Public discussion of business AI tends to default to chat bots and generative writing tools. Statistics Canada actually asked Canadian businesses which specific applications they use, and the ranked answer looks somewhat different from the public conversation.
Key takeaways
Before ranking what Canadian businesses use AI for, it is worth restating the base: only 19.2% of Canadian businesses reported using AI at all over the 12 months preceding Statistics Canada’s Q2 2026 survey. Everything below describes what that 19.2% is actually doing — it is not a description of the whole economy.
Among businesses that used AI in the last 12 months, the leading applications were data analytics (36.6%), text analytics (34.5%), virtual agents or chat bots (28.2%), natural language processing (27.0%), and large language models (24.8%). Further down the list: speech or voice recognition (20.6%), marketing automation (19.8%), machine learning (18.2%), recommendation systems (17.9%), image or pattern recognition (14.0%), decision-making systems (13.7%), deep learning (13.1%), robotic process automation (5.0%), machine or computer vision (4.6%), augmented reality (3.0%), neural networks (2.3%) and biometrics (1.8%). The ordering is not what most public commentary about “business AI” would predict — analytics, not conversational AI, sits at the top.
Comparing the same categories against Q2 2025 shows where growth actually concentrated. Data analytics rose from 26.4% to 36.6%, decision-making systems rose from 5.7% to 13.7%, and deep learning rose from 6.6% to 13.1% — each roughly doubling. Large language models grew more modestly, from 19.1% to 24.8%. Two categories actually fell: marketing automation dropped from 23.1% to 19.8%, and text analytics dipped slightly from 35.7% to 34.5%. The growth is not uniform across every AI category, and the fastest-growing categories are not the ones that dominate public conversation about generative AI.
Layering industry onto the applications data shows the mix is not one national pattern repeated everywhere. In information and cultural industries, virtual agents or chat bots lead at 50.9%, followed by data analytics at 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%, with text analytics at 44.2%. A generic statement like “Canadian businesses mostly use AI for chat bots” is true for one industry and false for two others in the very same dataset.
Among AI-using businesses with 100 or more employees, 50.5% used virtual agents or chat bots and 40.4% used data analytics. Among AI-using businesses with 1 to 4 employees, data analytics led at 32.5% and text analytics at 29.4%, with chat bots further down the list. Larger organizations appear to lean more heavily on customer-facing conversational tools; smaller ones lean more on analysing the data they already have. Neither pattern is more “correct” — they reflect different operational realities.
The least-used categories are worth naming precisely because they contradict some of the more dramatic public claims about AI. Robotic process automation sits at 5.0%, machine or computer vision at 4.6%, augmented reality at 3.0%, neural networks at 2.3%, and biometrics at 1.8% among AI-using businesses. These are the categories most associated with fully autonomous, physically embedded or highly specialized AI — and they are, by a wide margin, the least common applications Canadian businesses report actually running. The everyday reality skews toward analytics run on existing digital records, not toward robots or biometric systems.
It is worth reading the applications ranking against the adoption context from a separate release: in the third quarter of 2025, two-thirds (66.7%) of Canadian businesses reported no plans to adopt AI over the next 12 months, and 78.1% of those said it was simply not relevant to what they produce. The applications ranked above describe what a minority of Canadian businesses are doing, in detail; they say nothing about the majority, who are not using AI applications of any kind and, on their own account, do not expect to soon.
A companion StatCan analysis of adoption and productivity found that firms with certain capabilities already in place adopt AI at meaningfully higher rates — firms using data analytics are 15.0 percentage points more likely to adopt AI than firms that are not, and firms using advanced robotics are 8.1 percentage points more likely, alongside R&D activity, cloud computing and ICT training for employees. Read together with the applications ranking above, this suggests the businesses topping the data-analytics application category are largely businesses that already had an analytics practice before AI entered the picture, not businesses starting from nothing.
Reading the ranking correctly
The 36.6% at the top of the applications list describes the share of AI-using businesses — itself 19.2% of all Canadian businesses — that report using data analytics as one of their AI applications. That works out to roughly 7% of all Canadian businesses. The ranking is genuinely informative about relative popularity among adopters; it is not a claim that a given application is common across the whole economy.
None of this is a ranked list of what a business should adopt. StatCan is reporting what is already in use, which reflects which industries have adopted fastest and which applications were easiest to bolt onto existing digital workflows — not which application produces the best result for a given task. A business choosing where to start is better served by matching an application to a specific, named task it already performs than by copying whatever tops a national ranking.
Related: does AI apply to my industry, and how to read AI statistics without being fooled.
Data analytics, used by 36.6% of AI-using businesses, ahead of text analytics (34.5%) and virtual agents or chat bots (28.2%) (StatCan, Q2 2026).
Not overall — they rank third nationally, though they lead specifically in information and cultural industries (50.9%) and among the largest businesses (50.5% at 100+ employees) (StatCan, Q2 2026).
Decision-making systems and deep learning both roughly doubled between Q2 2025 and Q2 2026 (5.7% to 13.7%, and 6.6% to 13.1% respectively), a faster rate of growth than large language models over the same period (StatCan).
A short call is enough to place a specific idea against what similar Canadian businesses are actually doing.
