세계의 화학 분야 AI 시장 규모 조사 : 구성요소별, 비즈니스 용도별, 최종사용자별, 지역별 예측(2022-2032년)

The Global AI in Chemicals Market is valued at approximately USD 1.14 billion in 2023 and is anticipated to grow with a healthy growth rate of more than 39.72% over the forecast period 2024-2032. AI in chemicals refers to the application of artificial intelligence technologies to the chemical industry to enhance processes, optimize production, and drive innovation. AI techniques, such as machine learning and data analytics, are used to analyze complex chemical data, predict outcomes, and improve the design of chemical products and processes. This includes optimizing reaction conditions, identifying new materials, and enhancing quality control. AI helps accelerate research and development, reduces operational costs, and enhances safety by predicting potential risks. The integration of AI in chemicals facilitates more efficient and precise operations, leading to advancements in product development and process optimization within the industry. Furthermore, advanced analytics and machine learning algorithms enable precise cost and performance estimations, while AI-driven automation streamlines experimental procedures, thereby enhancing efficiency, accuracy, and safety.

The growing demand for AI in research and development is significantly driving the AI in chemicals market. As the chemical industry seeks to accelerate innovation and streamline R&D processes, AI technologies provide critical support by analyzing vast amounts of data, predicting experimental outcomes, and optimizing chemical processes. AI facilitates the discovery of new materials, improves reaction conditions, and enhances product development through advanced algorithms and machine learning. This capability allows researchers to make data-driven decisions more efficiently and effectively, thereby reducing time and costs associated with traditional R&D methods. Consequently, the increasing reliance on AI to advance research and development fuels the expanding demand for AI solutions within the chemical sector.

The key region in the Global AI in Chemicals Market include North America, Europe, Asia Pacific, Latin America, and Middle East & Africa. Geographically, North America is expected to hold the largest share of the AI in Chemicals market in 2023, driven by robust R&D funding and strategic government initiatives promoting AI. The region's strong focus on innovation and digital transformation drives the adoption of AI technologies to enhance chemical processes, optimize production, and accelerate product development. Major corporations and research institutions in North America are leveraging AI to gain competitive advantages, improve operational efficiency, and foster innovation. Additionally, supportive government policies and substantial funding for AI-driven initiatives contribute to North America's leadership in this rapidly growing market. Furthermore, the Asia-Pacific region is poised to grow at the fastest CAGR, fueled by its diverse chemical industry and supportive governmental policies.

Major market players included in this report are:

• IBM
• Schneider Electric
• Google
• Microsoft
• SAP
• AWS
• NVIDIA
• C3.ai
• GE Vernova
• Siemens

The detailed segments and sub-segment of the market are explained below:

By Component:

• Hardware
• Software
• Services

By Business Application:

• R&D
• Production
• Supply Chain Management
• Strategy Management

By End User:

• Basic Chemicals
• Advanced Materials
• Active Ingredients
• Green & Biochemicals
• Paints & Coatings
• Adhesives & Sealants
• Water Treatment & Services
• Other End Users

By Region:

• North America
• U.S.
• Canada
• Europe
• UK
• Germany
• France
• Spain
• Italy
• ROE
• Asia Pacific
• China
• India
• Japan
• Australia
• South Korea
• RoAPAC
• Latin America
• Brazil
• Mexico
• Rest of Latin America
• Middle East & Africa
• Saudi Arabia
• South Africa
• RoMEA

Years considered for the study are as follows:

• Historical year - 2022
• Base year - 2023
• Forecast period - 2024 to 2032

Key Takeaways:

• Market Estimates & Forecast for 10 years from 2022 to 2032.
• Annualized revenues and regional level analysis for each market segment.
• Detailed analysis of geographical landscape with Country level analysis of major regions.
• Competitive landscape with information on major players in the market.
• Analysis of key business strategies and recommendations on future market approach.
• Analysis of competitive structure of the market.
• Demand side and supply side analysis of the market.

Table of Contents

Chapter 1. Global AI in Chemicals Market Executive Summary

• 1.1. Global AI in Chemicals Market Size & Forecast (2022-2032)
• 1.2. Regional Summary
• 1.3. Segmental Summary
  • 1.3.1. By Component
  • 1.3.2. By Business Application
  • 1.3.3. By End User
• 1.4. Key Trends
• 1.5. Recession Impact
• 1.6. Analyst Recommendation & Conclusion

Chapter 2. Global AI in Chemicals Market Definition and Research Assumptions

• 2.1. Research Objective
• 2.2. Market Definition
• 2.3. Research Assumptions
  • 2.3.1. Inclusion & Exclusion
  • 2.3.2. Limitations
  • 2.3.3. Supply Side Analysis
    • 2.3.3.1. Availability
    • 2.3.3.2. Infrastructure
    • 2.3.3.3. Regulatory Environment
    • 2.3.3.4. Market Competition
    • 2.3.3.5. Economic Viability (Consumer's Perspective)
  • 2.3.4. Demand Side Analysis
    • 2.3.4.1. Regulatory frameworks
    • 2.3.4.2. Technological Advancements
    • 2.3.4.3. Environmental Considerations
    • 2.3.4.4. Consumer Awareness & Acceptance
• 2.4. Estimation Methodology
• 2.5. Years Considered for the Study
• 2.6. Currency Conversion Rates

