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Emmanuelle Vaast to deliver keynote at BPM 2026

The 2026 BPM Conference will feature Emmanuelle Vaast, Desautels Chair in Digital Technology Management and professor of Information Systems at the Desautels Faculty of Management, McGill University, as a keynote speaker. 

In her keynote address, Vaast will examine how the growing availability of digital trace data is reshaping research on organizational processes. Organizations increasingly generate detailed digital records of decision‑making, work and coordination, creating new opportunities to analyze how processes unfold. At the same time, Vaast argues, these digital traces raise important questions about how organizational processes are represented, interpreted and understood.  

“Emmanuelle Vaast’s expertise in digital trace data and organizational processes makes her an exciting addition to this year’s keynote program,” says Ravi de Costa, associate dean, research, LA&PS. “Her work shows how emerging data sources are reshaping the ways we are able to study and understand organizational life, and we look forward to the critical insights she will share with our community.” 

Drawing on scholarship in digital technologies, organizing and process theory, Vaast will explore how trace data are produced through specific technologies, infrastructures and categorizations that shape which aspects of organizational processes become visible.  

Join this keynote examining how digital trace data is reshaping the study of organizational processes and practices. Register by July 14 for early bird rates. 

Emmanuelle Vaast profile photo

About the Speaker

Emmanuelle Vaast is associate dean of research at the Desautels Faculty of Management, McGill University. Her research examines how social practices emerge and evolve with the implementation and use of new technologies and how these shifts are connected to changing organizational processes. She has a particular interest in methodological questions and has focused extensively on the opportunities and challenges of combining methods when analyzing electronically collected data.