OUR PERSPECTIVES
Purpose
Although the overall layoffs and discharges rate has changed very little in recent years and remains exceptionally low by historical standards, the last year has been characterized by a number of high-profile layoff announcements. These cuts have been particularly notable because they have often focused partly or entirely on organizations’ white-collar workers, and artificial intelligence adoption has repeatedly been cited as a driving factor. Whether this rationale is true or simply a convenient excuse remains to be seen. In any case, concern about job displacement driven by rapidly evolving automation technologies is growing, particularly in white-collar fields including HR.
In October 2025, SHRM published Automation, Generative AI, and Job Displacement Risk in U.S. Employment. This data brief used an analysis of the SHRM 2025 Automation/AI Survey to estimate the share of employment with high levels of automation, generative AI (GenAI) use, and nontechnical barriers to automation displacement. The estimates reported were produced by first generating occupation-level estimates and then aggregating them to examine groups of interest (e.g., overall employment and employment within major occupational groups). Due to this approach, we can also examine individual occupations and define unique occupational groups that capture a specific part of the employed population. In this data brief, we repeat many of the analyses from Automation, Generative AI, and Job Displacement Risk in U.S. Employment, but focus exclusively on 10 individual occupations that are specific to HR.
KEY FINDING NO. 1
19.1% of HR Employment (393,000 Jobs) Is at Least 50% Automated
Based on the May 2024 BLS OEWS data, total wage/salary HR employment in the U.S. stands at just over 2 million jobs. Collectively, our analysis of the SHRM 2025 Automation/AI Survey data suggests that at least 50% of tasks are automated in 19.1% of these jobs, meaning that nearly one-fifth of HR jobs meet our threshold for “high automation level.” Even so, the estimated share of jobs meeting this threshold varies considerably across HR occupations, from a low of 11.6% (farm labor contractors) to a high of 27.2% (compensation, benefits, and job analysis specialists).
To put these findings in greater context, our analysis of total U.S. employment in Automation, Generative AI, and Job Displacement Risk in U.S. Employment found that about 15.1% of U.S. wage/salary jobs were at least 50% automated, meaning that the average HR job in the U.S. is somewhat more likely to be highly automated than the average U.S. job in general. Furthermore, the first three HR occupations in Figure 1 crack the top 100 of the 831 occupations studied when ranked by share of employment that is at least 50% automated.
In short, if we limit our attention purely to current automation level, then our findings suggest that a notable fraction of HR employment is already highly automated, which raises the specter of displacement risk. This has become a prominent topic in HR circles, especially as tools for automation (e.g., AI software for evaluating resumes) have become an increasingly visible part of work in the HR field in recent years. However, for reasons we will discuss below, the findings reported in Figure 1 fail to capture key details about HR employment that are likely to significantly mitigate job displacement risk, at least in the near term.
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KEY FINDING NO. 2
11.9% of HR Employment (244,000 Jobs) Is at Least 50% Done Using Generative AI
In addition to asking about the degree to which tasks in their current job are automated, we also asked respondents to report the percentage of tasks in their current job that are completed using GenAI. Estimates for the share of employment that is at least 50% completed using GenAI is reported in Figure 2, both overall and for individual HR occupations.
Compared to our overall results reported in Automation, Generative AI, and Job Displacement Risk in U.S. Employment, Figure 2 suggests that jobs in the HR field are substantially more likely to complete at least 50% of their tasks using GenAI tools, with 11.9% of HR employment estimated to reach or surpass that threshold (compared with 7.8% for overall U.S. wage/salary employment).
Having said this, it is still the case that the vast majority of HR employment (88.1%) does not meet this GenAI use threshold, and the prevalence of GenAI use varies significantly by HR occupation, from a low of 5.4% (farm labor contractors) to a high of 17.8% (training and development managers). Even so, these findings reinforce the growing narrative that GenAI and other emerging technologies have seen particularly rapid adoption in HR. As such, it is reasonable to expect that this technological wave will have a particularly transformative effect on HR workers, even if outright job displacement is limited.
