Abstract:
Canada is facing a skilled labour shortage, with a large portion of organizations of all sizes reported their growth being constrained by labour shortages, challenges recruiting skilled labour, and challenges retaining this labour. These labour shortages are not only limiting growth but slowing the adoption of new technologies, including AI. The implications are significant, as other countries surge ahead in digital transformation and productivity, Canadian firms (particularly SMEs), risk falling behind. Ultimately, this session aims to move beyond identifying barriers to a scalable, actionable framework that ensures AI adoption contributes to inclusive growth and long-term resilience in Canada’s innovation ecosystem.

Summary of Conversations 

The discussion highlighted the national paradox of leading in AI research but lagging significantly in technology adoption, particularly among small and medium enterprises (SMEs). Participants noted that the rapid, often underestimated, pace of technological change is transforming every sector and role, including skilled trades, due to the emergence of generative and embodied AI. A central theme was the urgent need for a national skills strategy, positioning AI literacy as a foundational requirement to navigate deepfakes, misinformation, and ethical pitfalls like algorithmic bias. Key barriers to adoption included structural challenges, a widespread lack of trust, inconsistencies in measuring AI use, and a critical digital divide related to access, skills, and affordability. The conversation emphasized the necessity of responsible deployment, focused on governance, accountability, and the integration of social sciences and ethical thinking.

Take Away Messages/Current Status of Challenges

• The AI paradox is a fundamental structural issue, as the country excels in AI invention but struggles with national adoption, particularly by SMEs which make up 90% of the private sector workforce.
• The rate of AI-driven change is consistently underestimated, rapidly shifting duties and roles across all industries, including physical labor, due to advancements in agentic and embodied AI systems.
• A multi-faceted digital divide—encompassing infrastructure access, skills, and affordability—threatens to widen existing inequalities and heighten the risk of marginalization for underrepresented groups.
• There is a pervasive trust deficit and significant ethical concern stemming from fears about fairness, data bias (e.g., in hiring), privacy, and lack of accountability, which actively hinders widespread, responsible deployment.
• Small and medium enterprises (SMEs) face a capacity gap—lacking dedicated technology staff, surplus funds, and application knowledge—to effectively identify and implement practical AI use cases across their value chain
• The economic impact of AI adoption is unclear and subject to debate, with high-end projections of global GDP increase contrasting with more modest estimates, creating uncertainty on the required scale of investment.
• The increasing use of algorithmic workforce management is generating high levels of stress and negatively impacting the mental health of workers who feel constantly monitored and subject to optimization algorithms.

Recommendations/Next Steps

• Elevate AI literacy to a foundational skill for the entire population to ensure citizens can critically evaluate information, mitigate risks from deepfakes and misinformation, and foster responsible use.
• Shift AI training focus from tool use to oversight, accountability, and ethical considerations, implementing a risk-based competency framework immediately, independent of legislative timelines.
• Adopt an interdisciplinary approach by integrating social science expertise into AI governance, competency framework development, and research to explicitly address ethical and social justice implications.
• Cultivate an adaptive and entrepreneurial mindset through education and upskilling, preparing the workforce not just with technical AI skills but with the ability to sense new opportunities and align technology with real-world applications.
• Require the government to model trustworthy AI adoption by training its own workforce to transparently procure, deploy, and audit AI systems.
• Develop context-specific and role-specific training that blends deep AI knowledge with sector expertise (e.g., AI and Health, AI and Finance), including micro-certifications for rapid deployment.
• Implement mandatory training for managers and top executives to ensure organizational leadership understands the full scope of transformational change, risks like “shadow AI,” and the need for new governance policies.

Advance national ethical and participatory frameworks to strengthen data protection, ensure transparency, and strategically position the country as a global leader in disclosure standards for trustworthy AI.