Call for Proposals in AI Skills & Adoption

To position Canada as a global leader in AI products, we need a robust pipeline of applied AI talent. This involves not only developing technical skills but also ensuring that future professionals have the practical experience and business acumen needed to drive innovation and bring AI solutions to market.

Projects are encouraged to encompass the following elements:

• 1. Work-Integrated Learning: Providing practical, hands-on experiences through internships, co-ops, or practicums in collaboration with industry leaders.
• 2. Skill Development: Equipping learners with in-demand AI/ML/DS competencies and commercialization skills.
• 3. Equity, Diversity, and Inclusion (EDI): Increasing the participation of women and other underrepresented groups in AI fields.
• 4. Industry-Academic Partnerships: Aligning academic curricula with industry needs to ensure that students gain relevant and applicable skills and fostering collaboration to offer rich and diverse learning experiences.
• 5. Employment Outcomes: Setting measurable objectives for post-program employment rates in AI roles, ensuring a clear pathway to careers in the AI industry.

This area of interest aims to promote the widespread adoption of made-in-Canada AI technologies, driving innovation, improving efficiency and enhancing competitiveness across various sectors. By fostering the adoption of AI, we can ensure that Canadian organizations remain at the forefront of technological advancements and are well-equipped to tackle emerging challenges in the AI-driven economy. 

Projects should focus on developing the skilled workforce needed to implement and manage AI solutions across sectors, which may include but are not limited to:

• 1. Sector-Wide AI Competency Building: Enhancing AI fluency and skills across multiple organizations within a sector through training and development programs. Training can be targeted at both employees and leadership teams, ensuring organizations can effectively implement and manage AI tools. Internal training programs for individual organizations are not eligible; projects must promote AI adoption on a larger scale.
• 2. Shared AI Resources and Infrastructure: Creating shared AI tools, platforms, or infrastructure—such as data centers or cloud computing resources—that multiple organizations can access, along with training programs to equip users with the skills to use these resources effectively. This enables even smaller entities to leverage advanced AI technologies while building the talent needed to fully benefit from these tools.
• 3. Collaborative AI Learning Networks: Establishing networks that connect organizations within and across sectors to share knowledge, best practices, and resources for AI adoption, fostering a collaborative approach to workforce readiness.
• 4. Responsible AI Practices: Promoting safe and ethical AI adoption by equipping individuals with the knowledge and skills to apply data privacy, security, and ethical guidelines in AI systems. Through targeted training and best practices, this ensures that talent is prepared to implement AI responsibly.