of respondents said that their QA insights reach the rest of the organization.
Held back by poor integration and a lack of automation, QA insights can remain trapped in the contact center, with no automated way to surface broader patterns and put them in front of leadership or the teams who can act on them.
FULLY AUTOMATED QA REQUIRES A NEW LEVEL OF CUSTOMIZA- TION AND SEGMENTA- TION...
WHAT THE NEW QA SHOULD LOOK LIKE
In the modern contact center, QA should be fully automated, accounting for the entire interactions across every channel and acting as a continuous loop to improve the entire CX that:
• Evaluates both human and AI interactions.
• Identifies what worked.
• Feeds those lessons back into real-time coaching and the virtual agents themselves.
Here is what this new automated set of QA processes should have, how it should be used, and what it can enable.
38 CONTACT CENTER PIPELINE
1. OUT WITH THE OLD, IN WITH FULLY AUTOMATED QA, NOT JUST THE PIECES
Automating your QA system doesn’ t just swap out the human reviewer with an AI-generated scorecard. It should fundamentally change what gets evaluated, the speed of insights, and what happens to those insights once an interaction ends.
2. ABILITY TO EVALUATE EVERY INTERACTION
Automated QA analyzes interactions across every channel, from voice and email to SMS and web chat, providing service leaders with a far more robust view of their service quality.
Rather than clipping samples from calls at random, management gains a rich view of what’ s happening across the entire CX to catch blind spots or patterns and gather more data to work from.
3. REAL-TIME AGENT IMPROVEMENT
QA doesn’ t work if it doesn ' t help your human agents improve in real time; it should never wait until the interaction ends to act.
[ From our research, across industry and media sites ] QA feedback helps agents improve, even though most conversations are never reviewed. That helpful feedback then lands on only a minority of interactions.
Automated QA makes it easier to quickly address gaps and errors in real time. You don’ t have to wait weeks to discuss a clunky call with a human agent or step out of a meeting to correct a team member.
Real-time scoring and AI-powered agent assist identify what your human agents need while they ' re still on a call or as they’ re answering a chat, surfacing relevant information or guidance in the moment. The team member gets help right when they need it. The customer gets a better experience.
4. IDENTIFY WHAT NEEDS TO IMPROVE
QA insights shouldn ' t live in a report or end with a coaching conversation. Automated QA goes beyond human-agent scoring to address and improve the entire agentic system.
Automated analysis can surface recurring customer issues, knowledge gaps, ineffective workflows, and places where an AI agent isn ' t handling a request as intended.
Those patterns give leaders a clearer picture of where the broader operation needs attention, allowing you to fix handoff moments and workflows or provide targeted coaching before the customer feels a blip.
5. CUSTOMIZED, INTEGRATED SCORECARDS
Of course, handing QA over to AI doesn ' t mean contact centers can simply set it and forget it. Organizations still have to decide what they ' re looking for, how quality should be measured, and what happens when the system finds a problem.
After all, AI can ' t evaluate an interaction well if the organization hasn ' t established what a successful outcome looks like.
Fully automated QA requires a new level of customization and segmentation because quality doesn ' t mean the same thing for every interaction. A customer calling to resolve a billing issue may require a different scorecard from a prospect calling to make a purchase decision.
Organizations should be able to define scorecards around their specific use cases, while AI evaluates interactions against those criteria. And those scorecards need to apply to both human and AI interactions.
But you have to evaluate AI differently from humans. A human agent can be evaluated on whether they provided accurate information, followed the right process, and resolved the customer ' s issue. AI needs to be held to the same standards, but the workflows themselves need to be evaluated more closely.
So, put in the processes that ask whether the AI:
• Used the right tool?