AI models are revolutionizing even the most inherently analog industries. In 2024, Demis Hassabis and John Jumper of DeepMind were awarded a share of the Nobel Prize in Chemistry for their AI model AlphaFold2, which helped solve a 50-year-old problem: Predicting the complex structures of proteins. One application of this model is creating images of enzymes capable of decomposing plastic. Artificial general intelligence (AGI) and machine learning (ML) are helping chemicals companies do business in an environment of geopolitical upheaval, uncertainty and volatility. Chemical companies are operating in an atmosphere of oversupply, unpredictable international policy shifts and a global energy transition.
The energy industry is generally ahead of the AI-adoption curve compared to the chemical sector, but the potential use cases in chemicals are profound and extensive — especially when catalyzed by generative AI (GenAI). Although there are challenges related to data, trust and integration, the potential benefits for efficiency, decision-making and innovation could help chemicals companies compete more effectively and build risk resilience in an impossibly challenging environment.
Disrupting the Disruptions
The chemicals industry is currently undergoing a significant shift with the increasing application of AI, like the previous phase when ML and AI first gained traction a decade ago. GenAI could not have come at a more crucial time, on the heels of a half-decade of sharp disruptions that have asked the industry to transform its systems, adapt to new regulations, decarbonize and reimagine supply chains. Now, as uncertainties in trade policy and geopolitics plague the industry, businesses are focusing on protecting margins, navigating the oversupply issue, diversifying their supplier base and decentralizing operations to build resilience against future disruptions.
GenAI: The Access Layer to More Advanced Modeling Capabilities
Chemical companies are already using GenAI copilots and large language models (LLMs) for time-consuming tasks to unlock operational efficiencies. It helps in managing colossal datasets and getting quick answers from tomes of technical, legal and compliance documentation. The chemicals sector is a process industry, with numerous use cases in R&D and manufacturing. However, if one asked which top two business functions — if done well — would best help chemical companies survive and thrive, most leaders would top their lists with supply chain and market intelligence. Most sectors are now in a race to make the best use of GenAI models and tools, which has become critical to competing.
Traditional AI/ML analytics tools are currently more prominent in use cases such as supply chain optimization and inventory optimization. AI can be used with sensors in plants for predictive maintenance and to detect problems. It can also predict the quality of output much faster than traditional lab testing, providing a time advantage.
Increasingly, GenAI is going to be a propellant, an access layer to more advanced modeling capabilities. We’re going to see a democratization of advanced capabilities, opening access to less technically advanced users. Companies can gain an advantage when LLMs allow their people to interact with advanced systems through natural language processing (NLP). A chemical company’s procurement decision-maker, for example, will be able to justify their decisions through a couple of clicks instead of hours of taxing manual analysis.
Democratizing Advanced Analytics for Smarter Procurement
GenAI and traditional AI can assist in monitoring purchasing needs, anticipating shortages, deciding when and how much to buy and managing supply chain risks. Chemicals and commodities products cascade downstream to automotive, packaging, medical, construction and clothing, with the forces of regulatory and trade policies exerting influence on the inextricably linked markets of Europe, the U.S., the Middle East and Asia. It’s like a just-in-time global web — and when you pull one thread, the closer nodes feel it immediately, but nothing is entirely isolated.
AI helps overcome some of the limitations to procurement optimization, in seeing not just a few possible puzzle pieces, but the entire picture so that we can make decisions based on this holistic view. It’s not just improved line of sight; it’s the speed to decide at the right time. AI can help us monitor some intricate dynamics to anticipate shortages or assess the impact of a shortage. Procurement leaders can ask their GenAI copilots questions such as what’s the dynamic around different sectors and how does that tie back to the chemicals they actually produce.
GenAI can Save the Day in Value Chain Visibility and Risk Management
Deloitte named value chain resilience as one of the top five priorities for chemicals industry in its 2025 report, which it boils down to:
- Decentralization/diversification
- Collaborative planning with suppliers and customers
- Monitoring supply chain resilience
GenAI can play an essential role in making all of these drivers more effective — helping with monitoring, tracking and forecasting. Further, agentic AI tools will be talking to each other, saving more time and sharing intelligence, not only among departments but also across organizations.
Uncertainty and volatility, along with the resulting market fluctuations, were standard operating conditions as we entered Q2 2025. GenAI digital assistants can enable analysts to access intelligent data on trends in feedstocks, capacity and production information, price differentials and forecasts in real-time. This offers companies up-to-the-minute insights to reduce losses and take full advantage of short-term market opportunities.
Cost Optimization Through AI
Cost efficiency is the #1 trend in chemicals. Aside from improved procurement decisions and time-saving operations, AI helps producers in cash preservation and cost optimization with predictive maintenance, lower material consumption and reduced energy usage. By democratizing access to a clearer understanding of market data, AI can empower more individuals within a company to make cost-conscious decisions. In the future, AI could support more efficient negotiations with suppliers by providing better information and structuring arguments to get favorable pricing agreements and reduced procurement costs.
Strategic Considerations for AI Adoption
Everybody is running on the AI adoption wheel, grappling with decision-making focused on delivering the best ROI. However, the challenging part of innovation is not creating technical systems; it’s driving buy-in and making people comfortable. Questions abound: Build or buy, LLM or SLM, purpose-built agentic or open-source provider. Depending on the budget, urgency and purpose, some chemical companies may elect to work with big players like OpenAI and DeepSeek, which will drive non-industry specific innovations such as language interpretation, reasoning and math.
For certain highly regulated industries like chemicals and energy, it is likely more sensible to build onto systems where others have already driven the innovation, so you don’t need to pay for every rotation of the innovation cycle. Out-of-the-box GenAI solutions are good at summarizing but not good at understanding where information gaps exist, which requires industry-specific context, since much relevant data is not readily available on the public web.
AI Breakthroughs Reshape the Chemicals Industry
In many ways, the chemicals sector is the ‘industry of industries’ in terms of interdependencies and influence. It would not prove cost-effective if 1,000 plastics and synthetic resins manufacturing companies in the U.S. each developed their own GenAI model. The aforementioned ‘collaborative planning with suppliers and customers’ is mission-critical in several aspects, not the least of which is the rising necessity to enter more partnerships between chemicals industry companies and suppliers and customers. This would ideally include data sharing, executed through associations and third-party intermediary providers.
AI tools are redefining the chemicals industry, enhancing supply chain resilience, procurement and decision-making amid market volatility. Better and faster visibility can lead to optimized inventory levels, reduced logistics costs and improved responsiveness to market changes. As companies navigate complex regulations and global dependencies, AI enables smarter operations and democratized access to advanced insights — crucial advantages in a sector powering our entire lives.
