Executives Overestimate How Much Employees Use AI at Work

Most executive believe that AI is already integrated into their everyday work, but new research suggests that confidence may be misplaced. A growing gap between what leaders think is happening and how employees actually use AI is quietly undermining organisations’ ability to turn AI ambition into real-world impact.

According to a new report from Multiverse, 59% of the business leaders think that AI is part of employees’ daily work, but only 42% of employees say they actually use it that often. Moreover, 23% of executives believe staff are already handing over full tasks to AI systems, but only 8% of employees say that they are doing this.

This perception gap widens further around data-driven decision-making. A majority of business leaders believe employees use AI for data analysis, but fewer than 40% of workers agree. There is a noticeable gap around streamlining complex, multi-step workflows, and a smaller gap when it comes to using AI for routine administrative work.

AI adoption varies by seniority and role

Multiverse’s research found that mid-level managers are far more likely to use AI regularly than junior employees. This creates a widening adoption gap within organisations, where those earlier in their careers are missing out on skills and confidence that will be critical in the future.

“AI is not a monolithic tool, and its application varies wildly between a junior developer, a middle manager, and a CEO. The 30% gap in adoption we see between seniority levels is a clear signal that the one-size-fits-all approach to AI is failing. To bridge this divide, businesses must move beyond generic training and implement custom AI upskilling paths tailored to the unique daily workflows of every individual,” said Gary Eimerman, chief learning officer at Multiverse.

Limited formal AI training for executives

According to the report, many leaders have received minimal structured AI education. Instead, they are often self-teaching through informal experimentation, which weakens their ability to guide teams effectively through transformation. Most leaders and employees also agree that regular, role-specific training is essential to keep pace with rapid AI development. One-off or generic training approaches are considered insufficient in enterprise environments.

How can organizations close the AI adoption gap?

Organizations are encouraged to close the gap between AI strategy and everyday practice by improving visibility into how AI is actually being used across teams. Leaders should avoid assuming adoption is widespread and instead rely on data, regular feedback, and frontline engagement to understand where AI is and isn’t adding value. This clearer view helps prevent overconfidence at the top and ensures AI initiatives are grounded in real workflows rather than high‑level assumptions.

This research also points to the need for structured, role‑specific AI upskilling rather than informal experimentation or generic training. Moreover, AI use varies widely by seniority and role, and organizations should invest in tailored learning pathways that support junior employees as well as managers, address cultural resistance, and build confidence over time. Regular, applied training tied directly to job tasks is also seen as important for turning AI enthusiasm into sustained adoption and measurable impact.