In specialty chemicals, new digital tools can help researchers explore ideas faster, but they depend on reliable data and close collaboration with scientists who understand how materials behave. At Lubrizol, digital transformation aims to connect technical records, computing resources, and research teams so they can make better-informed decisions and develop more sustainable materials.
Connecting data with scientific expertise
Lubrizol is building a digital foundation that links business processes, modern data platforms, and cloud infrastructure to support research and development. The goal is to bring decades of technical data together with the knowledge of chemists and engineers, including their understanding of customer needs and the physical limits of their work.
With that foundation, data scientists can search for patterns, test hypotheses, and model possible outcomes before researchers run physical experiments. The models can help teams examine more possibilities, while scientists remain responsible for interpreting results and applying their expertise.
Girish Rao, vice president of information technology at Lubrizol, describes the role of these tools as support for scientists rather than a substitute for their judgment. Better access to data and modeling can inform research decisions, while experiments and scientific expertise remain central to the work.
Building teams around shared goals
Scientists, data scientists, and IT teams bring different priorities to a project. Researchers focus on whether experiments are valid, data scientists consider model performance, and IT teams pay attention to security and the ability to scale platforms.
Lubrizol brings these groups together in “fusion teams” and starts with small pilots. Working on shared objectives gives team members a chance to build trust and see the value the collaboration can create. Rao says that once that trust is established, the work goes better.
Cloud infrastructure is another part of the approach. It lets the company access computing resources and modern tools without the substantial investments involved in building and maintaining its own data centers. That flexibility can help teams use digital capabilities as their needs change.
Modeling sustainability trade-offs
Digital modeling could be especially useful when researchers look for more sustainable materials. It can help them evaluate more formulations, explore alternatives such as starch and corn-based materials, and consider trade-offs over a product’s life cycle.
A promising result in a laboratory does not guarantee that a material will succeed in production. Teams still need to scale manufacturing while meeting performance expectations, regulatory requirements, and financial constraints. These demands can pull in different directions.
AI may help researchers model multiple variables and compare possible choices as they work through those demands. That can support decision-making, but the practical challenge remains: a material must work beyond the laboratory and meet the requirements involved in bringing it to market.
Combining tools across research and manufacturing
Rao expects AI, automation, advanced modeling, digital twins, and connected data to converge. In his view, connecting these capabilities could help make laboratories more intelligent and manufacturing plants more efficient.
The opportunity depends on how well organizations bring the pieces together. Data, models, cloud tools, and scientific expertise each contribute something different; connecting them can help teams move from exploring an idea to assessing whether it can work in practice.
For specialty chemicals, that means using digital tools to extend researchers’ ability to test possibilities while keeping decisions grounded in material behavior, customer needs, and the constraints of production. The combination may help accelerate innovation while supporting the search for more sustainable materials.