I am a strategy scholar who studies how organizations make decisions when attention, information, and judgment are limited. For most of my career, that has meant studying how managers search for strategies, how organizations combine judgment, and how the models they use shape what they can see. In recent years, that research program has led directly to a newer question: what changes when artificial intelligence begins to do some of the cognitive work that strategy once assumed was exclusively human?

I am the Alexander M. Nick Professor and Chair of the Strategy Area at the Ross School of Business, University of Michigan. Before joining Michigan, I taught strategy at INSEAD.

The Path Here

I grew up in Chile and studied Computer Science at the University of Chile, graduating in 1996 as valedictorian. I later earned a PhD in strategy from Wharton, where my committee included Dan Levinthal, Nicolaj Siggelkow, Jitendra Singh, and Sid Winter.

Before academia, I was head of research at an asset management firm and founder and CEO of an internet startup. Those experiences still shape how I think. They are why I care about decision processes that can survive uncertainty, feedback, disagreement, and real consequences rather than only elegant theory.

What Connects the Work

The first thread is search: how organizations look for better strategies when they cannot see the whole landscape. That line includes the NK-model work in How much to copy?, Positioning on a multi-attribute landscape, A note on how NK landscapes work, and Government as landscape designer: A behavioral view of industrial policy.

The second thread is aggregation. I have studied how organizations combine judgments, how structure shapes performance, and why firms should not be treated as unitary actors with clean, stable preferences. That work runs from Organizational decision making, Organizational structure as a determinant of performance, and Individual and organizational antecedents of strategic foresight to Revisiting the unitary actor assumption.

The third thread is representation: the internal mental models managers use, the external visuals and frameworks they rely on, and the distributed representations that organizations build by combining many partial views. Papers such as Mental representation and the discovery of new strategies, External representations in strategic decision making, and The power and limits of distributed representations sit in that stream.

The newest thread is what I call unbounding rationality. The claim is that AI changes a foundational assumption of strategy by relaxing some of the cognitive limits on which the field was built. That idea appears in Unbounding rationality, in the forthcoming Harvard Business Review piece AI is revolutionizing strategic decision-making, in the Handbook of Artificial Intelligence and Strategy, and in the Strategy Science special issue Can AI do strategy?.

Research, Teaching, and Service

I serve as Senior Editor for Strategy Science. Earlier, I was an associate editor for Management Science and a senior editor for Organization Science. In 2025, Ross named me Researcher of the Year.

Teaching is not separate from the research. I teach strategy, AI & Strategy, and doctoral seminars at Ross, and many of the questions I work on are sharpened by the classroom. The paper Learning strategic representations grew directly out of that belief, as does my continuing work on what AI means for management education. More on that is on the Teaching page.

Beyond the CV

I do not run a consulting practice, license proprietary frameworks, or try to turn every idea into a product. When I write or speak for practitioners, I try to do so in the same spirit as the academic work: evidence first, clear argument, and no hype. That is also how I approach the talks collected on the Talks page.

For the full professional record, see my CV. For academic, media, or institutional inquiries, use the Contact page.