This article was originally published on Forbes.com on May 27, 2025.

AI is rapidly becoming ubiquitous in every corner of our society, impacting individuals and industries alike. The potential of AI in the chemical industry is no exception. This is something I've seen firsthand during my time in the industry, especially within my role at Trinseo.

The multiplier effect AI brings to the harnessing of all forms of information promises to revolutionize the chemical development and manufacturing space. AI is enabling researchers to analyze, predict and optimize solutions more efficiently than ever before, all while empowering manufacturing operations to run more effectively and efficiently by boosting speed, reducing costs, reducing waste, reducing downtime and enabling more sustainable manufacturing practices.

Author George Couros, in his book, The Innovator's Mindset, says, "Technology will never replace great teachers, but technology in the hands of great teachers is transformational."

I think this same sentiment can be applied across industries and to our use of AI. In the hands of scientists, engineers, operations leaders and innovators of all kinds, AI has the potential to fundamentally transform how we operate in the chemical industry.

The Five P's Of AI

In business school, they teach about the four P's of marketing: product, price, promotion and place. AI may have its own P’s, especially when we take a look at the specialty chemicals and manufacturing industry.

1. Product Development

AI can comb through massive databases of complex chemical structures to predict the effectiveness of new compounds before they undergo real-world testing in the lab. In chemical engineering experiments involving the synthesis of new compounds or the optimization of chemical processes, vast amounts of data must be analyzed quickly and accurately.

The ability to accelerate the R&D process and model performance, all while helping reduce costs, creates a significant advantage for companies that leverage what AI can bring to the table. It's potential for integration into the stage-gate process for product development is extremely exciting, and we have seen the benefits in our organization already in terms of increasing speed and accuracy.

2. Prototyping

Rapid prototyping has always been a key component in the research and development process. AI can surface thousands of design iterations based on desired parameters such as cost, weight, material strength and thermodynamics, for example. This frees up scientists and engineers to experiment with a much broader design pallet to develop products that are more efficient, durable and cost-effective.

AI can also have an outsized impact in the product manufacturing process by optimizing operations ensuring the highest quality, maximizing throughput, reducing waste, reducing production costs and, ultimately, increasing margins.

3. Predictive Analytics

Researchers commonly depend on simulations to predict how materials, systems or processes will react under various conditions. In chemical engineering, AI models can simulate complex reactions and processes, such as catalytic reactions, combustion or the behavior of new materials under different environmental conditions. For example, in the development of new chemical catalysts, AI can predict the optimal conditions and structures for catalysts that can increase reaction efficiency, minimize by-products or reduce energy consumption.

In materials science, AI algorithms can predict the properties of materials based on their unique molecular structure, enabling researchers to identify materials with the desired characteristics before physically synthesizing them.

4. Preventive Maintenance

AI shows great potential in manufacturing and preventive maintenance. Traditionally, manufacturers rely on scheduled maintenance or reactive repairs when machines fail. These approaches are costly and time-consuming, and they often lead to unexpected downtime.

By using sensors and algorithms to analyze machine data such as temperature, vibration and pressure, manufacturers can predict when a machine is likely to fail. This predictive maintenance approach helps manufacturers schedule maintenance only when necessary, avoiding unplanned downtime, reducing repair costs and extending the lifespan of machinery, all while improving productivity.

This is an area we are really focused on in my organization, given the capital intensity of chemical manufacturing and the importance of productivity and efficiency, especially in the use of energy given it's impact on overall costs of operations.

5. Process Optimization

Whether in a manufacturing environment or in a lab, the optimization of processes can improve efficiency, maximize time and resources, reduce costs, increase productivity and increase agility. In chemical R&D, researchers monitor complex reactions involving a range of variables. AI-powered systems can continuously analyze real-time data from sensors and instruments, predicting the outcomes of these reactions and suggesting adjustments to improve yield, safety or efficiency.

This capability makes AI an indispensable tool in fields like the specialty chemicals industry that rely on precision and data-driven decision-making. For example, in chemical manufacturing, AI systems can analyze real-time data from sensors and control systems to optimize variables such as temperature, pressure and flow rates.

What's Next?

Yogi Berra famously said, as only he could, "It's tough to make predictions, especially about the future." As I said at the beginning of this article, who knows what's next when it comes to AI? In fact, who knows what's next when it comes to anything involving the human imagination and our quest to see beyond the horizon?

With respect to the integration of AI in the chemical industry, from my perspective, it's important to really understand what challenges you are trying to address. Technology for technology's sake isn't the answer. So make sure you're accurately defining your problem to be solved.

And while the use of AI is going to be unique to the needs of every organization, I do see the potential for some standardization, especially in the form of education and training for scientists and engineers entering our industry—so they can get up to speed quickly in the application of this technology.

I like to think that the motivation of innovators in any field is simply to make something better. It is part of an innovator's DNA. And if AI can help make a lot of things better in a number of industries by bringing to life the ideas of innovators, then what's next should be pretty exciting.