EXPOSED
The core of this job is walking floors — inspecting rooms and restrooms, checking chemical dilution and equipment, coaching a crew that often has high turnover and language diversity, and covering shifts when someone no-shows. AI is already eating the paperwork tier: scheduling, labor-hour forecasting, supply ordering, inspection scoring apps, and incident write-ups. What remains is physically present crew leadership and on-the-spot judgment about priorities during an outbreak, VIP arrival, or flood, which no software or robot currently handles.
Dipped in 2020, then grew past where it started.
Median pay $40,780 → $49,100 -3.7% 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
+2.5%
Percentage only. The projection counts a different population from the 178,760 above — it includes self-employed workers, which for this occupation is most of them, so the two headcounts are not comparable.
Growing, and only partly exposed
The BLS expects +2.5% more of these jobs by 2034, and at 51/100 the work is only partly exposed — some tasks are automatable, the core of the job is not. Nothing here is in tension.
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.
~33,000 openings a year on average, including replacing people who leave.
ButlerMaid SupervisorLinen SupervisorJanitor SupervisorJanitorial ManagerLaundry SupervisorBuilding SupervisorCleaning SupervisorCustodial SupervisorCustodian SupervisorHouse SuperintendentHousekeeping ManagerExecutive HousekeeperJanitorial SupervisorLinen Room SupervisorSanitation SupervisorHousekeeper SupervisorHousekeeping InspectorMaintenance SupervisorBuilding SuperintendentHousekeeping SupervisorHouse Cleaner SupervisorCleaning Staff SupervisorHotel Cleaning Supervisor
Holding it up: embodiment . Weakest point: liability shield .
Mixed — a routine tier and a judgment tier Scheduling, par-level supply ordering, labor-hour forecasting and inspection scorecards are already app-driven, but the irreducible half — verifying a restroom was actually cleaned after the tablet says it was, retraining a new hire on bloodborne pathogen procedure at the cart, and physically covering a no-show — keeps this at 13 rather than in the automatable band.
Hands-on in uncontrolled environments You are in occupied guest rooms, stairwells, loading docks and mechanical closets across a whole building every shift, handling chemicals and jammed floor machines in spaces nobody controls for a robot; it sits at 15 rather than 18 because a meaningful share of the week is spent at a desk on rosters, payroll edits and vendor calls.
No licence, no signature requirement No state licence gates this job — OSHA HazCom and bloodborne pathogen training plus maybe a CMI or IEHA certificate is the ceiling, and when a slip-and-fall claim lands it is the property or contractor entity named, not you personally, which is why this is a 3 and not a 10.
Meaningful discretion Deciding which of forty dirty rooms gets cleaned first when the hotel is oversold, pulling a crew off routine work to isolate a contamination event, and documenting a write-up that will survive a grievance are genuine discretionary calls, but they run inside a brand standard, a labor contract and a spec sheet someone else wrote, keeping this at 11 rather than 15.
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 (13/20 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 (3/20) is whether the law requires a licensed human to sign. Trust premium (9/20) is whether buyers specifically pay for a person. Judgment and accountability (11/20) 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 51 points (45%).
Embodiment (15/20) 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 first-line supervisors of housekeeping and janitorial workers 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 66/100, still EXPOSED.
Outbreak-response and pathogen-escalation authority formalized in facility protocols — supervisor owns the call to close a wing, escalate to sporicidal disinfectant, or divert crew mid-shift, with that decision logged and auditable after a C. diff or norovirus cluster. Post-COVID hospital and cruise-line protocols have already pushed some of this into writing.
Task-mix shift: once scheduling, labor forecasting, inspection scoring and supply ordering are fully automated, what remains is the genuinely hard tier — recruiting and retaining crews at 100%+ annual turnover, multilingual on-floor coaching, immigration-document and wage-hour compliance conversations, and physical triage during floods and VIP turnarounds. Also rises if autonomous floor scrubbers proliferate, since exception handling, charging, and fault recovery land on the supervisor.
Healthcare environmental-services supervision becoming a named, certifiable accountable role: CMS Conditions of Participation or Joint Commission infection-control standards requiring a designated, trained EVS supervisor to sign off on terminal-clean verification (ATP/UV audit logs) for isolation rooms, plus OSHA HazCom/bloodborne-pathogen enforcement naming the on-site supervisor as the responsible trainer of record. AHE's CHESP credential already exists as the vehicle; a mandate rather than a preference is what changes the score.
Wage-theft and joint-employer enforcement in contract cleaning: state laws like California's AB 1513/property-services provisions and NYC-style building service worker rules extending personal or named-supervisor accountability for break records, training certification, and subcontractor labor compliance.
Union contracts (SEIU 32BJ and West Coast property-services agreements) that specify minimum on-site supervisor-to-cleaner ratios and bar remote or shared supervision, so building owners are contractually buying a physically present human lead rather than a dispatch app.
The limit. Trust premium is near its ceiling: cleaning is bought on price per square foot by procurement departments, and no end customer knowingly pays extra for a human supervisor. The realistic upside is concentrated in healthcare, food-processing, and unionized commercial real estate; in schools, offices, and hotels there is no visible route above the current score.
| New York-Newark-Jersey City, NY-NJ | 11,670 | $59,990 +22% |
| Los Angeles-Long Beach-Anaheim, CA | 5,190 | $61,270 +25% |
| Washington-Arlington-Alexandria, DC-VA-MD-WV | 5,140 | $50,840 +4% |
| Miami-Fort Lauderdale-West Palm Beach, FL | 4,510 | $45,440 -7% |
| Chicago-Naperville-Elgin, IL-IN | 3,910 | $51,280 +4% |
| Houston-Pasadena-The Woodlands, TX | 3,680 | $38,500 -22% |
| Dallas-Fort Worth-Arlington, TX | 3,310 | $45,290 -8% |
| Las Vegas-Henderson-North Las Vegas, NV | 3,200 | $51,460 +5% |
| San Francisco-Oakland-Fremont, CA | 2,680 | $70,400 +43% |
| San Jose-Sunnyvale-Santa Clara, CA | 1,010 | $69,080 +41% |
| Seattle-Tacoma-Bellevue, WA | 1,610 | $67,910 +38% |
We have no reported case of a named organisation automating this occupation. Not one deployment, not one announcement.
That is worth saying out loud next to a score of 51. The verdict above is about what the work exposes — what current AI could do to these tasks. It is not a claim that anyone has done it. For this occupation those two things have come apart completely: the capability argument is on this page, and the evidence column is empty.
Has AI actually changed your work? One tap, anonymous, and the running tally is public. Nothing else is asked of you.
Rather than check back: get the digest and we'll tell you what changed — or watch a single occupation from its own page.