High-low split: 75% of executives think AI is working, employees disagree

A new survey reveals a significant disconnect between executives and employees regarding AI implementation success, with nearly three-quarters of executives believing their generative AI approaches are strategic and successful, while less than half of employees agree. This gap threatens long-term AI adoption and organizational productivity, highlighting critical change management challenges that companies must address to realize AI’s full potential.

What you should know: The perception gap between leadership and workforce creates serious operational and security risks across organizations.

• Nearly 75% of executives surveyed by Writer, an AI software company, believe their companies’ approach to generative AI is well-controlled and highly strategic, with similar percentages claiming success over the past year.
• Less than half of employees share this optimistic view, creating what experts describe as a fundamental trust and adoption crisis.
• Only one-third of employees believe their organizations have high AI literacy levels, compared to nearly two-thirds of executives who think they do.

The security problem: Employee frustration is driving risky workarounds that compromise organizational data governance and security protocols.

• 35% of employees use their own money to pay for generative AI tools they use at work, with about 15% spending $50 or more monthly out of pocket.
• Nearly one-third of employees admitted to “sabotaging” their companies’ AI strategies by entering company information into non-approved tools, using unauthorized AI, or failing to report security leaks.
• “When employees don’t feel supported, they’re more likely to shadow-IT their way into productivity,” says Christina Inge, an AI instructor at Harvard University.

What’s driving the disconnect: Executives have privileged access to AI tools and resources that don’t translate to the employee experience.

• “When you’re somebody at the top of the food chain, you’ve got the best tools, the best access, the best employees on your team,” explains May Habib, CEO of Writer.
• Employees often face tool limitations, workflow friction, and lack of practical training or access, according to Inge.
• Many workers are expected to learn AI tools as “a side gig on the weekend as an extracurricular with no incentive, no extra pay,” Habib notes.

Why this matters: The leadership-employee gap undermines the strategic value of AI investments and creates organizational friction that kills productivity gains.

• “When executives think they’re succeeding while employees struggle with inadequate tools, you get organizational friction that kills productivity gains,” says Eric Vaughan, CEO of IgniteTech, an AI software firm.
• Companies risk being outrun by organizations that successfully align executives and employees on AI capabilities and limitations.
• The disconnect represents a leadership problem rather than a technology issue, requiring measurement of engagement alongside productivity.

The solution path: Successful AI transformation requires executive involvement, voluntary adoption programs, and sustained cultural change efforts.

• “I spent my first month using every AI tool we implemented, not watching demos or getting briefings, but doing work with them,” Vaughan explains about his hands-on approach.
• Companies should make early AI adoption voluntary and create “AI office hours” where employees can get help.
• At IgniteTech, the cultural shift took over a year to stick, with previously skeptical employees eventually bringing AI application ideas to leadership meetings.

What they’re saying: Industry leaders emphasize that both executives and employees share responsibility for bridging the AI adoption gap.

• “So much of the disconnect is a lack of understanding about what needs to happen right for the real and lasting change,” Habib says, adding that “employees need to own up to” learning new skills.
• “If your employees aren’t curious about the tools you’re implementing, you haven’t actually transformed anything,” Vaughan warns.
• “Most companies quit too early” on AI culture transformation efforts, according to Vaughan.