Explainable AI Quality Management: Evidence vs. Blind Trust

Would you jump out of a plane with a parachute someone else packed, without checking it first? Most of us wouldn’t. Not because we assume the packer did a bad job, but because the stakes are too high to take it on faith. We’d want to see the folds, check the lines, and know the reserve is there.

That’s exactly how many contact center QA analysts and leaders feel about AI quality management. Mallory Partain, a Quality Product Manager at Verint, dove into this topic on a recent Master Class webinar. Watch How AI is Transforming QM into Quality Excellence on demand.

The promise of automated, AI quality management is compelling: score every interaction instead of a 1 to 3% sample, spot trends early, and coach at scale. But when an AI tells you an agent failed a compliance question or needs coaching on professionalism, your name is tied to the outcome. You need to be able to check the work and be confident in the answer.

The good news is you can. The right approach to AI quality management doesn’t ask you to trust it blindly. It’s built so that every score, every insight, and every recommendation comes with evidence, and a human makes the call at every point that matters.

Trust is the top concern, and it should be

Quality management in contact centers is table stakes. Yet, according to Ryan Strategic Advisory’s 2026 global survey of more than 800 enterprise CX decision makers, only 32% of enterprise organizations have AI quality management in place. That means about two-thirds of enterprise contact centers still run quality the traditional way: an analyst pulls a small random sample, listens to each call, and scores it by hand. That’s true even in organizations already using AI for routing, self-service, or agent assist.

On the master class webinar, Mallory asked attendees what was holding them back from automating QM. The most common answer was clear: we don’t trust AI to be accurate.

Why “trust me, it works” isn’t good enough

Picture buying a used car. The salesman pats the hood and says, “Trust me. It works.” No inspection report, no service history, no mechanic allowed to look underneath. Even if the car runs perfectly, you’d be right to walk away. The problem isn’t the car. It’s that you have no way to confirm what you’re being told.

Too much AI works like that salesman. It produces a score or a recommendation and expects you to accept it. When an agent asks, “Why did I get this score?” the supervisor has nothing to point to except “the system said so.” That’s not defensible to an agent, to a compliance auditor, or to a leadership team deciding where to invest.

To trust AI recommendations in the contact center, you have to be able to look under the hood of every result, check it against the source, and reject it if it doesn’t hold up.

Show your work: the math-test standard for AI quality management

Remember math tests in school? Getting the right answer was never enough. Your teacher wanted to see your work, every step, so they knew you understood the problem and didn’t just guess or copy. A right answer with no work earned partial credit at best.

AI quality management should be held to the same standard. In Verint QM Intelligence, AI reasoning makes every evaluation score explainable. Each score comes with an explanation of why it was given and a link to the evidence in the interaction. If the AI says an agent didn’t confirm the customer’s identity, you can see the reasoning and jump to the exact moment in the conversation to check it yourself.

That changes the conversation in three ways:

• Analysts can audit the AI. Instead of re-scoring calls from scratch, they spot-check the reasoning and evidence. If there’s a discrepancy, they can recalibrate the question and easily test the change against real interactions with Verint Automated Form Validator before going live with the change. They can tweak until they are satisfied the AI is scoring correctly and then push the change into production.
• Supervisors can defend every score. When an agent asks why they got a score, the supervisor can show the evidence, not just the number.
• Leaders get consistency. The same criteria are applied the same way across every interaction, individual, and team, which makes scores easier to compare and harder to dispute.

AI isn’t a black box when it shows its work. It’s a fast, thorough first draft that a QA Analyst or supervisor can verify.

Human judgment at every key decision point

Explainability is only half of trust. The other half is control. AI quality management should do the heavy lifting of listening, scoring, and spotting patterns across 100% of interactions. People, however, should make the decisions. Here’s where the QA analyst stays in the loop.

