AI in Manufacturing Market Size, Share, Forecast, & Trends Analysis by Component, Technology (ML, NLP, Computer Vision), Application (Predictive Maintenance & Machinery Inspection, Cybersecurity, Supply Chain Optimization), End-use Industry, and Geography—Global Forecast to 2031

The AI in Manufacturing Market was valued at $9.8 billion in 2023. This market is expected to reach $84.5 billion by 2031 from an estimated $11.8 billion in 2024, at a CAGR of 32.6% during the forecast period 2024-2031.

AI in Manufacturing Market Size & Forecast

Key factors driving the market’s growth include the increasing adoption of smart manufacturing and Industry 4.0 technologies, rising emphasis on predictive maintenance and quality control, and a growing demand for automation and greater efficiency across manufacturing processes. Additionally, the rise in manufacturing activities in emerging economies and the rising utilization of AI in supply chain & logistics management are expected to offer growth opportunities for industry players.

AI in Manufacturing Market Growth Drivers

Increasing Adoption of Smart Manufacturing & Industry 4.0 Technologies

Industry 4.0, also known as the Fourth Industrial Revolution, refers to the integration of advanced technologies such as Artificial Intelligence (AI), the Internet of Things (IoT), cloud computing, and cyber-physical systems into manufacturing processes. Smart manufacturing is a technology-driven approach that utilizes interconnected machines to monitor the production process. It combines advanced technologies, including artificial intelligence, robotics, IoT devices, and digital twins, which optimize manufacturing procedures through automation for minimizing costs and maximizing productivity. Deployments involve embedding sensors into manufacturing machines to enable data collection, self-monitoring, and predictive maintenance to track the status and performance of the manufacturing process, which benefits manufacturers by making the production process efficient, transparent, and flexible. The key applications of Industry 4.0 and smart manufacturing are the use of AI and machine learning algorithms to analyze vast amounts of data generated by connected devices and sensors on the factory floor. This data is used to identify patterns, predict equipment failures, and optimize production processes.

AI helps manufacturers enhance automation, efficiency, and quality control. It also helps monitor the condition of machinery and alert operators when maintenance is required, thereby reducing downtime and increasing productivity. Hence, as manufacturers seek to leverage the benefits of these technologies, they are increasingly adopting AI-powered solutions to gain a competitive edge in the rapidly evolving market. Major players across various industries are investing in AI solutions to support their Industry 4.0 and smart manufacturing initiatives.

Rising Emphasis on Predictive Maintenance & Quality Control

Predictive maintenance and quality control are crucial aspects of modern manufacturing operations that are driving the demand for AI solutions in the industry. Advanced equipment, such as IIoT and robots, generates immense data daily. Therefore, manufacturers rely on AI-based predictive maintenance to ensure minimum downtime and maximum returns on investment from their equipment. Predictive maintenance refers to the practice of monitoring the condition of machinery and equipment to predict when maintenance will be required, while quality control involves ensuring that products meet the required standards and specifications. Predictive maintenance systems gather data to generate insights that reduce downtime by predicting equipment failure. They enable self-monitoring, improve production capacity, help avoid downtime, lower maintenance costs, enhance safety, and report manufacturing issues in real time. Quality control helps ensure that products meet customer expectations and regulatory requirements. It helps companies increase customer satisfaction, reduce product returns and recalls, improve brand reputation, and comply with industry standards and regulations.

Manufacturers are leveraging AI and machine learning algorithms to analyze data from sensors, maintenance logs, and other sources to identify patterns and anomalies that indicate potential failures or degradation, allowing them to minimize disruptions in production and maximize asset utilization by scheduling maintenance activities proactively. Additionally, AI-powered solutions can be used to automate inspection and detect defects by analyzing data from various sources, including images, sensor readings, and production logs. AI systems enable manufacturers to take corrective actions promptly by identifying deviations from quality standards and assisting in root cause analysis. Hence, manufacturers are increasingly turning to AI solutions to gain a competitive edge and optimize their operations, reduce costs, and improve product quality. Major players in the market are focusing on implementing advanced AI-based intelligent solutions to minimize maintenance costs, equipment failure, and downtime.

