The materials and process industries sit at an inflection point with AI. The question is no longer whether AI can do something interesting in R&D — it can. The question is whether it can do something commercially useful, repeatedly, within the constraints of a real manufacturing environment. Greg Mulholland has been building the answer to that question at Citrine Informatics.

Greg and I discussed how AI is changing the pace and precision of materials development: turning scattered lab data into directed R&D guidance, encoding scientific intuition so it doesn’t walk out the door when a chemist retires, and enabling application engineers to respond to customer requirements in days instead of weeks.

Who should listen:

* Materials and chemicals executives running R&D or innovation functions

* Leaders trying to understand what AI can actually deliver in a capital-intensive, manufacturing-constrained environment

* Innovation leaders navigating the gap between AI potential and commercial reality

Topics covered:

* Turning scattered, under-specified lab data into usable digital assets for R&D

* Capturing scientific intuition and heuristics to augment your best chemists and formulators

* Using bounded search spaces to keep AI recommendations manufacturable, scalable, and within cost and supply constraints

* Enabling application engineers and business developers to respond to customer requests in days instead of weeks

Show Notes

* Greg Mulholland on LinkedIn: https://www.linkedin.com/in/gregorymulholland/

* Citrine Informatics: https://citrine.io/

* Kendall Justiniano on LinkedIn: www.linkedin.com/in/kendalljustiniano

* Join live conversations — Roundtable membership: https://www.growth-arc.com/roundtable

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