Contact center AI investment is accelerating, but fragmented data threatens to block the shift from cost center to value center. Verint's State of Contact Center AI 2026 report found that 87% of contact center leaders plan to increase spending on automated agent assistance, while 62% now view the contact center as a value center where returns outweigh operational costs.
That marks a clear break from years of measuring contact centers mainly through cost, speed, and containment. AI is pushing the function closer to revenue, retention, and customer journey performance. But disconnected systems could slow that transition.
Anna Convery, CMO at Verint, said the research is built for leaders navigating that tension: "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."
The performance case is strong but raises the proof standard
Verint's report shows contact center AI has moved beyond experimentation. AI now supports agents, supervisors, forecasting, quality programs, and customer-facing interactions. The performance numbers back the investment: 94% of organizations say AI tools have increased human agent performance. Seventy-five percent report gains from AI in agent training, and 78% report gains in evaluation and performance management.
Those results strengthen the internal case for spending. But they also raise the bar for what counts as proof. A contact center claiming value center status must show more than lower handle times. It must demonstrate better resolutions, stronger loyalty, cleaner journeys, and more productive teams. That requires connecting AI performance to customer outcomes - and systems that track those outcomes across the full journey.
Fragmented data creates a real tax on AI value
The report identifies data availability and customer journey tracking as key blockers to value center transformation. The average contact center uses technology from three or more providers. Twenty percent use five or more. Customer records, interaction histories, workforce systems, quality tools, and channel data often sit in separate platforms.
When that happens, AI sees only part of the customer journey. Personalization weakens. Automation suffers. Measurement gets harder. Broken integrations disrupt workflows, and closed ecosystems make it harder to adapt as needs change. Rebecca Wettemann, CEO and Principal Analyst at Valoir, framed production readiness as the real test for customer-facing AI: "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.'"
For support teams, the practical question is whether the organization can feed AI with accurate, connected, and usable customer context. Without that foundation, even well-funded AI for Customer Support programs will underdeliver.
Supervisors become the next AI battleground
Verint found that 94% of organizations see value in additional AI-powered tools for supervisor support. That includes real-time coaching, automated quality scoring, and performance pattern detection. Supervisors sit at the pressure point between strategy and execution. They must improve agent performance, protect customer experience, manage compliance, and keep teams engaged.
AI can surface patterns faster than manual reviews and support more consistent coaching. But the value still depends on data quality. A supervisor dashboard pulling from incomplete systems may create confidence without clarity. Leaders considering AI for Call Center Supervisors need to ask whether the tools reflect the real customer journey, not just a partial view.
Customers are not yet sold on AI-led service
The report warns that customer perception still needs work. While 64% of consumers have seen AI's impact on customer service, only 62% believe that impact has been positive. Customers judge AI by outcomes - whether the experience feels easier, faster, and more relevant. They do not care whether the brand has invested in the latest automation layer.
Only 44% of organizations say AI has significantly reduced routine and repetitive work for agents. Fifty-three percent say it has somewhat reduced it. AI is improving performance before it fully changes the work itself. For support leaders, that means AI programs must start with moments that matter across the customer journey and measure whether automation actually improves the experience.
Why this matters for customer support teams
The highest-leverage move for support leaders in 2026 may not be launching another bot. It may be unifying customer data, simplifying technology stacks, and giving supervisors better real-time visibility. The organizations that connect their systems so every agent, supervisor, and decision shares the same view of the customer will be the ones that turn AI investment into measurable business value. Customers will feel the difference when the support experience is built on a complete picture of what matters.
Your membership also unlocks:
