The global artificial intelligence in manufacturing market size was valued at USD 4.10 billion in 2023 and is projected to grow at a compound annual growth rate (CAGR) of 44.2% from 2024 to 2030. The rapid progress of artificial intelligence (AI) within the manufacturing sector is gaining momentum through the implementation of sophisticated technological innovations including analytics, augmented reality, virtual reality, smart packaging, and additive manufacturing within production facilities. Sectors presently undergoing their digital transformation are anticipated to embrace AI-driven services in the foreseeable future. The resilience of manufacturing firms and their increasing demand for sustainable solutions stand as pivotal catalysts augmenting the expansion of AI adoption within the manufacturing industry.
The fourth industrial revolution, Industry 4.0, is centered on the application of cyber-physical models and digital integration across the linked value chain. This fundamental change has accelerated the development of advanced technological capabilities and the widespread proliferation of digital ecosystems, particularly in the industrial sector. The growing digital revolution in manufacturing not only creates new opportunities in a variety of fields but also increases efficiency by tapping pre-existing values more efficiently. This transition correlates with the adoption of artificial intelligence in the manufacturing sector, enabling innovation and efficiency benefits throughout production processes.
The advancement of automation within the manufacturing sector and the increasing demand for integrating big data are driving the expansion of AI in the manufacturing market. Additionally, the extensive utilization of machine vision cameras in various manufacturing applications including machinery inspection, material movement, field service, and quality control is a significant driver propelling the growth of AI in manufacturing. Furthermore, major market players are implementing diverse strategies, such as introducing new products and fostering innovation, to broaden their product portfolios and uphold competitiveness in the rapidly evolving market. For instance, in April 2023, Databricks launched the Databricks Lakehouse specifically tailored for manufacturers, featuring pre-developed AI solutions and a diverse array of applications. Major market players across the world, including DuPont, Honeywell, Rolls Royce, and Shell have adopted the Databricks platform.
AI in manufacturing market exhibits a high level of innovation, accompanied by a rapid pace of market growth. This market is characterized by continuous advancements in technology, primarily fueled by progress in machine learning algorithms, the accessibility of vast datasets, and the continual augmentation of computing capabilities. Consequently, this environment fosters a consistent stream of pioneering AI applications, frequently challenging existing industry norms and paving the way for the creation of entirely new sectors within the market landscape.
Top players in the market engage in a substantial level of merger and acquisition (M&A) activities. This trend is propelled by comparable drivers, encompassing the aspiration to access fresh AI technologies and skilled talent, the imperative to consolidate within a swiftly expanding market, and the rising strategic significance attributed to AI applications within manufacturing operations.
The market also witnesses escalating regulatory scrutiny, primarily prompted by apprehensions regarding potential adverse effects of AI technology, including issues like algorithmic bias, privacy breaches, and workforce displacement. Governments globally are actively formulating regulatory frameworks to oversee the progression and implementation of AI in manufacturing. These emerging regulations hold the potential to profoundly influence the market by shaping the trajectory of AI technology development and its adoption within manufacturing processes.
End-user concentration remains a pivotal factor within the market, owing to diverse industries propelling the demand for AI solutions. The consolidation of this demand within a handful of end-user industries presents opportunities for companies specializing in crafting AI solutions tailored to these specific manufacturing sectors. However, this scenario simultaneously poses challenges for companies endeavoring to establish their presence and compete effectively in an increasingly saturated market environment for AI solutions within manufacturing.
The hardware segment led the market and accounted for 42.1% of the global revenue in 2023. Developments in dedicated AI chips and processors played a crucial role in the manufacturing industry. These hardware advancements are tailored to meet the specific computational requirements of AI algorithms, resulting in quicker and more effective processing of complicated datasets. Businesses allocate resources to create specialized hardware specifically optimized for tasks related to machine learning, which consequently improves efficiency and allows for the implementation of more advanced AI applications within manufacturing settings. These specialized chips are instrumental in managing complex neural network computations, thus facilitating the progression of AI-centric operations across the industry.
Software solutions offer extensive applicability across a diverse spectrum of production processes owing to their exceptional versatility and adaptability. Given its innate flexibility, software stands out as the optimal choice for a sector characterized by varied requirements. The swift development, testing, and deployment capabilities inherent in software enable rapid implementation—a crucial advantage in the manufacturing landscape. This adaptability proves indispensable in an industry that necessitates prompt responses to fluctuations in the market and technological advancements. Furthermore, software integration into existing industrial machinery and workflows is notably straightforward and seamless.
