AI Delegation Index

AI Delegation Index

What work could you hand off to an AI agent?

Tile and Stone Setters

Low

AI agents have a narrow supporting role here, mainly helping with preparation or documentation rather than doing the physical work.

Where agents can help most

  1. 1

    Mark tile layout and check fit

    Use an agent to turn blueprints and site measurements into a marked layout plan for tile work.

  2. 2

    Assemble decorative mosaic sections

    Use an agent to organize a decorative mosaic pattern from the approved design and your tile list.

  3. 3

    Set tile and finish grout joints

    Use an agent to build a set-by-set checklist for placing tile and finishing grout from the room plan and your material notes.

Search another job

O*NET-SOC 47-2044.00 · #669 of 923

Result context

How to read this result

Apply hard tile, stone, and comparable materials to walls, floors, ceilings, countertops, and roof decks.

National position
#669 of 923 occupations
Top 73% of occupations
Overall AI delegation potential
Low
Potential of score-contributing workflows
Limited · 59/100
Meaningful work covered
29%

The overall rating combines how useful the best agent workflows are with how much of the occupation they address. It is not an estimate of job automation or replacement.

Recommended agent uses

3 workflows you could delegate to AI

1

Mark tile layout and check fit

How you could use an agent

Use an agent to turn blueprints and site measurements into a marked layout plan for tile work. You give it the room dimensions, fixture locations, and pattern notes, and it helps you lay out reference lines, check material needs, and spot places where the pattern may not fit cleanly.

Where you stay involved

You verify the layout on the actual surface, decide how to handle tight corners or fixtures, and make the final call before any tile is set. You stop and ask for direction if the plan needs to change.

Review level: Medium

2

Assemble decorative mosaic sections

How you could use an agent

Use an agent to organize a decorative mosaic pattern from the approved design and your tile list. You provide the sketch or pattern notes, and it helps you arrange the pieces, track spacing, and create a paper or bench layout you can follow when you set the section.

Where you stay involved

You decide whether the pattern still matches the approved design, adjust piece placement by hand, and approve the assembly before it is fixed in place. You handle any mismatch in design or tile selection yourself.

Review level: Medium

3

Set tile and finish grout joints

How you could use an agent

Use an agent to build a set-by-set checklist for placing tile and finishing grout from the room plan and your material notes. You give it the layout, mortar type, and work sequence, and it helps you keep track of alignment checks, grout cleanup, and the next area to finish.

Where you stay involved

You apply the mortar, set and tap the tile, keep checking level and line, and decide when the joint work looks right. You stop if the tile starts drifting or the bond looks wrong.

Review level: Medium

Supporting analysis

Why this occupation's AI delegation potential is Low

Underlying methodology score

51 / 100

This technical score determines the qualitative rating; it is not an estimate of the share of the occupation that can be automated.

Importance & frequency82
AI capability64
Digital actionability46
End-to-end leverage40
Safety & reversibility64
Meaningful-work coverage
29%
Physical-work modifier
Moderate
Safety modifier
Limited
Qualitative judgment
No material constraint
O*NET task evidence
25 tasks

O*NET 31.0 · methodology 3.3.0. Every workflow passes an action-level physical-execution and protected human-and-veterinary clinical-action gate. Documentation workflows must own a complete digital loop and use digital task evidence only; support-only workflows are disclosed separately and excluded from scoring. Artistic, editorial, normative, and policy-dependent work receives a transparent human-judgment constraint. National ranking within 923 scored O*NET occupations under methodology 3.3.0.

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