Inside the Chemicals Giant Betting AI Can Speed Up New Materials

For a specialty chemicals manufacturer, the hardest part of adopting artificial intelligence is not the technology itself. It is connecting new tools to reliable data, scientific expertise, and the practical realities of getting products to market. That is the challenge Lubrizol, one of the industry's large specialty chemicals producers, is working through — and the company's approach offers a window into how AI is landing in old-economy industries.

The goal, according to a piece published by MIT Technology Review, is to give researchers the tools to test ideas faster, make better-informed decisions, and develop more sustainable materials without compromising performance. Girish Rao, Lubrizol's vice president of information technology, says the company is building a digital foundation that connects business processes, modern data platforms, and cloud infrastructure to support its research and development teams.

Data meets chemistry

The idea is to combine decades of accumulated technical data with the deep expertise of chemists and engineers who understand both customer needs and the physical constraints of their work. Data scientists can help identify patterns, test new hypotheses, and model possibilities before researchers conduct physical experiments.

Rao emphasizes, however, that the point is not to sideline scientific judgment. "It's not about replacing scientific judgment; it is about giving our scientists better tools," he says in the piece.

That distinction matters in a field where a promising formulation in a laboratory is only the first step. Scaling production while meeting performance expectations, regulatory requirements, and financial constraints is still a tightrope walk — and AI's ability to model multiple variables could help teams navigate those competing demands.

Fusion teams and small pilots

Scientists, data scientists, and IT teams approach problems from different perspectives, prioritizing experimental validity, model performance, and platform security and scalability, respectively. Lubrizol's answer is to bring the groups together in "fusion teams" aligned around shared objectives, starting with small pilots to build trust.

"Once the trust is built between the team members and they know what value they're about to create, things go a lot better," Rao says.

Cloud infrastructure also gives the company flexibility to access computing resources and modern tools without making the substantial investments required to build and maintain its own data centers, according to the piece.

The sustainability angle

These capabilities could prove especially valuable in advancing sustainability. Digital modeling can help researchers evaluate more formulations, explore materials such as starch and corn-based alternatives, and assess trade-offs across a product's life cycle.

Looking ahead, Rao expects AI, automation, advanced modeling, digital twins, and connected data to converge as laboratories become more intelligent and manufacturing plants more efficient. The opportunity, he suggests, lies in bringing these capabilities together responsibly and at scale.

The piece was produced by Insights, MIT Technology Review's custom content arm, in partnership with Infosys — a reminder that much of the public conversation about industrial AI is being shaped by the vendors selling the transformation, not just by the companies living through it. Even so, Lubrizol's framing — small pilots, trust first, scientists in charge — is a useful counterweight to the breathless version of AI adoption.

Written by the BidAsk Newsroom. Based on reporting from the sources below.