AI Adoption & Productivity

Canada's AI adoption story is best read as a definition problem before it is read as a ranking. The official Statistics Canada baseline asks whether a business used AI to produce goods or deliver services. On that narrow and defensible measure, formal adoption rose from 6.1% in Q2 2024 to 12.2% in Q2 2025. That is real acceleration, but it still means most employer businesses were not yet reporting AI inside core operations.

Broader measures show much more activity. KPMG Canada's 2025 survey found 51% of employees using generative AI at work, while SME-focused surveys from CFIB and BDC find substantial task-level use of AI-enabled tools. Those numbers are not contradictions; they measure looser workflow use, prompted recognition, or employee behaviour rather than formal enterprise deployment.

The sector split is stark. Information and cultural industries, professional/scientific/technical services, and finance/insurance are the clear leaders in the official data, all above 30% formal use. Accommodation and food services, agriculture, and transportation/warehousing sit near 1-2%. The pattern is structural: data-heavy service sectors can adopt faster than physical, small-margin, operationally complex sectors.

The biggest official barrier is not fear of regulation or job loss. In Statistics Canada's Q3 2025 data, 78.1% of non-adopting businesses said AI was not relevant to their goods or services. That makes use-case discovery, sector-specific examples, training, vendor fit, privacy/security confidence, and ROI measurement central to the next phase of adoption.

Productivity evidence is promising but still conditional. Surveyed workers and SMEs report time savings, but scaled enterprise ROI is much thinner: KPMG found only 2% of business leaders reporting measurable ROI. The safest conclusion is that AI can raise productivity when paired with data readiness, workflow redesign, training, and management discipline; it is not a plug-in macroeconomic shortcut.