Nearly Every Contact Center Wants to Be a Value Center: Here’s What’s Stopping Them
New research finds that almost every contact center is now a value center or wants to become one – and that the biggest thing standing in the way is data.
The case for treating the contact center as a value center rather than a cost center is not a new concept.
What is new is how little disagreement is left. In The State of Contact Center AI 2026, 62% of respondents say they run a value center. And, among those who still call it a cost center, 59% want to make the shift.
Between them, that is very nearly everybody. On ambition, the argument is over.
When asked what is holding programs back from making the move, the most significant blocker is data – its availability, and the ability to track a customer across their journey.
Contact centers already know what they are worth to the business, but many struggle to unlock that value due to an inability to connect and harness data.
Not budget, not appetite, not AI – data
When leaders were asked what was standing in the way of moving to a value center, the obstacle that ranked highest was the availability of data and the ability to follow a customer across their journey.
That may come as a surprise because contact centers are many things, but being short of data is not one of them. It is one of the most data-rich functions in the enterprise, and has been for decades. Every call, chat, message, survey response, and desktop action generates a record of what customers want and where the business succeeds or fails.
The problem is not volume. It is reach. Data that sits in a system you can’t query, in a format you can’t analyze, or in a silo you can’t access, is ultimately just storage.
Fragmentation is the hidden tax on AI ROI
The average contact center runs on solutions from three or more providers (Verint, The State of Contact Center AI 2026). Among the largest organizations, a quarter run five or more (Verint, The State of Contact Center AI 2026). A fragmented stack and fractured data act as a hidden tax on AI ROI.
The tax is paid in places that never show up on a business case. An insight that exists in the voice channel but can’t be tested against digital. A trend visible in survey data that can’t be traced back to the conversations that caused it. A customer journey that has to be reassembled by hand, by an analyst, weeks after the moment when acting on it would have mattered.
Each individual tool may perform exactly as sold. The interactions between them are where the value leaks out, and no single dashboard shows you that leak.
This is the strongest argument for treating openness as a requirement rather than a preference. If your CX data can only be analyzed inside the system that captured it, every future decision about analytics, AI, and automation gets made for you by whichever vendor holds the recording. An ecosystem that lets data move – across channels, across applications, across the AI you have already invested in – empowers you to make your own decisions.
You already own the most valuable data in your business
The AI market is competing hard for data, and the data it is competing for is largely public. Most analytics tools built on general models are working from information that everyone else can also access. They can tell you about the market. They cannot tell you why your customers left last quarter.
Your interaction data can. It is proprietary, domain-specific, and it records what your customers said in their own words about your products, your policies, and your processes. Nobody else has it.
Daniel Ziv, Verint’s Global VP of AI and Analytics, has a name for it:
Historically, contact centers were considered a cost center, and the focus even of AI in the last few years has been cutting costs in the contact center. There’s definitely an opportunity there, but much more important is the insights, the data the contact center is generating. I call it the AI oil. Refining that oil faster than the competition is what is really going to change organizations and the role of the contact center.
The word “refining” is key here. Data you can’t extract insights from is not an asset, and speed of extraction is vital. Two organizations can hold equally valuable interaction data and only one of them will act on it while it still matters.
That requires a refinery.
The refinery: what contact center intelligence looks like in practice
Verint CX Analytics is built to turn structured and unstructured interaction data into insight you can act on and outcomes you can measure.
Analyze every channel in one place. Omnichannel interaction analytics surface AI-generated trending topics across voice and digital interactions together, rather than one channel at a time.
Get the transcription right. Analysis inherits the quality of the text underneath it. Exact Transcription Bot is measured by customers at above 90% transcription accuracy and above 95% categorization accuracy (Verint customer-measured results).
Ask questions in plain language. Traditional call studies take weeks of analyst time and specialist skills. With Genie Bot, a leader can ask a question about customer interactions in natural language and get to an answer in minutes. The interval between question and insight is where most CX value is currently lost.
Show the evidence. Every Genie insight is validated with quotes from the actual customer and agent conversations behind it. Explainability isn’t a compliance nicety here – it’s the reason an operations leader will act on a finding rather than ask for a second opinion.
Insight that arrives as a number the CFO recognizes
The value-center shift is a financial argument, and it has to be made in financial terms.
Genie can export its analysis into branded executive presentations built around the financial impact of an insight, the projected ROI of acting on it, and a priority matrix for deciding what to do first. That turns an analytics output into something that survives contact with a budget conversation.
This is what separates the organizations that made the value-center case from the ones still trying to. The leaders seeing real returns aren’t the ones who spent the most on AI; they’re the ones who closed the gap between what they bought and what it delivered.
The destination isn’t a cheaper contact center
The value-center shift gets described as a change in how the contact center is measured. It is really a change in who the contact center serves.
As Ziv puts it:
So rather than just being treated as a contact center — how can we cut costs — it’s what insights do I learn today, right now, that I could guide the rest of the business with. The product teams, the marketing team, all these teams need to know what’s happening and then make changes. So, it’s flipping it around. It’s the center of insights. It’s what’s driving competitive advantage AI, not the frontier models that have access to public data, but the data that you have that’s unique to your organization.
Here’s the message that will really grab your CEO’s attention. A contact center that reduces its own cost is a good line item. Contact center analytics that tell product which feature is generating complaints, tell marketing which promise isn’t being met, and tell finance which process is quietly leaking revenue is a different kind of function entirely.
The data to do that already exists inside your organization. All you need is the refinery.
Read the research in full
The State of Contact Center AI 2026 covers what 602 contact center leaders are investing in, where returns are landing, and where they aren’t – including the full picture on the value-center shift and the data barriers behind it.
Frequently asked questions
A contact center value center is measured by the business value it creates, such as revenue, retention and insight, rather than by cost alone. In The State of Contact Center AI 2026, 62% of leaders say they already run one (Verint, 2026).
Data. Leaders rank data availability, and the ability to follow a customer across their journey, as the top barrier. Fragmented stacks are a big cause: the average contact center uses three or more providers (Verint, 2026).
Contact center intelligence uses AI to analyze customer interactions across voice and digital channels and turn them into insights the wider business can act on, such as product issues, churn signals and process fixes.
It connects what customers say to financial outcomes. Tools like Verint Genie Bot answer plain-language questions in minutes, back each insight with real conversation quotes, and estimate the projected ROI of acting on it.