As artificial intelligence becomes more deeply embedded in everyday life and work, organizations are investing heavily in tools and employee training. But new research from Texas A&M University suggests a one-size-fits-all approach may miss a fundamental truth: people don’t all respond to AI the same way.

In a paper published in the journal Customer Needs and Solutions, Dr. Shrihari Sridhar, the Joe Foster ’56 Chair in Business Leadership and senior associate dean at Texas A&M University’s Mays Business School, and his co-author, Dr. Huachao Gao, say that AI adoption has often been treated as if people share a single attitude toward the technology.

Their findings suggest the opposite: people experience AI in markedly different ways depending on their individual perspectives and the context in which they’re using it — a concept they call “AI-heterogeneity.”

An AI model builder developed at UC Irvine can autonomously design particle physics theories, a task traditionally only carried out by humans. The approach enables efficient exploration of new theoretical frameworks, helping uncover promising neutrino models for scientific study. Applications could expand to other theoretical model-building problems in the future. The National Science Foundation and the Department of Energy provided research support.