한글목차

세계 산업용 AI 시장의 미래는 유리 제조, 반도체 제조, 자동차, 기계, 화학, 석유 및 가스, 포장 시장에 기회가 있으며, 유망한 것으로 평가되고 있습니다. 세계 산업용 AI 시장은 2025-2031년 연평균 복합 성장률(CAGR) 30.4%로 성장할 것으로 예상됩니다. 이 시장의 주요 촉진요인은 산업계의 자동화 및 효율화에 대한 수요 증가, 산업 환경에서의 센서 사용 증가, 인더스트리 4.0 기술 채택 증가 등입니다.

• Lucintel은 유형별로는 소프트웨어 분야가 예측 기간 중 높은 성장세를 보일 것으로 예상하고 있습니다.
• 용도별로는 유리 제조가 가장 높은 성장이 예상됩니다.
• 지역별로는 APAC 지역이 예측 기간 중 가장 높은 성장을 보일 것으로 예상됩니다.

산업용 AI 시장의 전략적 성장 기회

산업용 AI 시장은 기술 발전과 진화하는 산업적 요구에 힘입어 주요 용도에서 몇 가지 전략적 성장 기회를 제공합니다.

• 예지보전 확대: AI 기반 예지보전은 큰 성장 잠재력을 가지고 있습니다. 장비의 다운타임과 유지보수 비용을 줄임으로써 제조 및 에너지를 포함한 다양한 산업 분야에 큰 이점을 제공할 수 있습니다.
• 스마트 제조의 성장: 스마트 제조 기술의 부상은 AI 통합의 기회를 가져오고, AI는 공정 자동화, 품질관리, 실시간 모니터링을 강화하여 제조 운영의 효율성과 생산성을 향상시킬 수 있습니다.
• 공급망 최적화의 발전: AI를 통한 공급망 최적화는 중요한 성장 분야로, AI는 수요 예측, 재고 관리, 물류를 개선하여 보다 효율적이고 신속한 공급망을 실현할 수 있습니다.
• AI를 활용한 로봇공학 개발 : 조립, 검사 등의 작업에 AI를 적용하는 것은 성장 기회를 가져다주며, AI를 탑재한 로봇은 산업 공정의 정확성, 유연성, 효율성을 향상시킬 수 있습니다.
• 프로세스 최적화 강화: 프로세스 최적화를 위한 AI 솔루션이 인기를 끌고 있습니다. AI는 대량의 데이터를 분석하여 비효율적인 부분을 찾아내고 개선을 권고할 수 있습니다.

이러한 전략적 성장 기회는 산업용 AI가 다양한 분야에서 혁신과 효율을 촉진할 수 있는 잠재력을 보여줍니다. 기업은 예지보전, 스마트 제조, 공급망 최적화, 로봇공학, 공정 최적화에 집중함으로써 새로운 시장 동향과 니즈를 활용할 수 있습니다.

산업용 AI 시장 활성화 요인 및 과제

산업용 AI 시장은 다양한 촉진요인과 과제에 영향을 받아 성장과 시장 개발에 영향을 미치고 있습니다. 이러한 요인에는 기술적, 경제적, 규제적 고려사항이 포함됩니다.

산업용 AI 시장 성장 촉진요인으로는 다음과 같은 것이 있다:

• AI 기술의 발전: AI 알고리즘과 머신러닝 모델의 지속적인 개선이 산업용 AI의 성장을 가속하고 있습니다. 이러한 발전은 다양한 산업 분야에서 AI의 능력과 응용을 강화할 것입니다.
• IoT 채택 증가 : AI와 IoT 장비의 통합은 중요한 촉진제이며, IoT는 실시간 데이터 수집 및 분석을 가능하게 하고, AI와 결합하여 산업 공정의 모니터링, 제어 및 최적화를 강화합니다.
• 업무 효율성 향상에 대한 요구: 업무 효율성 향상에 대한 요구가 산업용 AI의 도입을 촉진하고 있으며, AI 솔루션은 프로세스 최적화, 예지보전, 자동화를 개선하여 비용 절감 및 생산성 향상으로 이어집니다.
• R&D 투자: R&D에 대한 투자 증가가 산업용 AI의 혁신을 촉진하고 있습니다. 자금 조달과 협업은 새로운 기술과 애플리케이션 개발을 지원하고 산업을 발전시킬 수 있습니다.
• 데이터베이스 의사결정의 중요성: 데이터베이스 의사결정이 산업용 AI의 성장을 가속하고 있으며, AI는 데이터 분석을 통해 귀중한 인사이트를 제공하고 산업 운영에서 보다 정보에 입각한 전략적 의사결정을 가능하게 합니다.

