Is Your AI Strategy Failing Before It Starts? Tom Redman, The Data Doc, Explains Why

We’ve all heard it by now: “AI will transform everything.”

But if that’s true, why do so many companies feel frustrated with their AI investments? Why are leaders spending millions only to get dashboards no one trusts and predictions that don’t deliver?

Meet Tom Redman, better known as The Data Doc. In his feature with The Executive Outlook, Tom uncovers the uncomfortable truth behind failing AI projects: it’s not the algorithm, it’s the data.

And if businesses don’t fix it, no amount of AI will save them.

Who Is Tom Redman?

Tom Redman has been a leading voice in the data community for decades. From his early career at Bell Labs to his widely read Harvard Business Review articles and his book People and Data: Uniting to Transform Your Organization, he has been one of the few consistently urging leaders to tackle data quality at the root.

But Tom isn’t just a researcher or author. As The Data Doc, he’s worked with global companies banks, oil corporations, tech firms, healthcare giants, helping them see what really holds back progress.

And here’s his message: AI isn’t failing because of lack of innovation. It’s failing because of bad data.

Data Isn’t Just Broken, It’s Distrusted

Here’s the reality: in most organizations, employees spend 20–30% of their time fixing data before they can even do their jobs.

Imagine hiring 10 people but only getting 7 worth of work. The rest are “data janitors,” patching problems instead of solving them.

The result? A culture of frustration. Employees stop trusting the systems. Leaders stop trusting the reports. And customers stop trusting the brand.

Tom calls this “dangerous tolerance.” Companies accept bad data as normal. But in the age of AI, that’s a recipe for disaster.

Why AI Won’t Save You

AI is powerful, but it isn’t magic.

“AI is only as smart as the data you feed it,” Tom explains. “If that data is wrong, your AI will only create bigger problems, faster.”

That’s why so many businesses spend millions on AI and still feel like they’re falling behind. They skipped the hard work of cleaning and governing their data.

And the bigger the decisions you’re making with AI, the more dangerous that gap becomes.

Real-World Example: When Trust Breaks Down

One global organization came to Tom with what looked like a technical problem: billing and invoicing errors.

But it wasn’t just about numbers. It was about trust. Customers were frustrated. Employees were wasting hours fixing mistakes. And leadership had no reliable view of what was real.

Tom’s team didn’t just plug in a new AI tool. They went back to basics—measuring error rates, cleaning up data sources, and fixing broken processes. Over time, trust returned. Customers noticed. Employees had more time to focus on meaningful work.

The lesson? You don’t solve data problems with more tech. You solve them with leadership.

The Big Data Myth

For years, businesses believed more data meant better decisions. But Tom says that’s a myth.

More data often means more confusion. More silos. More noise.

What matters isn’t the volume of data, but whether people believe in it. And belief comes from quality.

The Data Doc’s Prescription

So, how do you fix it? Tom recommends a simple but powerful approach:

• Start small. Tackle one issue the business really cares about.

• Measure the pain. His “Friday Afternoon Measurement” reveals how much time is wasted on bad data.

• Fix root causes. Don’t just clean data, stop the errors from being created in the first place.

• Make leaders accountable. Without executive buy-in, nothing changes.

• Empower employees. Everyone should be responsible for improving data, not just IT.

It’s not glamorous. But it works.

Leadership > Tools

Tom insists the real problem isn’t technical, it’s cultural.

“Most companies don’t have a data problem. They have a leadership problem,” he says.

Leaders keep chasing the newest tool or AI trend, thinking it will fix everything. But unless they commit to changing how their organizations think about and manage data, nothing improves.

AI can’t lead. Tools can’t lead. Only people can.

Building Smarter Data Teams

When Tom helps companies build data teams, he doesn’t just look for technical experts. He looks for:

• Curiosity

• Problem-solving ability

• Communication skills

• Business impact mindset

Because in the end, technology changes fast. But people who understand how to create trust and meaning from data are the ones who drive real transformation.

Why This Matters for You

Whether you’re:

• A startup founder betting on AI to grow your business

• A student entering the data science field

• A leader frustrated with wasted investments

Tom’s message is clear:

• AI won’t save you if your data is broken.

• Data quality is about trust, not just numbers.

• Leadership, not dashboards, is the missing piece.

The Bottom Line: Trust Is the New ROI

For Tom Redman, the formula is simple:

Better data → Better trust → Better decisions → Better results.

That’s the chain reaction every business leader should care about.

Because in the end, AI isn’t about technology. It’s about people believing in the decisions being made. And that belief starts with data they can trust.

Want to Learn More?

This feature is part of a powerful series from The Executive Outlook, where leaders like Tom Redman share real-world strategies that work.

🎧 Watch the podcast episode on Youtube here.

#Business #Leadership #Technology #Innovation #Productivity #Entrepreneurship #Future #DigitalTransformation #AI #Success