産業用AI市場レポート：動向、予測、競合分析 (2031年まで)

世界の産業用AI市場の将来は、ガラス製造、半導体製造、自動車、機械、化学、石油・ガス、包装市場に機会があり、有望視されています。世界の産業用AI市場は、2025年から2031年にかけてCAGR 30.4%で成長すると予想されています。この市場の主な促進要因は、産業界における自動化と効率化の需要の増加、産業環境におけるセンサーの使用の増加、インダストリー4.0技術の採用の増加です。

• Lucintelでは、タイプ別ではソフトウェア分野が予測期間中に高い成長を遂げると予測しています。
• 用途別では、ガラス製造が最も高い成長が見込まれています。
• 地域別では、APACが予測期間中に最も高い成長が見込まれます。

産業用AI市場の戦略的成長機会

産業用AI市場は、技術の進歩と進化する産業ニーズに後押しされ、主要なアプリケーションにおいていくつかの戦略的成長機会を提示しています。

• 予知保全の拡大：AI主導の予知保全には大きな成長の可能性があります。機器のダウンタイムとメンテナンスコストを削減することで、このアプリケーションは製造業やエネルギーを含むさまざまな業界に大きなメリットをもたらします。
• スマート・マニュファクチャリングの成長：スマート製造技術の台頭は、AI統合の機会をもたらします。AIはプロセスの自動化、品質管理、リアルタイムのモニタリングを強化し、製造オペレーションの効率性と生産性を高めることができます。
• サプライチェーン最適化の進展：AIによるサプライチェーン最適化は、重要な成長分野です。AIは需要予測、在庫管理、ロジスティクスを改善し、より効率的で迅速なサプライチェーンを実現します。
• AIを活用したロボティクスの開発：組立や検査などの作業におけるロボット工学へのAIの応用は成長機会をもたらします。AIを搭載したロボットは、産業プロセスの精度、柔軟性、効率を高めることができます。
• プロセス最適化の強化：プロセス最適化のためのAIソリューションが人気を集めています。大量のデータを分析することで、AIは非効率を特定し、改善を推奨することができます。

こうした戦略的成長機会は、産業用AIがさまざまな用途でイノベーションと効率化を推進する可能性を浮き彫りにしています。予知保全、スマート製造、サプライチェーン最適化、ロボット工学、プロセス最適化に注力することで、企業は新たな市場動向とニーズを活用することができます。

産業用AI市場の促進要因・課題

産業用AI市場は様々な促進要因・課題の影響を受け、その成長と市場開拓に影響を与えています。これらの要因には、技術的、経済的、規制的な考慮事項が含まれます。

産業用AI市場の促進要因には、以下のようなものがある：

• AI技術の進歩：AIアルゴリズムと機械学習モデルの継続的な改善が産業用AIの成長を促進しています。これらの進歩により、さまざまな産業分野におけるAIの能力と応用が強化されます。
• IoTの採用増加：AIとIoTデバイスの統合は重要な促進要因です。IoTはリアルタイムのデータ収集と分析を可能にし、AIと組み合わせることで産業プロセスの監視、制御、最適化を強化します。
• 業務効率化の要求：業務効率の向上に対するニーズが、産業用AIの採用を後押ししています。AIソリューションは、プロセスの最適化、予知保全、自動化を改善し、コスト削減と生産性向上につながります。
• 研究開発への投資：研究開発への投資の増加が、産業用AIのイノベーションを促進しています。資金調達とコラボレーションは、新しいテクノロジーとアプリケーションの開発をサポートし、業界を発展させます。
• データ駆動型意思決定の重視：データ主導の意思決定が産業用AIの成長を促進しています。AIはデータ分析から貴重な洞察を提供し、産業運営においてより多くの情報に基づいた戦略的な意思決定を可能にします。

産業用AI市場の課題は以下の通り：

• 統合の複雑さ：AIと既存の産業システムとの統合は複雑で困難な場合があります。互換性とシームレスな運用を確保するには、多大な労力と専門知識が必要となります。
• 高い導入コスト：AIソリューションの導入コストは高額になる可能性があります。これには、技術取得、システム統合、トレーニングに関する費用が含まれ、組織によっては障壁となり得ます。
• データのプライバシーとセキュリティに関する懸念：データのプライバシーとセキュリティに関する懸念は重要な課題です。AIシステムが規制に準拠し、機密情報を確実に保護することは、信頼を獲得し、セキュリティを維持する上で極めて重要です。

産業用AI市場に影響を与える促進要因・課題は、継続的なイノベーションと適応の必要性を浮き彫りにしています。これらの要因に効果的に対処することが、成長機会を活用し、市場の障害を克服する鍵となります。

目次

第1章 エグゼクティブサマリー

第2章 世界の産業用AI市場：市場力学

• イントロダクション、背景、分類
• サプライチェーン
• 業界の促進要因と課題

第3章 市場動向と予測分析 (2019年～2031年)

• マクロ経済動向 (2019～2024年) と予測 (2025～2031年)
• 世界の産業用AI市場の動向 (2019～2024年) と予測 (2025～2031年)
• 世界の産業用AI市場：種類別
  • ソフトウェア
  • ハードウェア
• 世界の産業用AI市場：用途別
  • ガラス製造
  • 半導体製造
  • 自動車
  • 機械
  • 化学
  • 石油・ガス
  • 包装
  • その他

第4章 地域別の市場動向と予測分析 (2019年～2031年)

• 世界の産業用AI市場：地域別
• 北米の産業用AI市場
• 欧州の産業用AI市場
• アジア太平洋の産業用AI市場
• その他地域の産業用AI市場

第5章 競合分析

• 製品ポートフォリオ分析
• 運用統合
• ポーターのファイブフォース分析

第6章 成長機会と戦略分析

• 成長機会分析
  • 世界の産業用AI市場の成長機会：種類別
  • 世界の産業用AI市場の成長機会：用途別
  • 世界の産業用AI市場の成長機会：地域別
• 世界の産業用AI市場の新たな動向
• 戦略的分析
  • 新製品の開発
  • 世界の産業用AI市場の生産能力拡大
  • 世界の産業用AI市場における企業合併・買収 (M&A)、合弁事業
  • 認証とライセンシング

第7章 主要企業のプロファイル

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

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.

Gain valuable insights for your business decisions with our comprehensive 150+ page report.

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.

If you are looking to expand your business in this market or adjacent markets, then contact us. We have done hundreds of strategic consulting projects in market entry, opportunity screening, due diligence, supply chain analysis, M&A, and more.

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