The Tool Desk
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Why are employees resisting AI at work?
“Resistance” can describe several different situations: anxiety about jobs, lack of confidence or training, doubts that a tool fits the work, or reluctance to be the first person on a team to use it. Those causes call for different responses. A mandate may increase usage counts without resolving any of them.
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Job concerns are real, but they are expectations—not job-loss data
In a February 2025 Pew Research Center survey of US workers, 52% said they felt worried about future AI use at work, 33% felt overwhelmed, 36% hopeful and 29% excited. Asked about their own prospects, 32% expected workplace AI to lead to fewer job opportunities for them, while 6% expected more. These figures describe workers’ perceptions; they do not measure observed job losses. Pew Research Center’s findings are US-specific.
Some employees need more support, not more pressure
Training gaps can make a tool feel like an added burden. In EY’s 2025 survey of 1,148 US desk workers at companies with at least $1 billion in revenue, 59% cited insufficient AI-skills training as an organizational barrier. Separately, an OECD report published in 2025 summarizing a 2022–23 survey of 840 enterprises in G7 countries found that roughly every second AI-adopting enterprise had difficulty retraining or upskilling staff. Staff reluctance to retrain or upskill was cited by 45% of manufacturers and 34% of ICT enterprises. The OECD figures concern enterprises and are older than the worker surveys, not a direct measure of current employee attitudes.
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Peer norms and leadership shape whether tools get used
Gartner reported that 37% of surveyed employees who could use AI did not because their coworkers were not using it. The finding came from a July 2025 survey of 2,986 employees. It suggests adoption may be social as well as individual: people can hesitate when use is unfamiliar or unsupported around them.
McKinsey’s report, published January 28, 2025, drew on a survey of 3,613 employees and 238 C-level executives conducted in October and November 2024. It says employees were more ready than leaders assumed and identifies leadership as the biggest barrier to success. The report’s findings are primarily about US workplaces, despite surveying participants in the US and five other countries. Taken together, these studies do not establish one universal leading cause; they show why employee concerns should be examined alongside leadership, communication, peer behavior, workflow fit and governance.
How can companies get employees to use AI?
Start by finding a real work problem, then involve the people who do that work in testing a solution. The following practices are evidence-aligned ways to improve a rollout, not guaranteed fixes: the cited surveys do not establish the causal effect of each one.
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- Listen before setting a usage target. Ask employees which tasks are frustrating, what they worry AI could change, and where they would trust it to help. Provide a safe way to respond, then explain what feedback changed—or why a proposed change was not made.
- Define the tool’s role and boundaries. Describe the task it is meant to assist with, which decisions remain human, what information employees may enter, and where to raise a concern. Do not promise job security or productivity gains unless the company can substantiate those claims.
- Pilot one specific workflow with willing employees. Include people from the roles affected, rather than choosing only enthusiastic technology specialists. Test output quality, errors, time, rework, workload and employee experience. If the workflow does not improve, investigate tool fit or process design instead of treating the result as an employee failure.
- Train for actual tasks and skill levels. Pair short instruction with practice on work employees recognize, safe-use guidance and a clear route to help. Do not assume everyone has the same baseline confidence or digital skills.
- Make use visible without turning it into a contest. Share practical examples from the pilot, including limitations and corrections. Gartner’s finding about coworkers’ behavior makes peer norms worth attention, but employees should not be pressured to use AI where it is unsuitable.
- Measure outcomes, not just activity. Combine usage data with employee feedback. Logins or prompt counts alone do not show value; assess quality, timeliness, rework, confidence and whether the workflow improved.
- Coordinate workforce and technical governance. Involve HR alongside business, technology, security and legal teams so training, employee impacts, data rules and risk controls are considered together.
What AI training do employees need?
Training should let people perform a defined task safely and judge whether the tool’s result is good enough to use. A generic introduction may explain what AI is, but it does not necessarily prepare someone to apply it in a particular role.
- Task practice: use examples drawn from the employee’s real workflow, with time to try the tool and ask questions.
- Safe-use rules: clarify what data can be entered, what uses are permitted and how to handle sensitive information or uncertain output.
- Review and escalation: show how to check results, correct errors and reach a person when the tool is unreliable or the task requires human judgment.
- Ongoing support: offer a practical channel for help and update instruction when tools or approved workflows change.
EY’s survey found an association between clearly communicated agentic-AI strategy and higher reported use and confidence. Because this was a survey association—not a causal test—and its sample was US desk workers at large companies, it should not be taken as proof that a particular communication or training program will work everywhere.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should managers introduce AI at work?
Managers should explain the purpose, boundaries and human responsibilities before asking a team to change its habits. Gartner recommends including employee impacts in AI governance, piloting with collaborative and digitally curious staff, and tailoring learning to people’s adoption attitudes and usage. These ideas help turn a rollout into a workflow decision rather than a technology announcement.
Questions to answer before a pilot
- Which specific task is the tool meant to improve, and for whom?
- What information may employees use, and what must stay out of the tool?
- Who checks the output, and who remains accountable for the final decision?
- How will the team judge quality, errors, rework, workload and usefulness?
- How can employees raise concerns or suggest a change without being penalized for honest feedback?
EY’s 2025 survey covered US desk workers at large companies and focused on agentic AI; it should not be generalized to every occupation or every kind of workplace AI. OECD’s enterprise results reflect a 2022–23 survey. Pew, McKinsey, EY and Gartner ask different questions of different populations, so their percentages should not be treated as directly comparable. Together they support a practical conclusion: adoption is shaped by people and organizational conditions as well as by the technology itself.
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