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We’ve spent the past few years celebrating the AI boom. After years of record investment, it’s time to ask the question that many boardrooms still avoid: Why are so many companies investing billions in artificial intelligence without seeing meaningful returns?
Companies are pouring unprecedented sums into artificial intelligence, yet the business case is becoming surprisingly difficult to defend. While 70 percent of businesses now actively use AI, more than 80 percent report that it has had no measurable impact on productivity or employment. That should concern every executive who believes another AI investment, another model or another pilot project will finally deliver the breakthrough they’ve been promised.
Technology is not the problem. But I also don’t believe the answer is simply deploying AI more widely. Productivity gains alone were never going to create a lasting competitive advantage. Those gains are quickly copied, margins shrink and the benefits are competed away. The organizations that truly pull ahead will be the ones that fundamentally rethink how they create value using assets their competitors cannot easily replicate.
That’s exactly where I believe many companies are making their biggest mistake. They’re investing aggressively in artificial intelligence while treating the one asset capable of making those investments transformative as if it’s too dangerous to touch: their own most valuable data.
I don’t believe this is because AI has reached its limits. I believe we’ve built an AI boom on information that companies are too afraid to use. That statement often surprises people because they assume I’m arguing executives should become more comfortable exposing sensitive information to AI systems. I am arguing the exact opposite.
They should be deeply concerned.
Organizations today hold enormous volumes of customer records, financial transactions, healthcare information, intellectual property, operational knowledge and decades of institutional expertise. Those datasets could dramatically improve AI systems and keep organizations competitive with startups that carry a lower regulatory burden. But they also carry legal, regulatory and reputational risks if handled carelessly.
Leaders understand that reality better than anyone.
The mistake is assuming the only options are to expose sensitive data or avoid using it altogether. Until recently, this may have been true. But leaders do not need to give up hope. As technologies that rely on this data advance, so too do the methods for protecting it.
But without this awareness, leaders find themselves having to choose one of those two extremes.
The greatest AI risk isn’t that employees are using artificial intelligence. It’s that they’re using it without the guardrails their organizations should have built first. A 2026 AI security report by Zscaler found a 93 percent year-over-year surge in sensitive enterprise data being shared with AI applications, including financial records, healthcare data, source code and personally identifiable information. We call it shadow AI, but it’s really a leadership problem. When organizations fail to provide secure ways to use AI, employees inevitably create their own.
Others respond by locking everything down. Critical datasets remain isolated, AI initiatives rely on sanitized information with limited strategic value, and organizations continue investing millions in artificial intelligence while leaving their most valuable knowledge untouched.
Neither approach fuels innovation. One creates unnecessary risk. The other guarantees disappointing returns. From where I sit, both failures stem from the same misconception: that privacy and AI innovation are fundamentally incompatible.
What concerns me most is that this false choice is now shaping enterprise strategy at precisely the moment when AI depends more than ever on trusted data. The next generation of AI will not be defined by who licenses the largest model or deploys the most agents. It will be defined by who can safely apply proprietary knowledge that competitors cannot replicate.
That is why regulation should not be viewed as the enemy of innovation. In fact, it is pushing organizations toward better infrastructure. Financial institutions operating under the European Union’s Digital Operational Resilience Act (DORA) are already expected to strengthen how sensitive information is protected throughout its lifecycle, including while it’s in use, while the EU AI Act is introducing new obligations for organizations deploying general-purpose AI in regulated environments. These developments are not designed to slow AI adoption. They reflect a growing recognition that trust will determine whether AI succeeds at enterprise scale.
I’ve spent much of my career working with organizations responsible for highly regulated data. Whether in healthcare or financial services, I have seen the same pattern repeat itself. Teams are not lacking ambition. They are constrained by the belief that unlocking insight requires compromising privacy.
That assumption has become one of the most expensive misconceptions in modern business.
The organizations that ultimately lead the AI economy will not be the ones making the loudest announcements or chasing every new model that reaches the market. They will be the ones who stop viewing privacy as a barrier and begin treating it as infrastructure. They will recognize that protecting sensitive information and extracting value from it are no longer competing priorities.
Business leaders are right to reject reckless experimentation. They should question every vendor promising effortless AI transformation. They should demand stronger governance, greater transparency and technologies that can withstand regulatory scrutiny, all while delivering on their organizational goals.
What they should not do is allow skepticism to become paralysis.
Every day that valuable enterprise data sits unused is another day competitors move closer to solving problems that once seemed impossible. Every delayed initiative leaves untapped opportunities to improve patient outcomes, strengthen fraud detection, optimize supply chains, accelerate scientific discovery or deliver better customer experiences.
The AI race will not be won by organizations with the biggest budgets. It will be won by those who finally learn how to use the information they have spent decades collecting without sacrificing the trust that made collecting it possible in the first place.
Artificial intelligence was never supposed to replace human judgment. It was supposed to amplify it.
But judgment is only as good as the knowledge behind it. If enterprises continue treating their most valuable data as something too dangerous to use, they won’t just limit the potential of AI. They’ll limit their own future.
Jackie Peters is the founder and CEO of Blind Insight, where she focuses on privacy-preserving AI infrastructure and secure enterprise data collaboration. She has spent her career working across healthcare, financial services and emerging technologies, motivating her to spend the last decade helping organizations unlock value from sensitive data while maintaining privacy, security and regulatory compliance.
The views expressed in this article are the writer’s own.
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