Text analysis

Organizations are adopting AI tools faster than they can tell which of their people will actually use them. This project asks whether structured executive interviews, transcribed and coded, predict which employees go on to adopt AI in their work, and whether leadership experience with transformation, technology, and scaling, observable from career histories alone, bears on an organization’s capacity to direct that adoption. The setting is an AI assessment platform used by private equity firms, in which structured interview transcripts and career records are linked to the organizations those executives lead. The aim is a measurement approach that treats AI adoption as a predictable consequence of what an interview reveals about a person and a team, rather than as a matter of tools or training alone.

Paper available upon request.

The company builds AI assessment software for private equity firms. Its product predicts executive performance from structured interview data and guides interviewers toward the follow-up questions that matter. The models sit on a matrix of outcomes and evidence, from career histories through short and long interview transcripts, each with its own validation protocol, and the product is in beta with clients.

I lead all of the modeling work: measurement design, model development, validation, and research and development.