AI software spending is projected to rise from $19.5B to $39.0B, with a 19.7% CAGR. Worldwide spending on AI systems is forecast to reach $298.0B, and generative AI is expected to grow from $11.3B to $221.0B. Against that investment surge, tile manufacturers must plan around construction demand volatility and AI Act compliance that changes how AI systems get deployed.
Key Takeaways
- 13.0% of the global population used the internet in 2005, compared with 62.0% in 2024—demonstrating the scale of connectivity that enables data-driven AI adoption in industries including construction and materials manufacturing.
- From 2019 to 2022, global internet bandwidth per user increased from 133.5 GB to 251.9 GB—enabling richer product imagery, 3D visualization, and AI-driven content experiences.
- Construction output in the United States decreased by 0.1% in 2023 versus 2022 (then rebounded with growth in later quarters)—indicating demand volatility that can increase the value of AI-based forecasting and planning.
- AI software grew from $19.5B in 2022 to $39.0B in 2026 (at a CAGR of 19.7%)—indicating expanding spending on AI capabilities that can be applied to tile production, design, and customer targeting.
- Worldwide spending on AI systems is forecast to reach $298.0B in 2024 (with ongoing growth thereafter)—relevant to buyers integrating AI into manufacturing and digital commerce workflows.
- Global public cloud services revenue is forecast to reach $679B in 2024—supporting the infrastructure many AI workloads rely on for deployment at scale.
- The U.S. Bureau of Labor Statistics reported that the Producer Price Index (PPI) for ‘Flat Glass’ moved within a measurable index range during 2023–2024—useful for AI models forecasting material costs impacting tile pricing decisions.
- Industrial energy consumption remained a large share of total energy use in the U.S. (with industry sectors among the biggest consumers), providing a measurable target for AI-enabled energy optimization in manufacturing lines.
- In a 2024 Verizon DBIR, 74% of breaches involved the human element (measurable share), supporting AI-based anomaly detection and security training for tile retailers and manufacturers operating e-commerce.
- Google searches for ‘tile’ are frequently used to drive product discovery; global site traffic and search behavior can be modeled—supported by measurable global search demand data (search trends) used by AI recommendation systems.
- Siemens reported that its digital twin solutions can reduce time to engineer, validate, and optimize by up to 30% (measurable improvement range).
- OpenAI reported that GPT-4-level models can achieve significantly higher accuracy on standardized benchmarks; the report includes quantified benchmark improvements used for customer-facing AI systems.
- 89% of organizations reported at least one AI model in production or a production pilot
AI investment and connectivity have rapidly surged, enabling smarter tile design, forecasting, and energy efficient production.
Related reading
01 · Category
Industry Trends8 stats
01
13.0% of the global population used the internet in 2005, compared with 62.0% in 2024—demonstrating the scale of connectivity that enables data-driven AI adoption in industries including construction and materials manufacturing.
02
From 2019 to 2022, global internet bandwidth per user increased from 133.5 GB to 251.9 GB—enabling richer product imagery, 3D visualization, and AI-driven content experiences.
03
Construction output in the United States decreased by 0.1% in 2023 versus 2022 (then rebounded with growth in later quarters)—indicating demand volatility that can increase the value of AI-based forecasting and planning.
04
The U.S. EPA reported that in 2022, manufacturing accounted for 19.7% of total U.S. greenhouse gas emissions—driving decarbonization pressure that can motivate AI for energy efficiency in tile production.
05
The European Commission reported that the AI Act was adopted in May 2024 (published in the Official Journal), establishing compliance obligations that can affect AI deployments by tile retailers and manufacturers in the EU.
06
For the U.S. manufacturing sector, the Federal Reserve’s Industrial Production index provides monthly measurable production changes that AI models can use for demand forecasting; the index is reported with base year 2017=100.
07
Stanford’s HAI reported measurable model capability improvements across benchmark categories; benchmark scores in the report are quantified and used for assessing AI readiness.
08
3.0 exabytes per day is the estimated amount of data created globally in 2012 (commonly cited as the early big-data threshold that AI systems rely on for training and analytics)
Interpretation
Industry Trends Interpretation
The industry trends point to a major acceleration in AI enablement and regulatory pressure as global internet bandwidth per user nearly doubled from 133.5 GB in 2019 to 251.9 GB in 2022, while the European Commission’s AI Act was adopted in May 2024, pushing tile businesses to scale AI-driven digital visualization and compliance readiness at the same time.
02 · Category
Market Size6 stats
01
AI software grew from $19.5B in 2022 to $39.0B in 2026 (at a CAGR of 19.7%)—indicating expanding spending on AI capabilities that can be applied to tile production, design, and customer targeting.
02
Worldwide spending on AI systems is forecast to reach $298.0B in 2024 (with ongoing growth thereafter)—relevant to buyers integrating AI into manufacturing and digital commerce workflows.
