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In a Workday-commissioned survey, 75% of respondents said they were comfortable working alongside AI agents, but only 30% were comfortable being managed by one. The distinction matters: the first figure measures reported comfort with AI assistance, not a prediction that 75% of all employees will use AI.
What Workday’s survey measured
The report, “AI Agents Are Here—But Don’t Call Them Boss,” was released on August 12, 2025. Hanover Research conducted the survey in May and June 2025 for Workday, a vendor of enterprise HR, finance, and AI software. Its 2,950 respondents were full-time decision-makers and software-implementation leaders—not a representative sample of every worker.
The respondents came from North America (706), Asia-Pacific (1,031), and Europe, the Middle East and Africa (1,213). Workday’s announcement and methodology describe the sample and findings. Because the sponsor sells enterprise AI and workplace software, the results are best read as a vendor-sponsored survey of technology-aware business leaders, not as an independent measure of all employees’ views.
Why comfort changes when AI becomes the boss
Workday reported different comfort levels for three distinct roles:
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Outbyte PC Repair FREEClear out junk files and repair common Windows errorsFree Scan →Outbyte Driver Updater FREEFix the driver behind crashes, sound loss and screen glitchesFind Drivers →| AI role | Respondents comfortable |
|---|---|
| Working alongside AI agents | 75% |
| Being managed by an AI agent | 30% |
| AI operating in the background without human knowledge | 24% |
An AI assistant might draft a message, summarize information, answer an IT question, or suggest skills to develop. A system acting as a manager could assign work, evaluate performance, or influence promotion or discipline. A hidden system adds a separate concern: workers may not know that AI shaped a decision about them.
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These figures measure comfort, not actual adoption, opposition, or future behavior. They are not contradictory: respondents can welcome help with a task while resisting a system that exercises authority over their work or livelihood.
Optimism comes with concerns about pressure and judgment
Workday said 82% of surveyed organizations were expanding their use of AI agents, and nearly 90% of surveyed employees believed the agents would help them get more done. Those expectations came alongside concerns: 48% worried that productivity gains could increase pressure, 48% were concerned about declining critical thinking, and 36% about reduced human interaction.
The tension is practical. If automation saves time, an employer might use that time to reduce routine work—or raise output expectations. Similarly, delegating repetitive tasks can free people for more complex work, but relying on automated recommendations without scrutiny can weaken human judgment. The survey records expectations and concerns; the published release does not establish that AI agents improve productivity, accuracy, fairness, or employee outcomes.
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Trust rises with exposure, but the survey does not prove why
Workday reported that 36% of respondents whose organizations were exploring AI agents trusted their organization to use them responsibly, compared with 95% among respondents further along in adoption. That is an association, not proof that exposure caused trust to rise. Hands-on use might reduce uncertainty, but organizations further along could also have stronger governance—or respondents already favorable toward AI may be more likely to move into implementation.
Trust also depends on the task. Workday said respondents were more comfortable with uses such as IT support and skills development than with sensitive areas including hiring, finance, and legal matters. A chatbot answering a routine IT question is not equivalent to a system ranking job candidates or influencing an employee’s pay or performance rating.
Finance workers show how context shapes AI expectations
Within the finance-worker group, Workday reported that 76% believed AI agents could help address shortages of CPAs and finance professionals, while 12% worried about job loss. Reported use cases included forecasting and budgeting (32%), financial reporting (32%), and fraud detection (30%). These figures describe that occupational group’s views and expected uses; they do not show that AI has resolved staffing shortages or improved those functions.
What the 75% figure does—and does not—say
The result is not a forecast that three-quarters of workers will use AI, nor evidence that workers generally approve of AI-managed workplaces. It describes reported comfort among a sample of business decision-makers and software-implementation leaders. Frontline and hourly workers, job seekers, and employees without a role in technology decisions may have different experiences and views.
The survey is useful for identifying a boundary worth testing in workplace policy: assistance can feel different from authority. It cannot establish how representative that boundary is across the workforce or whether a particular AI deployment is safe, fair, or effective.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How employers can apply the distinction
Organizations considering workplace AI can make the system’s role and accountability clear before deployment. “A human is involved” is not enough if that person cannot inspect the recommendation, understand its limits, or change the result.
- Disclose use: Tell employees when AI is involved, what information it can access, and whether it is recommending, deciding, or carrying out an action.
- Keep people accountable: For decisions affecting hiring, pay, promotion, discipline, or termination, assign a human owner with authority to review the evidence and override the system.
- Provide a challenge route: Give affected workers a meaningful way to ask how a decision was made and seek review.
- Test for errors and bias: Check data quality and outcomes, and monitor whether managers simply accept AI rankings without independent review.
- Measure more than speed: Track work quality, errors, workload, fairness, and training—not only output. Check whether time saved reduces pressure or merely raises targets.
- Define the system’s authority: Distinguish an assistant, recommender, supervised executor, autonomous workflow component, and manager or evaluator. Each role warrants different oversight.
Workday’s research summary presents the company’s broader findings and recommendations. Its central survey signal is not blanket enthusiasm: respondents were more comfortable with AI as a visible helper than as an invisible or autonomous authority.
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