산업용 AI 시장이 해결해야 할 과제는 다음과 같습니다:

• 통합의 복잡성: AI와 기존 산업 시스템과의 통합은 복잡하고 어려울 수 있습니다. 호환성과 원활한 운영을 보장하기 위해서는 많은 노력과 전문 지식이 필요합니다.
• 높은 도입 비용: AI 솔루션의 도입 비용은 매우 높을 수 있습니다. 여기에는 기술 획득, 시스템 통합, 교육 관련 비용이 포함되며, 조직에 따라서는 장벽이 될 수 있습니다.
• 데이터 프라이버시 및 보안 문제: 데이터 프라이버시 및 보안 문제는 중요한 문제이며, AI 시스템이 규정을 준수하고 기밀 정보를 보호하는 것은 신뢰를 얻고 보안을 유지하는 데 있으며, 매우 중요합니다.

산업용 AI 시장에 영향을 미치는 촉진요인과 과제는 지속적인 혁신과 적응의 필요성을 강조하고 있습니다. 이러한 요인에 효과적으로 대응하는 것이 성장 기회를 활용하고 시장 장애물을 극복하는 열쇠입니다.

목차

제1장 개요

제2장 세계의 산업용 AI 시장 : 시장 역학

• 서론, 배경, 분류
• 공급망
• 업계의 촉진요인과 과제

제3장 시장 동향과 예측 분석(2019-2031년)

• 거시경제 동향(2019-2024년)과 예측(2025-2031년)
• 세계의 산업용 AI 시장 동향(2019-2024년)과 예측(2025-2031년)
• 세계의 산업용 AI 시장 : 유형별
  • 소프트웨어
  • 하드웨어
• 세계의 산업용 AI 시장 : 용도별
  • 유리 제조
  • 반도체 제조
  • 자동차
  • 기계
  • 화학
  • 석유 및 가스
  • 포장
  • 기타

제4장 지역별 시장 동향과 예측 분석(2019-2031년)

• 세계의 산업용 AI 시장 : 지역별
• 북미의 산업용 AI 시장
• 유럽의 산업용 AI 시장
• 아시아태평양의 산업용 AI 시장
• 기타 지역의 산업용 AI 시장

제5장 경쟁 분석

• 제품 포트폴리오 분석
• 운영 통합
• Porter's Five Forces 분석

제6장 성장 기회와 전략 분석

• 성장 기회 분석
  • 세계의 산업용 AI 시장의 성장 기회 : 유형별
  • 세계의 산업용 AI 시장의 성장 기회 : 용도별
  • 세계의 산업용 AI 시장의 성장 기회 : 지역별
• 세계의 산업용 AI 시장의 새로운 동향
• 전략적 분석
  • 신제품 개발
  • 세계의 산업용 AI 시장의 생산능력 확대
  • 세계의 산업용 AI 시장에서의 기업인수합병(M&A), 합병사업
  • 인증과 라이선싱

제7장 주요 기업의 개요

• Siemens
• IBM
• Microsoft
• Cisco Systems
• General Electric Company
• Rockwell Automation

영문목차

The future of the global industrial AI market looks promising with opportunities in the glass manufacturing, semiconductor manufacturing, automotive, machinery, chemical, oil & gas, and packaging markets. The global industrial AI market is expected to grow with a CAGR of 30.4% from 2025 to 2031. The major drivers for this market are increasing demand for automation and efficiency among industries, growing use of sensors in industrial settings, and rising adoption of industry 4.0 technologies.

• Lucintel forecasts that, within the type category, the software segment is expected to witness higher growth over the forecast period.
• Within the application category, glass manufacturing is expected to witness the highest growth.
• In terms of regions, APAC is expected to witness the highest growth over the forecast period.

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Emerging Trends in the Industrial AI Market

Industrial AI is evolving with several key trends that are reshaping how industries operate and leverage technology.

