by The artificial intelligence (AI) in chemical market is experiencing significant growth as AI enables optimization of production processes, cost reduction, risk management, and data-driven decision making in the chemical industry. AI provides chemical manufacturers with innovative tools to improve plant safety and operations. It analyzes vast amounts of complex data generated during chemical production and offers insights to enhance efficiency. For example, AI-powered computer vision systems and robots assist in quality inspections, predictive maintenance helps schedule repairs before breakdowns occur, and machine learning algorithms aid in real-time optimization of production metrics.
The Global Artificial Intelligence (AI) in Chemical Market Demand is estimated to be valued at US$ 1.40 BN in 2024 and is expected to exhibit a CAGR of 31% over the forecast period from 2024 to 2031.
Key Takeaways
Key players: Key players operating in the artificial intelligence (AI) in chemical market are 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, and Petrochem Middle East.
Key opportunities: The AI in chemical market offers opportunities for chemical companies to reduce operating costs, minimize wastage, increase productivity and optimize asset utilization through real-time data monitoring and advanced analytics. It also enables predictive maintenance and quality control.
Global expansion: Major players are investing in AI technology and collaborating with startups to expand globally. They are also acquiring small AI companies to integrate novel solutions into their production facilities and distribution networks across world regions like North America, Europe, Asia Pacific, Latin America, and Middle East & Africa.
Market drivers: One of the major drivers for growth of AI in the chemical market is rising adoption of AI-powered systems for improved decision making. AI assists chemical manufacturers in predictive analytics, optimization of complex chemical processes, risk assessment, and supply chain management through machine learning algorithms. This helps increase operational efficiency and reduce production downtime.
PEST Analysis
Political: Government policies and regulations play a key role in adoption and implementation of AI technologies. Changes in trade policies between countries can impact cross border investments in AI technologies.
Economic: Global and regional economic growth directly impacts the chemicals industry demand. Economic uncertainties may delay investments in advanced AI systems. However, AI can help optimize operations and reduce costs, supporting economic goals.
Social: Chemical companies aim to enhance safety, quality and sustainability with AI. Social acceptance of AI depends on addressing job disruption concerns and fair treatment of data. Public-private partnerships on AI skills training can help address social challenges.
Technological: Advanced machine learning and deep learning techniques are enabling new applications in chemistry, materials development and manufacturing. Integration of AI across business functions requires continuous technology upgrades. Data availability, computing power and software capabilities impact the pace of AI adoption.
North America remains the largest geographical market for AI in chemicals in terms of value, driven by large investments in R&D and presence of global tech companies. Europe is also a major regional market supported by initiatives to support advanced manufacturing through AI. Asia Pacific is expected to show highest growth led by government initiatives and infrastructure development in countries such as China and India.
The fastest growing regional market for AI in chemicals is expected to be Asia Pacific from 2024-2031 supported by rapid economic growth, developments in industrial AI and digitalization efforts across industries in China, India, Japan, and South Korea. National AI strategies aim to boost manufacturing competitiveness through emerging technologies such as ML, computer vision and autonomous robots.
