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Artificial Intelligence (AI) in Chemical Market Size is valued at USD 1.2 billion in 2024 and is predicted to reach USD 14.1 billion by the year 2031 at a 28.0% CAGR during the forecast period for 2025-2034.
Artificial intelligence (AI) is a game-changer that can make chemical industries more efficient and effective. The automation of processes, the improvement of manufacturing settings, and the revelation of chemical reactions are all ways this technology makes operations more productive. AI is utilized to expedite the innovation process between the process and product development stages. Chemical businesses use machine learning and advanced analytics algorithms with historical data to define costs and performance precisely. Many chemical sectors turn to specialized mathematical methods and models when anticipating catalyst aging, complex dye solubility, and the ideal chemical combination. The chemical industry faces increasing demand to enhance sustainability while decreasing environmental effects. AI can facilitate the creation of more environmentally friendly chemical solutions. Furthermore, the market is anticipated to be propelled by increased government investments in research and development to optimize production processes.
However, the market growth is hampered by the high investment criteria for the safety and health of Artificial Intelligence (AL) In Chemical Market and the product's inability to prevent fog in environments with dramatic temperature fluctuations or high artificial intelligence (AL) in chemical, because building a factory in the chemical sector usually necessitates a large initial expenditure. Large initial investments are required to implement new technology, such as AI, because of the high cost of acquiring necessary hardware and software, training employees, funding research and development, and integrating AI solutions into current operations.
High implementation costs are preventing many small and medium enterprises from embracing AI despite its immense potential to streamline operations, increase productivity, and shorten product development cycles in the chemical sector. However, the COVID-19 pandemic contributed to the expansion of AI in the chemical sector by highlighting the widespread usage of the technology for the detection and screening of current COVID-19 treatments. Global markets expanded during the pandemic based on AI-based discoveries rather than the months-long and equally expensive conventional vaccine recognition techniques.
The Artificial intelligence (AI) in the chemical market is segmented based on type, application, and end-use. Based on type, the market is segmented into hardware, software, and services. By application, the market is segmented into the discovery of new materials, production optimization, pricing optimization, load forecasting of raw materials, product portfolio optimization, feedstock optimization, and process management & control. By end use, the market is segmented into base chemicals & petrochemicals, specialty chemicals, and agrochemicals.
The production optimization artificial intelligence (AI) in the chemical market is expected to hold a major global market share in 2022. Production optimization can enhance a company's financial, time management, organizational, and ecological elements. When establishing objectives, many things come into play, including the company's resources and investment capacity, consumer needs, and the industry's overall state of the market.
The hardware industry makes up the bulk of acrylic acid ester usage because specialist hardware components like AI memory and processors are in high demand. AI algorithms are used for more complicated operations, especially in countries like the US, Germany, the UK, China, and India.
The North American artificial intelligence (AI) in the chemical market is expected to register the highest market share in revenue in the near future. This can be attributed to the fact that chemical companies are investing more in research and development to improve their manufacturing processes and because of a growing awareness of digitalization approaches. In addition, Asia Pacific is projected to grow rapidly in the chemical market's global artificial intelligence (AI) because of the growing funding for cutting-edge research and development in this area. The expansion of healthcare facilities in the area is another factor that will boost the market's growth.
