The headline says Canadian finance teams get less from AI than everyone else. The footnote says the headline comes from 53 very large companies. Your controller should read both.
- Only 36% of Canadian respondents in KPMG’s 2026 AI in finance survey report better forecast accuracy, against 64% globally.
- The Canadian sample is 53 companies, all with revenue of at least US$250 million.
- The useful lesson for a mid-sized firm is measurement, not scale: 64% of Canadian respondents track AI KPIs, against 85% globally.
AI in finance in Canada looks like it’s falling behind. KPMG Canada’s AI in finance 2026 findings, published this month, show 36% of Canadian respondents reporting improved forecast accuracy against 64% globally. The number is real. It also comes from 53 Canadian companies with at least US$250 million in revenue. If you run finance for a 60-person firm, the gap tells you less than it seems.
TL;DR
- KPMG’s Canadian findings show 83% of respondents past the planning stage for AI in finance, against 92% globally.
- Canadian results trail on outcomes: 36% versus 64% on forecast accuracy, 45% versus 71% on decision speed.
- The Canadian sample is 53 respondents from large companies. 43% of them have 1,000 to 5,000 employees.
- Our estimate: a sample of 53 carries a margin of error near 13 points either way. The direction holds. The precision doesn’t.
- The part that transfers to a mid-sized firm is the measurement gap. Track what AI changes before you buy more of it.
What did KPMG find about AI in finance in Canada?
KPMG’s 2026 survey found Canadian finance functions adopting AI broadly but reporting smaller gains than global peers. 57% say AI return meets expectations and 19% say it exceeds them. Canada also trails on audit evidence for AI, 66% versus 82%, and on tracking AI KPIs, 64% versus 85%.
The headline numbers, straight from the KPMG Canada page:
| Measure | Canada | Global |
|---|---|---|
| Past planning for broad AI use in finance | 83% | 92% |
| Report improved forecast accuracy | 36% | 64% |
| Report faster decision-making | 45% | 71% |
| Can efficiently produce AI audit evidence | 66% | 82% |
| Track and act on meaningful AI KPIs | 64% | 85% |
Agentic AI is moving too. 77% of Canadian respondents say they’re past planning for it. They rank greater responsiveness (41%) and competitive advantage (37%) ahead of lower costs (27%) as the bigger opportunity.
Why does the sample size matter for Canadian businesses?
Because every Canadian figure above comes from 53 respondents at companies with at least US$250 million in revenue. Our rough calculation puts the margin of error near 13 points. The gap on forecast accuracy survives that. The exact size of the gap doesn’t, and none of it describes a 10 to 500 person business.
This is the part most coverage skips. KPMG is clear about it in the “About the survey” box at the bottom of the page. The global study covered 1,013 senior leaders across 20 jurisdictions, surveyed online in March 2026. Revenue floor: US$250 million, or US$500 million in the United States. Of the 53 Canadians, 43% came from companies with $1 billion to $5 billion in revenue and 1,000 to 5,000 employees.
That’s a fair sample for what it measures. Large Canadian finance functions. It’s the wrong mirror for the firms most Canadians work at. Statistics Canada’s second-quarter AI analysis found 19.2% of all Canadian businesses using AI. KPMG’s respondents are already at 83% past planning. Those are two different countries.
The contrarian take, then: stop reading this report as “Canada is behind, spend to catch up.” Read it as “big Canadian firms bought first and measured second.” A mid-sized finance team can skip that mistake entirely.
What does the AI finance gap cost a mid-sized firm?
Directly, nothing. The cost comes from copying the wrong fix. A 60-person company that buys an enterprise AI finance platform to close a gap measured at billion-dollar firms spends real money chasing someone else’s benchmark. The cheaper lesson is the KPI gap.
Look at the bottom two rows of that table again. Canadian respondents are 16 points behind on audit evidence and 21 points behind on tracking AI KPIs. That’s a measurement problem. You can’t show better forecasts if nobody wrote down forecast error before the tool arrived.
That’s where a smaller team has the advantage. One controller can baseline three numbers in an afternoon: days to close the month, forecast error last quarter, and hours spent on reconciliations. A billion-dollar finance function needs a steering committee to do the same thing.
What should finance leaders do next?
Baseline before you buy. Write down close time, forecast error and reconciliation hours today, then run one AI use case for a quarter and compare. Ask vendors for results from firms your size, not from the KPMG sample. Keep a human reviewing anything that reaches a lender or the CRA.
- Record three baselines this week: days to close, last quarter’s forecast error, and monthly hours on reconciliations.
- Pick one use case, not a platform. Variance commentary drafts and invoice matching are common starting points.
- Check your existing accounting software for AI features already included in your plan before buying anything new.
- Ask every vendor: what changed for a client under $50 million in revenue, measured how, over what period?
- Keep a paper trail. KPMG’s respondents trail on AI audit evidence. Log what the tool produced and who approved it.
- Stop comparing yourself to billion-dollar peers. Compare against your own baseline next quarter.
The skeptic’s view
KPMG’s numbers are the best available read on Canadian finance and AI, and dismissing them on sample size is too easy. The gap is large and it points one way across every outcome measure. Mid-sized firms often follow the practices of large ones within a few years, so the warning is worth taking early.
That’s fair. The direction is the signal. Our point is narrower: take the lesson about measurement, and leave the enterprise budget and tooling to the enterprises.
What to watch
- 28 October 2026. The Bank of Canada’s Monetary Policy Report. Watch its language on AI and business productivity.
- Year-end close. The first close where you have a baseline. Compare days to close against the same period last year.
- KPMG’s 2027 edition. Watch the Canadian sample size, and any sign of mid-market firms in it.
Our prediction: KPMG’s next edition will still show Canadian forecast accuracy gains trailing the global figure by at least 10 points, because the measurement gap takes longer to close than the adoption gap.
FAQ
How does Canada compare on AI in finance?
In KPMG’s 2026 survey, 36% of Canadian respondents reported improved forecast accuracy from AI, against 64% globally, and 45% reported faster decisions, against 71% globally.
How many Canadian companies were in the KPMG AI in finance survey?
53 Canadian respondents, part of 1,013 senior leaders across 20 jurisdictions surveyed in March 2026. All worked at organizations with revenue of at least US$250 million.
Is AI worth it for a small finance team in Canada?
It can be, if you measure first. Record close time, forecast error and reconciliation hours, run one use case for a quarter, and compare against your own baseline.
What is the best first AI use case for a finance team?
Start with a repetitive task that has a clear before and after, such as drafting variance commentary or matching invoices, and keep human review on anything external.
Closing analysis
The KPMG gap is a warning about large Canadian firms that bought AI before they decided what to measure. A mid-sized finance team can read the same page and do the opposite: measure first, buy small, and let its own numbers decide.
For Alberta finance teams mapping a first project, the Alberta AI Advantage blueprint lays out the starting steps. Finance leaders compare notes on the AI Podcast.
Sources
- KPMG Canada, AI in finance 2026, Closing the value gap, September 2026. kpmg.com
- Statistics Canada, Analysis on artificial intelligence use by businesses in Canada, second quarter of 2026, 11-621-M2026010. www150.statcan.gc.ca
- Bank of Canada, Canadian businesses’ use of AI, what the evidence shows, Sparks, August 2026. www.bankofcanada.ca
Related reading
- AI and the month-end close
- Why most Canadian AI pilots never prove a return
- AI for business in Canada, our reference hub
Disclosure
The author has no relevant financial, advisory, or board relationships with any party named in this column.
Zak Hussein writes on AI for Canadian business owners and operators. He is the founder of AI Magazine Canada and CEO of ORKA AI.
