Floor Layers and Tile Setters

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 4 task statements that define Floor Layers and Tile Setters (ISCO-08 7122) score an average of 0.10 on a 0–1 exposure scale — more exposed than about 3% of the 427 placed occupations. Roughly 0% of its tasks fall somewhere on the exposed part of the gradient, and the typical task lands in the Not exposed 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 4 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Assembling carpet, tiles or other materials and laying them on floors according to design and other specifications;”

Scores 0.13 on the 2025 scale. The task of assembling carpet, tiles, or other materials and laying them on floors according to design and specifications is a highly manual and tactile activity that necessitates significant physical effort, dexterity, and spatial reasoning. Generative AI lacks the capability to directly automate hands-on, physically intensive tasks, although it can assist with planning, optimization, and potentially offering virtual simulations or instructions for the layout. Similar tasks such as installing wall cladding and laying floor surfaces, which involve comparable physical labor and craftsmanship, received low automation scores (e.g., 0.0881, 0.12). These tasks emphasize manual dexterity and the need for human judgment in adjusting to real-time conditions and ensuring quality, making them largely inaccessible for AI automation, particularly within the current technological landscape. Given these factors and understanding that the task is in a well-digitized country like Poland, an adjusted score of 0.1 aptly reflects the minimal potential for automating this task through Generative AI, highlighting the primarily physical nature that cannot be replaced by digital automation tools.

Moving fastest, 2023 → 2025

“Assembling carpet, tiles or other materials and laying them on floors according to design and other specifications;”

Model capability on this task changed by +0.02 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 7122, 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.

• Tile and Stone Setters
• Floor Layers, Except Carpet, Wood, and Hard Tiles
• Carpet Installers
• Floor Sanders and Finishers

In context

Part of the 7 - Craft and related trades workers major group. Return to the full gradient to see how the whole group sits.

Floor Layers and Tile Setters sit at the 3rd percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Floor Layers and Tile Setters rank in the 3rd 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 0% of this occupation's tasks fall into an exposed gradient band. (ILO / Gmyrek et al. (2025))
• Mean task exposure rose by 0.00 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Assembling carpet, tiles or other materials and laying them on floors according to design and other specifications;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Floor Layers and Tile Setters". https://singulariki.com/gradient/7122-floor-layers-and-tile-setters.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.