Supervisors of Building and Grounds Cleaning Workers
United States · BLS Employment Projections 2024–34 (figures for SOC 37-1011 occupational group)
AI exposure in published research
Figures below are reproduced from external datasets without modification. Where a dataset does not cover this occupation, the value is shown as — rather than as zero.
Data provider: OpenAI · "GPTs are GPTs"
Time basis: 2023 baselineex-ante estimate
Scored against GPT-4-generation capability.
- Human rater basis
- 25.5%
- GPT-4 rater basis
- 26.5%
β = direct exposure (E1) + 0.5 × exposure when tools are available (E2), per the source repository's definition.
26 rated tasks · 10 tasks with β ≥ 0.5 (38.5%)
- Version:
- gh-main-0471612
- License:
- MIT License · Copyright (c) 2024 OpenAI
Data provider: Anthropic Economic Index
Time basis: Published 2026-03-05composite index
- Observed exposure
- 4.4%
Theoretical exposure index weighted by measured Claude usage, per the original report's definition.
22 tasks · usage observed in 2 · mean 7.4%
Data: Anthropic Economic Index — labor_market_impacts, CC-BY, https://huggingface.co/datasets/Anthropic/EconomicIndex
AI Changing Work uses Anthropic Economic Index data under CC-BY; Anthropic does not endorse or sponsor this site or its analyses.
- Version:
- hf-lmi-2026-03
- License:
- CC BY 4.0
- Observation period:
- — (not applicable to this release)
- Model:
- — (not stated by the source)
- Definition source:
- https://www.anthropic.com/research/labor-market-impacts
2023 prediction vs observation-based index published 2026-03-05
One card (GPTs are GPTs) is a 2023 estimate of what AI could theoretically do; the other (Anthropic Economic Index) is built from observed usage and was published on 2026-03-05 — that is its publication date, not the period it observed. They measure different things, so the two figures cannot be added, averaged, or ranked against each other. The older figure is kept here as a baseline for comparison rather than removed.
Caution: the Anthropic figures — observed exposure and task penetration — take the Eloundou β as one of their inputs. The two sides resembling each other is therefore not evidence that the earlier prediction came true; reading it that way is circular reasoning.
The Anthropic figures are measured on Claude users, who are not the whole economy and not the whole workforce.
The mapping of O*NET tasks and occupation codes was performed by AI Changing Work. The source figures themselves were not modified.
These indices are not forecasts. Which point in time each one belongs to is stated on the badge on its card.
Task Breakdown
- Schedule cleaning shifts and assign work areas to staff
- Inspect cleaned areas and ensure quality standards are met
- Order cleaning supplies and manage inventory records
- Train new employees on cleaning procedures and safety protocols
About This Occupation
If you work as a Supervisor of Building and Grounds Cleaning Workers, AI is reshaping your profession. With an automation risk of 16/100 and overall exposure at 18%, this role faces limited but growing transformation. The highest-impact area is ordering cleaning supplies and managing inventory records at 65% automation, where automated reordering systems track consumption patterns and trigger purchase orders when stock levels fall below thresholds. Scheduling shifts is partially automated at 52%, with AI-powered workforce management tools optimizing coverage based on building occupancy data and historical cleaning needs. Physical inspection of cleaned areas (8%) and training new employees (12%) remain firmly human tasks requiring hands-on presence and interpersonal skills. This is classified as a 'mixed' role. BLS projects +5% growth through 2034, with median annual wage of $43,980 and roughly 263,400 supervisors employed. While robotic cleaning equipment is emerging in commercial settings, the supervisory role remains essential for quality assurance, staff management, and handling the varied and unpredictable situations that arise in building maintenance.
ISCO-08 classification
Cleaning and Housekeeping Supervisors in Offices, Hotels and Other Establishments
Indirect mapping — this occupation is linked to the ISCO unit group by a rule-based fallback.
Definition
ILO original text (English)
Cleaning and housekeeping supervisors in offices, hotels and other establishments organize, supervise and carry out housekeeping functions in order to keep clean and tidy the interiors, fixtures and facilities in these establishments.
Definition & vocabulary source
Source: International Labour Organization (ILO) — ISCO-08 Structure
License: ILO CC BY 4.0
Official occupational information
- ONET37-1011.00
First-Line Supervisors of Housekeeping and Janitorial Workers
Directly supervise and coordinate work activities of cleaning personnel in hotels, hospitals, offices, and other establishments.
View original
Frequently Asked Questions
The Anthropic Economic Index puts observed exposure at 4.4%. The OpenAI "GPTs are GPTs" rubric puts occupation-level β at 25.5% under human raters. Both figures are reproduced from published research without modification.
They come from two published datasets: the Anthropic Economic Index (labor_market_impacts, CC BY 4.0) and the OpenAI "GPTs are GPTs" exposure rubric (MIT License, Copyright (c) 2024 OpenAI). AI Changing Work maps them onto O*NET occupation and task codes and does not calculate exposure scores of its own. AI Changing Work uses Anthropic Economic Index data under CC-BY; Anthropic does not endorse or sponsor this site or its analyses.
No. They are a diagnosis of exposure as measured at the time each source dataset was published. AI Changing Work publishes no prediction of future automation or job displacement for this occupation.