Han Hendriks – Trinseo’s Chief Technology & Sustainability Officer. We drive forward-thinking material solutions for a better tomorrow.

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AI has changed how researchers access and process information with its ability to search across multiple data sources simultaneously and reduce time spent gathering input.

AI's ability to accelerate research cycles, in and of itself, will be crucial for freeing researchers for higher-level tasks, but what I find most compelling is AI's ability to reveal unexpected solutions.

For example, MIT and Duke researchers announced last year that they had used machine learning models to test hundreds of polymer combinations and discovered a trait that made the compounds stronger.

The machine learning model was able to predict the effects on other compounds with similar features, so this discovery would likely never have been possible using a conventional trial-and-error approach.

With limited time and resources, the scope of traditional research methods has to be predefined. AI lifts that constraint by allowing us to explore much broader possibilities. ​By compressing development timelines, expanding the design space we can explore and surfacing non-obvious solutions, AI's creating a fundamentally different model of innovation where speed, scale and creativity reinforce each other.​

Based on my experience in the materials solutions sector, here are a few ways that AI is already transforming R&D:

Innovation At A New Pace

One of AI's clearest effects is that it shortens innovation cycles. Work that once took years can now happen in weeks or days, achieving more with the same resources. This acceleration spans the full development process, from discovery and design through prototyping, testing and manufacturing.​

The impact's already tangible. In automotive, Volkswagen Group is using AI-driven virtual twins to simulate, test and refine vehicles digitally before physical production, reducing engineering cycles and accelerating time to market.​​

In my organization, AI has been systematically developed and integrated into our innovation workflow. One of the benefits has been that AI has analyzed the scientific research done throughout the decades together with other data at a scale and speed that are impossible for humans. As a result, it identifies patterns, correlations and trends that might otherwise go unnoticed.

I've found, however, that creativity and innovation still come from human capital. Humans' role is now to ask the right questions, challenge assumptions and guide AI to explore perspectives. The true value comes from combining AI's speed and analytical power with human creativity and critical thinking.​

Why Scientific Rigor Is Still Paramount

​​Unlike many other industries, chemical products must pass multiple layers of validation, including internal physical laboratory testing, third-party accreditation testing, safety and regulatory reviews, customer qualifications and real-world application trials.

But I want to be direct about something: Serious research methodology, robust testing and disciplined interpretation remain nonnegotiable. AI is a powerful accelerant, not a substitute for scientific rigor. ​

AI-generated insights cannot be simply or readily implemented. While AI can accelerate analysis and decision-making as well as simulate scientific testing, real-world performance remains the ultimate proof point.​

The human factor is still the gatekeeper. For example, in our organization, there are different "gates" during the innovation process where the team will make judgments and validations.​

In other words, the foundation still has to be solid, and humans will still be held accountable at all times.

The Business Value Of AI In R&D

​According to McKinsey, AI could enhance R&D throughput by up to 75% in industries aligned with scientific discovery like chemicals and pharmaceuticals.

Likewise, for companies that compete on innovation, embedding AI into the R&D operating model is increasingly a competitive requirement.​ McKinsey estimated elsewhere that AI can accelerate R&D by 20% to 80% in complex industries, unlocking $360 billion to $560 billion in annual value depending on the sector. ​​

Faster innovation cycles mean shorter time to market and earlier revenue generation. Broader exploration of the design space reduces the risk of missing breakthrough solutions. Improved productivity means R&D teams can take on more, with greater impact per dollar invested. ​​

​​Successfully adopting AI, however, requires understanding both its strengths and limitations.

One of those limitations is that AI relies heavily on the context it is given, so learning how to ask good questions directly impacts the quality of results. Developing these skills takes training, experimentation and sharing best practices. As AI continues to evolve at a rapid pace, continual learning will be essential to unlock its full potential.​​​

Redefining Innovation Advantage

From what I've seen, the next source of innovation advantage will come from combining AI's speed with human creativity, experience and judgment. The strongest innovators will be those who use AI to ask better questions, process evidence more effectively and act on insight faster.​​

​​I would also argue that the scientists and engineers most likely to drive AI adoption in R&D are the ones who are already intrinsically curious, the people who are never quite satisfied with the boundaries of what is known and are always asking what else might be possible. ​

That disposition is precisely what AI rewards. It amplifies curiosity. It turns a question that would have taken months to explore into one that can be answered in days. ​

For curious minds, AI is not a tool that replaces thinking but rather one that lets them think at a different scale. That natural alignment will be a significant driver of adoption as AI becomes more embedded in R&D workflows.​

Looking Ahead​

The focus for the next phase is clear: Further increase adoption, quantify productivity and business impact, and expand AI capabilities while maintaining a secure and compliant operating environment.​ ​

AI is becoming an integral part of our R&D operating model, supporting both innovation and competitiveness. As the steam engine once transformed manufacturing by unlocking scalable mechanical power, AI has the potential to reshape R&D in a similarly fundamental way.

The value we capture will depend on how effectively we combine this capability with rigorous science, human judgment and the curiosity that has always driven discovery.​​

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