1. You decide what matters. Your business and QA teams define the evaluation criteria and agree on which values matter, such as a wire fee, a balance, or a due date. The AI checks against your standards, not its own.
2. You test before you trust. With Automated Form Validator, you describe a rule in plain language, and QM Intelligence runs it against a set of real calls before it goes live. You review the results and decide whether the rule is ready to publish.
3. You choose who to coach and on what. AI flags agents performing below benchmark and shows where each one is falling short. The supervisor picks which agents to work with and which focus areas to address. For example, as Mallory showed in her demo, you might tackle only the biggest gap first so the agent isn’t overwhelmed.
4. You approve every recommendation. “Nothing goes to an agent automatically,” Mallory emphasized. “The supervisor reviews each one first.” Supervisors can listen to the linked calls, then edit, send, or reject each recommendation.
5. You verify the outcome. A record of every recommendation that was sent or rejected shows managers how coaching is happening across the team. Performance tracking after coaching shows whether it actually worked, and automated workflows close the loop on quality disputes.

In other words, AI packs the parachute, and you inspect it before anyone jumps.

What checking the AI’s work looks like in practice

An AI Coaching Recommendation with evidence. Say the AI recommends coaching an agent on closing: always ask “Is there anything else?” before ending the call. It doesn’t just name the problem. It shows what the agent says today (“Thanks for calling, have a great day.”) and exactly what to say instead. Then it shows how often the pattern appeared across interactions: the agent’s recent pattern showed up in 439 of 500 sampled interactions, with direct links to the calls. That isn’t a hunch from one bad call. It’s a habit, backed by the agent’s own conversations.

A compliance check with proof attached. On a bank’s international wire transfer form, one question matters a lot: did the agent quote the correct wire fee? As Mallory explained, transcript-only scoring can’t answer that, because “it only knows what was said. It doesn’t know what was on the screen.” Verint Automated Answer Validation can answer that, because it is able to capture and compare the desktop screen to the conversation/transcript. A bot icon on the QA Analyst’s dashboard shows which answers the AI completed, and hovering over the highlighted evidence shows the value captured from the desktop. Every automated answer can be traced back to its source.

In the following example, the agent told the customer the fee was $35, while the agent’s desktop showed $45. That $10 discrepancy, which the customer would have found on their statement, was flagged with the relevant part of the conversation highlighted, so a supervisor could confirm it in seconds and take corrective action immediately.

Trust that’s earned shows up in results

When AI shows its work and people stay in control, the outcomes follow. In Verint’s State of the Contact Center AI 2026 research, 78% of contact center and CX leaders said AI has improved agent performance, and 94% of organizations see value in AI-powered tools for supervisor support. Verint customers are seeing real business outcomes:

• BNP Paribas increased agent quality by 38% in five months.
• DenizBank raised quality and compliance scores by 30% in one year.
• A healthcare brand lifted supervisor capacity by 33%, saving $1.5M.

Just as important is what changes for your people. When AI quality management handles the scoring, QA analysts get back the time they spent listening to calls. Supervisors spend less time hunting for interactions to review and more time with their agents. Your quality team stops being the group that grades calls and becomes the group that tells the business what’s happening with customers, your agents, and your operations.

Questions to ask before you trust any AI quality management tool

Whether you’re evaluating Verint or anyone else, bring your own mechanic to look “under the hood.” Ask:

• Does every score come with an explanation and a link to the evidence?
• Can I test rules and forms on real interactions before they go live?
• Does anything reach an agent without a person reviewing it first?
• Can supervisors edit or reject AI recommendations, and is there a record of those decisions?
• Can the AI check what agents said against what was on their screen, or what the customer said versus what the agent recorded, not just the transcript?
• Can I track whether coaching actually improved performance?

If the answer to any of these is “Trust me, it works,” keep shopping.

The bottom line

Your skepticism about AI is an asset. It’s the same instinct that makes a great QA analyst: don’t accept a conclusion until you’ve seen the evidence. The right AI doesn’t ask you to set that instinct aside. It’s built around it, showing its work on every score, linking every insight to the source, and leaving every key decision in human hands.

AI can pack the parachute faster and more thoroughly than any team working by hand. But you should always be able to inspect it, and you should always be the one who decides when it’s ready.

Want to see AI reasoning, AI Coaching Recommendations, and Automated Answer Validation in action? Watch Mallory’s full Verint Master Class, How AI is Transforming QM into Quality Excellence, on demand, or request a demo today.