Chapter 3. Global AI in Chemicals Market Dynamics

• 3.1. Market Drivers
  • 3.1.1. Growing demand for AI in research & development
  • 3.1.2. Adoption of advanced digital techniques
  • 3.1.3. Increased emphasis on improved batch production scheduling
• 3.2. Market Challenges
  • 3.2.1. High initial investment and operational costs
  • 3.2.2. Regulatory concerns and data privacy issues
• 3.3. Market Opportunities
  • 3.3.1. Expansion in emerging markets
  • 3.3.2. Technological advancements and innovations
  • 3.3.3. Collaboration between AI developers and chemical manufacturers

Chapter 4. Global AI in Chemicals Market Industry Analysis

• 4.1. Porter's 5 Force Model
  • 4.1.1. Bargaining Power of Suppliers
  • 4.1.2. Bargaining Power of Buyers
  • 4.1.3. Threat of New Entrants
  • 4.1.4. Threat of Substitutes
  • 4.1.5. Competitive Rivalry
  • 4.1.6. Futuristic Approach to Porter's 5 Force Model
  • 4.1.7. Porter's 5 Force Impact Analysis
• 4.2. PESTEL Analysis
  • 4.2.1. Political
  • 4.2.2. Economical
  • 4.2.3. Social
  • 4.2.4. Technological
  • 4.2.5. Environmental
  • 4.2.6. Legal
• 4.3. Top investment opportunity
• 4.4. Top winning strategies
• 4.5. Disruptive Trends
• 4.6. Industry Expert Perspective
• 4.7. Analyst Recommendation & Conclusion

Chapter 5. Global AI in Chemicals Market Size & Forecasts by Component 2022-2032

• 5.1. Segment Dashboard
• 5.2. Global AI in Chemicals Market: Component Revenue Trend Analysis, 2022 & 2032 (USD Billion)
  • 5.2.1. Hardware
  • 5.2.2. Software
  • 5.2.3. Services

Chapter 6. Global AI in Chemicals Market Size & Forecasts by Business Application 2022-2032

• 6.1. Segment Dashboard
• 6.2. Global AI in Chemicals Market: Business Application Revenue Trend Analysis, 2022 & 2032 (USD Billion)
  • 6.2.1. R&D
  • 6.2.2. Production
  • 6.2.3. Supply Chain Management
  • 6.2.4. Strategy Management

Chapter 7. Global AI in Chemicals Market Size & Forecasts by End User 2022-2032

• 7.1. Segment Dashboard
• 7.2. Global AI in Chemicals Market: End User Revenue Trend Analysis, 2022 & 2032 (USD Billion)
  • 7.2.1. Basic Chemicals
  • 7.2.2. Advanced Materials
  • 7.2.3. Active Ingredients
  • 7.2.4. Green & Biochemicals
  • 7.2.5. Paints & Coatings
  • 7.2.6. Adhesives & Sealants
  • 7.2.7. Water Treatment & Services
  • 7.2.8. Other End Users

Chapter 8. Global AI in Chemicals Market Size & Forecasts by Region 2022-2032

• 8.1. North America AI in Chemicals Market
  • 8.1.1. U.S. AI in Chemicals Market
    • 8.1.1.1. Component breakdown size & forecasts, 2022-2032
    • 8.1.1.2. Business Application breakdown size & forecasts, 2022-2032
    • 8.1.1.3. End User breakdown size & forecasts, 2022-2032
  • 8.1.2. Canada AI in Chemicals Market
• 8.2. Europe AI in Chemicals Market
  • 8.2.1. UK AI in Chemicals Market
  • 8.2.2. Germany AI in Chemicals Market
  • 8.2.3. France AI in Chemicals Market
  • 8.2.4. Spain AI in Chemicals Market
  • 8.2.5. Italy AI in Chemicals Market
  • 8.2.6. Rest of Europe AI in Chemicals Market
• 8.3. Asia-Pacific AI in Chemicals Market
  • 8.3.1. China AI in Chemicals Market
  • 8.3.2. India AI in Chemicals Market
  • 8.3.3. Japan AI in Chemicals Market
  • 8.3.4. Australia AI in Chemicals Market
  • 8.3.5. South Korea AI in Chemicals Market
  • 8.3.6. Rest of Asia-Pacific AI in Chemicals Market
• 8.4. Latin America AI in Chemicals Market
  • 8.4.1. Brazil AI in Chemicals Market
  • 8.4.2. Mexico AI in Chemicals Market
  • 8.4.3. Rest of Latin America AI in Chemicals Market
• 8.5. Middle East & Africa AI in Chemicals Market
  • 8.5.1. Saudi Arabia AI in Chemicals Market
  • 8.5.2. South Africa AI in Chemicals Market
  • 8.5.3. Rest of Middle East & Africa AI in Chemicals Market

Chapter 9. Competitive Intelligence

• 9.1. Key Company SWOT Analysis
• 9.2. Top Market Strategies
• 9.3. Company Profiles
  • 9.3.1. IBM
    • 9.3.1.1. Key Information
    • 9.3.1.2. Overview
    • 9.3.1.3. Financial (Subject to Data Availability)
    • 9.3.1.4. Product Summary
    • 9.3.1.5. Market Strategies
  • 9.3.2. Schneider Electric (France)
  • 9.3.3. Google
  • 9.3.4. Microsoft
  • 9.3.5. SAP
  • 9.3.6. AWS
  • 9.3.7. NVIDIA
  • 9.3.8. C3.ai
  • 9.3.9. GE Vernova
  • 9.3.10. Siemens

Chapter 10. Research Process

• 10.1. Research Process
  • 10.1.1. Data Mining
  • 10.1.2. Analysis
  • 10.1.3. Market Estimation
  • 10.1.4. Validation
  • 10.1.5. Publishing
• 10.2. Research Attributes