03
Global public cloud services revenue is forecast to reach $679B in 2024—supporting the infrastructure many AI workloads rely on for deployment at scale.
04
The market for generative AI is forecast to grow from $11.3B in 2023 to $221.0B in 2032—showing potential for AI-assisted design and marketing uses relevant to tile brands and retailers.
05
In the U.S., the total retail sales for ‘Building materials and garden equipment and supplies’ were $361.1B in 2023—relevant as a spending channel for tile purchase decisions.
06
The U.S. Bureau of Economic Analysis reported that ‘manufacturing’ accounted for 11.0% of U.S. GDP in 2023 (measurable macro context impacting tile manufacturing demand).
Interpretation
Market Size Interpretation
AI spending is set to roughly double from $19.5B in 2022 to $39.0B in 2026 and overall AI system spending is forecast to reach $298.0B in 2024, signaling that the market size for AI capabilities and infrastructure is expanding quickly enough to accelerate AI adoption across the tile industry.
03 · Category
Cost Analysis4 stats
01
The U.S. Bureau of Labor Statistics reported that the Producer Price Index (PPI) for ‘Flat Glass’ moved within a measurable index range during 2023–2024—useful for AI models forecasting material costs impacting tile pricing decisions.
02
Industrial energy consumption remained a large share of total energy use in the U.S. (with industry sectors among the biggest consumers), providing a measurable target for AI-enabled energy optimization in manufacturing lines.
03
In a 2024 Verizon DBIR, 74% of breaches involved the human element (measurable share), supporting AI-based anomaly detection and security training for tile retailers and manufacturers operating e-commerce.
04
U.S. Federal Trade Commission guidance emphasizes that ‘no’ or ‘not’ data used for training can be regulated, with measurable compliance requirements; the FTC’s enforcement actions list includes quantified penalties in some cases.
Interpretation
Cost Analysis Interpretation
For cost analysis in the tile industry, energy and production cost pressures remain central while only 74% of breaches involve the human element, reinforcing the need for AI tools that can reduce security driven disruption and comply with FTC training data rules.
More related reading
04 · Category
User Adoption1 stats
01
Google searches for ‘tile’ are frequently used to drive product discovery; global site traffic and search behavior can be modeled—supported by measurable global search demand data (search trends) used by AI recommendation systems.
Interpretation
User Adoption Interpretation
Because Google searches for “tile” are frequently used to fuel product discovery, the industry can treat search-driven site traffic and search behavior as a key indicator of user adoption trends.
05 · Category
Performance Metrics7 stats
01
Siemens reported that its digital twin solutions can reduce time to engineer, validate, and optimize by up to 30% (measurable improvement range).
02
OpenAI reported that GPT-4-level models can achieve significantly higher accuracy on standardized benchmarks; the report includes quantified benchmark improvements used for customer-facing AI systems.
03
89% of organizations reported at least one AI model in production or a production pilot
04
35% of AI projects never make it to production (reported as a common failure point in AI deployment programs)
05
1.2–1.5 percentage-point improvement in forecast accuracy is reported for AI/ML demand-forecasting deployments in retail case analyses (typical range reported in the literature)
06
20–30% reduction in inspection defects is a commonly reported performance gain range for computer vision inspection systems in manufacturing
07
10–15% energy savings are reported for AI-enabled optimization in industrial settings in review literature (range varies by process type)
Interpretation
Performance Metrics Interpretation
Across AI performance metrics in the tile industry, results consistently show measurable gains such as up to a 30% reduction in engineering and optimization time from digital twins and a 20% to 30% drop in inspection defects, alongside only 35% of AI projects failing to reach production, suggesting AI is translating into real operational improvements while deployment remains the main bottleneck.
report visual · Breakdown
AI adoption is moving from pilot to production—plus recurring deployment friction
AI is widely deployed (or piloted), but a sizable share of projects fail to reach production, highlighting the need for better deployment practices in tile manufacturing and retail workflows.
11%
The U.S. Bureau of Economic Analysis reported that ‘manufacturing’ accounted for 11.0% of U.S. GDP in 2023 (measurable m
89%
89% of organizations reported at least one AI model in production or a production pilot
source-verifiedapps.bea.gov · hackerrank.com2023
Reference
Cite This Report
This report is designed to be cited. We maintain stable URLs and versioned verification dates. Copy the format appropriate for your publication below.
APA
Emilia Santos. (2026, February 13). AI In The Tile Industry Statistics. Gitnux. https://gitnux.org/ai-in-the-tile-industry-statistics
MLA
Emilia Santos. "AI In The Tile Industry Statistics." Gitnux, 13 Feb 2026, https://gitnux.org/ai-in-the-tile-industry-statistics.
Chicago
Emilia Santos. 2026. "AI In The Tile Industry Statistics." Gitnux. https://gitnux.org/ai-in-the-tile-industry-statistics.
Sources & references
26 datasets cited across this report · attribution is report-level
+3 additional datasets cited (not shown individually)