Montreal's AI ecosystem evolution unfolds in phases, with commoners as pioneers, orchestrating social, symbolic, and knowledge commons for success.

Ecosystem evolution: Montreal’s AI frontier

Montreal's AI ecosystem evolution unfolds in phases, with commoners as pioneers, orchestrating social, symbolic, and knowledge commons for success.

Scholars have increasingly been using “ecosystem” as a concept within and beyond social science, but less is known about how ecosystems emerge. Following the assemblage theory by Manuel DeLanda, it can be said that “ecosystem” is a concept with knobs that can be set to define different ecosystems. The knobs can be value propositions, structures, scopes, or functions. For instance, actors in an innovation ecosystem are defined according to scope, affiliation, or organizational boundaries. Accordingly, we can say that in an innovation ecosystem, the collective goal or the knob is set around the activities related to innovation.

Research on the emergence of ecosystems lacks a unified conceptual understanding, with debates revolving around bottom-up versus top-down approaches, as well as a combination of both. The literature is fragmented due to the interchangeable use of ambiguous terms across different ecosystem types without robust theoretical support. Consequently, there is a persistent call to study ecosystems, align them with theories, and unravel the mechanisms guiding their emergence.

In response to this need, our investigation focuses on the AI ecosystem in Montreal. We aim to shed light on the mechanisms driving the emergence of innovation ecosystems, recognizing the importance of clarifying theoretical foundations and terminology within the broader research landscape.

The AI ecosystem in Montreal

In under two decades, Montreal has emerged as a global hub for innovation in Artificial Intelligence (AI). The city boasts the largest AI academic community in North America, notably home to the Montreal Institute for Learning Algorithms (MILA), a globally recognized research institution contributing significantly to machine learning. Additionally, IVADO, a multidisciplinary center, serves as a crucial hub for professionals and researchers, fostering expertise in data science, operational research, and AI.

Montreal takes a leading role in promoting responsible AI through initiatives such as the Montreal Declaration for responsible AI development. Notably, the city’s AI landscape extends to healthcare, witnessing the establishment of the CHUM School of Artificial Intelligence in Healthcare (SAIH) in 2018 at the Montreal University Hospital Center (CHUM) in collaboration with the University of Montreal. The SAIH aims to support and facilitate the adoption, implementation, and promotion of AI within the healthcare system. The concentration of talent and expertise in health and AI has spurred collaborations among hospitals, universities, and research centers, fostering the growth of numerous Montreal-based companies incorporating AI in healthcare.

Montreal’s AI ecosystem evolution unfolds in phases, with commoners as pioneers, orchestrating social, symbolic, and knowledge commons for success.

Nasrin Sultana
Figure 1. The relationship between commoners, organizations, communities, and ecosystems in a
Credit. Author

With regards to the issue of factors and antecedents to the formation of the innovation ecosystem in AI in Montreal, it appears that none of the traditional top-down modes of explanation in terms of regional structural preconditions of the formation of an ecosystem of innovation applies to the case of AI in Montreal. There was no pre-existing anchor firm or no active and deliberate initial public funding to build an ecosystem of innovation. Instead, there are key local individual actors who were at the origin of the emergence of the AI ecosystem in Montreal. These key actors, called commoners, orchestrated the different steps of development of the ecosystem through their collective actions.

The mechanisms underlying the emergence of the AI ecosystem in Montreal

We observed that the main mechanism underlying the emergence of the AI ecosystem in Montreal is the articulation of a series of innovation commons by commoners. Based on the actions of commoners in different steps of the development of the ecosystem, we identified three sub-mechanisms: orchestration of social commons, orchestration of symbolic commons, and orchestration of knowledge commons.  At first, commoners orchestrate social commons by connecting actors from diverse backgrounds. Next, the decentralized structure of the community of actors leads to the orchestration of symbolic commons. Finally, the orchestration of knowledge commons occurs due to the collaboration between actors across boundaries and their attendant knowledge. 

Table 1. The emergence of the AI ecosystem in Montreal

The federal government of Canada and the provincial government of Québec have recently been increasingly investing in AI. However, such financial support was not at the origin of the formation of the AI ecosystem but came later in the process to reinforce already existing successful initiatives. In Montreal, the bottom-up initiatives of commoners led to the emergence of the ecosystem of innovation in AI; the top-down support of the AI ecosystem has come mainly during phase III of the emergence.