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KEY FINDING NO. 3
64.4% of HR Employment (1.32 Million Jobs) Faces Nontechnical Barriers to Automation Displacement
After asking respondents about the degree to which their current job is automated or completed using GenAI, the 2025 SHRM Automation/AI Survey posed the following question:
Assume that a technology existed that could do your current job completely autonomously. In such a scenario, do you think that there would still be real-world barriers that would prevent your job from being automated?
Based on our analysis of this survey data, we estimate that 63.3% of all wage/salary U.S. employment has at least one such nontechnical barrier. These barriers appear to be slightly more common in HR employment, with 64.4% of jobs having at least one nontechnical barrier to displacement.
Outside of farm labor contractors and payroll and timekeeping clerks, we estimate that well over 60% of jobs across the remaining eight HR occupations have at least one nontechnical barrier to displacement, including HR occupations in which jobs are comparatively likely to have a high automation level. For example, we estimate that 26.7% of jobs in the training and development managers occupation are highly automated (see Figure 1), which is the second-highest share observed across the 10 occupations in the HR field. At the same time, this occupation ranks first out of 10 in Figure 3, with 68% of training and development manager jobs having at least one nontechnical barrier to displacement.
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KEY FINDING NO. 4
Among Hr Employment With Nontechnical Barriers to Automation Displacement, Client Preferences Are the Most Common Issue
For survey respondents who definitively reported the presence of at least one nontechnical barrier to automation displacement in their current job, we asked a follow-up question about the nature of these barriers. In this question, respondents were able to cite multiple barrier types, including three specific categories (client preferences, legal and/or regulatory requirements, and cost-effectiveness) and an “other” category.
As discussed in Automation, Generative AI, and Job Displacement Risk in U.S. Employment, we estimate that — across all jobs for which at least one nontechnical barrier exists — the most common type of barrier is client preferences (73.6% of all employment with nontechnical barriers). Legal and/or regulatory requirements comes in a distant second (41.5%), closely followed by cost-effectiveness (36.4%).
The patterns observed in HR employment with at least one nontechnical barrier to displacement are broadly similar to what we see for overall employment with nontechnical barriers, with some notable exceptions. First, barriers related to client preferences are somewhat more common in HR employment than overall employment (78.7% versus 73.6%), whereas legal and/or regulatory requirements are a much more common barrier (51.2% versus 41.5%). Conversely, nontechnical barriers related to cost-effectiveness appear to be much less common among HR employment with nontechnical barriers than overall employment with nontechnical barriers (30.3% versus 36.4%).
These differences largely match any HR professional’s intuition and personal experience about the nature of HR employment versus overall employment. For example, HR jobs often heavily emphasize interpersonal communication, which suggests that client preferences will be an especially important nontechnical barrier to automation displacement. Similarly, HR workers often find their work deeply rooted in legal and/or regulatory issues that — at least at present — will likely forestall or greatly mitigate job displacement risk.
KEY FINDING NO. 5
9.3% of U.S. HR Employment (192,000 Jobs) Is at Least 50% Automated and Has No Definitive Nontechnical Barriers to Automation Displacement
Given that the 2025 SHRM Automation/AI Survey asked respondents about the extent to which tasks in their current job are automated and the presence or absence of nontechnical barriers to automation displacement, we are able to estimate the share of employment in individual occupations that simultaneously meets two conditions:
- At least 50% of tasks are automated.
- There are no definitive nontechnical barriers to automation displacement.2
As we discussed when reviewing the findings of Figure 1, meeting the first condition is important because it identifies jobs that are plausibly already automated enough to become fully displaced in the near future due to relatively minor technological advances or through the redistribution of nonautomated tasks across other positions. However, we noted that highly automated positions of this kind might be shielded from full displacement by nontechnical barriers, and, in fact, our survey data suggest that such barriers are quite common (even in highly automated jobs). Therefore, jobs that meet both of the conditions listed above plausibly face a significantly elevated level of displacement risk.
Figure 5 reports the share of U.S. HR employment that we estimate meets these two conditions, both overall and by individual occupation. Overall, we estimate that 9.3% of HR employment (about 192,000 jobs) is at least 50% automated and has no nontechnical barriers to displacement. This share varies widely by individual occupation, from a low of 5.3% (farm labor contractors) to a high of 12.7% (training and development managers).
To place these findings in greater context, Automation, Generative AI, and Job Displacement Risk in U.S. Employment reported that just 6% of all U.S. employment both is highly automated and has no nontechnical barriers to displacement. Furthermore, our finding that 10% or more of employment in 4 of 10 HR occupations meets both of these conditions is striking when one considers that only 84 of the 831 occupations studied meet the same threshold.
In short, our findings suggest that the HR field is notably more exposed to automation displacement risk relative to overall U.S. employment and includes some of the most at-risk occupations. Of course, it is important to note that our findings do not suggest that all HR workers face an imminent risk of being displaced by automation; in fact, we estimate that 90.7% of HR jobs do not fall into our high-risk category because they are not currently highly automated or they include at least one nontechnical barrier to automation displacement.
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CONCLUSION
This brief covered five key findings about the exposure of HR employment to automation, GenAI, and job displacement risk based on analyses associated with the 2025 SHRM Automation/AI Survey. Taken together with the overall results reported in Automation, Generative AI, and Job Displacement Risk in U.S. Employment, these findings suggest that jobs in the HR field are more exposed to high displacement risk, mostly because they are more likely to meet our “high automation” threshold (i.e., at least 50% of tasks are automated). Overall, we estimate that 9.3% of HR employment (192,000 jobs) is both highly automated and has no nontechnical barriers to automation displacement, a number over 1.5 times as large as the share we estimate for U.S. employment overall (6%).
Put simply, we expect that the current wave of technological change driven by increasingly advanced AI tools will displace some workers and that those in the HR field will be disproportionately affected. In fact, recent news reports suggest that HR workers have already been a common (though certainly not the only) target for replacement by AI. Notable examples include IBM laying off about 200 HR workers in early 2025 and Google offering buyouts to U.S.-based members of its People Operations team.
Despite this, a comprehensive assessment of our findings suggests that the vast majority of HR workers are unlikely to be completely displaced by automation technology in the immediate future. Instead, the rapid proliferation of AI tools is more likely to lead to the transformation of HR roles in the immediate future. Having said this, the rapid pace of technological advancement and evolving preferences (e.g., the willingness of customers to forgo human interactions) suggest that exposure to automation displacement risk may change suddenly and unpredictably. As such, SHRM is devoting significant resources to tracking these developments and their implications for the workforce at large and HR workers in particular.
Definitions
A key challenge when discussing this topic is clearly defining terms. For instance, automated processes have become so ubiquitous in day-to-day life that it is sometimes easy to forget that such processes often represent critical work tasks. Similarly, AI technologies can appear in subtle ways that may not immediately be associated with AI, such as improved editing features in word processing software. To avoid confusion, we defined the following terms for participants in the SHRM 2025 Automation/AI Survey:
Automation — The technique of making an apparatus, a process, or a system operate autonomously (i.e., without human intervention). An example would be manufacturing processes that complete routinized tasks without human input. A task is considered automated if it is completed using automation technology. The degree to which an occupation is automated depends on the extent to which tasks completed within that occupation are done via automation.
Artificial Intelligence — Any technology that can independently perform tasks that typically require human intelligence. One example would be self-driving vehicles.
Generative AI (GenAI) — Any AI technology that leverages training on an archive of content to generate brand-new, unique content (e.g., text or images). One example would be ChatGPT, which generates entirely novel and unique content based on a massive database of existing content that the software has been trained on.
To simplify the language of this brief and provide greater clarity to the reader, the following definitions will also be useful:
High Automation Level — We define a job as having a high automation level if at least 50% of the tasks in that job are automated. A central value that we estimate in our results is the share of employment in a given occupation (or collection of occupations) that meets this threshold. For example, we estimate that about 39.7% of employment in the “software developers” occupation is highly automated (i.e., at least 50% of their tasks are automated).
High GenAI Use Level — We define a job as having a high GenAI use level if at least 50% of the tasks in that job are completed using GenAI. A central value that we estimate in our results is the share of employment in a given occupation (or collection of occupations) that meets this threshold. For example, we estimate that about 23.3% of employment in the “software developers” occupation exhibits high GenAI use (i.e., at least 50% of their tasks are completed using GenAI).
