ARTIFICIAL INTELLIGENCE (AI) IN CHEMICAL MARKET SIZE AND SHARE ANALYSIS - GROWTH TRENDS AND FORECASTS (2024-2031)

The artificial intelligence (AI) in chemical Market size is valued at US$ 1.40 Bn in 2024 and is expected to reach US$ 12.51 Bn by 2031, growing at a compound annual growth rate (CAGR) of 36.7% from 2024 to 2031. Artificial intelligence (AI) is being widely adopted by the chemical industry to improve processes and discoveries. AI is helping chemists in various ways ranging from basic research to production. In research, AI techniques like machine learning and deep learning are augmenting human insights. Chemical companies are using AI to speed up drug discovery and materials development. AI analyzes huge databases of molecules and reactions to identify promising candidates. This is helping scientists explore chemical space far more efficiently than before.

Artificial Intelligence (AI) in Chemical Market Regional Insights

• North America has emerged as the dominant region in the artificial intelligence (AI) in chemical market with market  presence of 40%. This is due to presence of several major chemical companies in the U.S. and Canada that have adopted AI technologies on a large scale to drive efficiencies in their research and development (R&D) and manufacturing processes. These companies are investing heavily in developing new AI-based tools and platforms. For instance, many of them have set up separate research divisions solely focused on creation of advanced algorithms and applications. This shows their strong commitment towards incorporating AI-led digital transformation.
• Asia Pacific region, on the other hand, is witnessing the fastest growth and has huge untapped potential. Countries like China, India, Japan, and South Korea are actively promoting use of advanced technologies across industries. Their governments provide liberal funding and incentives to foster innovation. Also, chemical firms located in Asia Pacific aim to leverage AI to cope up with rising competitive pressures and costs. This is evident from increasing number of startups sprouting with focus on custom AI solutions for chemicals vertical. Availability of technical talent and low investment costs make the region attractive for global tech giants to establish their development centers.
• Japan's competence in robotics is projected to drive greater adoption of AI-powered robotics solutions in its extensive chemicals market. China, too, is anticipated to contribute significantly due to concentrated efforts of its companies to embrace smart manufacturing initiatives supported by AI. In India, presence of huge talent pool proficient in computing technologies stirs demand for local AI product development. Its thriving pharmaceutical and specialty chemicals clusters provide suitable application environment for customized AI tools. South Korea meanwhile has shown reliance on advanced predictive maintenance systems relying on AI and internet-of-things (IoT) for predictive inspection and diagnostics across plants.

Figure 1.  Artificial Intelligence (AI) in Chemical Market Share (%), By Region, 2024

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Analyst Viewpoint:

The artificial intelligence (AI) in chemical market is expected to grow significantly in the near future. The key market drivers of growth include the increasing demand for more productive and safer chemical processes. AI technologies such as machine learning and computer vision can help speed up discovery and optimize chemical synthesis routes. There is also a growing need to reduce environmental impact and improve sustainability in the chemical industry. AI can help develop greener chemical solutions. However, high investment and maintenance costs that are associated with AI systems may restrain the market growth initially.

North America will likely continue dominating the artificial intelligence (AI) in chemical market driven by extensive R&D activities from major players in the region. However, Asia Pacific is expected to emerge as the fastest growing regional market. This is due to rising industrial activities, growing adoption of advanced technologies, and government focus on developing domestic AI champions in countries such as China and India.

Within the chemical sectors, pharmaceuticals and biotechnology are projected to provide most lucrative opportunities for AI. This is because AI can greatly accelerate drug discovery processes and reduce costs. AI will also find increasing usage in specialized chemical areas such as agrochemicals, water treatment chemicals, and coatings. Going forward, small & medium chemical companies are expected to increasingly invest in AI technologies to boost efficiency, gain insights from vast datasets, and remain competitive.

Artificial Intelligence (AI) in Chemical Market Drivers

Automating chemical analysis:  With advancements in machine learning and artificial intelligence techniques, automating routine chemical analysis tasks has become highly feasible. AI systems powered by neural networks can leverage vast amounts of existing analytical data to perform tasks like compound identification, property prediction, and structure-activity modeling with superhuman levels of accuracy. This reduces reliance on human experts and frees them up to focus on more complex challenges.

By automating repetitive jobs like scanning analytical results, classifying spectra, or characterizing molecules, AI promises to significantly boost productivity in chemistry labs. Rather than manually examining each test outcome, compounds can be rapidly analyzed at scale and anomalous results flagged for further investigation. This allows researchers to screen far larger libraries in pursuit of hits. Systems trained on institutional databases also help extrapolate knowledge across an organization, thus ensuring consistent analysis over time even as staff rotates onto new projects. For instance, according to the data provided by the United Nations Economic and Social Council in  2021, early adopters are witnessing productivity increases of 30-40% through automation.

Optimizing production processes: In large-scale chemical manufacturing, AI is being implemented to drive significant efficiency and optimization. Neural networks can learn patterns in vast production datasets spanning variables like temperatures, pressures, material properties and throughputs. They then identify the most influential factors and their interactions to precisely determine the ideal operating conditions across interdependent unit operations. Rather than crude rule-based controls, AI enables autonomously maintaining a process at its maximum performance point.

By continuously monitoring output quality and adjusting parameters accordingly, AI ensures manufacturing consistency even as conditions inevitably drift over time. Any updates like shifts in raw material specifications or equipment wear can be automatically compensated for. Predictive maintenance tools also analyze equipment telemetry to pinpoint impending issues, thus reducing unplanned downtime. Combined with digital twin simulations, AI finds ways to dynamically reconfigure entire plants in response to changing demand or unforeseen outages. For instance, in 2021, according to a  report provided by the United Nations Industrial Development Organization highlights, AI technologies for predictive maintenance enabled a leading European chemical manufacturer to reduce unexpected downtime by 25%.

Artificial Intelligence (AI) in Chemical Market Opportunities

Predictive maintenance: Predictive maintenance through artificial intelligence can play a pivotal role in optimizing plant performance, safety and sustainability objectives in the chemical industry. With the use of advanced sensors, IoT devices and machine learning algorithms, AI-powered predictive maintenance solutions are able to extensively monitor equipment operations in real-time. They can analyze multiple operational parameters, detect anomalies, and accurately predict equipment failures even before any visible symptoms arise. This helps to avoid unexpected breakdowns and outages which can cause significant losses in production capacity and revenues. By implementing predictive maintenance strategies based on AI, chemical companies can transition from conventional reactive or preventive maintenance approaches to a more cost-effective and risk-averse reliability-centered model. For instance, , according to the  studies by the Department of Energy of the U.S. AI solutions help chemical plants reduce unplanned downtime by up to 30% .

 New product discovery: New product discovery could provide significant opportunities for innovation and growth in the AI chemical industry. C The experimental discovery and development of new chemical compounds is a long, costly and imperfect process. Chemists rely largely on trial and error approaches and established chemical libraries. However, AI and machine learning models are making drug and material discovery more efficient by revealing unexpected connections in existing data and simulating potential molecular properties and reactions at a scale impossible for humans alone. By analyzing vast datasets of chemical structures and corresponding characteristics, AI systems can point the way towards entirely new classes of materials and medicines with desirable and marketable characteristics. This represents a paradigm shift that could radically accelerate the pace of innovation in fields like pharmaceuticals, agriculture, manufacturing and more.

