In Conversation Series: Accelerating Closed-Loop Outcomes with AI

Everest Group’s Isaac Premsingh, Research Director, Digital and Embedded Technologies, and Dr. Shalini Sharma, Ph.D., Semiconductor Product Leader, led the first session in the “In Conversation Series” on May 21, exploring how AI was accelerating closed-loop learning and transforming experimentation.

Rising experimentation costs and growing system complexity exposed the limits of traditional Design of Experiments (DoE). Nonlinear variables, complex design spaces, and expensive trials forced R&D teams to rethink how they gained insights, making slow, trial-and-error methods increasingly impractical.

The discussion highlighted how predictive models, adaptive learning, and intelligent workflow design could reduce experiment burden, accelerate discovery, and unlock more efficient exploration of complex design spaces.

During this LinkedIn Live “In Conversation Series,” the panel discussed:

• How AI could help enterprise R&D teams move from DoE to closed-loop, adaptive experimentation
• Where AI created the most value in experimentation today: optimizing outcomes, reducing experiment burden, or expanding the design space
• What capabilities, data, and workflow changes organizations needed to make experimentation faster, smarter, and more scalable