Nontechnical Barrier to Automation Displacement — A nontechnical barrier to automation displacement is any barrier to displacement that does not relate to a limitation of the automation technology. In other words, nontechnical barriers to automation displacement can exist even when the technology exists to completely automate a given job. For example, client preferences for interpersonal interaction might present a barrier to displacing employment through automation.
HR Employment — A central goal of this brief is to estimate the share of HR employment in the U.S. that meets certain conditions (e.g., highly automated, high GenAI use, or presence of nontechnical barriers to automation displacement). In all cases, we measure HR employment at the occupational level using the May 2024 U.S. Bureau of Labor Statistics (BLS) Occupational Employment and Wage Statistics (OEWS) employment data, which is designed to measure wage/salary employment in nonfarm establishments.1 We define HR employment as including all employment in the following 10 occupations:
- Compensation and benefits managers (SOC code: 11-3111)
- Human resources managers (SOC code: 11-3121)
- Training and development managers (SOC code: 11-3131)
- Human resources specialists (SOC code: 13-1071)
- Farm labor contractors (SOC code: 13-1074)
- Labor relations specialists (SOC code: 13-1075)
- Compensation, benefits, and job analysis specialists (SOC code: 13-1141)
- Training and development specialists (SOC code: 13-1151)
- Payroll and timekeeping clerks (SOC code: 43-3051)
- Human resources assistants, except payroll and timekeeping (SOC code: 43-4161)
Note that we only use the employment values for individual occupations (i.e., “detailed occupations”) because these can be aggregated to reflect employment levels in larger sets (e.g., occupational groups or overall employment). For simplicity, the text of this data brief discusses U.S. employment in the present tense.
Methods and Data
A detailed review of our methodology and data sources is available in the methodological appendix for Automation, Generative AI, and Job Displacement Risk in U.S. Employment. However, a general outline of our approach is provided here for reference:
The Survey — The SHRM 2025 Automation/AI Survey was fielded in March and April of 2025, with a final sample size of 20,262 U.S. workers. From a demographic point of view, the sample is broadly representative of the overall U.S. workforce, though certain groups are slightly overrepresented or underrepresented. In addition to providing basic occupational information and demographic characteristics, respondents were asked a series of questions related to automation, GenAI use, and nontechnical barriers to automation displacement in their current job. This data brief focuses on four topics captured in the survey data:
- The share of tasks currently automated in the respondent’s current job.
- The share of tasks currently done using GenAI in the respondent’s current job.
- The presence of nontechnical barriers to automation displacement in the respondent’s current job.
- The types of nontechnical barriers to automation displacement that are present in the respondent’s current job.
Occupation-Level Estimates — A central priority in the analysis underlying this data brief was to identify the following values for each of the 831 occupations in the May 2024 BLS OEWS employment data:
- The probability that a job in any individual occupation is at least 50% automated.
- The probability that a job in any individual occupation is at least 50% done using GenAI.
- The probability that a job in any individual occupation has at least one nontechnical barrier to automation displacement.
- The probability that a job in any individual occupation is at least 50% automated and faces no definitive nontechnical barriers to automation displacement.
Once calculated, we combined these probabilities with occupation-level May 2024 BLS OEWS employment data for the 10 HR occupations we identified, which allowed us to produce estimates for the share of wage/salary employment that meets any of these conditions, both for each individual occupation and for all 10 grouped together.
Unfortunately, precisely estimating the probabilities listed above generally requires a significant amount of data, so, in general, we were unable to rely on direct evidence to estimate these values for each individual HR occupation. Instead, we adopted a “proximity-based” estimation method in which values for any given occupation were obtained by combining direct evidence (i.e., survey responses from people in the occupation in question) with data from respondents who are in similar occupations. In this approach, all final estimates for a given occupation are weighted averages in which respondents whose occupation is most similar to the occupation in question receive the most weight. The details underlying these calculations are provided in the methodological appendix.
1. See Technical Notes for May 2024 OEWS Estimates for more information on the BLS OEWS methodology. The data can also be downloaded directly from the BLS website.
2. In the original survey question about the presence of nontechnical barriers to automation displacement, respondents could answer “yes,” “no,” or “don’t know.” We classified a respondent as not having a definitive nontechnical barrier to automation displacement in their current job if they replied “no” or “don’t know.”