An Expert’s View: Could Your Contact Center Interaction Data Be Your Most Valuable AI Asset?
Read Verint GVP of AI and Analytics Daniel Ziv’s take on “data oil” and the economics of proprietary conversational data
Key takeaways
Your contact center conversations are a proprietary AI advantage gained through revealing customer buying signals, loyalty drivers, and pain points that competitors and public AI models cannot access.
While AI companies are paying people per minute to create training conversations, enterprises already own years — or even decades — of real customer interactions that can be transformed into AI-ready data to improve automation, CX, and business insights.
For contact center leaders, the opportunity isn't collecting more data — it's building the governance, transcription, redaction, and activation capabilities needed to turn existing conversation data into a continuously improving AI and CX engine.
Today, customers are so used to the disclosure of “This call may be recorded for quality assurance purposes.” at the start of contact center conversations that they often don’t even register it anymore. But it’s there, and according to Daniel Ziv, Verint’s GVP of AI and Analytics, that line is quietly becoming one of the most valuable phrases in the realm of enterprise AI. Why? Because the conversational data behind it may be worth more than most organizations realize.
While AI companies are paying people cents per minute to talk on the phone just to generate AI training data (The Guardian, 2026), enterprises are sitting on decades of something far more valuable: real customer and employee conversations about real problems, purchases, and decisions. We sat down with Daniel Ziv to unpack what he calls “data oil,” and why not “refining” and utilizing it at scale is a missed opportunity — and one of the biggest mistakes organizations can make.
Erik Joo: Why is conversational data suddenly so valuable in the AI era?
Daniel Ziv (D. Z.): For the first time, we’re seeing a market emerge where AI companies are paying for authentic human conversations because high-quality training data is becoming scarce. Enterprises already possess something much more valuable: years of conversations between customers and employees discussing real purchases, real problems, and real decisions. That makes conversational data a strategic asset, not just an operational byproduct.
You compare proprietary conversational data to oil. Can you walk us through that?
D. Z.: Having call recordings sitting in storage is a lot like sitting on an oil reserve: valuable in theory, but useless without the infrastructure to extract, refine, and deploy it.
Every contact center conversation contains signals about what makes customers buy, what makes them leave, what breaks their trust, and many more details. That’s not generic internet data, but proprietary, domain-specific, and unique to every organization that captures it.
The problem is that raw interaction recordings on their own don’t do anything. You need the full pipeline — namely, capture, governance, transcription, redaction, and activation — before that reserve turns into something AI can actually use.
Where does AI competitive advantage come from if everyone has access to the same models?
D. Z.: AI models are becoming increasingly accessible, but proprietary customer interaction data remains unique to each organization. Contact center conversations capture real customer needs, purchasing decisions, frustrations, and loyalty drivers in a way that public datasets can’t. When organizations can use that data to train and improve AI, they create experiences and insights that are difficult for competitors to replicate.
If the contact center data is already there, why isn’t every company using it?
D. Z.: Because capture is only step one, and most organizations stop there. They have years of customer conversations sitting in storage, but getting from a raw recording to something an AI model can responsibly learn from requires several more layers.
You need governance and security to manage the data across systems and keep it compliant as regulations evolve. You need transcription that’s accurate across languages, accents, and industry terminology. Redaction is another critical step, identifying and removing sensitive information like payment details, Social Security numbers, and health data before the data can be used. And then you need activation — actually putting AI capabilities to work on that refined data, whether that’s through virtual agents, coaching bots, analytics solutions, or other applications.
Most companies have step one, but very few have the full stack. That’s really the challenge. The full stack is what turns a dormant contact center archive into a compounding advantage instead of a compliance liability.
Is building a conversational data pipeline a one-time project? Or does it need to be ongoing?
D. Z.: It’s definitely an ongoing process, and that’s a critical point. Customer expectations, products, and competitive dynamics shift quickly nowadays, so the models need to keep learning from the latest conversations.
Treating this as a “set it up once” initiative misses most of the value. The organizations that build a continuous pipeline (that is where new conversations are constantly refining the models) are the ones that build a lead the rest of the market can’t easily close.
What is the ROI of investing in conversational data?
D. Z.: Many organizations initially focus on the immediate benefits, such as automation, improved customer experiences, and greater operational efficiency. Those outcomes are important, but they’re only part of the value.
Every interaction that is captured, governed, and prepared for AI contributes to a growing repository of proprietary customer data. Over time, that data becomes more valuable as it helps organizations continuously improve AI and CX performance, uncover new insights, and build capabilities that deliver measurable business and customer value.
How Verint helps contact centers activate conversational data
This is exactly what the Verint CX Automation Platform is built to do — it’s the full refinery, tapping into a vast reserve. Verint helps organizations:
- Capture customer interactions, spoken or voice, across channels,
- Govern and secure that interaction data,
- Transcribe voice conversations accurately across languages and industry contexts,
- Redact sensitive information,
- And then activate it through AI agents, real-time coaching, automated quality management, and CX intelligence.
Every call recorded and every transcript generated makes that data asset richer and the resulting AI solution smarter. No foundation model has access to it, which is exactly what makes it a durable competitive moat rather than a temporary edge.
Want to dig deeper into the topic of data oil?
Learn how Verint can help you tap into your data oil reserve and uncover millions of dollars in your customer conversations.
And join Daniel Ziv and guest speaker Beth Schultz, Vice President of Research & Principal Analyst at Metrigy, for a webinar on August 19 exploring how to extract, refine, and deploy your existing customer engagement data to turn it into a compounding AI advantage.
Frequently asked questions
Conversational data is the recorded and transcribed content of interactions between customers and employees, most of it captured in the contact center across voice, chat, email, and messaging. Unlike public web data, it is proprietary to the organization that captures it and reflects real purchases, real problems, and real decisions.
Foundation models are trained on public data that every competitor can access. Conversational data is unique to each organization and captures genuine buying signals, loyalty drivers, and friction points. Training and grounding AI on it produces insights and customer experiences that competitors cannot replicate from public sources.
Capture alone is not enough. Contact centers need five layers: capture across every channel, governance and security to stay compliant, accurate transcription across languages and accents, redaction of sensitive details such as payment and health information, and activation through AI agents, coaching, and analytics.
It can be, with the right controls. PII redaction removes personally identifiable information such as payment card numbers, Social Security numbers, and health data before any model sees it. Governance frameworks then manage retention, access, and consent so the data stays compliant as privacy regulations change.