2026 Global: Artificial Intelligence (Ai) In Manufacturing Market-Competitive Review (2032) report
Description
The 2026 Global: Artificial Intelligence (Ai) In Manufacturing Market-Competitive Review (2031) report features the global market size and projected growth/decline data for the period 2021 through 2032. The report primarily provides an examination of the business strategies for the ten largest global companies in the market and how their strategies differ.
Perry/Hope Partners' reports provide the most accurate industry forecasts based on our proprietary economic models. Our forecasts project the product market size nationally and by regions for 2021 to 2032 using regression analysis in our modeling. and Perry/Hope is the only market research publisher that utilizes both longitudinal (historical) and vertical (from market section to market division to market class) analysis, since we study every manufactured product in the countries we analyze. The report also provides written analysis on the market definition, market segments, and SWOT analysis (market strengths, weaknesses, opportunities, and threats).
The market study aims at estimating the market size and the growth potential of this market. Topics analyzed within the report include a detailed breakdown of the global markets for artificial intelligence (ai) in manufacturing market by geography and historical trend. The scope of the report extends to sizing of the artificial intelligence (ai) in manufacturing market market and global market trends with market data for 2024 as the base year, 2025 and 2026 as the estimate years with projection of CAGR from 2027 to 2032.
The report also features a list of the top ten largest global players in the market. A review of each company includes 1) an estimate of the market share, 2) a listing of the products and/or services in the market, and 3) the features of these products and/or services in the market. The report has a chapter on Comparative Business Strategies for the largest four players. An example of the Comparative Business Strategies analysis would be -- How does Netflix's business strategy to expand its market share in the global online streaming compare to Amazon Prime's business strategy through its video products and services?
The ten market players in this report and a brief synopsis of their participation in the market are:
OpenAI, Microsoft, NVIDIA, Siemens, ABB, Bosch, Rockwell Automation, FANUC, Cognex, and Honeywell are among the ten major companies shaping the Artificial Intelligence in Manufacturing market, each contributing distinct AI-enabled hardware, software, and services that optimize production, quality, and supply chains.
OpenAI supplies advanced large language models (LLMs) and generative AI that manufacturers integrate as copilots for process documentation, knowledge management, and decision support, accelerating problem diagnosis and operator assistance; its models are widely licensed for enterprise applications across industries. Microsoft embeds AI across Azure cloud services, industrial digital twins, and productivity suites, enabling manufacturers to deploy scalable AI training, inference, and MLOps pipelines alongside OPC/IoT integrations and domain-specific copilots that connect shop-floor telemetry to enterprise systems. NVIDIA provides the computation backbone for factory AI through GPUs, accelerated systems (DGX), and AI software stacks (CUDA, Triton, Isaac/Omniverse) used for training computer vision, predictive maintenance, and simulation workloads in manufacturing.
Siemens combines industrial automation expertise with AI-driven digital twins, edge analytics, and Xcelerator software to deliver model-based optimization, predictive maintenance, and virtual commissioning across discrete and process industries. ABB integrates AI into robotics, motion control, and process automation to enable adaptive control, vision-guided assembly, and autonomous material handling systems that increase throughput and flexibility. Bosch offers embedded AI for industrial IoT, sensor fusion, and edge analytics alongside software platforms that turn machine data into actionable insights for quality control, energy optimization, and traceability.
Rockwell Automation pairs control hardware with AI-enabled FactoryTalk software and analytics services to operationalize anomaly detection, asset health scoring, and production optimization inside MES and PLC ecosystems. FANUC embeds AI in industrial robots and vision systems to improve precision, real-time force/vision control, and predictive maintenance across high-volume manufacturing lines. Cognex specializes in AI-powered machine vision—deploying deep-learning inspection, OCR, and 3D vision solutions that automate defect detection, part identification, and robotic guidance in electronics, automotive, and logistics. Honeywell delivers AI-driven automation, process optimization software, and industrial cybersecurity for manufacturing, with domain solutions for process industries that combine process models, advanced control, and predictive analytics to reduce downtime and improve yield.
Together these companies span the stack—chips and acceleration (NVIDIA), cloud and platform services (Microsoft, OpenAI), industrial software and digital twins (Siemens, Rockwell, Honeywell), robotics and motion (ABB, FANUC), embedded sensors and edge AI (Bosch), and machine vision (Cognex)—enabling manufacturers to deploy AI for predictive maintenance, quality inspection, process optimization, autonomous material handling, and workforce augmentation at scale.
Perry/Hope Partners' reports provide the most accurate industry forecasts based on our proprietary economic models. Our forecasts project the product market size nationally and by regions for 2021 to 2032 using regression analysis in our modeling. and Perry/Hope is the only market research publisher that utilizes both longitudinal (historical) and vertical (from market section to market division to market class) analysis, since we study every manufactured product in the countries we analyze. The report also provides written analysis on the market definition, market segments, and SWOT analysis (market strengths, weaknesses, opportunities, and threats).
