A proof of concept is a small, deliberately limited test built to answer one question — does this particular approach actually work, on real material, well enough to be worth pursuing further — before any wider commitment of time, money or client exposure is made.
Statistics Canada’s own numbers show why that small first test matters in Canada specifically. “In the third quarter of 2025, 14.5% of businesses reported plans to use AI over the next 12 months, while two-thirds (66.7%) of businesses reported no plans and 18.9% were uncertain”. Among businesses with no plans to adopt, the reasons were not mainly cost: StatCan found “a lack of knowledge about AI capabilities (11.3%), concerns about privacy and security (8.1%), and the view that AI is not yet a mature enough technology (7.6%)”. A proof of concept exists to answer that last concern directly, on one firm’s own files, instead of taking a vendor’s word for whether the technology is ready.
A proof of concept is deliberately smaller than a benchmark and much smaller than a pilot. It is not trying to produce a defensible performance comparison, and it is not yet touching a real client file — it only has to answer whether the approach is workable at all, on a small, controlled set of already-known outcomes, before anyone spends the effort a proper benchmark or a live pilot would require.
A firm curious about AI-assisted document review might run a two-week proof of concept: take thirty already-closed files with a known, correct outcome, run the tool against them without touching any live file, and have a person check every result against what actually happened. If the tool gets close on most of those thirty, that is a case for testing further with a proper benchmark — not for handing the tool a live file on day one. If it does not, the firm has spent two weeks finding that out instead of finding it out from a client’s own file.
See also: what is an AI benchmark, what is an AI model.
Deciding whether a small test is enough to justify going further belongs before any wider commitment — ai-strategy-roadmapping covers how that decision gets made once the test is done.
