The honest answer to does AI apply to my industry is not universally yes, and it is not universally no — it depends which industry, and Statistics Canada’s own data shows the spread is large enough that the question is worth asking properly rather than assuming.
Key takeaways
Does AI apply to my industry is really two questions folded into one: does AI apply to industries broadly like mine, and does it apply to the specific workflow my business runs. Statistics Canada answers the first question with real numbers. The second question needs a more specific test, covered further down.
In the second quarter of 2026, businesses in information and cultural industries (42.3%), finance and insurance (40.4%), and professional, scientific and technical services (32.4%) were most likely to use AI to produce goods and deliver services. At the other end, AI use was least prevalent among businesses in agriculture, forestry, fishing and hunting (4.5%), wholesale trade (7.9%), and construction (9.2%). That is close to a nine-fold difference between the highest- and lowest-adopting sectors in the same national survey, in the same quarter — strong evidence that industry genuinely matters, and that a single national adoption figure hides more than it reveals.
Even among industries that do use AI, what they use it for is not the same tool applied everywhere. In information and cultural industries, the leading applications among AI users are virtual agents or chat bots (50.9%) and data analytics (48.3%). In finance and insurance, text analytics and large language models are tied at 38.8% each. In professional, scientific and technical services, data analytics leads at 48.6%, followed by text analytics at 44.2%. A business asking does AI apply to my industry is often really asking which of these applications, if any, matches something my business actually does — and the honest answer changes depending on which one is on the table.
A construction firm reading that 19.2% of Canadian businesses use AI and concluding it is behind the curve is comparing itself to the wrong baseline. Construction’s own reported use is 9.2%, roughly half the national figure, in the same StatCan release. A finance business reading the same 19.2% headline and concluding it is ahead is making the identical mistake in the other direction — its sector’s own figure is more than double the national average, at 40.4%. Either comparison to the economy-wide number, rather than the industry-specific one, produces a wrong read on where a given business actually stands.
Finance and insurance also carries an obligation the other industries in this ranking do not. Federally regulated banks, insurers, and trust and loan companies must comply with OSFI’s Guideline E-23 — Model Risk Management, effective May 1, 2027, which the guideline itself frames around “the surge in artificial intelligence / machine learning” models and requires a documented “model inventory, model risk ratings, and requirements for model lifecycle governance”. A construction firm adopting the same chat-bot or analytics tool answers to no equivalent sector-specific regulator — part of why does AI apply to my industry needs an industry-specific answer, not only a StatCan percentage.
Industry is a proxy for something more specific: whether a business’s existing workflow already produces the kind of data, volume and repetition that an AI tool can act on. StatCan’s adoption and productivity research backs this up directly — firms already using data analytics are 15.0 percentage points more likely to adopt AI than firms that are not, regardless of which industry code they carry. Two businesses in the same industry, one with a decade of structured customer data and one without, are not equally positioned to answer “does AI apply to us” — the industry label predicts less than the underlying capability does.
Deavo’s own guidance for people buying or selling AI and software businesses in Canada frames a related version of this question from the acquirer’s side: “buyers price proprietary data and a defensible model far above the wrapper around someone else’s API”. That is written for AI-business valuation specifically, not for “should my business adopt AI,” but the underlying distinction transfers: a business whose value would come from data and process it already owns is in a genuinely different position from one that would only ever be wrapping a third party’s tool around an unchanged workflow.
It helps to work through opposite ends of the StatCan spread directly. A professional services firm sits in a sector reporting 32.4% AI use, led by data analytics (48.6%) and text analytics (44.2%) — both applications that act on the kind of written records and case data a services firm already generates as a byproduct of normal work, which is exactly the “already has the underlying capability” condition described above. A construction firm sits in a sector reporting 9.2% (StatCan, Q2 2026) use, and the honest reason is structural, not cultural: a construction firm’s core output is physical, on-site and highly variable by project, which is precisely the kind of work the leading AI applications — analytics on structured data, text analytics on written records — are not built around. Neither firm is wrong about whether AI “applies” to them; they are describing genuinely different starting conditions.
A three-question filter
Instead of asking does AI apply to my industry in the abstract: (1) what is your industry’s own StatCan adoption figure, not the national average; (2) which specific application leads in that industry, and does it match a task your business actually performs repeatedly; (3) do you already have the underlying capability — structured data, a digital record of the workflow — that the adoption research shows actually predicts a successful fit. An industry with low adoption can still be a good fit for a specific business inside it, and a high-adoption industry does not guarantee fit for every business inside it either.
Related: how Canadian businesses actually use AI, and AI in Canadian small business.
Information and cultural industries lead at 42.3%, followed by finance and insurance at 40.4% and professional, scientific and technical services at 32.4% (StatCan, Q2 2026).
Agriculture, forestry, fishing and hunting (4.5%), wholesale trade (7.9%) and construction (9.2%) report the lowest use among Canadian industries in the same survey (StatCan, Q2 2026).
Not necessarily. Adoption correlates more with whether a business already has capabilities like data analytics or cloud computing in place than with its industry code alone — StatCan found data-analytics users are 15.0 percentage points more likely to adopt AI regardless of sector (adoption and productivity study).
A short call is enough to check your position against the actual StatCan figures for your sector.
