AI capabilities will be a prime differentiator for chemical and energy companies in gaining an unprecedented view and understanding of relentlessly complicated commodities markets – and the swirling crosswinds buffeting them.
Tariffs are on top of meeting agendas of boardrooms across the globe. Executives of globally connected chemical companies are confronted each day with quickly changing and fluctuating trading conditions. The US government is setting out to redraw global trade maps. Waves of uncertainty are eroding shorelines where slow growth, excess capacity, and the need for circular operating models are already camped out.
The US government expects that the redomiciling of manufacturing will prove a boon to the industry suffering from a manufacturing recession. However, the main immediate consequence for decision makers is the extreme uncertainty of the moment. Short-term impacts can be highly disruptive, such as higher costs for raw materials and additional fees on exports. Executives now must elevate risk management and scenario planning to understand their company’s exposure to tariffs and maximize short-term resilience.
Concurrently, we are in the midst of an AI revolution that no industry can afford to postpone. The extraordinary power of AI should be deployed by industry to unlock critical efficiencies and insights to increase earnings and enable companies to reimagine how they analyze chemical markets. Can AI help decision makers in the chemical industry navigate the storm? Here are some ways the chemicals industry can leverage the power of AI to build resilience against volatility and navigate geopolitical tumult, all while staying competitive and cutting costs.
AI agents are upskilling, fast
AI technology is advancing quickly, making innovations every few weeks. AI digital assistants are already enabling chemical firms to manage vast amounts of information and make operational processes more efficient. For example, AI is used by teams that assess market risks to scan the latest information and structure internal reports, drawing on both public and specialized paid information sources. AI is further used by teams who operate plants to quickly find and summarize technical documentation like configurations, quality control, safety regulations, and compliance reports.
See also: Boosting Supply Chain Management Through Analytics: A Deep Dive
AI spots the unknown unknowns in scenario planning
Market and pricing analysts and supply chain/logistics managers can deploy AI agents to help understand global risks to their supply chains. Currently, chemicals companies operate in a time of radically changing contexts for supply chain decisions due to tariffs and country trade relationship negotiations. High-performing companies will roll with the changes, responding intelligently with expeditious, data-driven adjustments.
AI can’t take away the current political and economic uncertainty, nor can it magically derive clear decisions where they aren’t possible. However, AI can help us keep our fingers on the pulse in real-time and do scenario planning on various future pathways. AI can help appraise alternative strategies and prepare informed pros and cons of company responses to each scenario. In ways that humans alone cannot, AI agents can help to spot both known unknowns and unknown unknowns. Front-runners in the industry are connecting AI agents with existing advanced predictive capabilities and models. With so many variables at play, advanced scenario modeling has become exceedingly complex for decision makers – and necessary.
See also: Unveiling the Future: The Convergence of Brain-Inspired AI and Supply Chain Dynamics
Executives need real-time, actionable data to understand market dynamics
US chemical companies must gain a proactive, real-time understanding of shifting regional market dynamics to stay on top of the effects of higher raw materials prices and supply chain disruptions. They can use AI copilots to grab instant, actionable data on supply and demand, price forecasts, and margin data. A buyer stepping off a long flight can now instantly access real-time market data on frequently traded commodities like ethylene and propylene, enabling informed negotiations. An AI agent has pulled only the most relevant data from extensive reports and analyses, removing the need for the buyer to burn valuable time wading through it all.
For example, an analyst might have a daily task of evaluating 70 or 80 pieces of news and other information on polymers by region from numerous parameters, like transactions, production issues, outages, planned disruptions, and market economics. The analyst can generate five actionable takeaways in five minutes instead of an hour. They can track currency movements more closely and recommend production and inventory changes to benefit from favorable exchange rates and lower exposure to short-term tariff impacts.
A data transformation imperative
The interdependent nature of our global energy, materials, and consumer products markets and supply chains requires a new level of precision data management. To get a handle on volatile short-term fluctuations, company data officers should ensure everybody has maximum data visibility in terms of where they have product, inventory capacities, and levels, and exposure to tariffs. If you haven’t organized and structured your data, now is the time to do so. Both traditional analytics and generative AI systems are only as good as the data you feed them. They must have data connections into validated sources of information, like the latest applicable tariffs, for example.
Traditional AI and cloud-based supply chain analytics systems are likely already in use at most chemicals companies. In this case, target functions in optimizations must be reviewed and potentially adjusted to put a greater emphasis on supply chain resilience, for example, to ensure they have a larger strategic inventory in each region. All of our modern digital systems — AI, traditional analytics engines, and towering SaaS tech stacks — are reliant on organizations’ data infrastructure and data management acumen. Companies reticent to make investments in data and technology will miss out on two-pronged benefits. It will not only allow them to navigate the storm better but also will make AI agents more effective in the (fast-approaching) future.
Protecting against protectionism
Global supply chains are a very intricately packed and arranged apple cart. Nobody can predict where all the apples will land if the apple cart is upended. Companies should closely monitor bilateral and multilateral trade negotiations, yet still be prepared for all scenarios. AI agents are poised to aid chemical companies as they execute various strategies to improve their positions and adapt, including cash preservation and cost optimization in a margin-squeezed environment. In an ecosystem where supply chains have been shaped over decades, stable, reliable supply chains have become business assets and competitive advantages. They are not changed overnight. Chemical companies invest for the long run. AI can help to model how the changing demographics, redrawn global trade flows, geopolitics, circularity regulations, and the energy transition will shape future global consumption patterns and opportunities for growth.
Become trade-war-proof by building risk resilience
Today’s moment of uncertainty calls for a big change in the way we analyze chemical markets. Over 60% of chemical firms are expected to adopt generative AI to improve their supply chain and inventory management. Front-footed chemicals companies will deploy digital technology and AI-powered market intelligence to trade-war-proof themselves by building risk resilience, unlocking operational efficiencies, and optimizing their investments in their value chain. AI capabilities will be a prime differentiator for chemical and energy companies in gaining an unprecedented view and understanding of relentlessly complicated commodities markets – and the swirling crosswinds buffeting them.
