Verint has found that contact center AI investment is accelerating, but fragmented data still threatens its business value.
The company’s State of Contact Center AI 2026 report found that 87% of contact center leaders plan to increase spending on automated agent assistance. It also found that 62% now view the contact center as a value center, where its value outweighs the cost of running it.
That marks a clear shift in how enterprises see the contact center. For years, many leaders measured it mainly through cost, speed, and containment.
Now, AI is pushing the function closer to revenue, retention, and customer journey performance. But Verint’s research suggests that disconnected systems could slow that transition. Anna Convery, CMO at Verint, framed the research as a guide for leaders moving from operational management to business impact:
“This report is built for contact center leaders navigating that tension, providing guidance on where to invest, what’s actually working, and what it takes to move from running a contact center to operating one that drives measurable business value.”
That line sets up the real story. Contact centers may have the appetite for AI, but many still lack the connected data foundation needed to turn that investment into value.
Contact Center AI Is Becoming a Value Center Test
Verint’s report shows that contact center AI has moved beyond the experimentation phase.
AI now supports agents, supervisors, forecasting, quality programs, and customer-facing interactions. That makes the technology more central to how enterprises manage customer experience.
The performance case already looks strong. Verint found that 94% of organizations say AI tools have increased human agent performance.
It also found that 75% report performance gains from AI in agent training, while 78% report gains from AI in evaluation and performance management. That gives CX leaders a stronger internal case for investment, but it also raises the standard for proof.
A contact center that claims to be a value center must show more than lower handle times. It must show better resolutions, stronger loyalty, cleaner journeys, and more productive teams.
That creates a tougher measurement challenge. Leaders need to connect AI performance to customer outcomes, and they need systems that can track those outcomes across the full journey.
Fragmented Data Could Slow AI Value
The report identifies data availability and customer journey tracking as key blockers to value center transformation. That finding cuts through a lot of AI noise. The issue is rarely whether a contact center's systems can feed AI with accurate, connected, and usable customer context.
Verint found that the average contact center uses technology from three or more providers. It also found that 20% use five or more.
That creates a real fragmentation tax. Customer records, interaction histories, workforce systems, quality tools, and channel data often sit in separate platforms.
When that happens, AI only sees part of the customer journey. That limits personalization, weakens automation, and makes measurement harder.
It also creates operational risk. Broken integrations can disrupt workflows, while closed ecosystems can make it harder for leaders to adapt as needs change. Speaking to CX Today, Rebecca Wettemann, CEO and Principal Analyst at Valoir, framed production readiness as the real AI test for customer-facing teams:
“But we hear that and people say, ‘wait a minute, I want to make sure before I put this in production that it’s actually going to do a positive interaction with my customer if it’s customer facing, and that it’s not burning through tokens.’”
Her point lands directly on the challenge facing CX leaders. AI can improve service, but only when teams can control outcomes, understand costs, and trust the data behind each interaction.