# Meter Readers, Utilities

> Read meter and record consumption of electricity, gas, water, or steam.

- **SOC code:** 43-5041.00
- **Canonical URL:** https://singulariki.com/roles/role-43-5041-00
- **Also known as:** Meter Reader, Meter Technician, Water Meter Reader, Water Use Inspector, Field Technician, Fieldman, Meter Reader Inspector, Meter Reading Clerk
- **Frame:** "AI exposure" means task overlap (how codifiable the work is), not jobs lost or a forecast. Every figure below is traced to a named public dataset.

## What this work is

**Core tasks** (O*NET):
- Read electric, gas, water, or steam consumption meters and enter data in route books or hand-held computers.
- Upload into office computers all information collected on hand-held computers during meter rounds, or return route books or hand-held computers to business offices so that data can be compiled.
- Walk or drive vehicles along established routes to take readings of meter dials.
- Verify readings in cases where consumption appears to be abnormal, and record possible reasons for fluctuations.
- Inspect meters for unauthorized connections, defects, and damage, such as broken seals.
- Report to service departments any problems, such as meter irregularities, damaged equipment, or impediments to meter access, including dogs.
- Leave messages to arrange different times to read meters in cases in which meters are not accessible.
- Connect and disconnect utility services at specific locations.
- Answer customers' questions about services and charges, or direct them to customer service centers.
- Update client address and meter location information.
- Report lost or broken keys.
- Perform preventative maintenance or minor repairs on meters.

**Emerging tasks** (O*NET):
- Dig dirt away from meters to take readings.
- Install new or replace broken meters.

## Skills, tools, capabilities

**Knowledge, skills & abilities** (O*NET, highest importance first):
- Customer and Personal Service _(knowledge)_
- Near Vision _(ability)_
- Public Safety and Security _(knowledge)_
- English Language _(knowledge)_
- Oral Comprehension _(ability)_
- Oral Expression _(ability)_
- Information Ordering _(ability)_
- Perceptual Speed _(ability)_
- Trunk Strength _(ability)_
- Mathematics _(knowledge)_
- Reading Comprehension _(essential_skill)_
- Active Listening _(essential_skill)_

**Skills in demand:**
- English Language _(Common Skill)_
- Information Ordering _(Specialized Skill)_
- Mathematics _(Common Skill)_
- Time Management _(Common Skill)_
- Speech Recognition _(Specialized Skill)_
- Reading Comprehension _(Common Skill)_
- Microsoft Word _(Common Skill)_
- Microsoft Windows _(Common Skill)_
- Microsoft PowerPoint _(Common Skill)_
- Microsoft Outlook _(Common Skill)_
- Microsoft Excel _(Common Skill)_
- Microsoft Access _(Specialized Skill)_

**Tools & technology:**
- Microsoft Office software _(hot technology, in demand)_
- Microsoft Access _(hot technology)_
- Microsoft Excel _(hot technology)_
- Microsoft Outlook _(hot technology)_
- Microsoft PowerPoint _(hot technology)_
- Microsoft Windows _(hot technology)_
- Microsoft Word _(hot technology)_
- Billing software
- Geographic information system GIS systems
- Graphing software
- Mapping software
- Meter reading software

## AI exposure & outlook

- **AI task-overlap index:** 45th percentile (Moderate) across all occupations — composite of current-era exposure studies (ai-exposure-index-v1).
- **Overall AI exposure (Felten et al.):** 18th percentile (Low) — source: felten_aioe.
- **LLM task exposure, γ (OpenAI / Eloundou):** 46th percentile (Moderate) — source: eloundou_gamma.
- **AI assistant applicability (Microsoft):** 75th percentile (High) — source: microsoft_applicability.
- **Frey–Osborne (2013, historical computerization estimate):** 71st percentile — kept separate from current-era studies.
- **Remote-capable (Dingel–Neiman):** no — task structure, not who actually works remote.
- **Projected employment (BLS 2024–34):** -12.0% growth (Declining); 1.3k annual openings; 20.1k → 17.7k jobs.
- **Pay & employment (BLS OEWS, May 2024):** median $49,180; 19,620 employed.

## Sources

- **O*NET** (30.3) — U.S. Department of Labor / National Center for O*NET Development. https://www.onetcenter.org/database.html
- **BLS Occupational Employment and Wage Statistics (OEWS)** (May 2024) — U.S. Bureau of Labor Statistics. https://www.bls.gov/oes/
- **BLS Employment Projections** (2024–2034) — U.S. Bureau of Labor Statistics. https://www.bls.gov/emp/
- **Microsoft “Working with AI”** (working-with-ai) — Microsoft Research. https://www.microsoft.com/en-us/research/
- **“GPTs are GPTs” (Eloundou et al.)** (arXiv 2303.10130) — OpenAI / academic. https://arxiv.org/abs/2303.10130
- **AI Occupational Exposure (AIOE)** (Felten, Raj & Seamans) — academic. https://github.com/AIOE-Data/AIOE
- **Frey & Osborne (2013)** (frey-osborne-automation) — academic. https://www.oxfordmartin.ox.ac.uk/publications/the-future-of-employment/
- **Dingel & Neiman (2020)** (dingel-neiman-workathome) — academic. https://github.com/jdingel/DingelNeiman-workathome

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_Generated from Singulariki's joined dataset; data snapshot 2026-06-02T21:00:32.945303+00:00. https://singulariki.com/roles/role-43-5041-00_