Learning‐by‐twinning: Organizational digital twins and the future of search
Prithwiraj (Raj) Choudhury, Yingxin Du, Megan LawrenceAbstract
Research Summary
Entrepreneurs and managers often need to evaluate strategic choices, such as redesigning an incentive plan or entering an unfamiliar market, before committing scarce resources. Existing offline search tools, including mental models, analogical reasoning, model building, and simulation, allow managers to weigh alternatives without acting, but each is limited by what a single decision maker can specify, recall, or imagine. We argue that advances in generative AI, combined with the growing availability of firm‐specific digital trace data, are transforming this tradeoff. We introduce the organizational digital twin (ODT), an infrastructure combining firm‐specific data, AI‐based behavioral simulation models, and governance rules which facilitates a new form of offline search that we call learning‐by‐twinning: search conducted by observing how a strategic choice unfolds within a digital replica of the firm. We identify three mechanisms through which learning‐by‐twinning creates value, namely expanded search, safer experimentation, and richer system‐level feedback as well as specify certain conditions under which these benefits may attenuate.
Managerial Summary
Strategic entrepreneurship depends on discovering and refining opportunities through search and experimentation under uncertainty. Enabled by advances in generative artificial intelligence and organizational data infrastructures, we theorize that a new form of entrepreneurial search—learning‐by‐twinning—allows managers to conduct systematic virtual experimentation prior to real‐world implementation. We introduce the concept of the organizational digital twin (ODT) as the technological infrastructure that makes this search and experimentation possible: an evolving simulation environment built from firm‐specific digital trace data and multi‐agent AI models capable of reproducing organizational routines, interactions, and decision processes. We argue that learning‐by‐twinning alters the traditional tradeoffs of offline search by expanding the set of alternatives managers can explore, enabling low‐risk experimentation, and revealing second‐order organizational consequences before strategic commitments are made.