Kevin Keenan, Vice President of Corporate Communications, Reltio
The request arrived with a tinge of excitement. CEOs need clarity, but time is ticking. Board members are pressing hard to understand why they haven't seen meaningful progress after spending millions of dollars on AI. Broad promises have been whittled down to modest pilots, half-hearted efforts, and perhaps worse, customer anger and a public relations crisis.
Here's the uncomfortable truth. Most enterprise AI efforts are stuck in IT purgatory because they are too costly to abandon and too overwhelming to realize. For example, an MIT study found that despite significant investments, 95% of organizations have zero measurable ROI on AI due to poor implementation and data strategy.
This tension reflects a broader pattern playing out in many companies. It's a bold AI ambition that tackles our fragile data foundations head-on. While organizations are eager to leverage Agentic AI for real-world benefits, most are realizing that this technology cannot outrun the broken data underlying it. Until this gap is closed, even the most promising AI strategies will have a hard time progressing beyond pilot purgatory.
The big promise of AI: Can it deliver?
For most executives, the possibilities of Agentic AI are both exhilarating and daunting. For example, an HBR survey of more than 400 global business leaders found that 91% believe Agentic AI will change the future of work, and 83% say effective implementation of Agentic AI is essential to staying competitive. However, only 38% feel their organizations are fully prepared to do so.
The gap between ambition and readiness defines this moment in AI. Manish Sood, Founder and CEO of Reltio, said:
“Few moments offer greater transformative potential and risk than the rise of Agentic AI. The power of technology to think and act introduces new levels of autonomy, speed and intelligence to business processes. But that power depends entirely on the quality of the data behind it.”
As McKinsey also reports, “When you pull the strings around these (AI) use cases, you get the data.”According to McKinsey research, 72% of large enterprises identify data management as one of the biggest challenges preventing them from scaling AI use cases.
Data is the great dichotomy of enterprise technology in our time. Data is both the most valuable asset and least quality resource for most businesses. LLMs are also the most confident liars in the tech industry, making them a potentially huge liability in the age of AI. Bad information and broken processes are captured and amplified.
AI can’t find the truth buried in the clutter of corporate data
Reltio
Having data locked within individual apps and systems creates major problems for businesses. When information is scattered across siled tools, it gets out of sync. Multiple versions of the same record appear in different places, and the idea of a single source of truth disappears. That's why you need to ask simple questions like, “How many new customers did you sign last quarter?” Four different answers can come from sales, marketing, finance, and IT. People learn not to believe numbers. LLM does not accept that confusion. I don't say “sources are contradictory.” Just give a confident response even if the underlying data is wrong.
Intelligent data and context are the answer. Winning companies are already using it
In the AI era, not all data is created equal. The companies that win will be those that not only collect more data, but also operationalize it in context. intelligent data.
Context-rich, intelligent data is trusted, continuously updated information that is leveraged in real-time to drive decision-making by both humans and AI. It's the difference between giving an AI agent a bunch of spreadsheets it doesn't understand and giving it a crisp 360° view of the information that's important to running your business.
Intelligent data is characterized by:
Without intelligent data and context, AI becomes an expensive scientific project. AI cannot fix or mask underlying data issues. it amplifies them.
Enterprise data rules are changing rapidly
AI is becoming an uncomfortable topic in the boardroom for data and IT leaders. There is investment, but no return. It doesn't have to be this way. Agent AI can provide: All you need is the right information and context to facilitate it.
Industry leaders are already setting the pace. Global fast food chains, retailers, pharmaceutical companies, hotel brands, financial institutions, manufacturers, and insurance companies are rapidly working toward building reliable real-time data backbones. They use them to power their fraud detection agents, provide accurate and up-to-date profiles to their customer service co-pilots, and replace static dashboards with intelligent workflows that operate on their own.
The new playbook is here. And those who learn the rules first form the market.
Explore new rules for intelligent data. see how Industry leaders are integrating trusted data to lead in the AI era.
This post was created by Reltio. Insider Studio.