Machine learning technology accounted for the largest market revenue share in 2023. Machine learning algorithms have significantly transformed predictive maintenance in the manufacturing sector. Through the analysis of past equipment data, these algorithms have demonstrated the ability to predict potential machinery failures before they manifest. This proactive method has empowered manufacturers to schedule maintenance activities more effectively, thereby avoiding unexpected downtimes and maximizing equipment efficiency. The adoption of machine learning for predictive maintenance represents a shift from reactive to proactive strategies, resulting in cost efficiencies and improved overall operational dependability across manufacturing facilities.
Computer vision technology is expected to register the fastest CAGR during the forecast period. Artificial intelligence integrated with computer vision techniques enhances task efficiency. Through computer vision, robots gain improved understanding of their surroundings within factory premises, enabling safer navigation around humans. In smart manufacturing environments, AI-driven computer vision aids in detecting faults and flaws in product outcomes, subsequently streamlining factory workflows. For instance, Micron Technology Inc. specializes in crafting precise memory technology on silicon wafers, a process prone to imperceptible defects. To address this challenge, the company has implemented AI-based computer vision methods in its manufacturing facilities, effectively identifying defects and significantly enhancing manufacturing efficiency and effectiveness.
Production planning application accounted for the largest market revenue share in 2023. AI-powered production planning systems are transforming demand prediction by incorporating sophisticated predictive analytics. These systems employ machine learning algorithms to scrutinize historical data, market trends, and diverse influential variables for generating precise forecasts of demand. Leveraging these insights, manufacturers optimized inventory levels, refined production schedules, and allocated resources with greater effectiveness, culminating in cost reductions and heightened operational efficiency.
The predictive maintenance & machinery inspection segment is expected to register the fastest CAGR during the forecast period. AI-powered predictive maintenance systems place a growing emphasis on remote monitoring and diagnostics. Employing AI algorithms, manufacturers were able to remotely observe equipment status, identify irregularities, and promptly diagnose potential problems in live scenarios. This emerging trend facilitates proactive decision-making, enabling timely maintenance or repairs to be conducted without the necessity for physical inspection, thereby mitigating disruptions in production operations.
The medical device segment led the market in 2023. AI holds significant potential in the medical device manufacturing sector due to its technology, encompassing quality control, yield optimization, and predictive maintenance, among other functionalities. Implementing machine learning and computing enhances engineers' roles by enabling learning from errors and improving precision. Medical device manufacturers can employ AI in diverse ways, including ensuring device quality and predictive maintenance based on data analysis. For example, AI predictive maintenance technology is employed to inspect a medical device for any potential issues. If the algorithm detects usability concerns, the device is prevented from proceeding to the engineering department.
The automobile sector is projected to witness the highest growth rate over the forecast period. The automobile sector experienced a significant amalgamation of AI technology with advanced connectivity elements in vehicles. AI-based systems were smoothly incorporated alongside sophisticated vehicle connectivity technologies, stimulating enhanced communication among vehicles, infrastructure, and various devices. This integration led to remarkable progress in vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication, coupled with the introduction of sophisticated AI assistants within vehicles. These improvements were geared toward enhancing the driving experience by enabling instantaneous traffic updates, predictive navigation, and tailored support for drivers, ultimately aiming to ensure safer and more streamlined journeys.
North America dominated the market and accounted for 33.9% share in 2023. The regional market's revenue growth is propelled by the presence of top-tier companies producing high-performance hardware components essential for running advanced AI models. The U.S. government's National Strategy for Advanced Manufacturing serves as a strategic blueprint outlining initiatives to revitalize the manufacturing sector, fortify national supply chains, bolster national security, and encourage investments in research and development. This strategy prioritizes research endeavors encompassing machine learning, data privacy, encryption, and risk assessment, aiming to facilitate the integration of AI within manufacturing processes. Moreover, it seeks to institute industry-wide standards, best practices, and software solutions to foster innovative business models that harness production data while ensuring data security and safeguarding intellectual property rights.
Asia Pacific is anticipated to witness significant growth in the artificial intelligence in manufacturing market. The Asia Pacific region made significant strides in advancing smart manufacturing in line with Industry 4.0 principles. The countries such as China, Japan, India in this region focused on leveraging AI-driven technologies to digitalize and enhance production procedures. With a strong emphasis on integrating IoT devices, AI analytics, and cyber-physical systems, the objective was to establish sophisticated factories capable of adaptive manufacturing, predictive analysis, and immediate data-informed decision-making. This strategic move positioned the region as a leader in technological innovation within the manufacturing sector.