AI in Manufacturing Market Opportunity

Rising Utilization of AI Across Various Sectors

Operational efficiency is important for optimal business performance. Low efficiency can lead to congested workflows, production, and order fulfillment, especially considering the growing scale of manufacturing operations. AI has emerged as an effective solution for the manufacturing industry due to its benefits, such as enhancing production efficiencies and bringing machine interactions closer to human interactions. AI allows manufacturers to extract & gather data, analyze patterns, and learn & adapt to new environments through machine intelligence, NLP, and computer vision technologies, enhancing production outcomes, improving process effectiveness, minimizing operational costs, and providing superior scalability & product development. For instance, according to an article published by Capgemini SE (France), computer vision allowed General Motors Company to detect more than 70 instances of component failure, preventing massive downtime, as a single minute could have cost the company up to USD 20,000. Furthermore, in 2019, Danone achieved a 30% reduction in lost sales by using machine learning to predict demand and modify its manufacturing strategies accordingly.

AI can enhance and extend human capabilities and help manufacturing companies achieve better outcomes. AI can be integrated with organizations’ existing software to analyze vast volumes of data and, at the same time, customize results. For instance, the BMW Group (Germany) uses AI to evaluate component images from its production line, which allows it to identify deviations from standards in real time. These benefits of AI are expected to create growth opportunities for market players, especially in emerging economies where manufacturing industries largely rely upon conventional techniques and technologies.

AI in Manufacturing Market Trend

Increasing Adoption of Computer Vision & Robotics in Manufacturing

Computer vision refers to technology that allows machines to interpret and process visual data from the surrounding environment, while robotics involves the use of automated machines to perform tasks traditionally done by humans. These technologies enable advanced inspection capabilities, including automated defect detection, predictive maintenance scheduling, and real-time data analysis, and help enhance product quality and compliance. Computer vision tools are used for inspection in several applications, such as assembly verification, part selection, and final inspection & tracking. As the manufacturing industry deals with the large-scale production of goods through the use of heavy-duty machinery, regular supervision & inspection are needed to prevent equipment downtime. Also, manufacturers are increasingly relying on computer vision technology to prevent damage to equipment and machinery, as manually inspecting every machine in a manufacturing plant is time-consuming, expensive, and error-prone. Robotics and automated systems help streamline production, improve efficiency, and reduce human intervention.

Additionally, AI-enabled robotics can be used to predict equipment failures before they occur, thereby minimizing downtime. AI-enabled robotics can also perform repetitive tasks faster and with greater accuracy, handle packaging and sorting, and help increase productivity in manufacturing operations. Manufacturers in the market are adopting these technologies to gain a competitive edge through improved product quality and efficiency.

AI in Manufacturing Market Analysis: Key Findings

By Component: the Hardware Segment to Dominate the Market in 2024

Based on component, the global AI in manufacturing market is segmented into hardware, software, and services segment. In 2024, the hardware segment is projected to dominate the global AI in manufacturing market with a share of 44.6%. This significant share is due to the rising adoption of AI hardware within the manufacturing sector, increased R&D investment in AI hardware development, and stringent regulations aimed at ensuring safe manufacturing practices.

However, the services segment is slated to register the highest compound annual growth rate during the forecast period. This growth is fueled by the rising use of smart manufacturing services and the shortage of skilled professionals.

By Technology: the Machine Learning Segment to Dominate the Market in 2024

Based on technology, the global AI in manufacturing market is segmented into machine learning (ML), natural language processing (NLP), context-aware computing, computer vision, speech and voice recognition, and machine reasoning. In 2024, the machine learning segment is projected to dominate the global AI in manufacturing market with a share of 67.7%. This significant share is due to the increasing use of machine learning technology for processing extensive data related to production, equipment, and products, which enhances the efficiency of time-consuming manufacturing processes, including quality control, supply chain management, equipment maintenance, and product design.

However, the natural language processing segment is slated to register the highest compound annual growth rate during the forecast period. This growth is fueled by the rising need for NLP technology to analyze machinery data for failure prediction and prevention, automate the analysis of inspection reports, ensure compliance with safety protocols through real-time monitoring, personalize services based on customer feedback, and optimize manufacturing processes through the evaluation of production data.

