AI Is Making Contact Center Agents Better — Now Quality Programs Need to Catch Up

Most of the conversation about AI in the contact center is about what it takes off agents’ plates. Much less is said about what it does for the agents themselves.

For The State of Contact Center AI 2026, we surveyed 602 contact center leaders across 17 countries and 29 industries and asked what happened to agent performance when they applied AI to the workforce.

Where AI is used in agent training, 75% say performance increased, 20% saw no change, and just 4% saw a decline. Where AI is used in agent evaluation and performance management, 78% say performance increased, 18% saw no change, and 2% saw a decline (Verint, The State of Contact Center AI 2026).

That’s roughly three in four organizations reporting that AI has made their human agents better at their jobs.

It’s a finding that may go under the radar because it doesn’t show up as a single dramatic number. Its effect is felt across many of the most important contact center metrics: lower handle times, higher first contact resolution, better CSAT, and lower agent attrition.

Supervisors want the same leverage

If AI is making agents better, what can it do for the people who coach them?

Contact center leaders are close to unanimous here. 94% see value in additional AI-powered tools for supervisor support, and only 4% rule it out (Verint, The State of Contact Center AI 2026).

The opportunity is leadership leverage: where can AI multiply what a supervisor can achieve? Three areas stand out — real-time coaching, automated quality scoring, and detecting performance patterns across teams.

A supervisor’s impact has always been limited by time. They can only review so many interactions, sit in on so many calls, and run so many coaching sessions in a week. Everything outside that window goes unseen. AI widens the window.

Quality is where agent performance gets measured — and where many programs lag

Investment intent is strong: 82% of organizations are increasing their spend on quality and speech and text AI in 2026 (Verint, The State of Contact Center AI 2026).

The foundation isn’t in place everywhere, though. 43% of organizations aren’t using contact center quality management today (Verint, The State of Contact Center AI 2026).

For those that are, many still run their quality programs in the traditional way: a small manual sample of interactions, scored after the fact. The agent gets a score for a handful of calls, weeks later, with little guidance on what to do differently on the next one.

Automating evaluation solves part of that. But even organizations that have moved to AI-powered scoring can be left asking: now that we have all this data, what do we do with it?

The organizations seeing a performance lift use AI to make agents better, and scoring is where that work starts.

Beyond scoring, into action

This is the thinking behind Verint Automated Quality Management: moving from sample-based quality management to AI-powered programs that turn every evaluation into action.

Verint evaluates up to 100% of voice and digital interactions — across both human agents and AI bots, held to the same performance standards. Scoring goes beyond checklist questions to cover sentiment, empathy, and other soft skills.

What happens after the score is where performance improves. Coaching opportunities are flagged automatically when quality drops or KPIs are missed, and when quality is integrated with workforce management, coaching sessions are scheduled automatically too. AI reasoning explains every evaluation score, so feedback is unbiased, easy for supervisors to explain, and consistent across individuals and teams.

That’s the supervisor leverage leaders are asking for. Time that used to go into finding and scoring interactions goes into coaching instead, and scorecards and dashboards show where performance is moving across agents, teams, and sites. The same evaluations surface compliance risks early, before they become widespread.

The results show up in agent performance. BNP Paribas increased agent quality by 38% in five months by automating its entire QM process.

Quantify the performance lift

If AI-driven quality and coaching is improving agent performance, that improvement should be visible in the numbers — and leaders should be able to prove it.

That means setting a baseline before rollout and tracking the metrics where the lift shows up: handle time, first contact resolution, CSAT, and attrition. It means tracking performance trends after coaching to see whether it changed how agents handle interactions. And it means connecting quality outcomes to the business results executive teams care about.

Do that, and agent performance becomes one of the easiest AI ROI stories to tell.

Build around better agents

None of this points to a contact center where AI replaces the human workforce. The direction is AI taking on the predictable work and human agents handling the complex — with the organizations getting the most from their CX investment putting AI on both sides of the interaction.

Quality and coaching are where the agent side of that equation gets measured, improved, and validated. Three in four organizations already report the lift. The question now is whether your quality program is built to deliver it.