Several startups are already applying AI to propel new product introduction in the chemical industry. one area showing promise is sustainable chemistry. By examining databases of natural product structures derived from millions of plant and microbial species, AI is revealing unexpected bio-inspired building blocks for creating non-toxic materials, plastics and other compounds. Another application is vaccine and therapy design. By simulating molecular docking and protein folding at the atomic scale, AI is helping scientists engineer precisely targeted immunotherapies and gene therapies that could treat previously intractable diseases. As the power of AI and amount of available data grows exponentially in the near future, its ability to discover or invent revolutionary new chemical products from first-principles will also scale dramatically. For instance, according to the United Nations Environment Programme (UNEP), the current chemical production capacity of 2.3 Bn tonnes, valued at US$5 trillion annually, is projected to double by 2030.

Artificial Intelligence (AI) in Chemical Market Trends

Adoption of machine learning and deep learning techniques: The chemical industry has increasingly turned to artificial intelligence technologies like machine learning and deep learning in years. By analyzing vast datasets containing properties, structures, and reactions of chemicals, machine learning algorithms can discover complex patterns that aid in research and product development. For example, machine learning has helped pharmaceutical companies design new drug compounds more efficiently. Rather than trial-and-error in labs, AI helps predict which molecular structures are most likely to be safe and effective medicines. This has significantly accelerated drug discovery timelines. Similarly, materials companies developing new polymers, catalysts or specialty chemicals have witnessed machine learning recommend optimal formulations. By examining huge libraries of past formulations, experiments and outcomes, machine learning identifies correlations that help formulate new products with targeted properties. For instance,  in 2021, according to a survey conducted by the American Chemistry Council, over 80% of large U.S. chemical firms are either actively implementing or piloting AI projects, up from just 30% five years ago .

Increased investments in AI startups by major chemical companies: Major chemical companies have recognized the potential of artificial intelligence to transform various aspects of their business and drive efficiencies. They are actively scouting for and investing in AI startups that are developing technologies focused on chemical industries. This shift towards funding and partnering with external innovators signal these large corporations are open to explore new ideas from outside. By investing in promising AI startups early, chemical giants are aiming to stay ahead of the curve in commercializing newest AI applications. The increased investments are also an acknowledgement of the success many AI startups are achieving in solving industry-specific challenges.

This trend of deeper involvement of large chemical players in the AI startup ecosystem is positively impacting the developing AI in chemicals market. It is providing fillip to more targeted research and development in the sector as startups get access to both funding and real-world industry data & problems from their new partners.

Artificial Intelligence (AI) in Chemical Market Restraints

• High investment requirements: The chemical industry typically requires huge capital investments to set up manufacturing facilities. Adopting new technologies like artificial intelligence also demands substantial upfront investments for purchasing advanced equipment and software, hiring skilled talent, conducting R&D, and integrating AI solutions into existing processes. While AI has great potential to optimize operations, increase productivity, and accelerate product development cycles in the chemical sector, the high costs that are associated with its implementation are prohibiting many small and medium players from embracing these technologies. Setting up the necessary Information Technology IT  infrastructure for gathering and processing large chemical data sets, developing custom AI algorithms, and training comprehensive machine learning models requires millions of dollars that most mid-sized chemical companies may not have access to. Even simple AI applications like using computer vision for quality inspection or predictive maintenance of plants need significant capital outlays. Without sufficient financial resources, leveraging AI becomes an unviable option for these firms. This acts as a major roadblock in the widespread adoption of AI across the vast chemically diverse industry. For instance, according to the United Nations Conference on Trade and Development (UNCTAD), global Foreign Direct Investment.(FDI )flows plunged by 35% in 2020, from US$1.5 trillion in 2019 to US$1 trillion.
• Counterbalance: Start small with pilot projects: Beginning with smaller, low-risk pilot projects to demonstrate the potential return on investment (ROI) without requiring substantial upfront capital can counterbalance the restraint. Once proven successful, these pilot projects can be scaled up gradually.
•  Lack of skilled workforce:  The lack of a skilled workforce is a major hurdle restraining the growth of artificial intelligence in the chemical market. While AI has the potential to revolutionize various processes and operations across the chemical industry, generating cost efficiencies, optimizing production and opening new doors of innovation, the shortage of professionals with AI skills is preventing the full realization of these benefits. Having AI experts who understand both technological and domain aspects is crucial for developing and implementing relevant AI solutions. However, chemical companies worldwide are facing challenges in recruiting and retaining talent who have the expertise to work on AI projects tailored to the unique needs of the chemical industry. According to a  survey conducted by the World Economic Forum, in 2021, 83% of chemical business leaders cited lack of available skills as a significant barrier for adopting AI. Without the manpower that can steer AI deployment and maximize its impact, chemical companies are hesitant to invest heavily in this promising area. Furthermore, retraining the existing workforce is also proving to be difficult. Chemical plant employees who have been performing routine tasks for years may find it hard to transition to more strategic roles requiring strong digital abilities. There is a worldwide shortage of opportunities for chemical professionals to continuously up skill themselves in AI-related areas through accessible programs. Unless tackled through collaborative efforts between industry, government and educators, this talent crunch will hamper the global chemical sector from scaling up AI adoption and harnessing advanced technologies to thrive in an increasingly competitive marketplace. For instance, in 2021, according to a survey conducted by the World Economic Forum, 83% of chemical business leaders cited lack of available skills as a significant barrier for adopting AI.
• Counterbalance: Looking for potential candidates in adjacent fields such as data science, computer science, or process engineering who may have applicable skills could be transitioned into the chemical industry with relatively little additional training.

