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AI Adoption & Productivity

Canada's AI adoption story is split: employees and leading sectors are using generative AI quickly, while the latest official business-use measure is still below one in five and productivity gains depend on training, data readiness, and workflow redesign.

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Briefing summary

Canada's AI adoption story depends on what is being counted. Statistics Canada asks whether a business used AI to make goods or deliver services. On that narrow measure, formal adoption rose from 6.1% in Q2 2024 to 12.2% in Q2 2025 and 19.2% in Q2 2026. That is real growth, but it still means most employer businesses were not reporting AI in core operations.

The adoption gap is where Canada's AI strength either turns into everyday productivity gains or stays concentrated in labs and leading firms.
Government choices on training, small-business financing, public buying, data readiness, and Canadian computing capacity depend on knowing which adoption number is being discussed.
The highest headline numbers mostly describe use and experimentation; the official baseline still shows Canada has a long way to go before AI is built into everyday business operations.

Read the full context

Broader surveys show more activity. KPMG Canada's 2025 survey found 51% of employees using generative AI at work, while CFIB's April 2026 research found 45% of Canadian businesses reported using GenAI to complete tasks. Those numbers are not contradictions; they measure looser workplace use rather than formal business-wide adoption.

The sector split is stark. Information, professional services, finance, and insurance lead in the official data. Accommodation and food services, agriculture, and transportation sit much lower. Data-heavy service sectors can usually test AI faster than physical, small-margin, or complex operations.

The barrier story depends on the denominator. In Statistics Canada's Q2 2026 analysis, 40.0% of businesses said AI was not relevant to the business, while cybersecurity or privacy concerns (13.4%) and cost (10.6%) led the barriers that limit use. In the Q3 2025 no-plan subgroup, 78.1% said AI was not relevant to their goods or services.

Productivity evidence is promising but still conditional. Workers and small businesses report time savings, but proof across whole companies is thinner: KPMG found only 2% of business leaders reporting measurable return on investment. AI can help when paired with good data, redesigned work, training, and management discipline.

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