Collaboration between actors in the AI ecosystem in Montreal

Due to their connection to multiple organizations and expertise, commoners in the AI ecosystem in Montreal play crucial roles in connecting actors throughout the history of the ecosystem. Therefore, to understand the scenario of collaborations between actors in the ecosystem, we developed a network model of the ecosystem by identifying the linkages between commoners and the organization with which they are directly or indirectly connected. 

Figure 2. The network diagram of the AI ecosystem in Montreal.
Credit. Author

From the different shapes used to represent commoners and organizations, we observe that commoners are indeed the agents connecting not only different commoners but also different organizational actors, universities, and research centers.

The implications of understanding the emergence of an ecosystem

Our observation of the bottom-up approaches in the emergence of the AI innovation ecosystem in Montreal sheds light on the challenges in innovation ecosystems at the beginning and during evolution. Ecosystems may require the combination of top-down exploration of policy alternatives by policymakers and bottom-up knowledge-intensive entrepreneurial activity to progress toward sustainable development. However, the top-down approach makes sense when the necessary information is available at the top level, and there exists a supportive system to communicate strategies and plans from top to down. The findings have important implications for understanding the importance of emerging technologies and the digitalization of industries, as well as for the development of firms, districts, clusters, cities, regions, and innovation to cope with digital transformations. 

Amid the challenges and ethical concerns of implementing AI solutions in different sectors, the rate of innovations in AI is steadily growing. For example, an intricate domain such as human psychology provides vast opportunities for using AI. Therefore, the government and industry bodies have important roles in facilitating the implementation of AI in different domains.

To begin with, the governments and individual actors in an innovation ecosystem need to identify different stakeholders and their innovation activities in an ecosystem. Next, it is important for industry bodies to adapt organizational structures and other policies and connect with different stakeholders to facilitate collaboration. Above all, depending on the phase of the ecosystem, it is important for governments to build proper infrastructure and create opportunities for testing and implementing innovations. Taking these initiatives will not only support innovations in the ecosystem but also increase regional capabilities. 


Journal reference

Sultana, N., Turkina, E., & Cohendet, P. (2023). The mechanisms underlying the emergence of innovation ecosystems: the case of the AI ecosystem in Montreal. European Planning Studies, 1-23.

Nasrin Sultana is a Ph.D. candidate at HEC Montréal with a specialisation in International Business. Her research interests include, but are not limited to, social network analysis, knowledge and technology transfer, management of innovation, innovation ecosystems, sustainability, foreign direct investment, and international business. She studies how different organisations are linked to understand the impact of such linkages on organisations, industries, and ecosystems at both local and global levels. Currently, she is studying the emergence of innovation ecosystems and investigating the linkages between artificial intelligence and healthcare ecosystems to understand how such linkages enable value creation in healthcare. She has published in refereed journals including the International Business Review, Sustainability, European Planning Studies, Administrative Sciences, Competitiveness Review, and International Journal of Case Studies in Management.

Ekaterina Turkina holds a PhD in Public and International Affairs from the University of Pittsburgh, USA. She is a full professor at HEC Montreal and a holder of Research Chair in Global Innovation Networks. Ekaterina is also an associate editor of Journal of Small Business and Entrepreneurship, as well as a member of International Advisory Board of International Journal of Productivity Management and Assessment Technologies. Her main research areas are social network analysis, innovation and inter-firm networks, industrial clustering, international business and international entrepreneurship. She has published numerous articles in refereed journals. She has written four books and was a recipient of several awards, including the Highly Commended Paper Award from the Journal of Enterprising Communities, Best Paper from the European Community Studies Association, and other. She was also the finalist for Alan Rugman Award that is given to most talented researchers in international business under 40 years old.

Patrick Cohendet is a full professor at HEC Montréal in the International Business Department. His research interests include the Theory of the Firm, Economics of Innovation, Economics of Knowledge, Economics of Creativity, and Knowledge Management. He is the author of 20 books and over 120 articles in refereed journals, such as Research Policy, Organization Science, Industrial and Corporate Change, Journal of Economic Geography, Long Range Planning, etc. He has supervised more than 80 Ph.D. candidates. He has conducted a series of economic studies on the economics of innovation for different international organisations such as the European Commission, the Council of Europe, the European Space Agency, or the Canadian Space Agency. He is the co-director of the research group Mosaic at HEC Montréal on the management of innovation and creativity, and co-editor of the academic journal "International Management".