BETA
Updated: Mar 24
BETA
Updated: Mar 24
Stable
Last Update: 3/13/2026
Your role’s AI Resilience Score is
74.7%
Median Score
Changing Fast
Evolving
Stable
This reflects the reliability of your score based on the number of data sources available for this career and how closely those sources agree on the outlook. A higher confidence means more consistent evidence from labor experts and AI models.
What does this resilience result mean?
These roles are expected to remain steady over time, with AI supporting rather than replacing the core work.
AI Resilience Report for
Floor Layers, Except Carpet, Wood, and Hard Tiles
They install and finish soft flooring materials like vinyl or linoleum to create smooth, durable surfaces in homes and buildings.
This role is stable
The career of Floor Layers, Except Carpet, Wood, and Hard Tiles is considered stable because it heavily relies on human skills like judgment, measurement, and hands-on craftsmanship that machines can't fully replicate. While robots can help with planning or identifying floor types, they struggle with the unpredictable and varied conditions at real job sites.
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View analysisChat with CoachLatest newsMore career infoAnalysisChatNewsMoreThis role is stable
The career of Floor Layers, Except Carpet, Wood, and Hard Tiles is considered stable because it heavily relies on human skills like judgment, measurement, and hands-on craftsmanship that machines can't fully replicate. While robots can help with planning or identifying floor types, they struggle with the unpredictable and varied conditions at real job sites.
Read full analysisContributing Sources
We aggregate scores from multiple models and supplement with employment projections for a more accurate picture of this occupation’s resilience. Expand to view all sources.
AI Resilience
AI Resilience Model v1.0
AI Task Resilience
CareerVillage's proprietary model that estimates how resilient each occupation's tasks are to AI automation and augmentation
Stable90.6%
90.6%
Microsoft's Working with AI
AI Applicability
Measures how applicable AI tools (like Bing Copilot) are to each occupation based on real usage patterns
Stable94.8%
94.8%
Will Robots Take My Job
Automation Resilience
Estimates the probability of automation for each occupation based on research from Oxford University and other academic sources
Evolving45.7%
45.7%
Althoff & Reichardt
Economic Growth
Measured as "Wage bill" which is a long term projection for average wage × employment. It's the total labor income flowing to an occupation
Evolving69.5%
69.5%
Medium Demand
Labor Market Outlook
We use BLS employment projections to complement the AI-focused assessments from other sources.
Learn about this scoreGrowth Rate (2024-34):
9.5%
Growth Percentile:
91.9%
Annual Openings:
2,700
Annual Openings Pct:
27.2%
Analysis of Current AI Resilience
Floor Layers (except CWH)
Updated Quarterly • Last Update: 2/17/2026
What's changing and what's not
Today, most floor-covering work is still done by people. Little of this job is fully automated. In labs and factories, researchers have built robots that can lay floor tiles faster than humans.
For example, one study found an autonomous tile‐laying robot could cover floors about 40% faster than a human team while matching quality [1]. This shows smart machines can handle repetitive, planned layouts. In practice, though, those devices apply mostly to tile or similar systems, not the vinyl or rubber floors in this job.
On real sites, conditions are unpredictable. Construction experts note that each jobsite is unique and changes a lot, which makes full automation hard [2].
In the shop and on the job, workers use technology to help but still rely on manual skill. Many installers use computer-aided design (CAD) programs to plan seam lines and layouts [3], and laser levels to draw straight guide lines, but people still cut the materials and apply glue by hand. Some robots can recognize floor patterns or textures – one experiment taught a robot to identify over 130 floor types with 95% accuracy [4] – but identifying a surface is different from actually laying it.
In short, today’s AI tends to assist (in planning or sensing), while the core tasks of cutting, smoothing, and adhering floors remain mostly manual. Until very advanced systems appear, floor layers continue shaping and finishing floors by hand [1] [2].
Sources
AI in the real world
Will this change soon? There are mixed pressures. On the plus side, the construction industry faces worker shortages and higher labor costs [1] [2].
Big tech firms note that companies are beginning to adopt AI-driven robots to help address labor gaps and boost efficiency [5]. In theory, a robot could take on repetitive tasks like spreading concrete or laying uniform panels. However, there are strong reasons for slow adoption.
Floor layers often work in cramped, varied environments (homes, offices, factories) – not a tidy factory floor – so building a robot to handle every twist is very hard [2]. The cost of a special robot is high, and on small jobs it may not pay off compared to a human crew.
Social and safety factors also matter: many homeowners and contractors expect the flooring to be done by a skilled person. Legal and quality concerns (nobody wants a crooked floor) make companies careful. In practice, the industry is more likely to use AI to help with design, scheduling, or safety, rather than replace workers outright.
In short, AI tools and machines are slowly creeping in, but the human skills of measurement, judgement, and hands-on finishing are still very valuable. Floor-layers today can feel confident that their craftsmanship is still needed while keeping an eye on new ways technology might assist their work [1] [5].
Sources
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More Career Info
Career: Floor Layers, Except Carpet, Wood, and Hard Tiles
Parent Careers
Employment & Wage Data
Median Wage
$54,340
Jobs (2024)
33,700
Growth (2024-34)
+9.5%
Annual Openings
2,700
Education
No formal educational credential
Experience
None
Source: Bureau of Labor Statistics, Employment Projections 2024-2034
Task-Level AI Resilience Scores
AI-generated estimates of task resilience over the next 3 years
1
Inspect surface to be covered to ensure that it is firm and dry.
2
Heat and soften floor covering materials to patch cracks or fit floor coverings around irregular surfaces, using blowtorch.
3
Roll and press sheet wall and floor covering into cement base to smooth and finish surface, using hand roller.
4
Cut flooring material to fit around obstructions.
5
Remove excess cement to clean finished surface.
6
Trim excess covering materials, tack edges, and join sections of covering material to form tight joint.
7
Disconnect and remove appliances, light fixtures, and worn floor and wall covering from floors, walls, and cabinets.
Tasks are ranked by their AI resilience, with the most resilient tasks shown first. Core tasks are essential functions of this occupation, while supplemental tasks provide additional context.
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