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AI is no longer a future technology. It is already changing how work gets done, how companies make decisions and how economies compete.
This special edition of Disruptors was recorded at the Creative Destruction Lab’s Super Session during Toronto Tech Week. Host John Stackhouse is joined by Fabien Curto Millet, Chief Economist at Google and Sonia Sennik, CEO of Creative Destruction Lab, to explore AI adoption, productivity, jobs and Canada’s competitiveness.
Fabien brings a global view of AI adoption: where the data is showing productivity gains, why the jobs conversation is more nuanced than the headlines suggest, and why simple interventions like training, guidelines and encouragement can unlock experimentation. Sonia brings the founder and commercialization lens from CDL, where hundreds of science-based startups are working across AI, health, energy, agriculture, manufacturing and more.
Together, they explore why AI is moving fast but unevenly, why some sectors and workers are pulling ahead while others remain cautious, and what leaders need to do to move from pilots to scaled workflow redesign. For Canada, the test is clear: the country has deep AI talent, strong institutions, and a global reputation in modern AI. The gains will depend on adoption – especially among SMEs, public institutions and the sectors that make up the bulk of the economy.
Think of it as an AI adoption blueprint for you and your organization.
FAQs
This episode examines AI adoption as Canada’s next productivity test. John Stackhouse speaks with Sonia Sennik of Creative Destruction Lab and Fabien Curto Millet, Chief Economist at Google, about jobs, productivity, business adoption and competitiveness.
The conversation was recorded at the Creative Destruction Lab Super Session on the University of Toronto campus.
Canada has deep AI talent and strong institutions, but the economic gains from AI depend on whether companies, SMEs, governments and workers can put the technology into production and redesign workflows around it.
The conversation argues for a more nuanced jobs discussion. AI will affect tasks inside jobs and change workflows, but current labour-market data does not support the simplest version of mass job-loss panic.
The leadership challenge is moving from experimentation to scale: giving workers permission and training, setting guidelines, choosing high-value workflows and redesigning operating systems rather than treating AI as a side tool.
Start with practical experimentation, train teams, create clear guidelines, identify lighthouse workflows and focus on AI to increase capacity, quality and speed – not just as a cost-cutting tool.
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