EXPOSED
The bulk of the job — writing job postings, screening resumes, scheduling interviews, maintaining HRIS records, answering benefits and policy questions, assembling onboarding packets — is text-and-database work that ATS automation and LLMs already do at usable quality. What holds is the live human work: candidate and hiring-manager negotiation, employee relations conversations where someone is upset or a complaint could become litigation, and judgment calls on accommodation, discipline, and termination that an employer wants a named person to own. Note the split: the recruiting-coordinator and benefits-administration tiers are far more exposed than the employee-relations and HR-business-partner tiers, and the modal worker today sits closer to the former.
Headcount grew steadily across the period.
Median pay $61,920 → $75,940 -1.9% in real terms
This line is counted by the Bureau of Labor Statistics — the one figure on this page that isn't a judgement of ours. Headcount moves on demand, offshoring, demographics and the business cycle, and automation is one term among several, often not the loudest.
So a falling line is not evidence that AI did it, and a rising one is not evidence that it won't. Both happen in this register: some occupations resist automation and shrink anyway, others are highly automatable and keep growing. The marked year is 2020.
BLS projection, 2024–2034
+6.2% 944,300 → 1,002,700 on the projections basis
Exposed, but growing
AI can already do a lot of these tasks, and the BLS still expects +6.2% more of these jobs by 2034. Demand for the output is growing faster than the work is being automated away — the mechanism BLS gives for software developers, and the combination people most often misread as an error.
Different clocks. The score is what current AI could do to this work today. The projection is how many of these jobs will exist in 2034. Everything between the two — how fast employers actually adopt, whether demand grows in the meantime — is why they can point opposite ways without either being wrong.
~81,800 openings a year on average, including replacing people who leave.
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Holding it up: trust premium . Weakest point: liability shield .
Mixed — a routine tier and a judgment tier Resume screening against a requisition, posting to job boards, scheduling loops, I-9 and E-Verify entry, benefits open-enrollment mailings and HRIS data hygiene are already vendor-automated in Workday/Greenhouse tiers, which is why this sits at 7 rather than 12 — only the intake interview, the offer negotiation call, and the sit-down with an employee who just got written up require a person in the room.
Some physical or field component You are on a laptop nearly all day, but the job still puts you on-site for new-hire orientation, badge and equipment handoff, career fairs, plant or store walk-throughs during an investigation, and physically pulling a personnel file from a locked cabinet — that's a 5, not a 0, and nowhere near the uncontrolled-environment work that earns 13+.
No licence, no signature requirement SHRM-CP or PHR is a resume signal, not a licence — no statute requires a credentialed human to approve a hire, an FMLA designation, or a termination, and when a discrimination claim lands it is the employer entity and its counsel that are named, not you personally.
Meaningful discretion You make genuine calls — whether a request is a reasonable accommodation under the ADA, whether an investigation substantiates a complaint, what the offer band should be — but those calls run through a policy handbook, a compensation matrix, and sign-off from legal or the VP of HR before they bind anyone, which caps this at 9 rather than the 14+ of someone who owns the final decision alone.
The verdict above describes this occupation as a whole. Almost nobody does the typical version of a job — tick what's actually in your week and see how your own mix sits.
Your task mix speaks to task resistance (7/20 here) — how much of the day's work current AI already does. That is the dimension the boxes above are about.
It cannot move the other three. Liability shield (4/20) is whether the law requires a licensed human to sign. Trust premium (10/20) is whether buyers specifically pay for a person. Judgment and accountability (9/20) is whether the role exists to own consequential calls. Those are facts about the occupation's standing, not about which tasks are in your week — a paralegal who does only trial exhibits still holds no licence. Together they are 23 of this occupation's 35 points (66%).
Embodiment (5/20) is also a property of the work rather than the worker, but we don't tag individual tasks as physical or not, so the picker can't tell you anything about it. That's a limit of this tool, not a claim.
Did we get the list right? Tell us what's missing — the tasks are written from the outside, and you're reading this from the inside.
No occupation passed every test: close enough to human resources specialists on skills and subject matter, at least 10 points more resistant, no big jump in training, no new licence, no pay cut, and not shrinking on its own. That happens for 223 of the 654 occupations here that aren't SAFE, and it is worth stating plainly rather than leaving the section off.
The usual reason is that exposure travels with the skill profile. The jobs most similar to yours tend to be exposed for the same reasons yours is, so the near neighbours don't clear the gap — and the ones that do are a different kind of work, not a transfer of what you already know. Read that as a limit of this method, not a verdict that you're stuck: it only compares whole occupations, and it cannot see specialisation, industry, or anything you'd bring that isn't in a federal skill survey.
Here is that claim on your own job rather than in the abstract. These are the three occupations closest to this one by skill and subject matter — the places the work would most naturally transfer — with what the register scores them:
That is the whole problem in three lines. The nearest work is not meaningfully safer, so there is no move here that trades a similar skill set for a better verdict. This is not us running out of ideas — it is what the neighbourhood looks like.
The moves above are yours to make. This is the other half: what would have to change in the world for the occupation itself to score higher. None of it is in any one person's gift, but it is where the floor actually comes from. Scores here are not a one-way ratchet. Only two of the five dimensions — task resistance and embodiment — track what machines can do. The other three track law, what buyers will pay for, and who is answerable, and those move in both directions, often in response to the same pressure AI creates. If every lever below landed, this occupation would score around 52/100, still EXPOSED.
Genuine two-tier occupation: if ATS/LLM tooling absorbs postings, scheduling, HRIS upkeep and tier-1 benefits Q&A, the residual job is workplace investigations, accommodation interactive process, termination risk assessment, union grievance handling and layoff selection review — work that is adversarial, evidence-based and discoverable in litigation. This raises the score for surviving roles without any new law, while cutting headcount; watch for job-title drift from 'HR Coordinator/Recruiter' to 'ER Specialist / HRBP / Investigations'.
EU AI Act Art. 14 human-oversight duties for high-risk employment systems, plus ADA/FEHA interactive-process requirements that accommodation decisions be individualized, push the named-owner role onto a specific HR person. If US enforcement (EEOC or state AG consent decrees) starts requiring an identified human decision-maker of record for hiring rejections, discipline and accommodation denials — as bias-audit and impact-assessment regimes already require an identified auditor — the accountable share of the role grows.
California's FEHA automated-decision-system regulations (in force Oct 1 2025) and Colorado SB 24-205 already make employers liable for discriminatory algorithmic screening and require records/impact assessments. The score rises materially only if a rule names a *person*: e.g. a state amendment or EEOC conciliation-decree pattern requiring a designated HR professional to review and attest to each adverse automated employment decision, on the model of NYC Local Law 144's independent bias-audit signature. Also watch SHRM/HRCI pushing for a licensed-practitioner tier, which does not currently exist anywhere.
Narrow route only: employees and unions bargaining that grievances, harassment complaints and accommodation requests be heard by a human, not a chatbot. Concrete precedent to watch — UAW, CWA and SAG-AFTRA contract language restricting algorithmic management, and state bills (e.g. proposed 'No Robo Bosses' style legislation in California, SB 7) barring automated systems from making final discipline or termination decisions. Employer-side buyers of HR services show little willingness to pay extra for a human.
The limit. No licensure exists for HR anywhere in the US, so liability_shield has a low ceiling — liability lands on the employer entity, not a named practitioner, and voluntary SHRM-CP/PHR credentials carry no legal force. Embodiment has no route. Realistic composite ceiling is mid-50s, and it applies to the employee-relations/HRBP tier; the modal recruiting-coordinator and benefits-admin worker gains almost nothing from these levers.
| New York-Newark-Jersey City, NY-NJ | 52,370 | $89,780 +18% |
| Los Angeles-Long Beach-Anaheim, CA | 36,850 | $80,220 +6% |
| Dallas-Fort Worth-Arlington, TX | 29,190 | $70,700 -7% |
| Chicago-Naperville-Elgin, IL-IN | 26,470 | $78,210 +3% |
| Washington-Arlington-Alexandria, DC-VA-MD-WV | 24,450 | $100,830 +33% |
| Boston-Cambridge-Newton, MA-NH | 21,770 | $90,270 +19% |
| Atlanta-Sandy Springs-Roswell, GA | 20,920 | $75,410 -1% |
| Houston-Pasadena-The Woodlands, TX | 18,320 | $72,550 -4% |
| San Jose-Sunnyvale-Santa Clara, CA | 7,970 | $112,140 +48% |
| San Francisco-Oakland-Fremont, CA | 16,420 | $103,140 +36% |
| Washington-Arlington-Alexandria, DC-VA-MD-WV | 24,450 | $100,830 +33% |
Meta · Amazon · AXA XL · Progressive · US Office of Personnel Management · Maya · US federal agencies
Philippine digital bank Maya described adopting an 'AI-first' approach in how it builds and structures its workforce.
JD Supra reports Connecticut has enacted new restrictions governing the use of AI in employment decisions.
Reports that 29 workers have filed legal claims against Meta over dismissals said to have been decided or executed using artificial intelligence.
G1 reports that Meta employees allege the company used AI systems in making layoff decisions, but say they lack evidence to prove it.
HR Dive reported that Meta used AI systems in layoff selection, with claims that certain groups of workers were disproportionately selected.
Reported that Meta used AI-based performance evaluation in staff cuts, and a US federal judge denied an emergency injunction sought by 26 employees alleging discrimination.
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