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Artificial Intelligence (AI) in manufacturing Market

ID: MRFR/ICT/6276-CR
189 Pages
Aarti Dhapte
September 2024

Artificial Intelligence (AI) in Manufacturing Market Research Report: By Application (Predictive Maintenance, Quality Control, Supply Chain Management, Robotics, Production Planning), By Technology (Machine Learning, Natural Language Processing, Computer Vision, Robotic Process Automation, Deep Learning), By Deployment Type (On-Premise, Cloud, Hybrid), By End Use Industry (Automotive, Electronics, Aerospace, Food and Beverage, Pharmaceuticals) and By Regional (North America, Europe, South America, Asia Pacific, Middle East and Africa) - Fore... read more

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Summary Table of Contents Segmentation Download PDF

Artificial Intelligence (AI) in manufacturing Market Summary

As per MRFR analysis, the Artificial Intelligence (AI) in Manufacturing Size was estimated at 4384.1 USD Billion in 2024. The AI in manufacturing industry is projected to grow from 5687.07 USD Billion in 2025 to 76730.09 USD Billion by 2035, exhibiting a compound annual growth rate (CAGR) of 29.72 during the forecast period 2025 - 2035.

Key Market Trends & Highlights

The Artificial Intelligence in manufacturing market is poised for substantial growth driven by automation and data-centric strategies.

  • North America remains the largest market for AI in manufacturing, showcasing a robust demand for advanced automation solutions.
  • The Asia-Pacific region is emerging as the fastest-growing market, fueled by rapid industrialization and technological adoption.
  • Predictive maintenance stands as the largest segment, while quality control is recognized as the fastest-growing segment within the industry.
  • Enhanced operational efficiency and supply chain optimization are key drivers propelling the adoption of AI technologies in manufacturing.

Market Size & Forecast

2024 Market Size 4384.1 (USD Billion)
2035 Market Size 76730.09 (USD Billion)
CAGR (2025 - 2035) 29.72%

Major Players

Siemens (DE), General Electric (US), IBM (US), Rockwell Automation (US), Honeywell (US), ABB (CH), C3.ai (US), Microsoft (US), SAP (DE), Oracle (US)

Artificial Intelligence (AI) in manufacturing Market Trends

The Artificial Intelligence (AI) in manufacturing Market is currently experiencing a transformative phase, characterized by the integration of advanced technologies that enhance operational efficiency and productivity. Companies are increasingly adopting AI-driven solutions to optimize processes, reduce waste, and improve quality control. This shift appears to be driven by the need for manufacturers to remain competitive in a rapidly evolving landscape. As organizations embrace automation and data analytics, the role of AI becomes more pronounced, suggesting a future where intelligent systems play a pivotal role in decision-making and resource management. Moreover, the current landscape indicates a growing emphasis on sustainability and smart manufacturing practices. Manufacturers are leveraging AI to not only streamline operations but also to minimize environmental impact. This trend reflects a broader societal shift towards responsible production methods. As the Artificial Intelligence (AI) in manufacturing Market continues to evolve, it seems poised to redefine traditional manufacturing paradigms, fostering innovation and resilience in the face of emerging challenges. The potential for AI to revolutionize the sector is substantial, as it offers solutions that could lead to unprecedented levels of efficiency and adaptability.

Increased Automation

The trend towards automation in the Artificial Intelligence (AI) in manufacturing Market is gaining momentum. Companies are increasingly implementing AI technologies to automate repetitive tasks, thereby enhancing productivity and reducing human error. This shift not only streamlines operations but also allows human workers to focus on more complex and strategic activities.

Data-Driven Decision Making

The reliance on data analytics is becoming more pronounced within the Artificial Intelligence (AI) in manufacturing Market. Organizations are utilizing AI to analyze vast amounts of data, enabling them to make informed decisions quickly. This trend suggests a move towards a more agile manufacturing environment where real-time insights drive operational strategies.

Sustainability Initiatives

Sustainability is emerging as a key focus in the Artificial Intelligence (AI) in manufacturing Market. Companies are leveraging AI to optimize resource usage and reduce waste, aligning with global sustainability goals. This trend indicates a growing recognition of the importance of environmentally responsible practices in manufacturing.