Recent Developments:

• In January 2023, Bayer entered into a strategic partnership with Google Cloud, which aimed at enhancing the capabilities of Bayer in the realm of quantum chemistry research. This collaboration is designed to pioneer new pathways in drug discovery, utilizing the advanced capabilities of machine learning. Bayer AG is a German multinational pharmaceutical and biotechnology company, one of the largest pharmaceutical companies and biomedical companies in the world. In fiscal 2022, the company employed around 101,000 people and had sales of €50.7 Bn
• Bayer is committed to driving sustainable development and generating a positive impact through innovation and growth. The company's brand stands for trust, reliability, and quality throughout the world .
• Google Cloud is a suite of cloud computing services offered by GoogleIt provides a range of modular cloud services, including computing, storage, data analytics, machine learning, and more Google Cloud Platform (GCP) runs on the same infrastructure that Google uses internally for its end-user products, such as Google Search, Gmail, and Google Docs
• In January 2023, researchers initiated a groundbreaking study leveraging the power of AlphaFold alongside artificial intelligence to accelerate the process of creating novel therapeutics targeting liver cancer.
• On January 16, 2023, Chemical.AI publicized their cooperation agreement with NovAliX, a leading global CRO specializing in drug development, thereby signifying a significant step forward in their collaborative efforts.
• Chemical.AI is an artificial intelligence company that focuses on developing tools for the chemical and pharmaceutical industries.
• In 2022, significant breakthrough when scholars from IIIT-Delhi devised an innovative AI-driven technique purposed for the detection of potentially cancer-causing agents within chemical compounds.
• In 2022, Sanofi secured a partnership with Exscientia, marked by a US$100 million investment earmarked for the advancement of 15 new small molecules for the treatment of cancer and immunological disorders, thereby showcasing their dedication to the future of healthcare. Exscientia is a global pharmatech company that uses artificial intelligence (AI) to discover better drugs faster . The company's mission is to encode, automate, and transform every stage of the drug design and development process by combining the latest AI techniques with experimental innovation . Exscientia's validated platform has delivered the first three AI-designed drugs to enter clinical trials.

Figure 2. Artificial Intelligence (AI) in Chemical Market  Share (%), By Type, 2024

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Top Companies in the Artificial Intelligence (AI) in Chemical Market 

• 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.

Definition: An effective instrument that can make chemical firms operate more quickly and intelligently is artificial intelligence. Automation, chemical reaction insights, and enhanced industrial environments are just a few ways that technology makes operations more productive.