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BPM 2026 Faculty of Liberal Arts & Professional Studies
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Keynotes

BPM 2026 features three outstanding keynote speakers representing the forefront of business process management, digital transformation and organizational innovation. Industry leaders, academic researchers and technology executives from around the world will share insights on emerging trends in automation, AI-driven process optimization, operational resilience and the future of enterprise transformation. Through inspiring presentations and interactive discussions, keynote sessions will provide attendees with practical strategies and forward-looking perspectives designed to help organizations navigate an increasingly complex and rapidly evolving business landscape.

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Tom Davenport

Tom Davenport is the President’s Distinguished Professor of Information Technology and Management and Faculty Director of the Metropoulos Institute for Technology and Entrepreneurship at Babson College, and a Fellow at both the MIT Initiative on the Digital Economy and the Stanford Institute for Human-Centered AI. He’s been named as one of the top 25 consultants, one of the 100 most important people in the tech industry, and one of the top 50 business school professors in the world. He has written 27 books, more than 300 articles for Harvard Business Review, and hundreds of other articles and blog posts. His most recent co-authored books are The New Science of Customer Relationships and Agentic Artificial Intelligence.

Organizations are beginning to realize that to truly get value from artificial intelligence, they need to redesign their processes. However, many aren't prepared for this marriage of process and AI for various reasons. In this presentation, Tom Davenport will describe his hopes for a rebirth of business process reengineering, the obstacles organizations face in accomplishing it, and some steps early adopters are pursuing. He will also describe the role of AI in incremental process improvement, and the value of process intelligence in facilitating the AI/process relationship. 

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Opher Baron

Opher Baron is a University of Toronto Distinguished Professor of Operations Management at the Rotman School of Management, University of Toronto and a cofounder and CEO of SiMLQ.

On the teaching front, Opher is especially proud of the modeling and analytics courses he introduced and teaches at Rotman. He has given numerous invited keynote lectures and seminars, chaired several conferences, clusters, and sessions, and is currently serving on the advisory board and editorial boards of several leading journals, including Operations Research, and Manufacturing & Service Operations Management.  

Many modern business processes operate under congestion: stochastic demand, shared and limited resources, complex routing, and tight service-level constraints. From healthcare and public services to logistics, financial operations, and large-scale customer support, these systems exhibit nonlinear behavior where small disruptions propagate quickly and performance deteriorates sharply.   

This keynote examines how Business Process Management can evolve to address congestion-driven environments by integrating process mining, queueing theory, simulation, and machine learning into AI-enabled digital twins. Using large-scale event-log data from real deployments, we illustrate how descriptive analytics must move beyond static dashboards toward data-driven process models that reveal how processes actually unfold in practice, capturing routing variability, rework loops, synchronization delays, and resource contention. Process mining provides structural visibility; queueing-aware modeling explains performance; machine learning supports prediction under uncertainty. 

Building on these foundations, predictive and comparative analytics enable counterfactual “what-if” evaluation of staffing, routing, prioritization, and scheduling policies before implementation. Finally, prescriptive analytics embedded within real-time digital twins allow organizations to intervene proactively, anticipating congestion, reallocating capacity, and mitigating cascading delays. Drawing on industrial deployments through SiMLQ, this talk demonstrates how combining process intelligence with congestion-aware operational modeling transforms BPM from retrospective analysis into continuous, real-time decision support. The implications extend across service industries where variability and resource contention define performance, resilience, and competitiveness.

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Emmanuelle Vaast

Emmanuelle Vaast is the Desautels Chair in Digital Technology Management, Professor of Information Systems, and Associate Dean of Research at the Desautels Faculty of Management of McGill University.  

Emma's research examines how social practices emerge and change with the implementation and use of new technologies and how these new practices are associated with changing organizational processes.  

Emma is interested in methodological issues. She has become increasingly fascinated by the opportunities and challenges of combining methods in the analysis of electronically-collected data, especially to theorize processes. 

Organizations generate detailed digital traces of decision-making, work, and coordination. This has led to huge opportunities to retrace and analyze organizational processes. At the same, this also raises a question for process research: how to understand organizational process when process is approached through data automatically captured by digital systems? 

In this talk, I draw on scholarship on digital technologies, organizing, and theorizing from process to discuss how digital traces reshape the study of organizational processes. A key point is that trace data are not exact representations of organizational life. They are produced through particular technologies, infrastructures, and categorizations that shape how and which processes become visible and understandable. 

This is importance since the growing availability of trace data has expanded the possibilities of studying process dynamics and oriented attention to forms of actions, interactions, sequences, and variation. Some aspects of organizational processes have become much more accessible. Analytical capabilities have tremendously expanded. Yet, other aspects of organizational processes remain difficult to apprehend through trace data alone. I thus suggest viewing trace data as partial yet consequential representations of organizing.  

The keynote argues for a productive dialogue between computational and trace-based approaches on the other hand, and qualitative approaches of organizational process on the other. Such a dialogue is especially valuable to develop rich understanding of organizational processes and their dynamics in the digital age.