Context and rationale
Artificial intelligence (AI) is increasingly recognized as a major driver of economic transformation and social innovation. Countries such as Canada have emerged as global leaders in AI research and development, supported by strong academic ecosystems, public investments, and dynamic startup environments. Despite this dynamism, many AI innovations developed in advanced research ecosystems struggle to reach and scale in emerging markets. This raises an important question: under what conditions can technological innovations developed in one context be successfully adopted and deployed in another?
African markets offer significant opportunities for AI applications, particularly in sectors such as healthcare, agriculture, climate resilience, and public services. However, the successful adoption of these technologies requires more than technical readiness. It depends on a range of factors, including regulatory environments, local innovation ecosystems, data governance structures, and the socio-economic conditions that shape technology adoption. Understanding these dynamics is essential for fostering responsible and context-sensitive technology transfer between regions.
This project is supported by the MITACS Accelerate program and conducted in collaboration with the International Centre of Expertise in Artificial Intelligence, with the support of the Open AIR Network. The project is administered by the University of Ottawa and led by Professor Thomas Hervé Mboa Nkoudou (University of Ottawa).
Objective
The project seeks to identify the opportunities and success factors that can facilitate the adoption of Canadian artificial intelligence innovations in African markets, particularly in Sub-Saharan Africa.
More specifically, the research will:
- examine the structural barriers limiting the diffusion of Canadian AI innovations in African contexts;
- analyze the institutional, regulatory, and socio-economic conditions shaping technology adoption;
- conceptualize the notion of critical scaling in the context of responsible and context-sensitive AI innovation.
Research approach and project phases
To operationalize this approach, the project combines empirical research, ecosystem analysis, and stakeholder engagement. The research activities are structured into four complementary phases implemented over a twelve-month period.
- Mapping Canadian AI innovations
The first phase focuses on identifying Canadian AI innovations that may have relevance for African markets. This includes mapping startups, research laboratories, and technology developers working in areas such as healthcare, agriculture, climate monitoring, and public services. Interviews with innovators and researchers will also help identify technologies that have reached technological maturity but have not yet been widely deployed internationally.
- Understanding African AI innovation ecosystems
The second phase examines the structure of African innovation ecosystems. This includes analyzing regulatory frameworks governing artificial intelligence and digital technologies, mapping innovation hubs and entrepreneurship networks, and identifying structural barriers that may limit the adoption of AI solutions. Particular attention will be given to data governance, digital infrastructure, and institutional capacities that influence innovation diffusion.
- Identifying success factors for technology transfer
In the third phase, the project investigates the conditions that enable successful technology transfer between Canada and African markets. Through case studies and stakeholder consultations, the research will identify institutional, regulatory, and partnership models that support responsible and context-sensitive innovation transfer.
- Conceptualising the notion of critical
The final phase focuses on conceptual synthesis. A central conceptual contribution of the project is the development of the notion of “critical scaling in responsible and context-sensitive AI innovation.”
Traditional approaches to scaling innovation often focus primarily on rapid market expansion. However, when technologies move across very different socio-economic contexts, scaling must be approached more carefully. Critical scaling emphasizes the need to align innovation strategies with local needs, governance frameworks, and institutional realities.
Rather than simply exporting technological solutions, this perspective encourages adaptive and collaborative approaches that involve local actors and ensure that technologies are deployed in ways that are socially relevant and ethically responsible.
Expected outcomes
Beyond its academic contributions, the project aims to generate policy-relevant insights and strengthen collaboration between innovation ecosystems. Expected outcomes include a mapping of Canadian AI innovations with potential applications in Africa, comparative analyses of innovation ecosystems, policy briefs on responsible technology transfer, and the development of conceptual tools to better understand how emerging technologies scale across different socio-economic contexts.
Alignment with Open AIR missions
By identifying the conditions that enable meaningful and responsible innovation transfer, the project contributes to broader discussions on innovation governance and sustainable development. It also aligns closely with the research agenda of the Open AIR Network, which examines how innovation systems, intellectual property frameworks, and governance structures shape inclusive and sustainable innovation in the Global South.