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Report Attribute |
Specifications |
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Market Size Value In 2024 |
USD 1.2 Bn |
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Revenue Forecast In 2034 |
USD 14.1 Bn |
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Growth Rate CAGR |
CAGR of 28.0% from 2025 to 2034 |
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Quantitative Units |
Representation of revenue in US$ Bn and CAGR from 2025 to 2034 |
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Historic Year |
2021 to 2024 |
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Forecast Year |
2025-2034 |
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Report Coverage |
The forecast of revenue, the position of the company, the competitive market structure, growth prospects, and trends |
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Segments Covered |
By Type, Application and End-user |
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Regional Scope |
North America; Europe; Asia Pacific; Latin America; Middle East & Africa |
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Country Scope |
U.S.; Canada; U.K.; Germany; China; India; Japan; Brazil; Mexico; France; Italy; Spain; Southeast Asia; South Korea |
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Competitive Landscape |
Manuchar N.V, IMCD N.V., Univar Solutions Inc., Brenntag S.E., Sojitz Corporation, ICC Industries Inc., Azelis Group NV, Tricon Energy Inc., Biesterfeld AG, Omya AG, HELM AG, Sinochem Corporation, Petrochem Middle East FZE. |
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Customization Scope |
Free customization report with the procurement of the report and modifications to the regional and segment scope. Particular Geographic competitive landscape. |
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Pricing And Available Payment Methods |
Explore pricing alternatives that are customized to your particular study requirements. |
Chapter 1. Methodology and Scope
1.1. Research Methodology
1.2. Research Scope & Assumptions
Chapter 2. Executive Summary
Chapter 3. Global Artificial Intelligence (AI) in Chemical Market Snapshot
Chapter 4. Global Artificial Intelligence (AI) in Chemical Market Variables, Trends & Scope
4.1. Market Segmentation & Scope
4.2. Drivers
4.3. Challenges
4.4. Trends
4.5. Investment and Funding Analysis
4.6. Industry Analysis – Porter’s Five Forces Analysis
4.7. Competitive Landscape & Market Share Analysis
4.8. Impact of Covid-19 Analysis
Chapter 5. Market Segmentation 1: by Type Estimates & Trend Analysis
5.1. by Type & Market Share, 2024 & 2034
5.2. Market Size (Value (US$ Mn)) & Forecasts and Trend Analyses, 2021 to 2034 for the following by Type:
5.2.1. Hardware
5.2.2. Software
5.2.3. Services
Chapter 6. Market Segmentation 2: by Application Estimates & Trend Analysis
6.1. by Application & Market Share, 2024 & 2034
6.2. Market Size (Value (US$ Mn)) & Forecasts and Trend Analyses, 2021 to 2034 for the following by Application:
6.2.1. Discovery Of New Materials
6.2.2. Production Optimization
6.2.3. Pricing Optimization
6.2.4. Load Forecasting of Raw Materials
6.2.5. Product Portfolio Optimization
6.2.6. Feedstock Optimization
6.2.7. Process Management & Control
Chapter 7. Market Segmentation 3: by End User Estimates & Trend Analysis
7.1. by End User & Market Share, 2024 & 2034
7.2. Market Size (Value (US$ Mn)) & Forecasts and Trend Analyses, 2021 to 2034 for the following by End User:
7.2.1. Base Chemicals & Petrochemicals
7.2.2. Specialty Chemicals
7.2.3. Agrochemicals
Chapter 8. Artificial Intelligence (AI) in Chemical Market Segmentation 4: Regional Estimates & Trend Analysis
8.1. North America
8.1.1. North America Artificial Intelligence (AI) in Chemical Market Revenue (US$ Million) Estimates and Forecasts by Type, 2021-2034
8.1.2. North America Artificial Intelligence (AI) in Chemical Market Revenue (US$ Million) Estimates and Forecasts by Application, 2021-2034
8.1.3. North America Artificial Intelligence (AI) in Chemical Market Revenue (US$ Million) Estimates and Forecasts by End User, 2021-2034
8.1.4. North America Artificial Intelligence (AI) in Chemical Market Revenue (US$ Million) Estimates and Forecasts by country, 2021-2034
8.2. Europe
8.2.1. Europe Artificial Intelligence (AI) in Chemical Market Revenue (US$ Million) Estimates and Forecasts by Type, 2021-2034
8.2.2. Europe Artificial Intelligence (AI) in Chemical Market Revenue (US$ Million) Estimates and Forecasts by Application, 2021-2034
8.2.3. Europe Artificial Intelligence (AI) in Chemical Market Revenue (US$ Million) Estimates and Forecasts by End User, 2021-2034
8.2.4. Europe Artificial Intelligence (AI) in Chemical Market Revenue (US$ Million) Estimates and Forecasts by country, 2021-2034
8.3. Asia Pacific
