AI in Manufacturing Market Size, Growth Insights, and Key Trends by 2024

The global AI in manufacturing market size was valued at USD 8.14 billion in 2019 and is projected to reach USD 695.16 billion by 2032, exhibiting a CAGR of 37.7% during the forecast period. This significant growth indicates a rising adoption of AI technologies within the manufacturing sector. Advancements in AI technologies enhance efficiency, productivity, and decision-making in the industry.

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Recent Industry developments of AI in manufacturing market:

The company is focus on expanding the AI based product portfolio. Google LLC is acquiring companies from different countries such as China, India, the U.K., and the U.S. to name a few. Along with thirty AI startups of USD 4 billion, Google LLC tops the AI acquiring companies list. The company is also focusing on implementing AI in manufacturing industries. It is offering Cloud AI to boost and maximize the speed of process along with protecting the health and safety of workers. Also, it is investing in creating solutions and tools to ease the deployment and usage of AI in the manufacturing industries.

• Siemens and Microsoft collaborate to elevate industrial AI, revolutionizing product lifecycle management. Integrating Siemens’ Teamcenter software with Microsoft Teams and Azure OpenAI Service’s language models enhances innovation and efficiency. This partnership fosters seamless cross-functional collaboration, driving advancements in design, engineering, manufacturing, and product operations, marking a significant leap in industrial technology integration.
• Google Cloud launches industry-focused Generative AI solutions for healthcare and manufacturing, aiming to enhance productivity and enable digital transformation. This move signifies a significant step in leveraging AI for industry-specific advancements.

Top Keyplayers of AI in manufacturing market:

• Microsoft Corporation (United States)
• Google LLC (United States)
• IBM Corporation (United States)
• Amazon.com Inc. (United States)
•  NVIDIA Corporation (United States)
•  Siemens AG (Germany)
• GENERAL ELECTRIC (United States)
• SAP SE (Germany)
•  Rockwell Automation, Inc. (United States)
•  Mitsubishi Electric Corporation (Japan)

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AI in Manufacturing Market Segmentation

Artificial Intelligence in Manufacturing Market Share, Size & Industry Analysis, By Offering (Hardware, Software, and Services), By Technology (Computer Vision, Machine Learning, Natural Language Processing), By Application (Process Control, Production Planning, Predictive Maintenance & Machinery Inspection), By Industry (Automotive, Medical Devices, Semiconductor &Electronics), and Regional Forecast Between, 2020-2032

Reasons to buy this report:

This report thoroughly examines the competitive dynamics, growth areas, market conditions, and trends in the AI in manufacturing market. It assists businesses in identifying opportunities for growth and development while highlighting potential challenges and risks. Stakeholders are assisted in developing more informed investment and market strategies by the report’s assessment of product portfolios and business segments.

A thorough grasp of the industry dynamics outlined in the report can help develop successful success strategies in the AI in manufacturing market and greatly improve decision-making. All things considered, this report is an excellent source of information for anyone wishing to learn more about this rapidly changing sector.

Key Report Highlights on the AI in Manufacturing Market:

• Market CAGR for the Forecast Period: Provides insights into the Compound Annual Growth Rate (CAGR) of the AI in Manufacturing Market from 2024 to 2032.
• Comprehensive Analysis of Growth Drivers: Offers a detailed evaluation of the key factors driving the growth of the AI in Manufacturing Market during the forecast period.
• Accurate Market Size and Share Estimates: Precise projections on the size and share of the AI in Manufacturing Market, underscoring its position within the larger market landscape.
• Forecasts on Emerging Trends and Consumer Behavior: Predicts future trends and shifts in consumer behavior relevant to the AI in Manufacturing Market.
• Regional Growth Analysis: Examines the growth of the AI in Manufacturing Market across key regions, including North America, Asia-Pacific (APAC), Europe, South America, the Middle East, and Africa.
• Competitive Landscape Overview: Detailed assessment of the competitive environment, offering insights into the key players within the AI in Manufacturing Market.
• Evaluation of Growth Barriers: Analyzes the challenges that may hinder the expansion of suppliers in the AI in Manufacturing Market.

Regional Atributes:

• North America (U.S. and Canada)
• Europe (U.K., Germany, France, Spain and Rest of Europe)
• Asia Pacific (Japan, China, India, Southeast Asia and Rest of Asia Pacific)
• Middle East & Africa (South Africa, GCC and Rest of Middle East & Africa)
• Latin America (Brazil, Mexico and Rest of Latin America)

Table of Contents:

• Introduction
  • Research Scope
  • Market Segmentation
  • Research Methodology
  • Definitions and Assumptions
• Executive Summary
• Market Dynamics
  • Market Drivers
  • Market Restraints
  • Market Opportunities
• Key Insights
  • Key Industry Developments – Merger, Acquisitions, and Partnerships
  • Porter’s Five Forces Analysis
  • SWOT Analysis
  • Technological Developments
  • Value Chain Analysis

TOC Continued…!

AI in Manufacturing Market Drivers and restrains:

• Drivers:
  • Technological Advancements: Innovations in AI technologies, such as machine learning and predictive analytics, enhance manufacturing processes and productivity.
  • Demand for Automation: The push for automation to improve efficiency, reduce labor costs, and minimize errors drives the adoption of AI solutions in manufacturing.
  • Need for Quality Control: AI-powered systems improve quality control by detecting defects, optimizing processes, and ensuring consistent product quality.
  • Increased Data Availability: The growth of data collection and IoT devices provides valuable insights for AI algorithms to optimize manufacturing operations.
  • Competitive Pressure: Manufacturers seek AI solutions to stay competitive by enhancing operational efficiency, reducing costs, and innovating production processes.
• Restraints:
  • High Implementation Costs: The initial investment in AI technologies, including software, hardware, and training, can be substantial for manufacturers.
  • Complex Integration: Integrating AI solutions with existing manufacturing systems and processes can be complex and require significant technical expertise.
  • Data Security Concerns: Increased reliance on AI and data-driven insights raises concerns about data security and privacy, necessitating robust protection measures.
  • Resistance to Change: Some manufacturers may resist adopting AI technologies due to existing workflows, fear of disruption, or lack of understanding of the benefits.
  • Skilled Labor Shortages: The need for skilled personnel to develop, implement, and maintain AI systems can be a challenge, impacting the overall effectiveness of AI adoption.

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