Artificial Intelligence In Manufacturing Market Forecasts to 2030 – Global Analysis By Component (Hardware, Software and Services), Deployment Mode (On-Premise and Cloud), Technology, Application, End User and By Geography

Artificial Intelligence In Manufacturing Market Forecasts to 2030 – Global Analysis By Component (Hardware, Software and Services), Deployment Mode (On-Premise and Cloud), Technology, Application, End User and By Geography

According to Stratistics MRC, the Global Artificial Intelligence in Manufacturing Market is accounted for $4.59 billion in 2024 and is expected to reach $22.86 billion by 2030 growing at a CAGR of 45.6% during the forecast period. Artificial Intelligence (AI) in manufacturing refers to the use of advanced algorithms and machine learning models to optimize production processes, improve efficiency, and reduce costs. AI can enhance areas such as predictive maintenance, quality control, supply chain management, and robotics automation. By analyzing vast amounts of data in real-time, AI-driven systems help manufacturers identify inefficiencies, predict equipment failures, and make data-driven decisions.

According to Capgemini’s report of 2019, European manufacturers are leading in implementing AI technology, Germany is at the top rank in adoption.

Market Dynamics:

Driver:

Adoption of industry 4.0

Industry 4.0 emphasizes the use of AI, the internet of things (IoT), robotics, and big data to create highly interconnected and intelligent production systems. AI enables real-time monitoring, predictive maintenance, and process optimization, enhancing efficiency and reducing downtime. As manufacturers shift towards smart factories and digitalization, AI becomes essential for automating decision-making, improving product quality, and achieving operational flexibility, thus driving the overall growth of AI in the manufacturing market.

Restraint:

Data privacy and security concerns

Data privacy and security concerns in Artificial Intelligence (AI) in manufacturing arise because AI systems rely on vast amounts of sensitive data from machines, processes, and networks. This data is often stored and processed in connected environments, making it vulnerable to cyberattacks, unauthorized access, and breaches. These risks deter some manufacturers from adopting AI solutions, as they may be cautious about potential data vulnerabilities, thus hampering the growth of AI in the manufacturing market.

Opportunity:

Increased demand for automation

AI-powered automation reduces human intervention, streamlines operations, and minimizes errors, leading to cost savings and improved productivity. Manufacturers are adopting AI for tasks such as robotic automation, predictive maintenance, and quality control, which enhance speed and precision. Automation also addresses labour shortages by filling skill gaps and handling repetitive tasks. As industries aim to boost output and remain competitive, the demand for AI-driven automation continues to rise, fuelling market growth.

Threat:

High implementation costs

AI in manufacturing has high implementation costs due to the need for advanced hardware, software, and specialized infrastructure, including sensors, data processing systems, and machine learning algorithms. Additionally, integrating AI with existing legacy systems requires significant customization, time, and skilled personnel, further increasing expenses. These upfront costs, along with ongoing maintenance and updates, present financial barriers, especially for small and medium-sized enterprises (SMEs).

Covid-19 Impact

The covid-19 pandemic accelerated the adoption of Artificial Intelligence in manufacturing as companies sought to overcome supply chain disruptions, labour shortages, and operational challenges. AI-driven automation, predictive maintenance, and demand forecasting became critical for maintaining production efficiency and adapting to fluctuating market conditions. However, initial investments slowed due to economic uncertainty and reduced capital expenditures. Despite this, the long-term impact has been positive, with increased focus on AI solutions for resilience, flexibility, and improved operational efficiency in manufacturing.

The supply chain management segment is expected to be the largest during the forecast period

The supply chain management segment is estimated to be the largest during the forecast period. Artificial Intelligence (AI) in manufacturing is revolutionizing supply chain management by optimizing operations, enhancing demand forecasting, and improving inventory management. AI-driven systems analyze large datasets to predict demand patterns, detect supply chain disruptions, and streamline logistics. Predictive analytics enable manufacturers to reduce excess inventory and prevent stockouts, while AI-powered automation helps in scheduling and resource allocation.