8.3.1. Asia Pacific Artificial Intelligence (AI) in Chemical Market Revenue (US$ Million) Estimates and Forecasts by Type, 2021-2034
8.3.2. Asia Pacific Artificial Intelligence (AI) in Chemical Market Revenue (US$ Million) Estimates and Forecasts by Application, 2021-2034
8.3.3. Asia-Pacific Artificial Intelligence (AI) in Chemical Market Revenue (US$ Million) Estimates and Forecasts by End User, 2021-2034
8.3.4. Asia Pacific Artificial Intelligence (AI) in Chemical Market Revenue (US$ Million) Estimates and Forecasts by country, 2021-2034
8.4. Latin America
8.4.1. Latin America Artificial Intelligence (AI) in Chemical Market Revenue (US$ Million) Estimates and Forecasts by Type, 2021-2034
8.4.2. Latin America Artificial Intelligence (AI) in Chemical Market Revenue (US$ Million) Estimates and Forecasts by Application, 2021-2034
8.4.3. Latin America Artificial Intelligence (AI) in Chemical Market Revenue (US$ Million) Estimates and Forecasts by End User, 2021-2034
8.4.4. Latin America Artificial Intelligence (AI) in Chemical Market Revenue (US$ Million) Estimates and Forecasts by country, 2021-2034
8.5. Middle East & Africa
8.5.1. Middle East & Africa Artificial Intelligence (AI) in Chemical Market Revenue (US$ Million) Estimates and Forecasts by Type, 2021-2034
8.5.2. Middle East & Africa Artificial Intelligence (AI) in Chemical Market Revenue (US$ Million) Estimates and Forecasts by Application, 2021-2034
8.5.3. Middle East & Africa Artificial Intelligence (AI) in Chemical Market Revenue (US$ Million) Estimates and Forecasts by End User, 2021-2034
8.5.4. Middle East & Africa Artificial Intelligence (AI) in Chemical Market Revenue (US$ Million) Estimates and Forecasts by country, 2021-2034
Chapter 9. Competitive Landscape
9.1. Major Mergers and Acquisitions/Strategic Alliances
9.2. Company Profiles
9.2.1. Manuchar N.V
9.2.2. IMCD N.V.
9.2.3. Univar Solutions Inc.
9.2.4. Brenntag S.E.
9.2.5. Sojitz Corporation
9.2.6. ICC Industries Inc.
9.2.7. Azelis Group NV
9.2.8. Tricon Energy Inc.
9.2.9. Biesterfeld AG
9.2.10. Omya AG
9.2.11. HELM AG
9.2.12. Sinochem Corporation
9.2.13. Petrochem Middle East FZE
9.2.14. Other Prominent Players
By Type
Artificial Intelligence (AI) In Chemical Market By Application
Artificial Intelligence (AI) In Chemical Market By End User
Artificial Intelligence (AI) In Chemical Market By Region-
North America-
Europe-
Asia-Pacific-
Latin America-
Middle East & Africa-
InsightAce Analytic follows a standard and comprehensive market research methodology focused on offering the most accurate and precise market insights. The methods followed for all our market research studies include three significant steps – primary research, secondary research, and data modeling and analysis - to derive the current market size and forecast it over the forecast period. In this study, these three steps were used iteratively to generate valid data points (minimum deviation), which were cross-validated through multiple approaches mentioned below in the data modeling section.
Through secondary research methods, information on the market under study, its peer, and the parent market was collected. This information was then entered into data models. The resulted data points and insights were then validated by primary participants.
Based on additional insights from these primary participants, more directional efforts were put into doing secondary research and optimize data models. This process was repeated till all data models used in the study produced similar results (with minimum deviation). This way, this iterative process was able to generate the most accurate market numbers and qualitative insights.
Secondary research
The secondary research sources that are typically mentioned to include, but are not limited to:
The paid sources for secondary research like Factiva, OneSource, Hoovers, and Statista
Primary Research:
Primary research involves telephonic interviews, e-mail interactions, as well as face-to-face interviews for each market, category, segment, and subsegment across geographies
The contributors who typically take part in such a course include, but are not limited to:
Data Modeling and Analysis:
In the iterative process (mentioned above), data models received inputs from primary as well as secondary sources. But analysts working on these models were the key. They used their extensive knowledge and experience about industry and topic to make changes and fine-tuning these models as per the product/service under study.
The standard data models used while studying this market were the top-down and bottom-up approaches and the company shares analysis model. However, other methods were also used along with these – which were specific to the industry and product/service under study.
To know more about the research methodology used for this study, kindly contact us/click here.