Artificial Intelligence (AI) in manufacturing Market Drivers

Enhanced Quality Control

Quality control is a critical aspect of manufacturing, and the Global Artificial Intelligence (AI) in Manufacturing Market Industry is increasingly leveraging AI to enhance this process. AI systems can analyze vast amounts of data in real-time, identifying defects and inconsistencies that human inspectors might overlook. For example, AI-powered visual inspection systems can detect anomalies in products with over 95% accuracy. This capability not only reduces waste but also ensures that products meet stringent quality standards. As manufacturers strive to maintain competitiveness, the integration of AI in quality control processes is likely to become a standard practice.

Market Growth Projections

The Global Artificial Intelligence (AI) in Manufacturing Market Industry is poised for substantial growth, with projections indicating a market size of 3.5 USD Billion in 2024 and an anticipated increase to 22.5 USD Billion by 2035. This growth represents a compound annual growth rate (CAGR) of 18.43% from 2025 to 2035. The increasing adoption of AI technologies across various manufacturing sectors underscores the industry's commitment to innovation and efficiency. As manufacturers continue to integrate AI into their operations, the market is expected to expand significantly, reflecting the transformative impact of AI on the manufacturing landscape.

Supply Chain Optimization

The Global Artificial Intelligence (AI) in Manufacturing Market Industry is also driven by the need for supply chain optimization. AI technologies facilitate better demand forecasting, inventory management, and logistics planning. By analyzing historical data and market trends, AI can predict fluctuations in demand, allowing manufacturers to adjust their production schedules accordingly. This adaptability can lead to a reduction in excess inventory and associated costs. As the market is expected to grow to 22.5 USD Billion by 2035, the role of AI in enhancing supply chain efficiency is becoming increasingly vital for manufacturers aiming to remain agile in a dynamic market.

Data-Driven Decision Making

The Global Artificial Intelligence (AI) in Manufacturing Market Industry is increasingly characterized by a shift towards data-driven decision making. AI systems can process and analyze large datasets, providing insights that inform strategic decisions. This capability allows manufacturers to identify trends, optimize processes, and enhance product development. For instance, companies utilizing AI analytics have reported improved decision-making speed by up to 50%. As the industry evolves, the ability to harness data effectively will likely become a key differentiator for manufacturers, driving further investment in AI technologies.

Labor Shortages and Skill Gaps

Labor shortages and skill gaps present significant challenges in the Global Artificial Intelligence (AI) in Manufacturing Market Industry. As the workforce ages and fewer skilled workers enter the field, manufacturers are turning to AI to fill these gaps. Automation and AI-driven systems can perform repetitive tasks, allowing human workers to focus on more complex responsibilities. This shift not only addresses labor shortages but also enhances overall productivity. The increasing reliance on AI solutions is indicative of a broader trend where manufacturers seek to leverage technology to mitigate workforce challenges and maintain operational continuity.

Increased Efficiency and Productivity

The Global Artificial Intelligence (AI) in Manufacturing Market Industry is witnessing a surge in demand for solutions that enhance efficiency and productivity. AI technologies, such as machine learning and predictive analytics, enable manufacturers to optimize operations by minimizing downtime and streamlining processes. For instance, companies employing AI-driven predictive maintenance have reported reductions in equipment failure rates by up to 30%. This efficiency translates into significant cost savings and improved output quality. As the market is projected to reach 3.5 USD Billion in 2024, the drive towards operational excellence remains a pivotal factor in the industry's growth.

Market Segment Insights

By Application: Predictive Maintenance (Largest) vs. Quality Control (Fastest-Growing)

The application segment of the AI in manufacturing market is witnessing varying growth and distribution across its key components. Predictive Maintenance holds the largest share, driven by the increasing demand for reducing downtime and maintaining equipment efficiency. Quality Control, while not the largest, is quickly gaining ground as manufacturers increasingly adopt AI for real-time monitoring and defect detection, enhancing product quality and reducing waste. The growth trends in this segment underscore the urgency of efficiency and cost reduction in manufacturing. Supply Chain Optimization and Robotics Automation are also growing, propelled by the need for streamlined operations and increased automation. Process Automation is an emergent area gaining traction as manufacturers seek to incorporate AI into more of their production processes.

Predictive Maintenance (Dominant) vs. Quality Control (Emerging)

In the realm of AI applications in manufacturing, Predictive Maintenance stands out as the dominant force, characterized by its ability to foresee equipment failures and optimize maintenance schedules, ultimately leading to enhanced operational efficiency. This approach leverages machine learning algorithms to analyze data from machinery, which helps avoid unplanned downtimes. In contrast, Quality Control represents an emerging application that focuses on maintaining product standards through automated inspection processes and anomaly detection. This segment utilizes AI-driven imaging and analysis tools to support manufacturers in ensuring high-quality outputs. As investments in AI technologies grow, both segments are poised to not only complement each other but also drive innovation in manufacturing practices.

By End Use: Automotive (Largest) vs. Consumer Goods (Fastest-Growing)

In the Artificial Intelligence (AI) in manufacturing market, the automotive segment leads with a substantial market share, driven by significant investments in automation, efficiency, and predictive maintenance. This sector's ability to leverage AI for enhancing production processes and ensuring safety standards has solidified its position as the dominant force within the industry. Following closely is the electronics segment, which also holds a significant portion, while pharmaceuticals and aerospace represent notable, yet smaller shares as they increasingly adopt AI technologies to optimize operations.

Automotive: Dominant vs. Consumer Goods: Emerging

The automotive sector remains the dominant player in AI adoption within manufacturing, characterized by continuous innovations in autonomous systems and smart manufacturing processes. AI enables automotive manufacturers to streamline operations, improve quality control, and reduce downtime, ensuring they keep pace with consumer demands for efficiency and safety. On the other hand, the consumer goods segment is emerging rapidly, adopting AI to refine supply chains and enhance customer engagement through personalized experiences. The agility with which this sector integrates AI solutions showcases its potential to evolve quickly, driven by consumer trends and the need for innovative product offerings.

By Technology: Machine Learning (Largest) vs. Robotic Process Automation (Fastest-Growing)

In the Artificial Intelligence in manufacturing market, Machine Learning has emerged as the largest segment, capturing a substantial share with its ability to analyze vast amounts of data and improve decision-making processes. Following closely, Natural Language Processing and Computer Vision also hold significant portions of the market. As businesses increasingly adopt AI technologies, the distribution among these segments reflects a growing recognition of their crucial roles in enhancing manufacturing efficiency and productivity. The growth trends in this segment are primarily driven by the rise of Industry 4.0, which emphasizes automation and data exchange in manufacturing technologies. Machine Learning continues to dominate due to its applications in predictive maintenance and quality control, while Robotic Process Automation is rapidly gaining traction as manufacturers seek to streamline operations and reduce errors. As organizations strive to remain competitive, the integration of these AI technologies is expected to accelerate, highlighting both current capabilities and emerging potentials in the market.

Technology: Machine Learning (Dominant) vs. Robotic Process Automation (Emerging)

Machine Learning is characterized by its capacity to learn from vast datasets and uncover patterns that are not immediately apparent, making it an essential tool for predictive analytics and optimizing manufacturing processes. Its dominance in the AI sector is attributed to its versatility and applicability across various manufacturing functions, such as supply chain management and equipment maintenance. In contrast, Robotic Process Automation is labeled as an emerging technology, revolutionizing how repetitive tasks are executed without human intervention. This segment is gaining momentum as manufacturers aim to enhance operational efficiency and reduce costs. By automating mundane tasks, RPA paves the way for enhanced productivity and allows human employees to focus on more strategic initiatives.

By Deployment Type: Cloud-Based (Largest) vs. On-Premises (Fastest-Growing)

The deployment type segment of the Artificial Intelligence in manufacturing market is characterized by three main categories: On-Premises, Cloud-Based, and Hybrid. Among these, Cloud-Based solutions currently hold the largest market share, driven by their flexibility, scalability, and ease of access. On-Premises solutions, while traditionally preferred for their security, are facing challenges in terms of market share due to the growing adoption of Cloud-based models. Meanwhile, Hybrid solutions are gaining traction as manufacturers look to blend the robustness of on-premises installations with the agility of cloud services.

Deployment Type: Cloud-Based (Dominant) vs. On-Premises (Emerging)

Cloud-Based deployment in AI for manufacturing is becoming increasingly dominant thanks to its ability to provide manufacturers with on-demand resources and advanced analytics capabilities. This model allows companies to leverage large datasets and complex algorithms without heavy upfront investments in infrastructure. On the other hand, On-Premises solutions are emerging, particularly as companies with stringent data security needs prioritize local control over their data. They offer robust performance and customization capabilities, but their growth is hindered by the rising popularity of Cloud-Based offerings. Meanwhile, Hybrid deployments are navigating the middle ground, allowing organizations to reap the benefits of both worlds by combining the scalability of the cloud with existing on-premises infrastructure.

By Functionality: Predictive Analytics (Largest) vs. Data Analysis (Fastest-Growing)

The Artificial Intelligence (AI) in manufacturing market is dominated by Predictive Analytics, which has established itself as the largest segment due to its ability to forecast outcomes and optimize processes. This segment utilizes historical data to predict future trends, giving manufacturers a significant competitive edge. Data Analysis, while smaller in share, has seen rapid adoption as more manufacturers recognize its value in interpreting vast amounts of data for better decision-making. The growth in the functionality segment is driven by the increasing need for manufacturers to enhance operational efficiency and reduce costs. The widespread integration of smart technologies and IoT in manufacturing processes has significantly boosted the popularity of Process Optimization and Predictive Analytics. Additionally, the push towards data-driven decision-making continues to elevate the importance of all functionality segments in the AI in manufacturing landscape.

Predictive Analytics (Dominant) vs. Process Optimization (Emerging)

Predictive Analytics has emerged as the dominant functionality in the AI in manufacturing market, enabling companies to leverage historical data for forecasting and improving processes. This approach allows for advanced insights into market dynamics, production efficiency, and strategic planning. Its capability to anticipate various operational challenges empowers manufacturers to proactively address issues, allowing for smoother production cycles. On the other hand, Process Optimization is an emerging functionality that focuses on refining manufacturing processes through AI-driven insights. This segment is gaining traction as businesses seek ways to streamline operations, minimize waste, and enhance productivity. While still growing, Process Optimization shows tremendous potential, supported by technological advancements that facilitate real-time adjustments to manufacturing processes.

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Regional Insights

The Global Artificial Intelligence (AI) in Manufacturing Market is projected to experience substantial growth across various regions, significantly contributing to its overall valuation. By 2024, North America leads with a valuation of 1.4 USD Billion and is expected to reach 9.0 USD Billion by 2035, showcasing its majority holding and importance in the market due to advanced technology adoption and infrastructure. Europe follows closely, with a valuation of 1.0 USD Billion in 2024, reaching 7.0 USD Billion in 2035, reflecting a significant commitment to integrating AI in manufacturing processes.

The Asia-Pacific (APAC) region, although valued at 0.8 USD Billion in 2024 and projected to grow to 4.5 USD Billion by 2035, plays a crucial role in market dynamics due to rapid industrialization and technology initiatives. South America and the Middle East and Africa (MEA) represent the smaller segments with respective values of 0.2 USD Billion and 0.1 USD Billion in 2024, expected to grow to 1.5 USD Billion and 0.5 USD Billion by 2035. The variations in market size among these regions highlight the diversity in manufacturing capabilities and the strategic importance of regional investments.

The demand for automation, efficiency enhancement, and predictive maintenance is driving the Global Artificial Intelligence (AI) in Manufacturing Market segmentation forward.

Figure 3: Artificial Intelligence (AI) in Manufacturing Market Regional Insights

Key Players and Competitive Insights

The Artificial Intelligence (AI) in manufacturing Market is currently characterized by a dynamic competitive landscape, driven by rapid technological advancements and increasing demand for automation. Key players such as Siemens (DE), General Electric (US), and IBM (US) are at the forefront, each adopting distinct strategies to enhance their market positioning. Siemens (DE) focuses on digital transformation and innovation, leveraging its expertise in automation to integrate AI solutions into manufacturing processes. General Electric (US) emphasizes partnerships and collaborations, particularly in the energy sector, to expand its AI capabilities. IBM (US) is heavily investing in research and development, aiming to enhance its AI offerings through advanced analytics and machine learning, thereby shaping a competitive environment that prioritizes technological leadership and strategic alliances.

The market structure appears moderately fragmented, with a mix of established players and emerging startups. Key business tactics such as localizing manufacturing and optimizing supply chains are prevalent among major companies, allowing them to respond swiftly to market demands. The collective influence of these players fosters a competitive atmosphere where innovation and operational efficiency are paramount, driving the adoption of AI technologies across various manufacturing sectors.

In November 2025, Siemens (DE) announced a strategic partnership with a leading robotics firm to develop AI-driven automation solutions tailored for the automotive industry. This collaboration is expected to enhance Siemens' product offerings, enabling manufacturers to achieve greater efficiency and flexibility in production lines. The strategic importance of this move lies in Siemens' commitment to staying ahead in the competitive landscape by integrating cutting-edge technologies into its solutions.

In October 2025, General Electric (US) unveiled a new AI platform designed to optimize energy consumption in manufacturing facilities. This platform utilizes predictive analytics to reduce operational costs and improve sustainability. The introduction of this platform signifies GE's focus on leveraging AI to address pressing environmental concerns while enhancing operational efficiency, thus reinforcing its competitive edge in the market.

In September 2025, IBM (US) launched an AI-driven supply chain management tool aimed at improving inventory accuracy and reducing lead times for manufacturers. This tool employs machine learning algorithms to analyze data in real-time, allowing companies to make informed decisions swiftly. The strategic significance of this launch is evident in IBM's effort to position itself as a leader in AI solutions that directly impact operational performance and supply chain reliability.

As of December 2025, current trends in the market include a pronounced shift towards digitalization, sustainability, and the integration of AI technologies. Strategic alliances are increasingly shaping the competitive landscape, enabling companies to pool resources and expertise to drive innovation. Looking ahead, competitive differentiation is likely to evolve from traditional price-based competition to a focus on technological innovation, enhanced supply chain reliability, and sustainable practices. This shift underscores the importance of adaptability and forward-thinking strategies in maintaining a competitive advantage in the rapidly evolving AI in manufacturing Market.

Key Companies in the Artificial Intelligence (AI) in manufacturing Market market include

Industry Developments

Recent developments in the Global Artificial Intelligence (AI) in Manufacturing Market highlight a period of significant growth and innovation. In September 2023, Nvidia announced a collaboration with Siemens to enhance AI-powered manufacturing solutions, which is expected to drive efficiency and reduce downtime in factories globally. In August 2023, IBM launched new AI tools targeting predictive maintenance for manufacturing, reinforcing its position in the sector. Within the last few years, strategic mergers and acquisitions have also shaped the landscape, with Microsoft acquiring Nuance Communications in April 2021, focused on conversational AI to improve industrial processes.

Similarly, in March 2022, Rockwell Automation announced the acquisition of Fiix Software to bolster its AI capabilities in maintenance and asset management. Notably, SAP's continuous investment in AI applications has driven its market valuation higher, marking a significant trend towards integration of AI for operational excellence. The market's valuation has surged, propelled by enterprises' increasing reliance on advanced technologies to optimize production and enhance decision-making processes. These strategic advancements demonstrate a robust shift towards AI as a cornerstone of modern manufacturing practices globally.

Future Outlook

Artificial Intelligence (AI) in manufacturing Market Future Outlook

The Artificial Intelligence in manufacturing market is projected to grow at a 29.72% CAGR from 2024 to 2035, driven by automation, data analytics, and enhanced operational efficiency.

New opportunities lie in:

  • Integration of AI-driven predictive maintenance solutions
  • Development of AI-enhanced quality control systems
  • Implementation of autonomous robotics for assembly lines

By 2035, the market is expected to be a cornerstone of manufacturing innovation and efficiency.

