The State of AI in Canadian Small and Medium Business, 2026
A synthesis of 2024 to 2026 data from Statistics Canada, CFIB, KPMG, IBM and Microsoft, read through the lens of what Canadian SMB owners actually need to decide in 2026.
Aggregated from public sources, each linked where it is cited.
Executive summary
Most organizations are already using AI, often without realizing it. It’s happening quietly, without strategy, policy or visibility. That’s what I call shadow AI: unmanaged, unsanctioned AI adoption creating risk without oversight.
Methodology and sources: The headline figures in this report come from Statistics Canada’s Canadian Survey on Business Conditions (its 2026 analysis of AI use and the third-quarter 2026 release) and from KPMG in Canada’s 2025 surveys. Other figures are drawn from the sources listed at the end of the report.
Seven points follow for Canadian SMB leaders planning their 2026 and 2027 AI investments. Each figure links to the source that published it.
- Adoption. According to Statistics Canada, 19.2% of Canadian businesses used AI to produce goods or deliver services in the 12 months to the second quarter of 2026, up from 12.2% a year earlier and 6.1% in 2024. CFIB’s 2026 research puts generative AI use in operations at nearly 45%, a broader measure that counts the tools wherever a business uses them.
- Utilization. In KPMG in Canada’s 2025 employee survey, 36% said they had received AI training but had not started using the tools, and 37% started after training and then stopped. Where a firm pays per seat, those are the seats that sit idle.
- Productivity. In the Microsoft and LinkedIn 2024 Work Trend Index, 90% of people who use AI at work said it saves them time, and 85% said it helps them focus on their most important work. How that time turns into revenue depends on how the firm bills (Section 3).
- Security posture. IBM’s 2024 Cost of a Data Breach Report puts the average Canadian breach at CA$6.32 million. Canadian organizations that used security AI and automation extensively had breach costs CA$2.84 million lower and breach lifecycles 54 days shorter.
- Governance gap. In Microsoft Canada’s 2025 SMB report, 58% of small and mid-sized businesses had internal policies to guide AI use, which leaves more than four in ten without one. In KPMG’s 2025 employee survey, only 48% said their employer had given them the training and guidance they need.
- Sector variance. Statistics Canada’s 2026 figures put AI use highest in information and cultural industries (42.3%), finance and insurance (40.4%) and professional, scientific and technical services (32.4%). It is lowest in agriculture, forestry, fishing and hunting (4.5%), wholesale trade (7.9%) and construction (9.2%).
- Vendor risk. Expect enterprise clients to ask about AI in vendor security reviews: which tools touch their data, where it is processed, and whether it trains a model. Section 6 sets out the four questions to prepare for.
Section 1. Adoption trajectory: where Canadian SMBs actually are
Canadian business AI use has tripled in two years. Statistics Canada’s measure of businesses using AI to produce goods or deliver services rose from 6.1% in the second quarter of 2024 to 12.2% in 2025 and 19.2% in 2026. Urban businesses (21.0%) use AI at about twice the rate of rural ones (9.9%).
Two structural forces are driving the Canadian acceleration. First, Microsoft 365 Copilot’s inclusion inside a widely-adopted productivity platform dropped the friction for Canadian businesses with existing Microsoft tenants. For SMBs weighing Copilot against the other major assistants before they standardize, our 2026 AI platform comparison tests Copilot, ChatGPT, and Claude head-to-head on Canadian SMB workflows.
Second, federal funding is pulling adoption forward. Budget 2024 announced a $2.4 billion package of AI initiatives, including the $100 million AI Assist Program for small and medium-sized businesses building generative AI into their products and services.
| Sector | AI use, 12 months to Q2 2026 | Typical AI use cases | Main constraint |
|---|---|---|---|
| Legal practices | 32.4% (professional, scientific and technical services) | Drafting, review, summarization | Privilege and Law Society confidentiality |
| Accounting and tax | 32.4% (professional, scientific and technical services) | Bookkeeping automation, audit prep | CPA professional conduct and CRA evidence |
| Healthcare clinics | 30.0% (health care and social assistance) | AI scribes, intake, triage | PHIPA, PIPA, and data residency |
| Manufacturing | 13.1% | Quality inspection, predictive maintenance | Data readiness and OT integration cost |
| Professional services (general) | 32.4% | Document drafting, research, client comms | Governance and acceptable-use policy gap |
| Financial services | 40.4% (finance and insurance) | Risk analytics, fraud, customer support | OSFI B-13 and vendor risk evidence |
| Construction and trades | 9.2% (construction) | Estimating, scheduling, jobsite documentation | Mobile workflow and document quality |
Source: AI use by sector from Statistics Canada, Table 33-10-1167-01, second quarter of 2026. Statistics Canada reports legal and accounting firms within professional, scientific and technical services. Use cases and constraints are Fusion Computing’s reading of each sector’s work and regulation.
Section 2. The utilization gap: licences bought and not used
If you license 50 Microsoft 365 Copilot seats and only 15 are used each week, you pay every month for 35 seats that produce nothing.