By Application: the Predictive Maintenance & Machinery Inspection Segment to Dominate the Market in 2024

Based on application, the global AI in manufacturing market is segmented into field services, quality management, cybersecurity, robotics & factory automation, predictive maintenance & machinery inspection, material handling, production planning, safety planning, energy management, and supply chain optimization. In 2024, the predictive maintenance & machinery inspection segment is projected to dominate the global AI in manufacturing market with a share of 20.6%. This significant share is due to the growing demand for self-monitoring systems, the need to cut costs associated with heavy equipment operation and maintenance, and the increasing requirement for real-time monitoring solutions in manufacturing facilities.

Moreover, the predictive maintenance & machinery inspection segment is slated to register the highest compound annual growth rate during the forecast period.

By End-use Industry: the Automotive Segment to Dominate the Market in 2024

Based on end-use industry, the global AI in manufacturing market is segmented into semiconductor & electronics, energy & power, pharmaceuticals, medical devices, automotive, heavy metals & machine manufacturing, fast-moving consumer goods, aerospace and defense, and other end-use industries. In 2024, the automotive segment is projected to dominate the global AI in manufacturing market with a share of 18.0%. This significant share is due to the rapid adoption of AI technologies among automotive manufacturers, a surge in demand for ADAS and autonomous vehicles, and an increasing demand for cars equipped with state-of-the-art safety features.

However, the medical devices segment is slated to register the highest compound annual growth rate during the forecast period. This growth is fueled by the rising demand for cutting-edge healthcare technologies and the need to ensure efficiency and quality in the production of medical devices.

Geographical Analysis

In 2024, Asia-Pacific to Dominate the Market in 2024

In 2024, Asia Pacific is anticipated to account for the largest share of 53.1% of the global AI in manufacturing market, followed by Europe, North America, Latin America, and the Middle East & Africa. The region’s significant market share is due to a surge in demand for automation, industrial robots, and Industry 4.0 technologies in the manufacturing sector, the growing use of cloud-based manufacturing solutions, and the presence of leading market players in Asia-Pacific.

Additionally, Asia-Pacific is slated to register the highest compound annual growth of 34.1% during the forecast period.

AI in Manufacturing Market: Key Companies

The report offers a competitive analysis based on an extensive assessment of the major players’ product portfolios and geographic presence and the key growth strategies adopted by them over the past 3–4 years. The key players operating in this market include Google LLC (A Subsidiary of Alphabet Inc.) (U.S.), International Business Machines Corporation (U.S.), Intel Corporation (U.S.), Microsoft Corporation (U.S.), NVIDIA Corporation (U.S.), Oracle Corporation (U.S.), Cisco Systems, Inc. (U.S.), Rockwell Automation, Inc. (U.S.), Amazon Web Services, Inc. (A Subsidiary of Amazon.com, Inc.) (U.S.), Siemens AG (Germany), General Electric Company (U.S.), SAP SE (Germany), Advanced Micro Devices, Inc. (U.S.), Robert Bosch GmbH (Germany), and Sight Machine Inc. (U.S.).

AI in Manufacturing Industry Overview: Latest Developments from Key Industry Players

• In April 2024, SAP SE (France) announced AI advancements in its supply chain solutions to enhance productivity, efficiency, and precision in manufacturing. AI-driven insights from real-time data will help companies use their data to make better decisions across supply chains, streamline product development, and improve manufacturing efficiency.
• In March 2024, Rockwell Automation (U.S.) opened a new Customer Experience Centre (CEC) in Singapore. The Experience Centre showcases the latest innovations in AI, robotics, and virtual reality and will help the region’s manufacturing, mining, and heavy industry sectors to embrace digital transformation.
• In June 2023, Siemens AG (Germany) collaborated with Intrinsic Innovation LLC (U.S.) to accelerate the integration of AI-based robotics and automation technology for automating and operating industrial production.
• In April 2023, Oracle Corporation (U.S.) introduced new AI and automation capabilities to help customers optimize supply chain management. The updates include new planning, usage-based pricing, and rebate management capabilities within Oracle Fusion Cloud Supply Chain & Manufacturing (SCM) and enhanced quote-to-cash processes in Oracle Fusion Applications.
• In April 2023, the Embassy of Things (U.S.), an industrial software provider, launched Generative AI to allow manufacturing and energy companies to train and test predictive maintenance models at the industrial edge.
• In September 2022, NVIDIA Corporation launched a high-precision edge platform, AI NVIDIA IGX, with advanced security and proactive safety features for industries including manufacturing, logistics, and healthcare.

AI in Manufacturing Market Research Summary

Key questions answered in the AI in manufacturing market report: