Leaders Think Employees Are On Board With AI-Most Aren't

Leaders Assume Employees Are Excited About AI. They're Wrong.

Ask executives how their people feel about AI and you'll hear optimism. In a survey of 1,400 U.S. employees, 76% of executives said employees are enthusiastic. Only 31% of individual contributors agreed.

That's not a small miss. It's a sign that many leadership teams are building AI plans on assumptions that don't match reality. Close the gap, or your AI roadmap stalls-no matter how smart the tech is.

The higher you sit, the rosier it looks

Executives overwhelmingly report positive emotions about AI-96% cite empowerment, hope, or openness among their top feelings. For individual contributors, that drops to 63%. And 33% of them feel more negative than positive (resistance, anxiety, fear of job loss), versus just 4% of executives.

Information gaps are just as wide. Only 30% of individual contributors say employees are well-informed about AI strategy and tools. Middle managers are at 58%. Executives? 80%. The same pattern shows up on participation: 80% of executives believe employee perspectives are heard in AI decisions, compared to 27% of individual contributors and about half of middle managers.

Translation: the higher the title, the more leaders overestimate awareness, excitement, and input. That optimism bias derails adoption and outcomes.

Employee-centricity is the strongest predictor of AI success

Organizations that score high on employee-centricity are 7x more likely to be AI mature. It's the single best predictor of success in the data.

In those companies, employees are 92% more likely to feel informed about AI strategy and 81% more likely to say their perspectives are considered. They're also 70% more likely to feel enthusiastic and optimistic about adoption.

It shows up in speed and usage. Employees in employee-centric organizations are 57% more likely to rate their company's AI pace as faster than competitors. And 83% of these firms use AI to support employees-skills, decisions, and job satisfaction-compared to 49% of the least employee-centric companies.

From anxiety to adoption: do this in the next 90 days

• Run monthly AI pulse checks. Measure understanding, emotions, and usage by team. Publish what you heard and what you're changing. The most AI-mature firms listen monthly; the least do it annually.
• Segment your workforce. Different groups need different approaches. Example: administrative "lifers" may worry AI removes the parts of work they enjoy, while "launchers" see it as a step toward mobility. Build distinct rollout plans for each.
• Explain the "why," not just the "how." Be explicit about goals, benefits for employees, and guardrails. Hold open Q&A. Share decisions and the trade-offs behind them.
• Co-create workflows, don't dictate tools. Run short design sprints with frontline teams to map pain points and test AI in real tasks. Let early adopters opt in and shape the playbook. At one global bank, leaders logged hours with advisors before launch and made the tool optional-driving strong buy-in across the firm (source).
• Invest in enablement and time. Provide scenario-based training and reserve capacity for experimentation. Create an internal "AI coach" network to support teams on real work. If you need structured programs, see role-specific options at Complete AI Training.
• Use AI for employees, not just efficiency. Prioritize tools that reduce drudge work, improve decisions, and support growth. Adoption accelerates when people feel the benefits in their day-to-day.
• Close the loop. Share what feedback changed, what's next, and the results so far. Celebrate real use cases, not demos.

Metrics that actually matter

• % of employees who feel informed about AI strategy (target: trend up monthly)
• % who say their perspective is considered in AI decisions
• Emotions index: positive vs. negative feelings by segment
• Adoption in daily workflows (by task, not licenses)
• Time-to-value: cycle time, quality, and error rates on AI-supported work
• Employee motivation and morale vs. competitors
• Intent to stay 12 months
• % of AI use cases aimed at employee enablement

What co-creation looks like in practice

At a major wealth manager, the AI assistant for advisors launched after extensive listening sessions. Advisors chose how and when to use it. That autonomy plus clear value created organization-wide engagement.

In project teams that co-created their rollout, tool usage doubled compared to teams that were told what to use and how to use it. Ownership beats mandates.

The payoff extends beyond AI

Employee-centric organizations report stronger fundamentals: respondents are 42% more likely to feel motivated frequently and 77% more likely to rate morale higher than competitors. They're 36% more likely to see themselves staying a year and 34% more likely to rate financial performance ahead of peers.

AI success starts with people. Listen. Co-create. Adapt in the open. The tech will follow the culture you build.