What the AI Boom Gets Wrong About Readiness

Over the past several months, I’ve had hundreds of conversations with CIOs, CTOs, and infrastructure leaders who are all asking the same question: What’s our AI strategy?

Some executives are already deep into experimentation, building custom models, and exploring new efficiencies. Others are still trying to decide what “AI strategy” even means for their business. They’re not lagging behind; they’re simply being cautious, waiting to see how this fast-moving technology evolves before committing major resources.

These aren’t technophobes. They’re experienced professionals who have seen hype cycles rise and fall. They remember the promises of big data, blockchain, and machine learning. Each wave came with its own surge of buzzwords and vendor claims, but not all of them delivered lasting value.

That’s why many IT leaders are skeptical about the current AI moment. They’ve learned that success comes not from chasing trends, but from strengthening the foundations beneath them.

Healthy Skepticism Is a Strength

Skepticism isn’t the same as resistance. It’s often a sign of wisdom.

In recent months, I’ve met leaders who are unsure about AI’s return on investment — and with good reason. A study from Constellation Research found that 42% of companies using AI in production haven’t yet achieved measurable ROI. Others worry about volatility in the vendor landscape. Why build a new system around a provider that might not exist next year?

Then there are leaders who simply want to protect what’s already working. Some are nearing retirement and prefer to rely on proven playbooks rather than introduce risky, untested tools. These are all reasonable positions to take.

But the pressure to do something with AI isn’t going away. Boards, CEOs, and even customers are looking for progress. The key is to respond strategically, not reactively. And that begins with one simple shift in focus: before you think about AI, think about your data.

AI Readiness Without the Hype

AI is only as effective as the data that fuels it. Regardless of how aggressively you plan to pursue automation or predictive analytics, the smartest way to prepare is by modernizing your data infrastructure.

These are what I call “no-regrets moves” — initiatives that strengthen your organization today while setting you up for future success.

1. Get Control of Unstructured Data

A few months ago, I spoke with a storage administrator at a large government agency. Let’s call him Bob.

Bob was three years from retirement and had seen every technology cycle come and go. He wasn’t convinced his agency needed AI. What did matter to him was the rising cost of storing and securing unstructured data, especially video from surveillance systems that was overwhelming local storage arrays.

Bob decided to consolidate everything into a unified cloud platform. The result was immediate: reduced costs, simplified management, and improved resilience.

But there was a bonus outcome too. Without even planning for it, Bob made his agency AI-ready. By organizing and centralizing their data, they created a foundation that could support future analytics or AI initiatives when the time comes. Whether or not they ever deploy AI, the investment paid off.

2. Break Down the Data Silos

Most organizations have already built strong processes for structured data. The challenge lies in managing the flood of unstructured data — documents, videos, images, design files, and emails — that lives in scattered systems across offices and devices.

According to IBM, 82% of enterprises report that data silos disrupt critical workflows.

Imagine you lead IT for an architecture or engineering firm. You know AI could help your teams analyze past projects, understand why certain bids failed, or identify potential risks before they happen. But if those files live in dozens of disconnected repositories, none of that is possible.

Bringing unstructured data together in a single, accessible system isn’t just good preparation for AI. It also improves collaboration, efficiency, and productivity across the business right now. Architects and engineers can work seamlessly across locations, while the data they generate becomes usable for future tools and innovations.

3. Balance the Edge and the Cloud

In manufacturing and other industries, data often needs to move between the edge and the cloud. Sensors and scanners on production lines generate information that must be processed locally for real-time insights, while larger datasets are archived or analyzed in the cloud for long-term trends.

The key is flexibility. Your infrastructure should make data available wherever it’s needed, whether it’s on the factory floor, in the data center, or in the cloud.

This balance not only improves operational efficiency but also ensures that when AI tools mature, your systems are ready to support them without costly redesigns.

The Real Opportunity Beneath the AI Buzz

AI is not a passing fad, but it will continue to evolve. The hype will fade, the tools will mature, and standards will eventually settle.

In the meantime, the most valuable work you can do is to build a solid data foundation.

Unified, accessible, and secure data will support your current operations and position your organization to take advantage of new technologies when the time is right. Whether or not you roll out an AI initiative this year is secondary. What matters is that your infrastructure is ready for whatever comes next.

A Practical Path Forward

The smartest leaders aren’t waiting for the perfect AI strategy. They’re investing in the fundamentals that make AI possible in the first place.

By improving how your organization stores, accesses, and manages data, you can prepare for the next decade of innovation without chasing every new trend or rewriting your playbook overnight.

You don’t need to master AI today. You just need to make your data ready for tomorrow.

And that’s a strategy you’ll never regret.