How Eastman used its data and analytics experience to get started with generative AI
Rather than trying to add AI to all its operations areas at once, the specialty materials company is looking for areas where its technical experience can enable deeper functionality.
Generative artificial intelligence appeared like a flash over a year ago. While the underlying algorithms had been in development for years, high-profile, publicly available interfaces brought it to the masses. Now many businesses are wondering, how can we get started using generative AI?
For Eastman, a global specialty materials company that produces a broad range of products found in items people use every day, the answer was to leverage its successful work with data and analytics to build more advanced implementations of generative AI tools. Rather than trying to add AI to all its operations areas at once, Eastman is looking for areas where its technical experience can enable deeper functionality.
“This approach lets us bring a digital service layer to the table to differentiate ourselves in the market and create a competitive advantage,” says Aldo Noseda, chief information officer at Eastman.1
Eastman has a history of incorporating data and analytics into its products. For example, it has an advanced intelligence service (with proprietary thermal stability measures) that will predict when a heat-transfer fluid used in its customers’ industrial processes is likely to degrade, allowing engineers to maintain optimal fluid quality, forecast predictive maintenance needs, and avoid costly downtime on manufacturing lines.
From projects like that, staff at Eastman know how to work with data and develop intelligent products and services. They are now building on this experience by experimenting with a generative AI guide that helps identify business opportunities. Still in the development stages, this tool reads and analyzes natural-language text files and generates insights that were previously difficult to unearth.
In one use case, the tool is being used to analyze procurement contracts. In another application, it’s being tested on extracting insights from sales call notes. These documents are generated by sales teams after every call but have historically been difficult to incorporate into knowledge systems because they are free text rather than structured data. Now, with the help of generative AI, the company is starting to unlock those insights. The tool analyzes the notes from sales calls to spot potential cross-sell opportunities that sales teams can explore.
“The engine can distill those insights and cross-references to new opportunities,” Noseda says.
Eastman isn’t stopping with AI. The company is continuing to develop its broader technical experience to drive one of its highest priorities: sustainability. Noseda says Eastman is building plants that will create recycled plastic and textile products. It will use blockchain to track and record from where feedstock comes, allowing customers to trace materials throughout the value chain.
Noseda says continuing to build on technical capabilities in this way is helping Eastman reach new markets: “We’re training the market to understand this is a premium opportunity. Everyone is talking about sustainability; we’re actually doing it.”