Canada’s leading research funding agencies—NSERC, CIHR, SSHRC, and the Canada Foundation for Innovation—have jointly responded to the federal government’s AI Strategy Task Force, highlighting the need to strengthen the country’s capacity for AI-driven research and discovery. The coordinated proposal calls for sustained investment in artificial intelligence infrastructure, research talent, and ethical data governance.
For laboratories, AI is shifting from a specialized tool to a foundational capability—one that will influence how research is conducted, analyzed, and shared. The national strategy signals a turning point in how science and technology will evolve within Canadian research environments.
From AI research to AI-powered science
The task force submission urges a transition from researching AI to using AI as a catalyst for innovation. This shift involves embedding machine learning, predictive modeling, and data-driven experimentation into daily laboratory operations.
The agencies emphasize that the next major breakthrough in any discipline will likely stem from applying AI to assist or accelerate research. For lab managers, this underscores the importance of preparing digital infrastructure—ensuring that instruments, databases, and cloud systems can support scalable, AI-enabled workflows.
Training researchers for an AI-ready workforce
A key component of the strategy—“talent as the future AI engine”—focuses on developing the workforce that will power Canada’s AI ambitions. The agencies recommend expanding AI literacy across all scientific disciplines, not only within computer science or engineering.
For laboratory leaders, this means rethinking training and recruitment strategies. Technical staff, research associates, and data specialists must understand how to apply and evaluate AI tools responsibly. Investing in professional development and interdisciplinary collaboration will position labs to attract and retain skilled personnel capable of driving data-centric research innovation.
Building secure and connected data infrastructure
Another central theme—“building the connective architecture”—addresses the need for secure, interoperable data systems. The agencies advocate for adopting FAIR data principles (Findable, Accessible, Interoperable, and Reusable) and aligning Canadian data practices with international standards while respecting Indigenous data sovereignty.
For lab operations, the implications are significant. Many facilities rely on datasets and instruments tied to short-term funding cycles. Without consistent investment, vital research data can be lost, limiting reproducibility and long-term innovation. The agencies stress that stable data stewardship funding is as essential as developing new AI technologies.
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What lab managers should do now
Canada’s AI for Science Strategy outlines a future where laboratory work is increasingly digital, automated, and data-intensive. Lab managers can begin preparing by:
- Assessing data infrastructure and cybersecurity readiness
- Reviewing policies for data sharing and storage compliance
- Integrating AI training into onboarding and continuing education
- Exploring partnerships that connect technical teams with AI specialists
As Canada builds a more connected and intelligent research ecosystem, laboratories that invest early in AI-readiness—through people, processes, and platforms—will gain a competitive edge in innovation and funding opportunities.
This article was created with the assistance of Generative AI and has undergone editorial review before publishing.
