Table of Contents
- 1. Questions
- 1.1. Research and Talent
- 1.2. Accelerating AI adoption by industry and government
- 1.3. Commercialization of AI
- 1.4. Scaling Canadian champions and attracting investments
- 1.5. Building safe AI systems and strengthening public trust in AI
- 1.6. Education and skills
- 1.7. Building enabling infrastructure
- 1.8. Security of the Canadian infrastructure and capacity
1. Questions
1.1. Research and Talent
- How does Canada retain and grow its AI research edge? What are the promising areas that Canada should lean in on, where it can lead the world? (i.e. promising domains for breakthroughs and first-mover advantage; strategic decisions on where to compete, collaborate or defer; balance between fundamental and applied research)
- How can Canada strengthen coordination across academia, industry, government and defence to accelerate impactful AI research? (i.e. mechanisms for cross-sector collaboration; integration of public and private research efforts; industry-sponsored research while preserving academic independence)
- What conditions are needed to ensure Canadian AI research remains globally competitive and ethically grounded? (i.e. infrastructure, talent and governance enablers; ethical standards and risk mitigation; alignment of applied research with business and societal needs)
- What efforts are needed to attract, develop and retain top AI talent across research, industry and the public sector? (i.e. differentiated enablers for research vs. applied talent; domestic vs. global talent strategies; targeted attraction programs and priority domains; international collaboration opportunities)
1.2. Accelerating AI adoption by industry and government
- Where is the greatest potential for impactful AI adoption in Canada? How can we ensure those sectors with the greatest opportunity can take advantage? (i.e. high-potential industries like health care, construction and agriculture; lessons from application-specific use cases like inventory management or financial forecasting)
- What are the key barriers to AI adoption, and how can government and industry work together to accelerate responsible uptake? (i.e. sectoral vs. cross-sectoral challenges, such as liability and small to medium-sized enterprise constraints; potential government policies, incentives and ecosystem supports)
- How will we know if Canada is meaningfully engaging with and adopting AI? What are the best measures of success? (i.e. metrics to distinguish experimentation, integration and transformation; sector-specific benchmarks and indicators of progress)
1.3. Commercialization of AI
- What needs to be put in place so Canada can grow globally competitive AI companies while retaining ownership, IP and economic sovereignty? (i.e. strategies for attracting investment and scaling internationally; balancing foreign capital with Canadian control of IP and corporate identity; economic security safeguards)
- What changes to the Canadian business enabling environment are needed to unlock AI commercialization? (i.e. barriers such as Canadian-controlled private corporation rules and foreign direct investment constraints; incentives, capital access and liability mitigation; sector-specific and cross-sectoral policy levers)
- How can Canada better connect AI research with commercialization to meet strategic business needs? (i.e. determining government’s role in linking academia, start-ups and industry; retaining Canadian-developed intellectual property; prioritizing sectors like life sciences, energy and defence for commercialization support)
1.4. Scaling Canadian champions and attracting investments
- How does Canada get to more and stronger AI industrial champions? What supports would make our champions own the podium? (i.e. barriers to scaling, including mentorship needs; effective mechanisms for transitioning between federal programs; tailored support across early-, mid- and late-stage growth)
- What changes to Canada’s landscape of business incentives would accelerate sustainable scaling of AI ventures? (i.e. alignment of business incentives and programmatic improvements to support scaling firms; mechanisms to retain and champion high-potential Canadian companies)
- How can we best support AI companies to remain rooted in Canada while growing strength in global markets? (i.e. strategies for long-term retention of scaled firms; balancing global competitiveness with domestic economic impact; government’s role in championing Canadian AI success stories)
- What lessons can we learn from countries that are successful at investment attraction in AI and tech, both from domestic sources and from foreign capital?
1.5. Building safe AI systems and strengthening public trust in AI
- How can Canada build public trust in AI technologies while addressing the risks they present? What are the most important things to do to build confidence? (i.e. risks posed by AI tools and services; drivers of public and business mistrust; educational and literacy strategies to foster informed confidence)
- What frameworks, standards, regulations and norms are needed to ensure AI products in Canada are trustworthy and responsibly deployed? (i.e. governance mechanisms for AI oversight; assurance of product integrity and ethical compliance; priority areas where trust issues are most acute)
- How can Canada proactively engage citizens and businesses to promote responsible AI use and trust in its governance? Who is best placed to lead which efforts that fuel trust? (i.e. public-facing strategies to explain AI systems; inclusive approaches to trust building; balancing transparency with innovation)
1.6. Education and skills
- What skills are required for a modern, digital economy, and how can Canada best support their development and deployment in the workforce? (i.e. enable rapid adaptation to technological change; programs for both AI-focused careers and broader workforce readiness)
- How can we enhance AI literacy in Canada, including awareness of AI’s limitations and biases? (i.e. workplace training programs or credentials; targeted engagements and public awareness campaigns; international best practices)
- What can Canada do to ensure equitable access to AI literacy across regions, demographics and socioeconomic groups? (i.e. collaboration with other levels of government; role of industry and private sector; educational and literacy strategies to foster informed confidence)
1.7. Building enabling infrastructure
- Which infrastructure gaps (compute, data, connectivity) are holding back AI innovation in Canada, and what is stopping Canadian firms from building sovereign infrastructure to address them? (i.e. strategies for derisking and promoting investment in different parts of the AI stack; government’s role in derisking; partnering with foreign capital)
- How can we ensure equitable access to AI infrastructure across regions, sectors and users (researchers, start-ups, SMEs)? (i.e. role of hyperscalers; open-source models; edge computing)
- How much sovereign AI compute capacity will we need for our security and growth, and in what formats? (i.e. economic models for AI forecasting; comparison of public and private sector demand)
1.8. Security of the Canadian infrastructure and capacity
- What are the emerging security risks associated with AI, and how can Canada proactively mitigate future threats? (i.e. current and downstream risks posed by AI technologies; anticipated needs in national security and defence; strategic foresight for evolving threat landscapes)
- How can Canada strengthen cybersecurity and safeguard critical infrastructure, data and models in the age of AI? (i.e. establishing policies and programs to protect sensitive assets, including data; building resilience into AI systems; leveraging international collaboration and partnership to meet global risks)
- Where can AI better position Canada’s protection and defence? What will be required to have a strong AI defensive posture? (i.e. coordination across public and private sectors; security-focused standards and frameworks; long-term preparedness for AI-driven security challenges)