The following are the leading companies in the artificial intelligence in manufacturing market. These companies collectively hold the largest market share and dictate industry trends. Financials, strategy maps & products of these artificial intelligence in manufacturing companies are analyzed to map the supply network.
Some of the key players operating in the market include Microsoft Corporation; IBM Corporation, Amazon.com, Inc.; and Google LLC (Alphabet Inc.)
Microsoft Corporation offers a wide range of AI capabilities and solutions, such as industrial Internet of Things, cloud computing, AI, and other smart manufacturing technologies. It also offers Azure Time Series Insights and Azure Digital Twins & Simulation, which helps in generating data representations of products, assets, and factories and helps in improving the process.
The IBM Maximo Application Suite uses AI algorithms to evaluate equipment performance and forecast maintenance requirements in industrial environments. The primary objective of the AI applications in this suite is to study correlations between asset reliability and various maintenance procedures to improve equipment uptime, reduce breakdowns, and increase overall operational productivity.
Spark Cognition Inc., General Vision Inc., and Sight Machine. are some of the emerging market participants in the market.
Spark Cognition specializes in AI-driven predictive analytics, focusing on various industries. They provide AI-powered predictive maintenance solutions that utilize machine learning algorithms to forecast equipment failure and optimize operations in manufacturing plants. This helps in reducing downtime, enhance productivity, and improve safety by analyzing data from sensors and other sources to predict potential issues before they occur.
General Vision Inc. offers machine vision systems that use AI algorithms for quality control, defect detection, and automated inspection processes in manufacturing facilities. Their technology enables high-speed and precise inspection, improving product quality and production efficiency in various manufacturing sectors.
In October 2023, Google Cloud introduced specialized Generative AI solutions tailored for healthcare and manufacturing industries, with the objective of improving efficiency and facilitating digital transformation. This initiative signified a significant stride in harnessing AI for industry-specific progress.
In April 2023, Siemens collaborated with Microsoft to enhance industrial AI, transforming the management of product lifecycles. The integration of Siemens' Teamcenter software with Microsoft Teams and Azure OpenAI Service's language models aims to boost innovation and effectiveness. This collaboration facilitated smooth cross-departmental cooperation, propelling progress in design, engineering, manufacturing, and product operations, representing a substantial advancement in integrating industrial technologies.
Market size value in 2024
USD 5.32 billion
Revenue forecast in 2030
USD 47.88 billion
CAGR of 44.2% from 2024 to 2030
Base year for estimation
2017 - 2022
2024 - 2030
Revenue in USD million/billion and CAGR from 2024 to 2030
Revenue forecast, company ranking, competitive landscape, growth factors, and trends
Component, technology, application, end-use, region
North America; Europe; Asia Pacific; Latin America; MEA
U.S.; Canada; Germany; U.K.; France; China; Japan; India; South Korea; Australia; Brazil; Mexico; KSA; UAE; South Africa
Key companies profiled
Amazon Web Services; Cisco Systems Inc; General Electric Company; Google LLC (Alphabet Inc.); IBM Corporation; Intel Corporation; Microsoft Corporation; Mitsubishi Electric Corporation; NVIDIA Corporation; Oracle Corporation; Rethink Robotics; Rockwell Automation Inc; SAP SE; Siemens AG
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This report forecasts revenue growth at global, regional, and country levels and provides an analysis of the latest industry trends in each of the sub-segments from 2017 to 2030. For this study, Grand View Research has segmented the global artificial intelligence in manufacturing market report based on component, technology, application, end-use, and region.
Component Outlook (Revenue, USD Million, 2017 - 2030)
Technology Outlook (Revenue, USD Million, 2017 - 2030)
Machine Learning (ML)
Natural Language Processing
Application Outlook (Revenue, USD Million, 2017 - 2030)
Predictive Maintenance & Machinery Inspection
Quality Control & Reclamation
End-Use Outlook (Revenue, USD Million, 2017 - 2030)
Semiconductor & Electronics
Energy & Power
Heavy Metal & Machine Manufacturing
Regional Outlook (Revenue, USD Million, 2017 - 2030)
Middle East and Africa (MEA)
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