The AI Reality Check: What the Data Says About AI ROI in the Contact Center

Contact center AI spending is up almost everywhere, but AI ROI hasn’t caught up. Across three Verint studies covering roughly 6,600 contact center leaders, agents, and US consumers, the pattern is consistent: spending is up almost everywhere, while the impact on routine work, customer experience, and agent retention lags behind.

That’s the AI reality check. It isn’t a case against AI. The technology works. The gap sits between buying AI and turning it into results that show up on the profit and loss (P&L) statement.

Here’s what the data shows:

• Nearly 9 in 10 contact center leaders are increasing AI spend in 2026
• 51% of consumers say businesses fall short when they need help
• 31% of agents say they’re likely to leave their role within six months

Below, we break down what each group told us and what contact center leaders can do to close the gap.

What is the AI reality check in the contact center?

The AI reality check is the gap between what organizations invest in contact center AI and what that investment returns on the P&L. AI activity is rising fast, but the metrics that move the business – handle time, deflection, churn, and agent capacity – often stay flat.

The pattern will be familiar to many contact center leaders. Executives push for AI to fix customer experience, and the budget follows. Teams launch pilots, vendors promise transformation, and months later the numbers that matter haven’t moved.

Verint’s State of Contact Center AI research, based on more than 600 contact center leaders, shows how broad that investment has become. Here’s the share of leaders reporting increased AI spend in 2026, by category:

• Agent assist AI – 87%
• Customer-facing AI, such as self-service and virtual assistants – 87%
• AI for insights and decision making – 86%
• Workforce management (WFM) and forecasting AI – 84%
• Quality, speech, and text analytics AI – 82%

There’s no laggard category. But broad investment doesn’t mean broad results. Buying AI isn’t the same as deploying it, and deploying it isn’t the same as scaling it.

Why isn’t contact center AI investment showing up on the P&L?

In most cases, the problem isn’t the technology. As Verint’s AI Reality Check guide for contact center and CX leaders explains, the harder part is operationalizing AI at scale, inside a real production environment, with real cost constraints. Underneath it sits the ongoing costs of running AI in production:

• Development hours – The time it takes to build, integrate, and govern AI inside your workflows.
• Model upkeep and data feeding – Ongoing maintenance and fresh data to keep performance from drifting.
• Change management – The training and process changes that help people work alongside AI.
• Token costs – Usage charges that grow as AI handles more of your interactions.

These costs don’t stop after go-live. They compound.

Has AI reduced routine work in the contact center?

Not as much as leaders hoped. 53% of organizations say AI tools have only somewhat reduced routine and repetitive work. So, while nine in ten are spending more, fewer than half report a significant reduction in the busywork that weighs on agents.

There’s real progress elsewhere. 62% of executive teams now describe the contact center as a value center rather than a cost center (Verint, The State of Contact Center AI 2026). But four in ten still see it as a cost center, and 59% of those want to make the shift. The number one thing holding them back is data availability, because AI is only as good as the data underneath it.

Staying stuck has a price: lost time, delayed savings, missed revenue moments, and customer loyalty at risk. So the question for CX leaders is no longer whether to adopt AI. It’s how to make it deliver business outcomes.

Why does the AI reality check matter for customer experience?

Because customers feel the gap first. Verint’s State of Customer Experience research, now in its fifth year, shows service perceptions moving the wrong way in the same year AI investment grew.

51% of consumers now say businesses fall short when they need help, up from 46% in 2025. It’s the first time in five years of this research that the figure has crossed half.

Are customer service expectations rising?

Yes, and quickly. 42% of consumers say their service expectations rose in the past year, compared to 19% in 2024.

That context matters for how we read the data. The 51% figure isn’t evidence that AI made service worse. It’s evidence that expectations are rising faster than delivery is improving, and AI is part of why expectations are rising.

How much does one bad customer experience cost?

Potentially, the whole relationship. 79% of consumers say they’d switch to a competitor after a single poor experience. Put that next to the other side of the same finding: 80% would repurchase and recommend after a single great one.

Same interaction, two opposite commercial outcomes. That’s why the contact center is more than a cost of doing business. It’s where loyalty gets decided, thousands of times a day.

How is AI changing the contact center agent experience?

In the short term, AI is making the agent’s job harder, not easier. That’s the counterintuitive finding from Verint’s State of Agent Experience research, which surveyed more than 1,000 agents in contact centers of 300 seats or more across five industries.

94% of agents expect AI to change their role within three years, and 61% expect to handle more complex and technical work as a result (Verint, The State of Contact Center AI 2026). When automation absorbs the simple contacts, what’s left for people is escalated, emotional, and harder.

Where can AI give contact center agents time back?

The research points to two clear opportunities:

• After-call work – Agents lose around three minutes per call to after-call work alone. Generative AI that summarizes interactions and updates CRM records can help give that time back to customers.
• Schedule flexibility – Nine in ten agents rank schedule flexibility as key to their job satisfaction. AI-powered scheduling that still meets service levels turns scheduling into a retention lever, not just an admin task.

Retention is the reason this matters. 31% of agents say they’re likely to leave their current role within six months. Every departure carries the cost of recruiting, onboarding, and training, plus the supervisor time it takes to bring a replacement up to speed.

How Verint helps contact centers move AI from promise to P&L

Most contact centers aren’t short on AI ambition. They’re short on AI that turns into outcomes quickly enough to show up in the numbers.

Verint’s approach is built around that problem. Its AI is designed to deploy inside existing contact center environments and deliver outcomes at each step, so value can build as investment grows instead of waiting on a long implementation.

Here’s one example. A major retailer used Verint to analyze customer conversations at scale across calls, chats, and surveys – tens of thousands of interactions, not a few hundred. One theme kept surfacing: customers were asking for payment options the retailer didn’t offer, such as Venmo, Apple Pay, and Klarna.

After adding them, the retailer saw conversion rates rise, generating an estimated $37 million in additional annual revenue.

That insight was in their customer data all along. The next one might already be sitting in yours.