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The knock comes with an edge. The CEO wants answers—and fast. The board is asking why, after millions spent on AI, there’s little to show for it. Promises of transformative impact have turned into underwhelming pilots, stalled initiatives, or worse: angry customers and a public relations crisis.

Agentic AI has captured imaginations across the C-suite. It’s expected to revolutionize everything, from customer experience and supply chains to fraud detection and forecasting. Executives don’t just want to explore AI; they expect results: new markets, faster decisions, operational savings, and a competitive edge.

But here’s the uncomfortable truth: most enterprise AI initiatives are stuck. And unlike digital transformation, time is not on your side. AI’s first-mover advantages are real and fleeting. By the time an organization figures it out, it may be too late to catch up.

AI’s massive promise: Can it possibly deliver?

CIOs and CDOs, you know the answer. No way. No one can simply roll out agentic AI and expect it to deliver business benefits. The biggest hurdle—once again, no surprise— is the enterprise data swamplands, the massive piles of fragmented, unreliable data sitting inside the once promising data warehouses, lakehouses, and everywhere else. The sad truth is, most enterprise data is buried in the graveyard of half-completed IT projects.

As McKinsey has reported, “pull the thread on these (AI) use cases, and it will lead back to your data.” In a survey,  of large companies identified managing data as one of the top challenges preventing them from scaling AI use cases. 

Data is the great enterprise tech dichotomy of our era. Data is simultaneously the most valuable asset and the lowest quality resource for most businesses. It is also a massive potential liability. Wrong data fed into AI models can have disastrous consequences. 

Exhibit 1: AI can’t find the truth buried in the enterprise data mess

Reltio

Data trapped within individual apps and silos is a significant problem for enterprises. When information is siloed in disparate applications, it often becomes inconsistent and outdated. Different versions of the same data can exist across various apps, creating confusion and making it difficult to maintain a single source of truth. This inconsistency leads to a lack of trust in the data, undermining decision-making processes and operational efficiency. Enterprises are left grappling with unreliable information that hinders their ability to make informed, data-driven decisions, ultimately stalling their digital transformation efforts.

Intelligent data is the answer. Winning companies are already using it

In the AI era, not all data is created equal. The enterprises that win will be the ones that don’t just collect more data—they operationalize intelligent data.

At Reltio, we define intelligent data as trusted, context-rich, continuously updated information that is mobilized in real time to drive decision-making by humans and AI alike. It’s the difference between feeding your AI agents a murky spreadsheet versus a crystal-clear 360° view of the customer, supplier, or product.

Here’s what sets intelligent data apart:

• Trusted: Continuously governed, deduplicated, and validated so that decisions—automated or human—are based on reality, not noise.
  
• Context-rich: Includes not just static attributes but the relationships, transactions, preferences, and behaviors that define how your business actually works.
  
• Continuously updated: Always current—data in motion, not in a monthly batch. Because if your AI agent sees yesterday’s truth, it might make today’s mistake.
  
• Unifying: Breaks through silos across CRMs, ERPs, and data lakes, connecting all relevant domains and sources into a single, interoperable semantic layer.
  
• Ready for activation: Delivered where it’s needed—in milliseconds—to fuel everything from real-time personalization and supply chain pivots to automated compliance checks and agentic workflows.
  

Without intelligent data, AI becomes an expensive science project. With it, you get a durable foundation that powers real-time operations, sharpens decision-making, and accelerates transformation.

The rules of enterprise data are rapidly changing

AI is becoming an uncomfortable boardroom conversation for data and IT leaders. It doesn’t have to be this way. The old rules of enterprise data—centralize it, catalog it, analyze it later—aren’t enough. AI demands faster, cleaner, more contextual data than most organizations are prepared to deliver. The pace of business doesn’t wait for a quarterly data refresh or a six-month transformation plan.

And while some companies are still debating governance frameworks, industry leaders are already setting the pace. They’re building real-time data backbones to fuel fraud detection agents. They’re arming customer service copilots with live, trusted profiles. They’re replacing “reporting dashboards” with intelligent workflows that act on their own.

The new playbook is here. And those who learn the rules first will shape the market.

Explore the new rules of intelligent data.  industry leaders are unifying trusted data to stay ahead in the AI era.