No — Statistics Canada’s own survey of Canadian businesses tracks them as separate categories, and the practical difference is real.
Short answer
No. Statistics Canada’s own survey of Canadian businesses treats them as separate, measured categories, and the practical difference matches: RPA follows a fixed script through a screen or an API, while an AI agent can vary what it does based on the content of what it is looking at.
The Canadian Survey on Business Conditions, run by Statistics Canada, lists AI applications used by Canadian businesses as distinct rows, not a single blended category. In the second quarter of 2026, among businesses using AI, “robotics process automation” sat at 5.0 per cent, “virtual agents or chat bots” at 28.2 per cent, and “machine learning” at 18.2 per cent — roughly a sixfold gap between RPA and virtual agents. StatCan’s own table heading spells it “robotics process automation,” not the more common “robotic process automation” — worth noting if you go looking for the row yourself.
RPA replays a fixed sequence of clicks, keystrokes or API calls against a screen or system, and it behaves identically every time it runs — it does not read or interpret what it encounters, it follows the script. An AI agent built on a language model instead reads the specific content in front of it — the wording of an email, the values in a document’s fields — and selects which of several defined actions applies. That is the practical line: fixed-sequence automation for work that never varies, an agent’s judgment where the input’s wording or structure genuinely changes case to case.
A concrete contrast makes the line clearer. An RPA script built to copy a supplier’s invoice number from a fixed field into an accounting system works reliably for that one supplier’s template — and breaks the moment a different supplier moves that field, renames it, or sends a scanned image instead of a structured file. An agent reading the same set of invoices can locate the invoice number regardless of where it sits on the page or what the supplier calls it, because it is reading the content rather than a fixed coordinate. That flexibility is also the risk: a script that breaks does so visibly, while an agent that misreads a number can produce a plausible, wrong answer with no obvious failure at all.
If what you are automating never varies — the same form, the same three fields, every time — RPA is cheaper and more predictable. If the input varies, an agent’s judgment earns its keep, but variability is also exactly where it can quietly go wrong, which is why it needs a review layer RPA does not. See whether AI agents need supervision and what an AI agent actually does.
See how the two get combined in a real integration.
