From molecule and materials discovery to new applications and customer acquisition, AI can create opportunities across a range of promising new use cases in chemicals.

It has been nearly two years since the launch of ChatGPT and other generative AI (gen AI) tools that revolutionized the way AI is perceived and consumed by industries, businesses, and other users. In that time, McKinsey experts estimate the capabilities unlocked by gen AI have helped accelerate levels of human performance by a decade, on average.

No industry remains untouched by the impact of gen AI, but adoption levels vary significantly. The chemical industry, in particular, remains a cautious adopter. A recent McKinsey Global Institute (MGI) survey estimates that energy and materials, which includes chemicals, has the lowest exposure to gen AI tools at 14 percent, compared with the cross-industry average of 23 percent. Meanwhile, chemical companies have significant untapped potential to leapfrog competitors using the recent advancements brought about by generative technologies.

This potential stems from the industry’s reliance on scientific data for innovation, the availability of (often fragmented) customer data, and the industry’s nuanced and complicated manufacturing processes. Simply stated, gen AI adds intelligence and completeness to these data, which can then be used to inform decision making, speed up processes, and improve overall efficiency. Altogether, our estimates show the application of gen AI across commercial, R&D, operations, and support functions in energy and materials can create anywhere from $80 billion to $140 billion in value.

Harnessing this new technology won’t be easy. Many use cases cannot be realized unless some degree of digitalization, technical capability, and scientific expertise is already in place. This article provides an overview of today’s most relevant opportunity areas and offers a pathway for chemical industry players to start taking concrete actions.

Chemicals and AI: An overview

The chemical industry plays a critical role in the global economy, providing essential materials for most other industries. Today, chemical companies face market forces that require new ways of thinking, including the search for newer materials to support the future innovation needs of the energy transition. Other forces include reinvigoration of growth with both new and existing customers, efficiencies in manufacturing and supply chain to fund growth and innovation, and significant talent and capability attrition as the workforce turns over.

“Gen AI” refers to applications that can process varied sets of unstructured data (such as lab notes, technical specification sheets, scientific literature, and sales presentations) as well as structured data (such as customer relationship management and transactional data) to aid synthesis, suggestions, and new content generation. In this way, gen AI generates new ideas by identifying patterns in data sets, particularly when it comes to complex tasks such as finding new applications, customer acquisition, and molecule or materials discovery. By contrast, more traditional, analytical AI typically solves specific tasks by analyzing data and making predictions based on structured data sets and predefined rules.

AI and gen AI are not competing technologies; rather, they can complement each other. The combination of the two technologies, referred to here as “gen AI,” has the potential to transform nearly all aspects of the chemical industry, revamping the ways companies operate and potentially unlocking billions of dollars in value. Although core pricing and forecasting models will likely continue to be based on traditional AI, generative technology allows organizations to prepare data faster, tap into internal and external sources, and support conversational abilities that can lead to pricing or forecasting insights.

Companies will likely need to have a combination of digitalization, technical capabilities, and scientific expertise before they can harness the potential of AI, and not every use case will apply across the board. The potential risks of AI must also be accounted for, including the accuracy of responses, security from cyberattacks, protection of competitively sensitive data, biased outputs, and the risk of intellectual property infringement.

The AI opportunity in chemicals

Innovation in the chemical industry has been slow. As a point of comparison, Amazon spent $73 billion on technology and infrastructure (a major component of which was R&D) in 2022, while the entire US chemical industry spent $13 billion. Some of this slowness is structural, arising from the industry’s asset-heavy nature, longer innovation cycles compared with software or consumer goods, enhanced regulatory considerations, and less-distributed customer base (primarily due to the B2B nature of offerings). However, the industry has also been a late adopter of new technologies and has subsequently been slow to deploy them to derive business value.

In the chemical industry, gen AI represents a substantial leap forward, making the generation of insights and creative processes, such as new molecular and marketing designs, more accessible and customizable. This democratization of technology can help companies, especially those with below-average performance, significantly enhance their operations.

Moreover, gen AI is reshaping competitive landscapes by enabling new ways to generate hypotheses using diverse data sources, augment individual creativity with systematic support, and embed tacit knowledge into institutional advantages. This evolution reduces entry barriers for new players, which can now use customer data more effectively and offer their products without traditional constraints. In addition, customers could gain the ability to easily compare and select suppliers, thanks to increased transparency. These shifts suggest that future competitive advantages in chemicals will rely heavily on the strategic use of AI.

About the authors

Lapo Mori is a partner in McKinsey’s Denver office; Matej Macak is a partner in the London office; RS Mallya Perdur is a partner in the Houston office, where Ian Wells is an associate partner; and Yashaswi Gautam is a senior partner in the Boston office, where Saumya Misra is a consultant and Zach Green is an associate partner.

Original article