As companies race to apply artificial intelligence, cloud computing, and advanced modeling, the central challenge is linking these technologies to reliable data, scientific expertise, and the practical demands of bringing products to market. For specialty chemicals manufacturer Lubrizol, digital transformation centers on equipping researchers with tools to test ideas faster, make better-informed decisions, and develop more sustainable materials without sacrificing performance.
Girish Rao, the company’s vice president of information technology, says Lubrizol is building a digital foundation that connects business processes, modern data platforms, and cloud infrastructure to support its research and development teams. The goal is to combine decades of accumulated technical data with the know-how of chemists and engineers who understand both customer needs and the physical constraints of their work. Data scientists can surface patterns, test hypotheses, and model possibilities before researchers commit to physical experiments. Rao stresses, however, that the aim is not to replace scientific judgment but to give scientists better tools.
Scientists, data scientists, and IT teams each bring distinct priorities, whether experimental validity, model performance, or platform security and scalability. Lubrizol brings these groups together in “fusion teams” aligned around shared objectives, beginning with small pilot projects to build trust. As Rao puts it, once team members trust one another and understand the value they are about to create, progress accelerates. Cloud infrastructure adds further flexibility, giving the company access to computing resources and modern tools without the heavy investment required to build and maintain its own data centers.
These capabilities could prove especially valuable for sustainability. Digital modeling allows researchers to evaluate more formulations, explore alternatives such as starch- and corn-based materials, and weigh trade-offs across a product’s full life cycle. A promising material in the lab is only the first step, however, since scaling production while meeting performance standards, regulatory requirements, and financial limits remains a delicate balancing act. AI’s capacity to model many variables at once could help teams navigate these competing demands.
Looking ahead, Rao expects AI, automation, advanced modeling, digital twins, and connected data to converge, making laboratories more intelligent and manufacturing plants more efficient. The opportunity lies in combining these capabilities responsibly and at scale. As Rao notes, the value comes not from any single technology but from connecting these processes, which will generate enormous amounts of data.
This webcast is produced in partnership with Infosys. This content was produced by Insights, MIT Technology Review’s custom content arm, not its editorial staff. It was researched and written by humans, with any AI tools used limited to production processes under human oversight.
Source: Innovating with AI in the special chemicals industry via MIT Technology Review