Why Trust Breaks in QA and How to Fix It

Most contact centers treat QA like a serious system while running it on standards that would never pass in any other part of the business.

Leaders sample a tiny percentage of interactions, behave as if those scores represent the entire customer experience, and then hang performance reviews, bonuses, and “culture” stories on that fragile foundation. Agents see the gap and label QA as a rigged game. Supervisors go through the motions. Executives reference the numbers in reviews, then look elsewhere when real decisions are on the line.

Everyone participates. Almost no one actually trusts it.

I was thinking about that gap during a conversation with Risa Eldridge, AVP of Product for AI, Data Platforms, and Integrations at CallMiner, at Enterprise Connect. Her world is 100 percent conversation analytics. As she put it, when you move to 100 percent of interactions, “you really move from anecdotal to data-driven decisions.” Once that level of visibility exists, “we only saw a few calls” stops working as cover. Leaders have to decide whether they are ready to manage from a fuller version of the truth.

That decision carries more weight than it first appears. The credibility of QA shapes how coaching lands, how metrics drive behavior, and whether the contact center has any real influence beyond its own walls.

The invisible ways QA loses credibility

QA loses credibility fast when agents see it scoring their performance in ways that do not reflect what their week really looked like.

An agent handles hundreds of conversations in a week. QA scores three of them. One is a disaster. That one appears in coaching. The message is straightforward: your worst moment counts more than your body of work.

Agents respond logically. They stop seeing QA as coaching and start seeing it as risk management.

Risa still meets teams who rely on “random pull” and wonder why belief in the process is so low. Agents tell supervisors, “I don’t do that all the time.” Often they are right. Supervisors know they are looking at thin evidence. Leaders sit above it all, pretending the sample is “directionally right,” while never basing serious bets on those same numbers.

That quiet double standard drains belief from the system. Once people learn that the process is uneven, they stop expecting it to help them grow. They focus on surviving it.

That is why broader visibility matters so much. When QA begins to reflect the full body of work, the conversation changes.

What leaders see when they finally stop guessing

Leaders who have only ever lived in a sampled world underestimate how different the work feels when they can see everything.

With partial QA, the questions sound like this:

• What really happened on that call?
• Is this just one agent having a rough week?

With 100 percent analytics, the questions get sharper:

• Where is this pattern concentrated?
• What keeps creating this friction?
• Which part of the business owns the fix?

That is the shift Risa described. Once agents and leaders are working from the same broad set of interactions, the argument over isolated calls starts to fade. “For the agents, what it really does is let them trust the data,” she told me. They can see that the behaviors getting coached are “not just a one-time thing, but things that are really prevalent.”

The examples become more useful too. Risa shared a story about a customer dealing with long calls and unexplained silence. On a thin sample it looked like noise. A few odd interactions. Nothing more. Once they analyzed all interactions, a clear pattern emerged. A group of agents was not disconnecting calls properly, which left customers sitting in dead air.

That allowed the team to target training at the right group, repair the process, and bring the metrics back into line. No debate. No guesswork. Just a visible cause and a visible correction.

This is where the value of full QA starts to show itself. The point is not just better oversight. The point is better judgment.

And once leaders can see the system more clearly, they also have to confront what their scorecards are actually rewarding.

How your scorecard punishes empathy

Many contact centers claim to value empathy. Their scorecards often tell a different story.

Risa named the collision point clearly. “Being empathetic requires connection,” she said. It is hard to build connection when the only thing in an agent’s mind is, “I have to get off this phone call as fast as I can.” The obsession with individual AHT creates constant tension. Agents know they should slow down for certain moments. They also know they will be measured on how quickly they move on.

That conflict changes behavior in predictable ways. People rush past acknowledgment. They shorten explanations. They protect the clock and hope the customer lets it go.

Conversation intelligence gives leaders a way to see this in much finer detail. AI can detect frustration, sarcasm, and subtler emotional cues in the customer’s language and tone, and it can help agents recognize the moments where empathy matters most.

Risa shared a billing example that should push more leaders to rethink how they define efficiency. A client faced lots of complaints about charges that were completely valid. The contract terms supported them. The numbers were right. Customers still felt burned.

When they used AI to compare calls that ended well with those that did not, the difference hinged on one behavior. On the better calls, agents took a moment to say, “I’m sorry that you’re affected by this,” or “Let me see what I can do to help you,” before they walked through the details. On the worse calls, agents skipped acknowledgment and went straight to the rules.

Charges did not change. Policy did not change. The outcome did. In many cases, a few seconds of genuine empathy prevented a second call.

That is the trap many organizations fall into. They talk about customer experience in one meeting and then reward speed in the next. The scorecard pulls the operation in one direction while the brand promise points in another.

Once you can see that conflict clearly, the next question is what to do with the flood of insight that follows.

The discipline that keeps leaders from drowning in data

Turning on 100 percent analytics can be exhilarating. It can also be overwhelming.

When leaders look at full conversation data for the first time, they see more problems than they knew existed. System outages. Policy confusion. Channel inconsistency. Script issues. Emotional breakdowns. The sheer volume can stall progress if everything feels urgent at once.

Risa’s answer is decisive and practical. Let the data reveal the biggest or most damaging pattern, choose that as your first target, and commit.

“The data will help you decide what the one thing is,” she said. Once you know that, you can take time to “embrace that and put it through to your organization and get the metrics out.” She also stressed the need to come back to it. Habits drift. People slide back. You have to revisit the issue, confirm the gains, and refresh coaching.

