Meter Readers and Vending-machine Collectors

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 7 task statements that define Meter Readers and Vending-machine Collectors (ISCO-08 9623) score an average of 0.38 on a 0–1 exposure scale — more exposed than about 72% of the 427 placed occupations. Roughly 100% of its tasks fall somewhere on the exposed part of the gradient, and the typical task lands in the Gradient 1 band.

Exposure is task overlap, not a verdict. A high score means a generative-AI model can do part of the content of these tasks — it says nothing about whether the work is automated, whether anyone uses AI for it today, or whether jobs are lost. The gradient is scored on the international ISCO-08 system; the rest of Singulariki is U.S. O*NET/SOC, bridged below by an approximate, many-to-many crosswalk.

How its tasks split across the gradient

Each of the 7 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Reading electricity, gas or water meters and recording consumption;”

Scores 0.75 on the 2025 scale. The task of reading electricity, gas, or water meters and recording consumption has a high potential for automation with Generative AI, particularly due to advancements in smart metering and data management systems. In comparison to similar tasks, such as remote readings of electronic meters, which received an adjusted score of 0.75 due to its high degree of automation potential through digital data transmission and minimal physical intervention, this task aligns closely. The primary functions involve data collection and entry, which can be managed efficiently by AI systems and smart metering technology. Considering Poland's strong technological infrastructure, where access to AI-enhanced systems is feasible, the physical aspect of manually reading meters is diminished in favor of automated, remote solutions. Therefore, the score reflects the high potential for generative AI to significantly automate the task by eliminating manual readings and enhancing data accuracy and efficiency in consumption recording.

Moving fastest, 2023 → 2025

“Proceeding along established routes to take readings of meter dials;”

Model capability on this task changed by +0.27 in two years — the gradient is not static, it is filling in.

U.S. occupations this maps to

The American O*NET/SOC roles that crosswalk to ISCO-08 9623, biggest by employment first, via the published (approximate, many-to-many) IBS O*NET-SOC ↔ ISCO-08 correspondence. These are the closest U.S. matches — not an asserted one-to-one identity.

• Coin, Vending, and Amusement Machine Servicers and Repairers
• Meter Readers, Utilities

In context

Part of the 9 - Elementary occupations major group. Return to the full gradient to see how the whole group sits.

Meter Readers and Vending-machine Collectors sit at the 72nd percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Meter Readers and Vending-machine Collectors rank in the 72nd percentile for GenAI task exposure — overlap with what generative AI can attempt, not a projection of displacement. (ILO / Gmyrek et al. (2025) GenAI exposure gradient)
• About 100% of this occupation's tasks fall into an exposed gradient band. (ILO / Gmyrek et al. (2025))
• Mean task exposure rose by 0.02 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Reading electricity, gas or water meters and recording consumption;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Meter Readers and Vending-machine Collectors". https://singulariki.com/gradient/9623-meter-readers-and-vending-machine-collectors.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.