Global industrial digitalization spending reached $1.6 trillion in 2023, signaling major budgets for automation. In manufacturing, AI adoption is already measurable, with 48% of firms using predictive maintenance enabled by AI and analytics. This report separates quantified results from the constraints companies still face, including EU AI Act obligations and model bias concerns.
Key Takeaways
- $29.21 billion global AI in manufacturing market size in 2023
- $13.5 billion AI in logistics market size in 2023
- $11.0 billion computer vision market size in 2022
- 24% of manufacturing companies reported using AI for demand forecasting as of 2022
- 19% of manufacturers reported using AI for supply chain optimization in 2022
- 48% of manufacturers report using predictive maintenance enabled by AI/analytics (2022 survey)
- 10% reduction in manufacturing defects associated with AI-based visual inspection (meta-analysis reported ranges)
- 20% improvement in OEE attributable to AI-enabled predictive maintenance (industrial study)
- 15% improvement in energy efficiency reported for AI-based process optimization projects (case-study compilation)
- EU AI Act has been adopted with targeted bans and obligations for high-risk AI systems, effective timeline set in 2024 (European Commission)
- Global industrial digitalization spending reached $1.6 trillion in 2023 (OECD/World Economic Forum referenced estimate)
- 42% of manufacturing organizations are concerned about model bias when deploying AI (survey)
- 48% of organizations expect AI implementation to reduce operating costs (survey)
- Up to 60% of AI project costs can be attributed to data labeling and preparation (peer-reviewed/industry analysis)
- The median cost of a data breach was $4.88 million in 2024 (IBM Cost of a Data Breach Report)
In 2023, AI in manufacturing grew to about $29.21 billion and can cut defects, downtime, and scrap.
Related reading
01 · Category
Market Size9 stats
01
$29.21 billion global AI in manufacturing market size in 2023
02
$13.5 billion AI in logistics market size in 2023
03
$11.0 billion computer vision market size in 2022
04
$6.6 billion of AI spend in the manufacturing sector in 2024
05
$15.7 billion global generative AI market size in 2023
06
$9.6 billion spend on AI software by manufacturing organizations in 2024 (global)
07
$10.53 billion global AI in manufacturing market size in 2023
08
12.3% CAGR for the global industrial computer vision market from 2024 to 2030
09
$1.2 trillion global manufacturing spending on digital technologies in 2023
Interpretation
Market Size Interpretation
The market size signals fast expansion in secondary industry AI with manufacturing leading at $29.21 billion in 2023 and generative AI reaching $15.7 billion in 2023, while ongoing AI spend continues to rise as shown by $6.6 billion in manufacturing in 2024 and $9.6 billion on AI software by manufacturing organizations in 2024.
02 · Category
User Adoption3 stats
01
24% of manufacturing companies reported using AI for demand forecasting as of 2022
02
19% of manufacturers reported using AI for supply chain optimization in 2022
03
48% of manufacturers report using predictive maintenance enabled by AI/analytics (2022 survey)
Interpretation
User Adoption Interpretation
In user adoption, manufacturing firms are still selectively applying AI with 48% using AI or analytics for predictive maintenance in 2022, while only 24% use it for demand forecasting and 19% for supply chain optimization, showing strong take-up in operations over planning functions.
03 · Category
Performance Metrics11 stats
01
10% reduction in manufacturing defects associated with AI-based visual inspection (meta-analysis reported ranges)
02
20% improvement in OEE attributable to AI-enabled predictive maintenance (industrial study)
03
15% improvement in energy efficiency reported for AI-based process optimization projects (case-study compilation)
04
2.5x faster changeover reported with AI-assisted scheduling in manufacturing operations (vendor study)
05
25% reduction in scrap rates from AI-based inline defect detection (peer-reviewed study)
06
33% improvement in yield reported by AI-enabled process control in semiconductor manufacturing (industry report)
07
5-10% reduction in inventory levels from AI-enabled demand sensing (peer-reviewed operations research)
08
16% reduction in unplanned downtime from AI-enabled predictive maintenance (systematic review, 2019–2022 evidence range)
09
18% reduction in energy intensity with AI/ML control strategies in process industries (meta-analysis)
10
14% improvement in forecasting accuracy (MAPE) with AI-based demand forecasting models versus baseline methods (peer-reviewed evaluation study)
11
2.7% reduction in scrap rate with AI-enabled defect classification compared with rule-based systems (comparative study)
Interpretation
Performance Metrics Interpretation
Across performance metrics, AI is consistently delivering measurable operational gains, ranging from a 10% drop in manufacturing defects up to a 33% yield improvement in semiconductors, with major wins in scrap reduction, OEE, and faster changeovers.
More related reading
04 · Category
Industry Trends3 stats
01
EU AI Act has been adopted with targeted bans and obligations for high-risk AI systems, effective timeline set in 2024 (European Commission)
02
Global industrial digitalization spending reached $1.6 trillion in 2023 (OECD/World Economic Forum referenced estimate)
03
42% of manufacturing organizations are concerned about model bias when deploying AI (survey)
Interpretation
Industry Trends Interpretation
With the EU AI Act adopted and bringing high risk AI obligations into focus in 2024 while global industrial digitalization spending hits $1.6 trillion in 2023, manufacturers also report that 42% are concerned about model bias, underscoring that industry trends are shifting toward responsible AI deployment as budgets and regulatory timelines accelerate.
05 · Category
Cost Analysis5 stats
01
48% of organizations expect AI implementation to reduce operating costs (survey)
02
Up to 60% of AI project costs can be attributed to data labeling and preparation (peer-reviewed/industry analysis)
03
The median cost of a data breach was $4.88 million in 2024 (IBM Cost of a Data Breach Report)
04
Training cost volatility: GPU compute costs for large model training can exceed $10 million per run for frontier-scale models (open technical report)
05
Energy is a major component of industrial AI operating costs: AI training can require tens to hundreds of MWh per large run (academic survey)
Interpretation
Cost Analysis Interpretation
Cost analysis shows that organizations expect AI to cut operating costs, with 48% anticipating lower expenses, yet the biggest spending pressures come from up to 60% of AI project costs tied to data labeling and preparation and from training energy and compute that can reach tens to hundreds of MWh and exceed $10 million per frontier-scale run.
report visual · Key figures
AI adoption and impact in manufacturing (survey & study metrics)
Manufacturers report AI use for planning and maintenance while studies show measurable performance gains from AI-enabled operations.
24%
24% of manufacturing companies reported using AI for demand forecasting as of 2022
19%
19% of manufacturers reported using AI for supply chain optimization in 2022
48%
48% of manufacturers report using predictive maintenance enabled by AI/analytics (2022 survey)
20%
20% improvement in OEE attributable to AI-enabled predictive maintenance (industrial study)
16%
16% reduction in unplanned downtime from AI-enabled predictive maintenance (systematic review, 2019–2022 evidence range)
source-verifiedstatista.com · ibm.com · sciencedirect.com · doi.org2022
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
Helena Kowalczyk. (2026, February 13). AI In The Secondary Industry Statistics. Gitnux. https://gitnux.org/ai-in-the-secondary-industry-statistics
MLA
Helena Kowalczyk. "AI In The Secondary Industry Statistics." Gitnux, 13 Feb 2026, https://gitnux.org/ai-in-the-secondary-industry-statistics.
Chicago
Helena Kowalczyk. 2026. "AI In The Secondary Industry Statistics." Gitnux. https://gitnux.org/ai-in-the-secondary-industry-statistics.
Sources & references
31 datasets cited across this report · attribution is report-level
+10 additional datasets cited (not shown individually)