By Kevin Keenan, VP, corporate communications, Reltio
The request lands with a chill. The CEO needs clarity, and the clock is ticking. Board members are pressing hard: After pouring millions into AI, why isn’t there meaningful progress? The sweeping promises have narrowed into modest pilots, half-built initiatives, and perhaps worse: angry customers and a brewing PR crisis.
Here’s the uncomfortable truth: Most enterprise AI initiatives are stuck in IT purgatory as they’re too costly to abandon and too underwhelming to deliver. MIT research, for example, found that despite massive investment, 95% of organizations are getting zero measurable ROI on AI due to poor implementation and data strategies.
The tension reflects a broader pattern playing out across many enterprises: Bold AI ambition running headfirst into brittle data foundations. Organizations are eager to harness Agentic AI for real gains, but most are discovering that the technology can’t outrun the broken data beneath it. Until that gap is closed, even the most promising AI strategies will struggle to advance past pilot purgatory.
AI’s massive promise: Can it possibly deliver?
For most executives, the potential for Agentic AI feels both exhilarating and daunting. For example, according to an HBR survey of more than 400 global business leaders, 91% believe Agentic AI will transform the future of work, and 83% say adopting it effectively will be essential to stay competitive. Yet only 38% feel their organizations are well prepared to do so.
That gap between ambition and readiness defines this moment in AI. As Reltio founder and CEO Manish Sood put it:
“Few moments offer as much transformative potential — and as much risk — as the rise of Agentic AI. The technology’s power to reason and act introduces a new level of autonomy, speed, and intelligence into business processes. But that power depends entirely on the quality of the data behind it.”
As McKinsey also reported, “pull the thread on these (AI) use cases, and it will lead back to your data.” In a survey, McKinsey found that 72% 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 in the age of AI because LLMs are the tech world’s most confident liars. It takes bad information and broken processes and amplifies them.
AI can’t find the truth buried in the enterprise data mess
Reltio
Data locked inside individual apps and systems creates real trouble for enterprises. Once information splinters across siloed tools, it drifts out of sync. Multiple versions of the same record surface in different places, and the idea of a single source of truth disappears. That’s why a simple question like “How many new customers did we sign last quarter?” can produce four different answers from sales, marketing, finance, and IT. People learn not to trust the numbers. An LLM won’t admit that confusion — it won’t say, “My sources conflict.” It will simply deliver a confident response, even when the underlying data is wrong.
Intelligent data and context 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 contextualized intelligent data.
Context-rich, intelligent data is trusted, 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 bunch of incomprehensible spreadsheets versus a crystal-clear 360° view of the critical information that your business runs on.
Here’s what sets intelligent data apart:
Trusted: Continuously governed, cleansed, and validated so that decisions (automated or human) are based on reality, not noise.Context-rich: Includes not just static attributes but the relationships, interactions, transactions, preferences, and behaviors that clearly outline to AI how your business actually works.Continuously updated: Always current, continuously updated, and never static.Unifying: Connects all information silos created across every business function, whether a department uses a CRM, an ERP, or 20 different SaaS applications; all relevant information is unified into a single source that becomes a single data layer for AI.
Without intelligent data and context, AI becomes an expensive science project. AI cannot fix or paper over the underlying data problems; it amplifies them.
The rules of enterprise data are rapidly changing
AI is becoming an uncomfortable topic in boardrooms for data and IT leaders. The investment is there, the returns are not. It doesn’t have to be this way. Agentic AI can deliver; it just needs the right information and context to fuel it.
Industry leaders are already setting the pace. Global fast-food chains, retailers, pharmaceutical companies, hotel brands, financial institutions, manufacturers, and insurers are moving fast to build trusted, real-time data backbones. They’re using them to power fraud-detection agents, equip customer-service copilots with accurate, up-to-the-minute profiles, and replace static dashboards with intelligent workflows that can 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. See how industry leaders are unifying trusted data to stay ahead in the AI era.
This post was created by Reltio with Insider Studios.