Market Segmentation

Artificial Intelligence (AI) in manufacturing Market End Use Outlook

  • Automotive
  • Electronics
  • Aerospace
  • Consumer Goods
  • Pharmaceuticals

Artificial Intelligence (AI) in manufacturing Market Technology Outlook

  • Machine Learning
  • Natural Language Processing
  • Computer Vision
  • Deep Learning
  • Robotic Process Automation

Artificial Intelligence (AI) in manufacturing Market Application Outlook

  • Predictive Maintenance
  • Quality Control
  • Supply Chain Optimization
  • Robotics Automation
  • Process Automation

Artificial Intelligence (AI) in manufacturing Market Functionality Outlook

  • Data Analysis
  • Process Optimization
  • Decision Making
  • Predictive Analytics

Artificial Intelligence (AI) in manufacturing Market Deployment Type Outlook

  • On-Premises
  • Cloud-Based
  • Hybrid

Report Scope

MARKET SIZE 20244384.1(USD Billion)
MARKET SIZE 20255687.07(USD Billion)
MARKET SIZE 203576730.09(USD Billion)
COMPOUND ANNUAL GROWTH RATE (CAGR)29.72% (2024 - 2035)
REPORT COVERAGERevenue Forecast, Competitive Landscape, Growth Factors, and Trends
BASE YEAR2024
Market Forecast Period2025 - 2035
Historical Data2019 - 2024
Market Forecast UnitsUSD Billion
Key Companies ProfiledSiemens (DE), General Electric (US), IBM (US), Rockwell Automation (US), Honeywell (US), ABB (CH), C3.ai (US), Microsoft (US), SAP (DE), Oracle (US)
Segments CoveredApplication, End Use, Technology, Deployment Type, Functionality
Key Market OpportunitiesIntegration of advanced predictive analytics enhances operational efficiency in the Artificial Intelligence (AI) in manufacturing Market.
Key Market DynamicsRising adoption of Artificial Intelligence in manufacturing enhances operational efficiency and drives competitive advantage.
Countries CoveredNorth America, Europe, APAC, South America, MEA

Market Highlights

Author
Aarti Dhapte
Team Lead - Research

She holds an experience of about 6+ years in Market Research and Business Consulting, working under the spectrum of Information Communication Technology, Telecommunications and Semiconductor domains. Aarti conceptualizes and implements a scalable business strategy and provides strategic leadership to the clients. Her expertise lies in market estimation, competitive intelligence, pipeline analysis, customer assessment, etc.

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FAQs

What is the projected market size for the Global Artificial Intelligence (AI) in Manufacturing Market by 2035?

The Global Artificial Intelligence (AI) in Manufacturing Market is expected to reach a value of 22.5 USD Billion by 2035.

What is the expected CAGR for the Global Artificial Intelligence (AI) in Manufacturing Market from 2025 to 2035?

The market is anticipated to grow at a CAGR of 18.44% from 2025 to 2035.

Which region is expected to hold the largest market share for the Global Artificial Intelligence (AI) in Manufacturing Market by 2035?

North America is projected to dominate the market with a value of 9.0 USD Billion by 2035.

What are the forecasted market values for Predictive Maintenance in the Global Artificial Intelligence (AI) in Manufacturing Market by 2035?

Predictive Maintenance is expected to be valued at 6.5 USD Billion by 2035.

Who are some of the key players in the Global Artificial Intelligence (AI) in Manufacturing Market?

Major players include IBM, Rockwell Automation, SAP, Infosys, and NVIDIA.

What is the market value for Quality Control in the Global Artificial Intelligence (AI) in Manufacturing Market in 2024?

The market value for Quality Control is projected to be 0.8 USD Billion in 2024.

How much is the Artificial Intelligence (AI) in Manufacturing Market for Supply Chain Management expected to grow by 2035?

The market for Supply Chain Management is expected to reach 4.5 USD Billion by 2035.

What will be the market value for Robotics within the Global Artificial Intelligence (AI) in Manufacturing Market by 2035?

The market for Robotics is forecasted to grow to 5.5 USD Billion by 2035.

What is the expected market size for the Global Artificial Intelligence (AI) in Manufacturing Market in 2024?

The overall market is expected to be valued at 3.5 USD Billion in 2024.

What is the growth forecast for the APAC region in the Global Artificial Intelligence (AI) in Manufacturing Market by 2035?

The APAC region is anticipated to grow to 4.5 USD Billion by 2035.

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