• Increased Use of Edge AI: Edge AI is gaining traction, allowing data processing and decision-making to occur closer to the source. This trend reduces latency and improves real-time response in industrial applications, enhancing operational efficiency and safety.
• Integration of AI with IoT: Combining AI with IoT devices is transforming industrial operations by enabling smarter monitoring and control. This integration enhances predictive maintenance, process optimization, and data-driven decision-making across various industrial sectors.
• Adoption of AI-Driven Robotics: AI-driven robotics are increasingly used in manufacturing for tasks like assembly, inspection, and material handling. These robots offer increased precision, flexibility, and efficiency, contributing to higher productivity and reduced operational costs.
• Focus on Predictive Maintenance: Predictive maintenance powered by AI is becoming a standard practice. By analyzing data from equipment sensors, AI can predict failures before they occur, minimizing downtime and maintenance costs while extending equipment lifespan.
• Enhanced Data Analytics: Advanced data analytics driven by AI are providing deeper insights into industrial processes. This trend enables better decision-making, process optimization, and identification of inefficiencies, leading to more informed strategies and operational improvements.

These emerging trends are significantly reshaping the industrial AI market by enhancing real-time processing, integration, and automation. As these trends advance, they are driving increased efficiency, productivity, and cost-effectiveness across industrial applications.

Recent Developments in the Industrial AI Market

Recent developments in the industrial AI market highlight the sector's rapid evolution and its impact on manufacturing and operational processes.

• Development of Advanced AI Algorithms: Innovations in AI algorithms are improving the accuracy and efficiency of industrial AI applications. These algorithms enhance machine learning models used in predictive maintenance, quality control, and process optimization.
• Expansion of AI-Enabled Robotics: The deployment of AI-enabled robotics is increasing in manufacturing environments. These robots offer advanced capabilities in tasks such as assembly, inspection, and material handling, improving operational efficiency and flexibility.
• Growth in AI-Powered Predictive Maintenance: AI-powered predictive maintenance solutions are becoming more prevalent. These solutions analyze data from equipment to predict failures and optimize maintenance schedules, reducing downtime and maintenance costs.
• Integration with IoT Devices: AI integration with IoT devices is advancing, enabling more intelligent monitoring and control of industrial processes. This integration supports real-time data analysis and enhances process optimization and efficiency.
• Focus on Customizable AI Solutions: There is a growing trend toward developing customizable AI solutions tailored to specific industrial needs. These solutions offer greater flexibility and adaptability, addressing unique challenges and requirements across different sectors.

These developments are transforming the industrial AI market by enhancing the capabilities and applications of AI technologies. They contribute to improved efficiency, flexibility, and cost-effectiveness in industrial operations.

Strategic Growth Opportunities for the Industrial AI Market

The industrial AI market presents several strategic growth opportunities across key applications, driven by technological advancements and evolving industry needs.

• Expansion in Predictive Maintenance: There is significant potential for growth in AI-driven predictive maintenance. By reducing equipment downtime and maintenance costs, this application offers substantial benefits to various industries, including manufacturing and energy.
• Growth in Smart Manufacturing: The rise of smart manufacturing technologies presents opportunities for AI integration. AI can enhance process automation, quality control, and real-time monitoring, driving efficiency and productivity in manufacturing operations.
• Advancement in Supply Chain Optimization: AI-driven supply chain optimization is a key growth area. AI can improve demand forecasting, inventory management, and logistics, leading to more efficient and responsive supply chains.
• Development of AI-Powered Robotics: The application of AI in robotics for tasks such as assembly and inspection offers growth opportunities. AI-powered robots can increase precision, flexibility, and efficiency in industrial processes.
• Enhancement of Process Optimization: AI solutions for process optimization are gaining traction. By analyzing large volumes of data, AI can identify inefficiencies and recommend improvements, leading to more effective and streamlined operations.

These strategic growth opportunities highlight the potential for industrial AI to drive innovation and efficiency across various applications. By focusing on predictive maintenance, smart manufacturing, supply chain optimization, robotics, and process optimization, companies can capitalize on emerging market trends and needs.

Industrial AI Market Driver and Challenges

The industrial AI market is influenced by various drivers and challenges, impacting its growth and development. These factors include technological, economic, and regulatory considerations.

