The #1 Reason AI Stalls in the Enterprise

Not long ago, I had a conversation with the CIO of a Fortune 500 financial services company. He shared a story that stopped me in my tracks. His CEO had asked a straightforward question: “How many customers do we have?”

It should have been easy to answer. Instead, every system—CRM, ERP, CDP, finance—gave a different number. And they weren’t even close—the answers were off by thousands, and to no one’s surprise, there was little trust in the data.

This isn’t unusual. In fact, it’s one of the most common issues I hear from leaders across industries. And it’s exactly why so many enterprises are struggling to deliver on the promise of AI.

Why AI fails without trusted data

Agentic AI, co-pilots, and autonomous systems cannot fix underlying, broken data. In fact, they amplify the weaknesses because of it. AI does not understand the difference between “trusted data” and “broken data.” There is only “data.” So instead of progress, you get stalled pilots, skeptical executives, and wasted resources.

The good news? That Fortune 500 company I mentioned earlier decided to tackle its data silos head-on. By investing in trusted data, they outpaced their peers in digital transformation. Today, they’re entering the agentic AI era with far more confidence than most of their competitors.

That’s the kind of transformation every enterprise should aspire to—and it all begins with trusted data.

10 Rules for Winning in the Age of Intelligence

To help leaders navigate this moment, I teamed up with Venkat Venkatraman, Professor Emeritus at Boston University, to write a white paper, “10 Data Rules for the Age of Intelligence.” Think of it as a practical guide for leaders who want to make AI work in the real world.

The rules cover everything from rethinking how you architect data to competing on speed instead of volume, to building trust as the foundation for autonomous systems. The big idea is simple: if your data isn’t unified, trusted, and real-time, AI won’t deliver the outcomes you’re hoping for.

I’ve attached the paper here. My hope is that it gives you some clear, actionable ways to start turning data into a true competitive advantage in this new era.

A closer look at financial services

Alongside the white paper, I also want to share an article from my colleagues Ansh Kanwar and Matthew Kehoe, who focus on the financial services industry. They explore one of the most pressing challenges banks face today: how to personalize every customer interaction without crossing the line into being invasive.

Their point is simple but powerful. In banking, trust is the ultimate currency. Customers want experiences that feel relevant and timely, but when the data driving personalization is siloed, inconsistent, or out of date, the results can backfire—sometimes irreversibly. AI can’t distinguish between “helpful” and “tone-deaf” unless it’s working from connected, consent-driven data.

As Ansh and Matthew argue, this isn’t just a technology issue. It’s a leadership challenge. Financial institutions that master data trust will define the personalization era, while those that stumble will find customers—and regulators—losing patience.

The takeaway

Whether you’re in financial services or any other industry, the lesson is the same: AI cannot run on broken data. Trusted, unified, real-time data isn’t a nice-to-have. It’s the difference between stalled initiatives and meaningful business outcomes.

That’s the opportunity in front of us—and I believe these resources will give you a useful place to begin.

Let’s stop asking “How many customers do we have?” and start building the data foundation to answer every question with confidence.