The market study aims at estimating the market size and the growth potential of this market. Topics analyzed within the report include a detailed breakdown of the global markets for artificial intelligence (ai) in manufacturing market by geography and historical trend. The scope of the report extends to sizing of the artificial intelligence (ai) in manufacturing market market and global market trends with market data for 2024 as the base year, 2025 and 2026 as the estimate years with projection of CAGR from 2027 to 2032.
The report also features a list of the top ten largest global players in the market. A review of each company includes 1) an estimate of the market share, 2) a listing of the products and/or services in the market, and 3) the features of these products and/or services in the market. The report has a chapter on Comparative Business Strategies for the largest four players. An example of the Comparative Business Strategies analysis would be -- How does Netflix's business strategy to expand its market share in the global online streaming compare to Amazon Prime's business strategy through its video products and services?
The ten market players in this report and a brief synopsis of their participation in the market are:
OpenAI, Microsoft, NVIDIA, Siemens, ABB, Bosch, Rockwell Automation, FANUC, Cognex, and Honeywell are among the ten major companies shaping the Artificial Intelligence in Manufacturing market, each contributing distinct AI-enabled hardware, software, and services that optimize production, quality, and supply chains.
OpenAI supplies advanced large language models (LLMs) and generative AI that manufacturers integrate as copilots for process documentation, knowledge management, and decision support, accelerating problem diagnosis and operator assistance; its models are widely licensed for enterprise applications across industries. Microsoft embeds AI across Azure cloud services, industrial digital twins, and productivity suites, enabling manufacturers to deploy scalable AI training, inference, and MLOps pipelines alongside OPC/IoT integrations and domain-specific copilots that connect shop-floor telemetry to enterprise systems. NVIDIA provides the computation backbone for factory AI through GPUs, accelerated systems (DGX), and AI software stacks (CUDA, Triton, Isaac/Omniverse) used for training computer vision, predictive maintenance, and simulation workloads in manufacturing.
Siemens combines industrial automation expertise with AI-driven digital twins, edge analytics, and Xcelerator software to deliver model-based optimization, predictive maintenance, and virtual commissioning across discrete and process industries. ABB integrates AI into robotics, motion control, and process automation to enable adaptive control, vision-guided assembly, and autonomous material handling systems that increase throughput and flexibility. Bosch offers embedded AI for industrial IoT, sensor fusion, and edge analytics alongside software platforms that turn machine data into actionable insights for quality control, energy optimization, and traceability.
Rockwell Automation pairs control hardware with AI-enabled FactoryTalk software and analytics services to operationalize anomaly detection, asset health scoring, and production optimization inside MES and PLC ecosystems. FANUC embeds AI in industrial robots and vision systems to improve precision, real-time force/vision control, and predictive maintenance across high-volume manufacturing lines. Cognex specializes in AI-powered machine vision—deploying deep-learning inspection, OCR, and 3D vision solutions that automate defect detection, part identification, and robotic guidance in electronics, automotive, and logistics. Honeywell delivers AI-driven automation, process optimization software, and industrial cybersecurity for manufacturing, with domain solutions for process industries that combine process models, advanced control, and predictive analytics to reduce downtime and improve yield.
Together these companies span the stack—chips and acceleration (NVIDIA), cloud and platform services (Microsoft, OpenAI), industrial software and digital twins (Siemens, Rockwell, Honeywell), robotics and motion (ABB, FANUC), embedded sensors and edge AI (Bosch), and machine vision (Cognex)—enabling manufacturers to deploy AI for predictive maintenance, quality inspection, process optimization, autonomous material handling, and workforce augmentation at scale.
Table of Contents
32 Pages
- 1.0 Scope of Report and Methodology
- 2.0 Market SWOT Analysis and Players
- 2.1 Market Definition
- 2.2 Market Segments
- 2.3 Market Strengths
- 2.4 Market Weaknesses
- 2.5 Market Threats
- 2.6 Market Opportunities
- 2.7 Major Players
- 3.0 Competitive Analysis
- 3.1 Market Player 1
- 3.2 Market Player 2
- 3.3 Market Player 3
- 3.4 Market Player 4
- 3.5 Market Player 5
- 3.6 Market Player 6
- 3.7 Market Player 7
- 3.8 Market Player 8
- 3.9 Market Player 9
- 3.10 Market Player 10
- 4.0 Comparative Business Strategies
- 4.1 Comparative Business Strategies of Player 1 and 2
- 4.2 Comparative Business Strategies of Player 1 and 3
- 4.3 Comparative Business Strategies of Player 1 and 4
- 4.4 Comparative Business Strategies of Player 2 and 3
- 4.5 Comparative Business Strategies of Player 2 and 4
- 4.6 Comparative Business Strategies of Player 3 and 4
- 5.0 Appendix
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