The factors responsible for driving the industrial AI market include:

• Advancements in AI Technology: Continuous improvements in AI algorithms and machine learning models are driving the growth of industrial AI. These advancements enhance the capabilities and applications of AI in various industrial sectors.
• Increased Adoption of IoT: The integration of AI with IoT devices is a significant driver. IoT enables real-time data collection and analysis, which, when combined with AI, enhances monitoring, control, and optimization of industrial processes.
• Demand for Operational Efficiency: The need for increased operational efficiency is driving the adoption of industrial AI. AI solutions offer improved process optimization, predictive maintenance, and automation, leading to cost savings and higher productivity.
• Investment in R&D: Increased investment in research and development is fostering innovation in industrial AI. Funding and collaboration support the development of new technologies and applications, advancing the industry.
• Focus on Data-Driven Decision Making: The emphasis on data-driven decision-making is driving the growth of industrial AI. AI provides valuable insights from data analysis, enabling more informed and strategic decisions in industrial operations.

Challenges in the industrial AI market are:

• Integration Complexity: Integrating AI with existing industrial systems can be complex and challenging. Ensuring compatibility and seamless operation requires significant effort and expertise.
• High Implementation Costs: The cost of implementing AI solutions can be high. This includes expenses related to technology acquisition, system integration, and training, which can be a barrier for some organizations.
• Data Privacy and Security Concerns: Concerns about data privacy and security are significant challenges. Ensuring that AI systems comply with regulations and protect sensitive information is critical for gaining trust and maintaining security.

The drivers and challenges impacting the industrial AI market highlight the need for ongoing innovation and adaptation. Addressing these factors effectively will be key to leveraging growth opportunities and overcoming market obstacles.

List of Industrial AI Companies

Companies in the market compete based on product quality offered. Major players in this market focus on expanding their manufacturing facilities, R&D investments, infrastructural development, and leverage integration opportunities across the value chain. Through these strategies, industrial AI companies cater to increasing demand, ensure competitive effectiveness, develop innovative products & technologies, reduce production costs, and expand their customer base. Some of the industrial AI companies profiled in this report include-

• Siemens
• IBM
• Microsoft
• Cisco Systems
• General Electric Company
• Rockwell Automation

Industrial AI by Segment

The study includes a forecast for the global industrial AI market by type, application, and region.

Industrial AI Market by Type [Analysis by Value from 2019 to 2031]:

• Software
• Hardware

Industrial AI Market by Application [Analysis by Value from 2019 to 2031]:

• Glass Manufacturing
• Semiconductor Manufacturing
• Automotive
• Machinery
• Chemical
• Oil & Gas
• Packaging
• Others

Industrial AI Market by Region [Analysis by Value from 2019 to 2031]:

• North America
• Europe
• Asia Pacific
• The Rest of the World

Country Wise Outlook for the Industrial AI Market

Major players in the market are expanding their operations and forming strategic partnerships to strengthen their positions. The image below highlights recent developments by major industrial AI producers in key regions: the USA, China, India, Japan, and Germany.

• United States: In the U.S., industrial AI is seeing increased adoption in predictive maintenance and supply chain optimization. Companies like GE and Siemens are integrating AI into their industrial systems to enhance operational efficiency and reduce downtime.
• China: China is focusing on AI-driven automation and robotics in manufacturing. Major players like Huawei and Alibaba are investing in AI technologies to improve production processes and boost industrial capabilities, supporting the country's "Made in China 2025" initiative.
• Germany: Germany is leveraging AI for Industry 4.0 initiatives, focusing on smart factories and advanced manufacturing. German firms are deploying AI for real-time process monitoring and quality control, aiming to enhance precision and reduce production costs.
• India: In India, industrial AI is being adopted to improve manufacturing efficiency and resource management. Startups and established companies are using AI to optimize supply chains and enhance production capabilities, supporting the country's industrial growth.
• Japan: Japan is advancing AI in robotics and automation, particularly for precision manufacturing and electronics. Japanese companies are integrating AI into production lines to enhance automation and increase manufacturing accuracy and speed.

Features of the Global Industrial AI Market

Market Size Estimates: Industrial AI market size estimation in terms of value ($B).

Trend and Forecast Analysis: Market trends (2019 to 2024) and forecasts (2025 to 2031) by various segments and regions.

Segmentation Analysis: Industrial AI market size by type, application, and region in terms of value ($B).

Regional Analysis: Industrial AI market breakdown by North America, Europe, Asia Pacific, and Rest of the World.

Growth Opportunities: Analysis of growth opportunities in different types, applications, and regions for the industrial AI market.

Strategic Analysis: This includes M&A, new product development, and the competitive landscape of the industrial AI market.

Analysis of competitive intensity of the industry based on Porter's Five Forces model.

