The AI Race Isn’t About Models Anymore. It’s About Data.

This article is sponsored by Tealium.

For the past two years, the AI conversation has been dominated by one question: Which model should we use? OpenAI or Anthropic? ChatGPT or Claude? Closed-source or open-source? As models have improved, they've offered us bigger context windows, better reasoning, faster inference, and even lower costs.

Looking at the Frontier Labs and the pace of innovation, it’s an understandable debate. The pace of innovation has indeed been breathtaking. But it’s becoming apparent this is the wrong conversation.

As frontier models rapidly converge in capability, the competitive advantage is shifting somewhere else entirely. Based on what I'm seeing as I work with clients, enterprise AI won’t be defined by who has access to the smartest model, but instead by who has the most trusted data.

No matter how powerful the models become, they can only reason with the information they're given. The issue is for many organizations looking to adopt AI, this is where the real problems begin.

AI Isn’t Creating a Data Problem—It’s Exposing One

Let's face it, Artificial intelligence didn’t suddenly break enterprise customer data. I would argue it's simply exposing weaknesses that organizations have been able to ignore or sweep under the rug for years. Who hasn't worked somewhere that had fragmented customer identities, duplicate CRM records. inconsistent consent, disconnected behavioral signals, missing/mixed-up metadata, or outdated customer profiles. Dozens of systems, each claiming to be the source of truth, is the norm not the exception.

These myriad issues have always existed, but traditional analytics, reporting, and marketing workflows are usually able to work around them. AI isn’t nearly as forgiving. Unlike traditional software, AI doesn’t just retrieve information—it interprets it, reasons over it, and increasingly makes recommendations or takes action on behalf of businesses and customers. When the underlying data is incomplete, outdated, or collected without clear permission, those problems don’t disappear. If anything, they’re amplified.

In fact, one could argue AI has become the most effective data quality audit enterprises have ever conducted.

Trust Is Becoming AI’s Competitive Advantage

For years, organizations have measured their data strategies by volume and hoarded data. More customer records, more events, more signals, more data lakes, add in a CDP, and so on. In the AI era, those metrics matter far less than one simple question: Can you trust the data you’re giving your AI? By trust, I mean more than just accuracy. Let's define trust as understanding where data came from, whether it’s current, whether it belongs to the right customer/profile, and whether that customer has actually given you permission to use it.

An AI agent won't necessarily know that a customer updated his or her email address yesterday if it’s buried in a disconnected CRM system. Nor will it know that a customer unsubscribed and revoked consent last week if that information isn’t shared promptly across every application. And it certainly won't know which of three conflicting customer profiles represents the truth.

AI is smart dumb. It has incredible powers of memory and reasoning, but always assumes the data it’s given is correct. When it isn’t, it doesn’t just make poor recommendations—it makes poor decisions, often at machine speed and enterprise scale. Worse, it presents these recommendations as fact—something we call hallucinations.

This is why trust is quickly becoming one of the most valuable assets in enterprise AI. As frontier models continue to improve and become increasingly interchangeable, proprietary customer data—and the confidence that it’s accurate, governed, and permissioned—is becoming one of the few sustainable competitive advantages organizations still control.

Consent Is Becoming AI’s Permission Layer

For years, consent has largely been treated as a boring compliance exercise—a requisite step to satisfy privacy regulations and reduce legal risk. Collect the consent, store the record, check a box, and move on. AI changes this equation.

As organizations deploy AI copilots, recommendation engines, and autonomous agents that can reason, personalize, and take action on behalf of customers, consent becomes far more than a legal artifact. I would argue it becomes the mechanism that determines what an AI system is allowed to know, infer, recommend, and even do.

Consider a simple example. A customer asks an AI assistant to recommend products based on his previous purchases. Straightforward enough. But what if the assistant also has access to website behavior, mobile app activity, location history, loyalty data, and interactions from a call center? Should it use all of that information to support interactions or formulate recommendations? Did the customer explicitly authorize those uses? Has their consent changed since those interactions were captured?

Ethically and legally, these are no longer theoretical questions. They are runtime decisions that AI systems will increasingly need to make every day and in real time. This is why consent is evolving from collecting and maintaining a static compliance record into a dynamic layer of enterprise infrastructure that sits below AI. Every AI interaction must be grounded not only in accurate customer data, but in a clear understanding of whether that data can be used for a specific purpose.

This is where companies like Tealium are helping redefine the conversation. Rather than treating consent as a disconnected legal workflow, Tealium had quietly rolled out features that integrate consent, identity, and customer data into the operational fabric of enterprise AI. Their approach isn't simply to enable more personalization—I would argue that's an outcome of this approach—but rather to ensure that every AI-powered interaction is built on data that is both trusted and permissioned.

AI Doesn’t Do Batches—Neither Can Your Data

There’s another assumption AI is quietly dismantling: enterprise data can move at yesterday’s pace.

For decades, most enterprise organizations have built customer data ecosystems around batch processing. In other words, data is collected throughout the day, transformed overnight, and made available to downstream systems the next morning. For reporting and traditional analytics, this is often good enough. But AI raises the bar.

An AI agent helping a customer, detecting a fraudulent transaction in real time, recommending next best action, or orchestrating a personalized experience can’t rely on yesterday’s data. It needs to know what just happened. Did the customer abandon their cart five seconds ago? Did they opt out of receiving emails this morning? Did they just open a new support case, or make a purchase moments before the conversation began?

In terms of customer experience, the value of AI isn’t just its ability to reason—it’s its ability to reason using the most current understanding of the customer. This is why real-time data is becoming a foundational capability for enterprise AI. Customer data must be current, not just accurate and consented.

This is another area where companies are investing heavily. By capturing customer interactions as they happen and making trusted, consent-aware data immediately available across the enterprise, they’re helping organizations build AI systems that respond to customers as they are—not as they were yesterday. This is another area where Tealium shines.

AI Winners Will Have the Strongest Data Foundation

To summarize, the first wave of enterprise AI was dominated by a single question: Which model should we use? That question still matters, but probably won’t define the ultimate winners. As foundation models continue to improve and become increasingly accessible, competitive advantage will shift toward something far more difficult to replicate: trusted customer data, governed identity, real-time context, and the confidence that every AI interaction respects customer consent.

The organizations that thrive won’t be the ones simply deploying more AI. Rather they’ll be the ones building the infrastructure that allows AI to make better decisions. Because in the end of the day, AI isn’t just an intelligence problem—it's really a data problem.

The companies that win the AI race won’t necessarily have the smartest models. They’ll have the data their AI—and their customers—can trust.

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Rio Longacre

Rio is an executive with 20+ years at the intersection of strategy consulting, AdTech, data, and media. He's a trusted advisor on customer experience, digital strategy, and marketing transformation. He's a partner at Credera, Omnicom's consulting arm. He's also a podcast host, writer, and public speaker focused on the future of advertising and AI-driven infrastructure.