KPMG in Canada’s 2025 employee survey shows how that happens. In it, 36% said they had received AI training but had not started using the tools because they were too busy, and 37% started after training and then stopped. Only 48% said their employer had given them the training and guidance they need.
To raise utilization, start with two training sessions per user: one on prompt patterns in a real workflow and one on governance. Check whether people use the tools afterward, and deal with the reasons they stop.
Ask a leader to show an approved workflow in their own work. Review usage each week and outcomes each month, then decide whether each seat is still useful.
Section 3. Productivity: the honest ROI math
AI saves time, but the way that time converts to business outcomes varies by firm type. Three patterns matter.
Billable firms (legal, accounting, consulting): reclaimed time either becomes billable work or disappears into shorter days.
Fixed-price firms: reclaimed time creates capacity. Margin improves when the firm uses that capacity for profitable work or to cut costs, after paying for AI licensing, rollout, training and review.
Operations firms (field services, manufacturing, distribution): AI mostly reduces variance. Gains show up as higher first-time-fix rates, faster dispatch, lower no-show rates, and reduced inventory carrying cost. The measurement frame has to match the operation.
See how your own AI rollout compares with the seven practices in Section 7.
Request an AI readiness scoping call
Section 4. Security posture and the AI-breach math
IBM’s figures describe breach costs and the use of AI in security operations. IBM’s 2024 Cost of a Data Breach Report puts the global average breach at US$4.88 million, and its Canadian edition puts the Canadian average at CA$6.32 million. Canadian organizations that used security AI and automation extensively had breach costs CA$2.84 million lower and breach lifecycles 54 days shorter.
Governance has not kept pace. In Microsoft Canada’s 2025 SMB report, 58% of small and mid-sized businesses had internal policies to guide AI use.
Deploying AI without governance introduces new attack surface without the controls that would mitigate it.
The AI-related incidents Fusion Computing sees in Canadian SMB clients fall into four kinds. The first two are client data pasted into consumer AI tools by well-meaning staff and shadow-AI deployments discovered during audit. The other two are vendor-AI tool configuration changes that silently expose new data, and AI-generated customer communications that include errors or inappropriate claims.
Section 5. Barriers to AI use by sector
Statistics Canada’s 2026 data shows which sectors most often report barriers to AI use. Cybersecurity or privacy concerns were the barrier businesses named most often (13.4%), and most often in information and cultural industries (30.9%) and in health care and social assistance (26.4%). The same two industries were the most likely to cite regulatory concerns (21.9% and 17.6%).
Set governance controls from your sector’s privacy, confidentiality and oversight requirements, and check which rules apply to your organization and each use.
Section 6. Vendor risk: the enterprise client gate
When an enterprise client reviews a supplier, AI questions can now sit in the security questionnaire, and a supplier without documented AI governance has nothing to hand over. For SMBs with enterprise client bases (or aspirations), AI governance has become revenue-relevant.
The questions enterprise clients are asking cluster around four themes. First, tool inventory: which AI tools does your firm use, and which of them touch our data? Second, data residency: where is our data processed and stored when your firm uses AI? Third, training data boundaries: can our data be used to train your AI vendor’s models? Fourth, incident response: what is your documented response to an AI-related incident involving our data?
Prepare evidence for each answer before a vendor review. Gaps can lead to follow-up questions or affect the client’s decision.
Section 7. Seven practices for an AI rollout
Fusion Computing recommends these seven practices for planning and reviewing an AI rollout.
- Executive sponsorship at CEO or managing-partner level. The AI steward sits on the leadership team.
- Documented governance before tool selection. Acceptable use policy, vendor-review register, incident-response runbook in place before the first license is purchased.
- Training pair, minimum. Every licensee gets at least two structured training sessions. One on prompt patterns, one on governance and responsible use.
- Utilization measurement. Weekly dashboards showing active use by team. Light users coached or downgraded; heavy users celebrated.
- Outcome measurement tied to business metrics. Billable conversion, margin impact, first-time-fix rate, no-show rate, whichever metric matches the operating model.
- Vendor-review register maintained as a live document. Every AI tool, every DPA, every SOC 2 report date, every incident disclosure, every renewal date, on one page an executive can share in a pitch.
- Quarterly governance review. Leadership-level review of tool inventory, policy drift, incident log, and vendor-security changes. Signed, dated, filed.
Section 8. What Canadian SMBs should plan for in 2026 and 2027
Want this benchmarked against your business? Fusion Computing delivers AI strategy and Microsoft Copilot deployments through three regional offices: AI services Toronto, AI services Hamilton, and AI services Vancouver. An AI readiness assessment starts with a free 60-minute scoping call, then a paid assessment at a fixed fee quoted in writing before work starts. The free AI readiness self-assessment on our tools page lets you score yourself first.
