Something interesting is happening inside most companies right now. Executives are spending record sums on AI tools and infrastructure. Their employees are quietly ignoring them — or actively working against them.
This is not a niche problem. According to Fortune’s reporting from April 16, more than 54% of workers bypassed their company’s AI tools in the past 30 days and completed the work manually instead. Another 33% haven’t used the tools at all. That means fewer than one in six employees are actually using the AI their employers paid for.
But the numbers get sharper. A new survey by Writer and independent research firm Workplace Intelligence, which polled 2,400 knowledge workers across the US, UK, and Europe including 1,200 C-suite executives, found 29% of employees admit to actively sabotaging their company’s AI strategy. Among Gen Z workers, that number jumps to 44%.
Sabotage includes entering proprietary data into public AI tools, using unapproved applications, generating low-quality output to make AI look ineffective, and outright refusal to use the tools at all.
Why Employees Are Pushing Back
The motivation breaks down into something quite human. Thirty percent of those who admitted to sabotage cited fear that AI would replace their job. The term FOBO — Fear of Becoming Obsolete — has emerged to describe a specific anxiety distinct from traditional job insecurity. It’s not about getting fired. It’s about becoming irrelevant before you even see the warning signs.
This fear is not entirely irrational. The same survey found that 60% of executives are considering laying off employees who refuse to adopt AI. Separately, 77% of executives said those who don’t become proficient in AI won’t be considered for promotions or leadership roles.
So the workers who resist AI to protect their jobs may be accelerating exactly the outcome they fear.
The Performance Gap Is Already Opening Up
Here is where the data gets important for any business leader watching this play out.
AI super-users — employees who have genuinely leaned into the tools — are already three times more likely to have received a raise or promotion in the past year. They’re also five times more productive than colleagues who have been slow to adopt.
Goldman Sachs economists found that workers with access to AI tools are saving 40 to 60 minutes per day. Compounded across a team of ten people, that’s roughly two full-time days of capacity created every week — without hiring anyone.
The productivity gap between AI-adopters and non-adopters is widening. Companies with teams that are genuinely using AI aren’t just more efficient. They can price differently, respond faster, and take on more work without proportionally growing headcount.
The Leadership Problem Nobody Is Talking About
Seventy-nine percent of organizations now face challenges adopting AI — a double-digit increase from 2025. And 54% of C-suite executives admit that deploying AI is actively tearing their company apart.
That tension usually comes from a mismatch between what leadership is mandating and what employees are experiencing. When a company drops a new AI tool on a team without training, context, or support, it doesn’t feel like progress. It feels like additional work that the employee doesn’t understand and doesn’t trust.
Trust is the actual bottleneck. Not technology. Sixty-seven percent of executives believe their company has already suffered a data leak or breach due to employees using unapproved AI tools — often because they found those tools more useful than the approved ones. When the sanctioned tools don’t work well, people find their own solutions. That creates security risk, inconsistent outputs, and a governance nightmare.
What This Means for Business
If you are running a business with more than a handful of employees, this data describes your organisation right now, whether or not you’re measuring it.
The companies that are pulling ahead aren’t the ones that spent the most on AI software. They’re the ones that treated AI adoption as a change management problem, not a technology problem. That means:
Starting with your data foundation. AI tools produce better outputs when they work with structured, clean, accessible data. Most companies haven’t done that work yet. That’s not a technology gap — it’s a data literacy gap.
Building capability, not just compliance. Mandating that employees use AI is not the same as helping them understand it. The resistance pattern in the data is mostly fear and confusion, not ideology. Employees who understand how a tool works and why it helps them are far more likely to use it.
Measuring actual output, not tool usage. The goal is not AI adoption. The goal is better business outcomes. Track that, and adoption will follow when the tools prove their value.
Creating space to learn. The companies with the highest AI super-user rates gave employees time and permission to experiment. That’s the investment most leaders aren’t making.
The KPMG survey found four in ten workers fear AI could take their job. Most of those fears aren’t going to materialise the way workers imagine. But the window for getting teams genuinely skilled up is shorter than most leaders think. The performance gap between AI-fluent organisations and those still managing resistance is growing every quarter.
The companies that close it fastest won’t be the ones that spent the most on tools. They’ll be the ones that invested in making their people genuinely capable with those tools.
Enterprise DNA works with businesses and individuals to build real AI and data capabilities — not just access to tools. If your team is stuck in the adoption gap, start here.