This report compares 21 high-relevance skills recommendations drawn from publications on the four named institutions' own sites — Statistics Canada, the Future Skills Centre, the Labour Market Information Council (LMIC) and the Brookfield Institute — in a targeted scan as of 10 October 2026, not a full catalogue of their archives. The rows span four independent institutional hosts; no single source supports more than half of them.
Formal research: tasks easy to codify face greater risk, so interpersonal and problem-solving skills build resilience; AI-complementary jobs still require skills to leverage AI. Guidance voiced in a StatCan-hosted podcast adds: centre education on communication, problem solving, emotional intelligence and adaptability; keep digital literacy critical; keep learning for life.
Make basic AI skills a core part of digital literacy for every field, not just tech. Learn AI's uses and implications, work with multi-modal AI, build critical thinking about when and when not to use it, and master data privacy, security and responsible use. Target reskilling at sectors most exposed to automation; build hybrid AI-plus-sector skill sets such as AI for healthcare professionals.
Build a mix of foundational, digital, complementary and specialized skills — but for most workers the growth is in communication, social, language and emotional skills, not advanced AI specialization. Employers adopting AI should train staff to use the tools; post-secondary should fold AI literacy into more programs; youth need work-integrated learning and human-in-the-loop collaboration skills.
Mostly pre-generative-AI, framed around automation and digitalization: everyday digital skills (Office/Excel, with SQL as a step up) beat niche programming; transferable skills beat credentials; critical thinking and imagination matter as much as technical pipelines. Mid-career workers need industry-led reskilling with placements, and AI teams need talent well beyond machine-learning experts.
Everyday digital skills and the basics of AI — what it does, its limits and implications — viewed through a critical lens (StatCan podcast, FSC, Brookfield).
Hands-on, occupation-specific training with the AI tools your workplace adopts, including multi-modal AI and human-in-the-loop workflows (LMIC, FSC).
Critically evaluate outputs; know privacy, security and ethics; decide when not to use AI at all (FSC, StatCan podcast).
Communication, teamwork, problem-solving, emotional intelligence, adaptability, creativity — the skills hardest for machines to replicate and the ones most workers actually need (LMIC, StatCan).
Pair AI literacy with your occupational depth — AI for healthcare, bridge roles between technologists and business (FSC, Brookfield).
Lifelong learning, employer training, work-integrated learning and industry-led mid-career reskilling with transition pathways out of shrinking sectors (all four).
| Institution | Publication | Skills recommended (top items) | Source |
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Method: 21 high-relevance recommendation rows extracted from publications on four institutional domains — statcan.gc.ca, fsc-ccf.ca, lmic-cimt.ca, brookfieldinstitute.ca — in a targeted scan as of 2026-10-10; not an exhaustive archive catalogue. Matrix counts tally how many rows per institution mention each of six skill themes by keyword; a row can count toward several themes. Table shows up to three skills per row for space; full skill lists are on the linked sources. Statistics Canada podcast guidance is labelled as discussion, not a statistical paper.