Workers may avoid AI because they cannot see how it helps with their job, object to using it, worry about privacy or security, or prefer familiar ways of working. But reluctance is not just an individual attitude: access to suitable tools, time to learn, practical training, clear rules, manager support and fit with existing workflows also shape whether employees try AI and keep using it.
Survey results vary by country, population and question, so there is no single universal ranking of barriers. The findings below distinguish U.S. employee surveys, a global worker survey and UK business research.
As an Amazon Associate I earn from qualifying purchases.
Why do workers avoid AI even when it is available?
Availability does not establish usefulness or trust. In Gallup’s February 2026 study of 23,717 U.S. employees, 39% of employees who did not use AI despite its availability said they did not believe it could assist with their work. In UK business research fielded from February 12 to May 2, 2025, lack of an identified need was also a commonly cited reason for not adopting AI.
Quick wins for a faster PC:
Scan for outdated or missing drivers - takes under a minuteDriver Scan →Repair Windows errors before they cause bigger problemsFix Now →That distinction matters: an organization can provide access without showing where a tool fits into a particular role. A generic instruction to “use AI” does not answer which task it should help with, what a good result looks like, or whether the time spent learning it is worthwhile.
#1 Best Overall
What concerns make employees reluctant to use AI?
Ethics, privacy, security and compliance
Among U.S. employees with AI available who did not use it, 43% cited ethical opposition and 43% cited data privacy, security or compliance concerns, according to Gallup’s 2026 study. Those concerns also affected infrequent users: 38% cited privacy, security or compliance. These findings describe reported reasons, not proof that every AI tool presents the same risks.
UK businesses in the 2025 Department for Science, Innovation and Technology research also raised data security and output accuracy concerns. For many businesses, however, those concerns did not prevent or delay deployment. The practical issue for workers is whether they know what information can be entered, which tools are approved, how outputs should be checked and who is accountable for the final work.
Job security and change
Some workers connect workplace AI with fewer opportunities or a changing 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 global survey, fielded in May and June 2026, 44% cited AI taking on more tasks among job-security risks; 57% cited economic volatility. These are different questions in different surveys, not comparable measures of the same fear.
PC Slower Than It Used to Be?
A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11Outdated Drivers Are Slowing You Down
One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchConcern about change can coexist with workload pressure. PwC described workers facing rising workloads, rapid change and external uncertainty; workers with little time or energy to learn a new tool may be less inclined to experiment, regardless of their view of AI’s potential.
Why do some workers stick with familiar methods?
Learning a new process has a cost, especially when the current one is reliable. In Gallup’s February 2026 study, 46% of U.S. employees with AI available who did not use it said they preferred to keep working as they do now. The same reason was cited by 36% of infrequent users. Familiarity is not necessarily hostility: workers may see no clear benefit that compensates for the disruption, review effort or learning time.
Usefulness also differs between non-users and people who try AI occasionally. Gallup found that 39% of non-users said AI could not assist their work, compared with 22% of infrequent users. That suggests the first hurdle can be identifying a suitable task; sustaining use also depends on whether the tool fits the workflow and earns a place in it.
Is the problem a lack of training, time or tools?
Often, access is only one part of readiness. The Conference Board’s July 2026 report on a global survey of nearly 1,300 workers found that 55.1% used generative AI or AI agents daily or weekly, while 33.3% had used organization-provided AI training in the prior six months. A further 28.3% said their organization provided no AI training.
In the same survey, 48.0% agreed that their organization provided enough work time to develop AI skills, and 47.6% agreed they had sufficient tools, access and resources. These are workers’ responses about support, not a measure of whether training was effective.
PwC’s 2026 global worker survey likewise found that 51% said they had access to the learning and development resources they needed, down from 59% the previous year. PwC reported that a group representing 56% of workers was less far along the AI learning curve, and that only about two in five in that group had the resources they needed. Taken together, these findings show why low adoption should not automatically be blamed on employee motivation: organizations may not have provided enough time, relevant instruction or practical access.
How do managers and workflow design affect AI use?
In Gallup’s February 2026 study, frequent AI use among U.S. employees with AI available was more common among those who strongly agreed that AI fit existing systems and processes: 88%, compared with 55% among those who did not strongly agree. It was also more common among employees who strongly agreed their manager supported AI use: 78%, compared with 44% among those who did not strongly agree.
These are associations, not evidence that manager support or workflow fit alone causes adoption. They do point to a workplace design issue: people are more likely to use a tool when it has a clear route into everyday work and their managers make experimentation acceptable. Gallup also associates use with organizational support for experimenting and clear AI policies. OECD findings from employer and worker surveys conducted in 2022 and reported in 2023 found higher worker trust in firms that consulted employees about new technologies.
What can employers do to reduce the barriers?
The evidence points to practical steps, not a guaranteed formula. Employers can make AI use easier to evaluate and safer to try by:
- Demonstrating role-specific use cases tied to real tasks, rather than setting a generic expectation to use AI.
- Explaining which tools are approved and what data may or may not be entered.
- Setting expectations for checking outputs and identifying who remains responsible for the final result.
- Providing role-relevant training, appropriate access to tools and protected time to practice.
- Involving workers in decisions about how new tools change their work.
- Integrating AI into existing systems and workflows where it provides a clear benefit, while allowing workers to decline poor-fit uses.
These actions respond to reported barriers in the Gallup, Conference Board, DSIT and OECD findings. The surveys do not establish that any one measure will cause adoption in every workplace.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to interpret the survey findings
The evidence spans different settings: Gallup surveyed U.S. employees in February 2026; Pew surveyed U.S. workers for its report published February 25, 2025; PwC’s global survey covered 49,364 workers in 48 countries and regions in May and June 2026; The Conference Board reported a global worker survey and enterprise-leader interviews in July 2026; and DSIT’s UK business research used 3,500 business interviews plus 100 qualitative interviews in 2025. OECD’s findings are based on 2022 employer and worker surveys, reported in 2023.
Those studies do not use one shared definition of AI use or ask identical questions. Their percentages should not be combined into a single league table. They do, however, show why “resistance” is too simple an explanation: workers may question AI’s value or ethics, while employers may leave gaps in training, time, resources, policy or workflow design.
Quick Recap
Sources
- Gallup, “AI in the Workplace: What Separates Adopters and Holdouts” (February 2026 study of U.S. employees).
- Pew Research Center, “Workers’ views of AI use in the workplace” (published February 25, 2025).
- PwC, “Companies risk losing their most AI-savvy employees while leaving majority behind” (published September 29, 2026; survey fielded May and June 2026).
- The Conference Board, “Report: Most Organizations Are Preparing Workers for Today’s AI, Not Tomorrow’s Jobs” (published July 28, 2026).
- UK Department for Science, Innovation and Technology, “AI Adoption Research” (business research fielded February 12 to May 2, 2025).
- OECD, “The impact of AI on the workplace: Main findings from the OECD AI surveys of employers and workers” (published 2023, based on 2022 surveys).
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