That creates a much healthier operating rhythm. You use full analytics to choose one high-leverage problem. You fix the process or design, not just the talk track. You mark the change and measure the effect. Then you return to it before muscle memory erases your progress.

Leaders who work this way build credibility fast. People can see that QA is doing more than documenting issues. It is helping the business solve them.

And once that pattern of action takes hold, the value of conversation intelligence expands beyond coaching.

When QA becomes your clearest market feedback

One of the most important things Risa said had nothing to do with scorecards. It had to do with who inside the business gets access to customer truth.

“Your conversation intelligence is market intelligence to your best customer,” she said. Your best customers are already telling you why they stay, what frustrates them, where competitors look stronger, and how your products behave in the real world.

Most organizations barely use this.

Risa’s pet water dispenser story makes that point concrete. A company launched a new product. Amazon reviews were bad, but vague. The written feedback did not give engineering anything precise to fix.

So they went into the calls.

They looked at who was calling for support and how they described the problem. The patterns were clear enough for product to act. Engineering redesigned the component, the updated version shipped, and reviews improved.

That loop started in the contact center and finished in the market.

Once leaders understand that, the contact center starts to look very different. It is no longer just the place where problems arrive. It becomes one of the clearest sources of insight in the business. Risa sees leading organizations pushing these findings to the right teams automatically. Product gets product issues. Marketing gets competitor mentions and brand comparisons.

That broader role raises another standard at the same time. If conversation intelligence is going to inform the business, it also has to reflect the business as customers actually experience it across channels.

Omnichannel: same promise, different reality

Most leaders now list omnichannel as a strategic priority. Far fewer deliver an experience that feels consistent across channels.

Risa sees this gap every day. Organizations are proud they can offer service across phone, chat, web, and email. That is the starting point. Real omnichannel means the customer gets the same quality of help regardless of where the interaction begins.

That standard exposes a lot of weak spots.

If customers in chat hit hard limits that phone agents can overcome, they notice. If email responses lack the options available in live channels, they notice that too. When leaders analyze interactions across channels, they can see which paths resolve problems and which ones push customers to switch channels out of frustration.

That matters because channel inconsistency does more than create inconvenience. It teaches customers that they need to work around the organization to get what they need.

The fix takes more than scripts or more automation. It requires a commitment to give each channel the same real ability to solve problems. Risa put it simply: organizations need to “meet you where you are,” rather than forcing customers into the lane that is easiest for the business.

As soon as you frame the work that way, another challenge comes into focus. Even when contact center leaders can see these patterns clearly, they still need the rest of the business to care.

The internal sales job you cannot skip

Even when contact center leaders see the power of conversation intelligence, they often struggle to get other functions to engage with it.

Risa’s advice on how to break through belongs in every leader’s playbook: start with what matters to the other person. “What’s in it for you?” is the question that determines whether people listen.

If you want product to engage, show them direct evidence of product issues and the customer language around them. If you want marketing to respond, bring them competitor mentions and brand perception data from real interactions. If you want finance to support investments, demonstrate how specific changes reduced repeat contacts and improved retention.

This is where many well-intentioned leaders lose momentum. They bring QA reports into rooms that do not care about QA reports. What other functions care about is risk, growth, retention, product performance, and customer behavior. The contact center leader who can translate frontline insight into those terms becomes much harder to ignore.

That translation work also changes how the contact center is perceived. It stops being the team that reports on issues after the fact and starts becoming a source of operating intelligence that helps other leaders make better decisions.

At that point, the scorecard itself starts to look overdue for an overhaul.

The scorecard rewrite your operation needs

Near the end of our Enterprise Connect conversation, I asked Risa what she would change first if she walked into a quality leadership role today.

Her answer cut straight to the point. She would remove metrics that are not tied to outcomes.

That means taking individual AHT and after-call work off the pedestal. Time still matters, but in a different role. Silence can point to system slowness. Hold time can reveal process friction. After-call work can expose manual burden that automation can absorb. Those measures still tell you something useful. They simply should not dominate the definition of good performance.

In their place, Risa would elevate outcome-based measures:

• Did we resolve the reason the customer contacted us?
• Did we prevent the need for a second contact on the same issue?
• Did the customer leave the interaction satisfied, even if we had to enforce a policy they did not love?

She would also evaluate humans and bots on the same foundation. Both exist to solve problems and improve the customer’s position. The channel or modality does not change the goal.

By this point, the logic becomes hard to escape. If you want to coach fairly, operate consistently, and learn from your customers at scale, you need broader visibility and better measures. A three-call sample cannot support that standard.

Which brings the piece back to the real issue.

The moment of truth for QA

The contact center industry no longer lacks technology. It lacks the leadership courage to use that technology at full power.

From Risa’s vantage point, the path is clear:

• See every interaction, not just a few.
• Treat QA as a way to find and remove obstacles, not just to police behavior.
• Align metrics with resolution and experience, rather than shaving seconds.
• Use conversation intelligence as a feed into product, marketing, and operations, not just coaching.
• Deploy AI to lower cognitive load, surface emotional cues, and handle repetitive work so humans can do the work humans do best.

If you keep running QA as a thin sampling exercise that nobody fully trusts, you will keep getting the same results: defensive agents, disengaged supervisors, skeptical executives, and customers who feel the cracks long before you see them on a dashboard.

If you are willing to raise the standard of what QA is and what it is for, the function becomes something else entirely. It becomes a source of truth that your people can believe in and a lever you can actually use to run the business.

The tools are already on your floor. The question is whether you are ready to let them change how you lead.