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Workers may avoid workplace AI because they do not see a useful task for it, object to its use, worry about privacy or security, or prefer familiar ways of working. Access alone does not resolve those concerns: training, time, approved tools, manager support, and fit with existing workflows also shape whether people try AI and keep using it. The barriers differ by worker and organization, so survey findings from different populations should not be treated as a single universal ranking.
What is stopping workers from using AI?
Among employees whose organizations made AI available, Gallup’s February 2026 survey of 23,717 U.S. employees found that non-users most often cited preferring their current way of working (46%). Ethical opposition and concerns about data privacy, security, or compliance each came next, at 43%. Another 39% said they did not believe AI could assist with their work. These are overlapping reasons, not mutually exclusive categories.
The distinction matters: someone may reject AI on principle, while someone else may be open to it but see no task where it helps. Others may try it occasionally but lack the time, confidence, or workflow support to use it regularly. Calling all of these cases “resistance” can hide problems employers are better placed to fix.
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They do not see a relevant use for their job
AI access does not automatically make a tool useful. In Gallup’s 2026 U.S. study, 39% of non-users with AI available said they did not believe it could help with their work, compared with 22% of infrequent users. UK business research also lists lack of an identified need among commonly cited reasons organizations do not adopt AI. A generic demonstration may not answer the practical question workers have: which task in my role would this improve?
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They have ethical objections
Gallup found that 43% of non-users with AI available reported ethical opposition to using AI. This can be a substantive disagreement about whether a task should be delegated to AI, rather than a gap in technical knowledge. Employers should make room for workers to raise concerns and clarify which uses are appropriate; training alone will not settle a disagreement about acceptable use.
They fear mishandling sensitive information
In the same Gallup survey, 43% of non-users cited data privacy, security, or compliance concerns. Such concerns also affected 38% of infrequent users. The UK Department for Science, Innovation and Technology’s 2025 business research found that businesses cited data security and output accuracy concerns, although for many businesses those concerns did not prevent or delay deployment. Workers need actionable rules: which tools are approved, what information may be entered, and how outputs should be checked.
Why access does not guarantee regular use
Familiar methods feel safer or more efficient
Preferring existing work habits was the most frequently cited reason among Gallup’s non-users with AI available: 46% said they preferred to keep working as they did. The same reason applied to 36% of infrequent users. A proven process may be faster than learning a new tool, particularly if the benefit is uncertain or the AI output requires substantial checking. Adoption is more plausible when a specific task shows a clear reduction in effort or an improvement in the result, without requiring workers to use AI where it is a poor fit.
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In The Conference Board’s July 2026 survey of nearly 1,300 workers, 55.1% said they used generative AI or AI agents daily or weekly, while 33.3% had used organization-provided AI training in the previous six months and 28.3% said their organization provided no AI training. Only 48.0% agreed their organization provided enough work time to develop AI skills, and 47.6% agreed they had enough tools, access, and resources. These responses describe workers’ reported access and support, not whether training was effective.
PwC’s 2026 survey of 49,364 workers across 48 countries and regions found that 51% had access to the learning and development resources they needed, down from 59% the previous year. PwC also reported that only about two in five of a group representing 56% of workers—those less far along the AI learning curve—had the resources they needed. Together, these findings point to an organizational constraint as well as an individual learning challenge.
Tools do not fit the workflow, or managers do not support their use
In Gallup’s 2026 survey, frequent use was more common among employees who strongly agreed AI fit their organization’s systems and processes than among those who did not strongly agree (88% versus 55%). It was also more common among those who strongly agreed their manager supported AI use than among those who did not (78% versus 44%). These are associations, not proof that workflow fit or manager support alone caused the difference. They do show why giving employees access without integrating the tool into normal work may not be enough.
Gallup’s report says, “Employees are more likely to incorporate AI at work when it fits naturally into their workflows and when their organization encourages and supports its use.” The OECD’s employer and worker surveys, conducted in 2022 and reported in 2023, also found higher worker trust in firms that consulted workers about new technologies.
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Some workers may hesitate because they connect AI adoption with future job opportunities or changes to their role. Pew Research Center’s February 2025 survey found that 32% of U.S. workers expected workplace AI to mean fewer job opportunities for them in the long run, while 6% expected more. In PwC’s 2026 global survey, 44% cited AI taking on more tasks as a job-security risk, compared with 57% citing economic volatility. These figures come from different surveys and questions; they do not establish that all workers fear displacement or that job concern is the main reason people do not use AI.
PwC also describes workers as facing rising workloads and rapid change. When people have little spare time or energy, learning another tool can feel like an added demand rather than support. Employers should address the work conditions around adoption as well as communicate how AI is expected to change tasks.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What employers can do to remove practical barriers
The evidence points to organizational steps that may make appropriate use easier, though none is a guaranteed fix:
- Demonstrate role-specific uses. Show how AI could help with a real task in a particular role, and be candid when it is not a good fit.
- Set clear data and review rules. Name approved tools, explain what information workers may enter, and specify how outputs must be checked and who remains accountable for the work.
- Provide protected practice time and practical training. Give workers time to learn in the context of their tasks, alongside access to the tools and resources they need.
- Involve employees in decisions. Ask workers where AI could help or create risks, and incorporate their feedback into rollout plans and policies.
- Make AI work with existing systems. Integrate approved tools into the processes employees already use where possible, rather than expecting people to build a separate routine around them.
- Equip managers to support responsible use. Managers should know what is allowed, how to answer questions, and how to support experimentation without pressuring workers into unsuitable uses.
- Explain how roles may change. Address job and task concerns directly instead of assuming that access or training will resolve them.
Gallup’s results link perceived support and workflow integration with more frequent use; The Conference Board documents gaps in training, time, and resources; and OECD findings associate worker consultation with greater trust. These findings support practical steps, but do not prove that any one intervention will cause adoption in every workplace.
How to interpret the survey evidence
The figures in this article are not directly interchangeable. Gallup studied U.S. employees in February 2026 and asked about reasons for non-use where AI was available. Pew’s figures describe U.S. workers’ expectations about job opportunities, while PwC surveyed workers in 48 countries and regions in May and June 2026. The Conference Board surveyed nearly 1,300 workers, and DSIT’s UK business research drew on 3,500 business interviews fielded from February 12 to May 2, 2025, plus 100 qualitative interviews. OECD’s findings come from employer and worker surveys conducted in 2022. Differences in geography, population, timing, and question wording mean these sources illuminate different barriers rather than establish one ranked list for every workforce.
Quick Recap
- Gallup: AI in the Workplace: What Separates Adopters and Holdouts
- Pew Research Center: Workers’ views of AI use in the workplace
- PwC: Companies risk losing their most AI-savvy employees while leaving majority behind
- The Conference Board: Most Organizations Are Preparing Workers for Today’s AI, Not Tomorrow’s Jobs
- UK Department for Science, Innovation and Technology: AI Adoption Research
- OECD: The impact of AI on the workplace: Main findings from the OECD AI surveys of employers and workers
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