The electronics segment is expected to have the highest CAGR during the forecast period

The electronics segment is anticipated to witness the highest CAGR during the forecast period. Artificial Intelligence (AI) in electronics manufacturing enhances efficiency and precision by automating tasks such as defect detection, predictive maintenance, and quality control. AI-powered computer vision systems enable real-time inspection, ensuring higher product quality and reducing human error. Machine learning algorithms optimize production processes, minimizing downtime and waste. AI also supports supply chain optimization and inventory management, improving operational flexibility.

Region with largest share:

Asia Pacific is projected to have the largest market share during the forecast period driven by strong industrial development, government initiatives promoting automation, and the rise of smart factories. Countries like China, Japan, and South Korea are leading in AI adoption, with significant investments in robotics, machine learning, and predictive analytics to enhance production efficiency. The region's robust manufacturing sector, combined with technological advancements and increasing demand for higher productivity and cost reduction, positions Asia Pacific as a key hub for AI-driven industrial transformation.

Region with highest CAGR:

North America is projected to have the highest CAGR over the forecast period, driven by advanced technology adoption, a focus on smart manufacturing, and the region's push for digital transformation. The U.S. leads the way, with manufacturers leveraging AI for predictive maintenance, quality control, and process optimization. The regions highly developed industrial sector, coupled with investments in automation and machine learning, supports increased efficiency and innovation. AI-powered solutions in robotics and data analytics are helping North American manufacturers improve productivity, reduce operational costs, and enhance competitiveness in global markets.

Key players in the market:

Some of the key players profiled in the Artificial Intelligence in Manufacturing Market include Siemens, General Electric (GE), IBM, Rockwell Automation, ABB, Honeywell, Microsoft, Bosch, Schneider Electric, SAP, NVIDIA, Intel, PTC, Oracle, Fujitsu, Sandvik, Teradyne, Zebra Technologies and Autodesk.

Key Developments:

In June 2024, Sandvik launched AI in the “Manufacturing Copilot”, the manufacturing software in alliance with Microsoft. This will provide customers a simple and more accessible experience with 24/7 intelligent customer assistance. The Copilot offers real-time updates and enables informed choices. This is the first step in the AI roadmap to enhance the customer experience.

In April 2024, Microsoft announced new industrial AI innovations from the cloud to the factory floor. This AI-driven shift is prompting many organizations to fundamentally alter their business models and re-evaluate how to address industry-wide challenges like data siloes from disparate data estates and legacy products, supply chain visibility issues, labor shortages, and the need for upskilling employees.

Components Covered:
• Hardware
• Software
• Services

Deployment Modes Covered:
• On-Premise
• Cloud

Technologies Covered:
• Machine Learning (ML)
• Natural Language Processing (NLP)
• Computer Vision
• Context-aware Computing
• Deep Learning
• Other Technologies

Applications Covered:
• Predictive Maintenance
• Machinery Inspection
• Quality Control
• Production Planning
• Inventory Optimization
• Supply Chain Management
• Yield Optimization
• Other Applications

End Users Covered:
• Automotive
• Electronics
• Energy & Power
• Pharmaceuticals
• Chemicals
• Food & Beverages
• Aerospace & Defense
• Other End Users

Regions Covered:
• North America
US
Canada
Mexico
• Europe
Germany
UK
Italy
France
Spain
Rest of Europe
• Asia Pacific
Japan
China
India
Australia
New Zealand
South Korea
Rest of Asia Pacific
• South America
Argentina
Brazil
Chile
Rest of South America
• Middle East & Africa
Saudi Arabia
UAE
Qatar
South Africa
Rest of Middle East & Africa

What our report offers:
- Market share assessments for the regional and country-level segments
- Strategic recommendations for the new entrants
- Covers Market data for the years 2022, 2023, 2024, 2026, and 2030
- Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
- Strategic recommendations in key business segments based on the market estimations
- Competitive landscaping mapping the key common trends
- Company profiling with detailed strategies, financials, and recent developments
- Supply chain trends mapping the latest technological advancements