A new survey caught my attention last week. Nearly 1 in 3 employees admit to actively sabotaging their company’s AI rollout. Among Gen Z workers, it’s 44%. For companies already dealing with AI adoption challenges, that number should be a wake-up call.
My first instinct was to take it with a grain of salt. The survey was conducted by Writer, a company that sells enterprise AI software, so they obviously benefit from CEOs believing their rollouts are failing. And self-reported sabotage has a certain bravado to it.
But here’s what I can’t dismiss: 75% of executives in the same survey admitted their company’s AI strategy is “more for show than a meaningful guide to outcomes.” That’s executives talking about their own companies. That number has teeth regardless of who’s asking the question.
When I talk to business leaders about AI adoption challenges, the conversation almost never starts with technology. It starts with:
“We bought the licenses and nothing changed.”
“Our team isn’t really using it.”
“We expected more traction.”
That’s not a technology problem. It’s a strategy problem. Employees are remarkably good at sensing the difference.
Here’s the part most coverage is skipping: some of the resistance is rational. The same data shows AI was the leading cause of job cuts in March 2026. If you’ve watched a colleague get replaced after a “productivity tool” was introduced, skepticism isn’t irrational. It’s learned. Leaders who chalk this up to poor communication are missing half the picture.
The other half matters too.
Workers who refuse AI are actually more vulnerable to layoffs than those who embrace it. 60% of executives said they’re considering cutting non-adopters. The resistance strategy backfires.
The fear is legitimate, but the response is self-defeating. That’s the dynamic leaders need to address honestly, not just communicate around.
In practice, failed AI rollouts almost always trace back to two questions nobody answered before launch.
1. What specific business outcome are we trying to achieve?
Not “AI transformation.” Something concrete:
- Faster proposals
- Fewer billing errors
- Shorter onboarding cycles
- Better response times
- Reduced manual work
When employees can clearly see that connection, AI tools stop feeling like surveillance and start feeling like support.
2. What actually changes for the people doing the work?
This is where most leaders think they’ve done the work and haven’t. The gap between what leadership communicates and what employees actually hear is almost always wider than anyone expects.
If the honest answer is:
“You’ll do twice the work for the same pay.”
People will figure that out quickly and respond accordingly. Companies getting real results from AI treated it like any other significant business change:
- Define the outcome
- Bring people along
- Measure what actually matters
Not AI for its own sake.
That’s the work we do with clients at BACS Consulting Group. If your rollout isn’t getting the traction you expected, the technology is probably fine.
The foundation might not be.
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Written by: Jeremy Kushner | CEO, Founder 
