Nobody has actually measured this for Canadian businesses. Here's what's measured instead, and why that's not a dodge.
Short answer
No Canadian source publishes a business-level time-savings figure for AI use, so no honest answer can give you a number — and any specific “X hours saved” claim you see elsewhere should be treated as unsourced until it names a real study. What is measured is which tasks Canadian businesses actually apply AI to, and that adopting it comes with real, documented setup costs that a simple “time saved” story leaves out.
Statistics Canada’s Canadian Survey on Business Conditions asks businesses whether, where, and how they use AI, and what holds them back — it does not ask them to estimate hours or dollars saved, and no other Canadian government source fetched for this page publishes a time-savings rate either. That gap is worth taking seriously rather than filling with a plausible-sounding figure: even at the scale of the entire national labour market, Statistics Canada’s own economists studying employment trends in AI-exposed occupations concluded: “It is unclear whether more recent trends reflect the advent of AI, other economic factors such as labour market adjustments after the COVID-19 pandemic, rapid demographic shifts, recent trade tensions with the United States or a combination of factors”. If isolating AI’s effect is that difficult across the whole economy, treating one business’s impression that “this saved time” as proof is harder still, not easier.
What Statistics Canada does track is the adjustment AI use requires. Of Canadian businesses that used AI in the last 12 months, 44.4% made changes in training or staffing practices due to AI use, with 32.0% providing AI-related training for existing employees and 21.6% for existing executives. Among businesses with 100 or more employees, that rises to 68.1% training existing employees and 30.2% using external consultants or vendors. For most businesses, the near-term, measured effect of adopting AI is a training and adjustment cost, not an immediate saving — any saving would have to outlast that setup period to be real.
Since no Canadian benchmark exists, the honest way to reason about this is structural: a time saving only holds up if checking the AI’s output doesn’t cost back what the tool saved. A task where a quick read catches any error — drafting a first pass, summarizing a long document — nets out very differently from a task where a mistake is costly and has to be checked line by line regardless of who or what produced it. See why most AI pilots stall for where that arithmetic commonly breaks down, and how long before AI shows results for the timing question this one is usually paired with.
There's no published number to lean on. There is a way to reason about it properly before you commit.