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This report answers the following 11 key questions:

• Q.1. What are some of the most promising, high-growth opportunities for the industrial AI market by type (software and hardware), application (glass manufacturing, semiconductor manufacturing, automotive, machinery, chemical, oil & gas, packaging, and others), and region (North America, Europe, Asia Pacific, and the Rest of the World)?
• Q.2. Which segments will grow at a faster pace and why?
• Q.3. Which region will grow at a faster pace and why?
• Q.4. What are the key factors affecting market dynamics? What are the key challenges and business risks in this market?
• Q.5. What are the business risks and competitive threats in this market?
• Q.6. What are the emerging trends in this market and the reasons behind them?
• Q.7. What are some of the changing demands of customers in the market?
• Q.8. What are the new developments in the market? Which companies are leading these developments?
• Q.9. Who are the major players in this market? What strategic initiatives are key players pursuing for business growth?
• Q.10. What are some of the competing products in this market and how big of a threat do they pose for loss of market share by material or product substitution?
• Q.11. What M&A activity has occurred in the last 5 years and what has its impact been on the industry?

Table of Contents

1. Executive Summary

2. Global Industrial AI Market : Market Dynamics

• 2.1: Introduction, Background, and Classifications
• 2.2: Supply Chain
• 2.3: Industry Drivers and Challenges

3. Market Trends and Forecast Analysis from 2019 to 2031

• 3.1. Macroeconomic Trends (2019-2024) and Forecast (2025-2031)
• 3.2. Global Industrial AI Market Trends (2019-2024) and Forecast (2025-2031)
• 3.3: Global Industrial AI Market by Type
  • 3.3.1: Software
  • 3.3.2: Hardware
• 3.4: Global Industrial AI Market by Application
  • 3.4.1: Glass Manufacturing
  • 3.4.2: Semiconductor Manufacturing
  • 3.4.3: Automotive
  • 3.4.4: Machinery
  • 3.4.5: Chemical
  • 3.4.6: Oil & Gas
  • 3.4.7: Packaging
  • 3.4.8: Others

4. Market Trends and Forecast Analysis by Region from 2019 to 2031

• 4.1: Global Industrial AI Market by Region
• 4.2: North American Industrial AI Market
  • 4.2.1: North American Industrial AI Market by Type: Software and Hardware
  • 4.2.2: North American Industrial AI Market by Application: Glass Manufacturing, Semiconductor Manufacturing, Automotive, Machinery, Chemical, Oil & Gas, Packaging, and Others
• 4.3: European Industrial AI Market
  • 4.3.1: European Industrial AI Market by Type: Software and Hardware
  • 4.3.2: European Industrial AI Market by Application: Glass Manufacturing, Semiconductor Manufacturing, Automotive, Machinery, Chemical, Oil & Gas, Packaging, and Others
• 4.4: APAC Industrial AI Market
  • 4.4.1: APAC Industrial AI Market by Type: Software and Hardware
  • 4.4.2: APAC Industrial AI Market by Application: Glass Manufacturing, Semiconductor Manufacturing, Automotive, Machinery, Chemical, Oil & Gas, Packaging, and Others
• 4.5: ROW Industrial AI Market
  • 4.5.1: ROW Industrial AI Market by Type: Software and Hardware
  • 4.5.2: ROW Industrial AI Market by Application: Glass Manufacturing, Semiconductor Manufacturing, Automotive, Machinery, Chemical, Oil & Gas, Packaging, and Others

5. Competitor Analysis

• 5.1: Product Portfolio Analysis
• 5.2: Operational Integration
• 5.3: Porter's Five Forces Analysis

6. Growth Opportunities and Strategic Analysis

• 6.1: Growth Opportunity Analysis
  • 6.1.1: Growth Opportunities for the Global Industrial AI Market by Type
  • 6.1.2: Growth Opportunities for the Global Industrial AI Market by Application
  • 6.1.3: Growth Opportunities for the Global Industrial AI Market by Region
• 6.2: Emerging Trends in the Global Industrial AI Market
• 6.3: Strategic Analysis
  • 6.3.1: New Product Development
  • 6.3.2: Capacity Expansion of the Global Industrial AI Market
  • 6.3.3: Mergers, Acquisitions, and Joint Ventures in the Global Industrial AI Market
  • 6.3.4: Certification and Licensing

7. Company Profiles of Leading Players

• 7.1: Siemens
• 7.2: IBM
• 7.3: Microsoft
• 7.4: Cisco Systems
• 7.5: General Electric Company
• 7.6: Rockwell Automation