AI is changing people operations by shifting particular tasks and decisions—not by replacing every HR, payroll, or workforce-management job in one stroke. Its clearest effects are on work processes and how people are managed; the available evidence does not establish payroll-specific automation rates, error reductions, or savings. For employers, the practical question is where AI can assist without making consequential decisions less fair, transparent, or accountable.
How is AI changing HR?
AI is best understood as a set of tools applied to tasks, not as one technology with one predictable effect on HR. It can influence how employers handle recruitment and workplace management, while changing the way people interact with processes and decisions. The result depends on how a system is designed and used.
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That distinction matters because a tool that speeds up an existing process can also scale its flaws. ILO Senior Economist Janine Berg describes this as a “paradox of automation”: AI may be introduced to address cumbersome screening or management, yet reproduce or amplify problems embedded in the process. That is a risk to assess, not evidence that every AI hiring system is discriminatory. Berg’s May 2026 analysis discusses the issue.
Recruitment and hiring
AI-powered recruitment tools raise questions about accessibility and discrimination as employers assess, acquire, and deploy them. The U.S. Department of Labor’s AI & Inclusive Hiring Framework offers employers ten focus areas for reducing unintentional discrimination and accessibility barriers. DOL says the framework draws on NIST’s AI Risk Management Framework and inclusive-hiring practices; it is a practical framework, not a guarantee that a tool will produce fair outcomes. Read DOL’s September 2024 announcement.
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Workplace management
Systems used to monitor or direct work can affect more than administrative efficiency. The ILO identifies possible effects on workers’ autonomy, pace and intensity of work, skill use, and relationships with peers and managers. Surveillance or reduced discretion can harm job quality. Worker involvement in design, feedback, and iteration can help make systems more responsive to the people whose work they affect. These are risks and design considerations, not proof that every monitoring tool has the same effect. The ILO’s 2024 report sets out this analysis.
Does AI exposure mean HR or other workers will lose their jobs?
No. Exposure describes how much work in an occupation could be affected by generative AI; it is not a forecast of job losses. The ILO’s 2025 global index concludes that job transformation is the most likely overall effect because most occupations include tasks that still require human input. The index uses task-level information, survey responses from 1,640 people across ISCO-08 major groups, expert discussions, and model predictions. It is an occupational exposure estimate, not an employer survey or payroll-specific forecast. See the ILO Working Paper 140.
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The index estimates that one in four workers globally is in an occupation with some generative-AI exposure. Clerical occupations have the highest exposure, and 3.3% of global employment falls in the index’s highest exposure category. Those figures measure estimated exposure—not the share of workers expected to be replaced.
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| ILO 2025 exposure estimate | Figure | How to interpret it |
|---|---|---|
| Workers globally in occupations with some generative-AI exposure | One in four | Some occupational tasks may be affected; this is not a job-loss rate. |
| Global employment in the highest exposure category | 3.3% | An occupational exposure estimate, not measured displacement. |
| Female employment in the highest exposure category globally | 4.7% | Higher than the corresponding global estimate for male employment. |
| Male employment in the highest exposure category globally | 2.4% | Exposure estimates vary by gender and country income. |
| Female versus male employment in the highest-gradient category in high-income countries | 9.6% versus 3.5% | ILO estimates for high-income countries, not a universal rate. |
| Employment with some exposure in low-income versus high-income countries | 11% versus 34% | Overall exposure rises with country income in the ILO index. |
All figures in the table are the ILO’s 2025 estimates from its refined global index; they are not observations of layoffs or forecasts for a particular HR team. The gender and income differences also caution against assuming that exposure is evenly distributed.
How is AI used in payroll?
Payroll-specific evidence here does not establish how widely AI is being used, which payroll tasks it automates in practice, or whether it reduces errors, processing time, or costs. Those outcomes should not be inferred from general claims about AI or from the presence of automation in payroll software.
It is also useful to distinguish generative AI from conventional payroll automation. Payroll systems may apply configured rules to recurring calculations and records; that alone does not show that a generative-AI system is making payroll decisions. If an employer is considering an AI feature around payroll, it should verify what the feature actually does, what records it uses, how it handles exceptions, and who reviews corrections before relying on it. No measured performance comparison is established by the sources cited here.
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Can AI make hiring fairer?
It can support efforts to identify barriers, but using AI does not itself make hiring fair. A system may introduce accessibility problems or carry flaws in an existing screening process into a faster or larger-scale workflow. Employers need to evaluate the tool and the process in which it is used, including whether applicants can access it and whether its outputs create unintended barriers.
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DOL’s inclusive-hiring framework is intended to help employers consider those issues when evaluating, acquiring, and deploying hiring technology. It is a resource for that work, not a certification of any system or a substitute for assessing actual outcomes. The broader DOL AI Best Practices roadmap, released in October 2024, also recommends governance, meaningful human oversight for significant employment decisions, transparency and worker input, rights protection, worker training, and protection of worker data. These are agency recommendations and should not be mistaken for a complete account of legal requirements.
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How should employers use AI at work responsibly?
Responsible deployment means treating AI as part of a work process that needs oversight—not as a neutral shortcut. DOL’s Best Practices provide a roadmap for developers and employers. A practical assessment can translate its recommendations and the ILO’s job-quality concerns into questions to answer before and during deployment:
- Define the task. Specify what the tool is meant to do and what it is not authorized to decide. Check whether it fits the task and performs accurately enough for that use.
- Set human review for consequential decisions. Identify who reviews outputs affecting hiring or other significant employment decisions, what they can change, and how errors are corrected.
- Check accessibility and disparate impact. Assess whether workers or applicants can use the system and whether its outcomes create barriers or unequal effects.
- Explain and consult. Tell affected workers what the system does, what information it uses, and how its output matters. Involve workers in design and gather feedback as the tool is used.
- Protect data and rights. Decide what worker data the system needs, who can access it, and how it will be secured.
- Preserve auditability and recourse. Keep enough information to understand how an output was reached, challenge a suspected error, and correct the underlying record or process.
- Evaluate effects on work. Consider whether the system changes autonomy, work intensity, skill use, or peer and manager relationships—not only whether a task appears faster.
- Check applicable law. Legal obligations vary by jurisdiction. The DOL materials cited here are guidance and frameworks, not a comprehensive legal analysis.
Worker participation is not just a communications step. In a DOL event readout, AFL-CIO President Liz Shuler said, “Workers are the experts in how technology impacts their work. Unions give them a seat at the table so that we design technology that enhances their jobs, not degrades them.” This is a stakeholder’s statement quoted by DOL, rather than a DOL research finding. The readout was published October 17, 2024.
What training do workers and managers need?
Training should help people understand AI’s capabilities and limits in the context of their work, including when to question an output and how to escalate an error. In February 2026, the U.S. Department of Labor released an AI Literacy Framework with five foundational content areas and seven delivery principles to guide workforce and education program design. It is a resource for shaping training, not evidence that a particular course will guarantee specific skills or employment outcomes. See the DOL framework announcement.
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1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitchesEmployers can pair general AI literacy with role-specific instruction: people who review system outputs need to understand the relevant workflow, the limits of the tool, and the route for correction. Workers also need a way to raise concerns about data use, accessibility, or effects on how work is managed.
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