Three things to plan for over the next 24 months:
First, the Critical Cyber Systems Protection Act, enacted through Bill C-8 in June 2026, requires designated operators to report cyber incidents once it comes into force. Those operators sit in federally regulated sectors such as banking, telecommunications, energy and transportation. Suppliers to those operators can expect the same expectations to reach them through their contracts. Firms that serve utilities, financial services, healthcare, and transportation should plan for tightened incident-reporting expectations whether or not they are directly designated.
Second, Quebec’s private-sector privacy law, as amended by Law 25, already covers automated decisions. An enterprise that bases a decision about a person exclusively on automated processing of their personal information must tell them, and let them submit observations to someone who can review it (section 12.1). Check whether the law applies to your activities, and assess consent and disclosure duties for each use before deployment.
Third, track sector-specific AI guidance from the privacy authorities, professional regulators and sector supervisors that apply to your business. Review changes before renewing tools or expanding their use.
Methodology and sources
This report is a synthesis of public 2024 to 2026 data from the following sources:
- Statistics Canada, Analysis on artificial intelligence use by businesses in Canada, second quarter of 2026
- Statistics Canada, Table 33-10-1167-01, use of AI by businesses, second quarter of 2026
- Statistics Canada, Canadian Survey on Business Conditions, third quarter 2026
- CFIB, AI Adoption and Workforce Training Investment in Canada (2026)
- KPMG in Canada, Canadian businesses adopting AI, but few are seeing ROI (2025)
- KPMG in Canada, Generative AI Adoption Index (2025)
- Microsoft Canada, Small and Medium-Sized Business Report (2025)
- Microsoft and LinkedIn, Work Trend Index (2024)
- IBM, Cost of a Data Breach Report (2024), Canadian edition
- Innovation, Science and Economic Development Canada, AI programs for SMEs (2024)
Each figure is quoted as its source published it. Where the report draws on Fusion Computing’s own client work, it says so and attaches no figure.
Scheduled updates
This report is a living document, refreshed as Statistics Canada and the other sources publish new data. The October 2026 update brought in Statistics Canada’s 2026 releases.
To discuss your AI readiness, contact us.
How to use this report
Three practical starting points for Canadian SMB leaders reading this:
If you have not yet deployed AI, start with the 7-practice checklist in Section 7 as your pre-deployment governance gate. Do not buy licenses until you have at least governance, training plan, and measurement framework in place.
If you have deployed AI and utilization is below 40%, pause and fix the utilization gap before buying more. Run a structured training-pair cycle. If utilization still lags at week 8, look at sponsorship and measurement.
If your team already uses AI regularly, review workflow outcomes, data handling, access and incident readiness before expanding. Keep the vendor register and governance records current so you can answer enterprise clients’ questions.
Request an AI readiness scoping call
Vertical deep-dives
This synthesis covers the Canadian SMB market at a high level. For sector-specific rollout guidance, deployment sequences, and compliance notes, see the vertical guides:
- AI for Canadian Accounting Firms: deployment guidance for accounting, tax records and insurer requirements.
- AI for Canadian Law Firms: adoption guidance on privilege, confidentiality and professional obligations.
- AI for Ontario Insurance Brokerages: RIBO-aware adoption for quoting, service, and policy workflows.
- AI for Canadian Healthcare Clinics: privacy and security considerations for scribes, triage and intake tools.
- AI for Canadian Professional Services Firms: the billable-hour math behind realistic AI ROI.
- AI for Canadian Field Services: dispatch, diagnosis, and documentation for HVAC, electrical, plumbing, and trades.
- Microsoft 365 Copilot ROI for Canadian Businesses: the break-even math for a Copilot seat.
Related reading: AI Services for Canadian Businesses | AI Acceptable Use Policy Template | Cybersecurity Services
Common Questions
Who publishes research on AI adoption by Canadian small businesses?
Statistics Canada publishes the official figures. Its Canadian Survey on Business Conditions asked about AI use each second quarter and AI plans each third quarter from 2024, until its final edition in the third quarter of 2026. Fusion Computing, a Canadian-owned, CISSP-led IT firm founded in 2012, publishes this report, which reads those figures alongside KPMG and other sources for owners of businesses with 10 to 150 employees. Fusion Computing refreshes it as new releases land.
How many Canadian businesses use AI in 2026?
Statistics Canada’s figures cover employer businesses of all sizes. It reports that 19.2% of Canadian businesses used AI to produce goods or deliver services in the 12 months before the second quarter of 2026, up from 6.1% in 2024, and 27.8% of businesses with 100 or more employees did. Separately, in the third quarter of 2026, 25.2% of businesses reported plans to use AI over the next 12 months. Use is highest in information and cultural industries, finance and insurance, and professional services.
What is the typical first-year ROI on Microsoft Copilot for an SMB?
It depends on use. Weigh first-year ROI against licensing, rollout, training and review costs, and count a financial benefit when saved time becomes margin, avoided cost or revenue. In KPMG in Canada’s 2025 employee survey, 36% said they had received AI training but had not started using the tools. Utilization is the number to manage, so measure it before buying more seats.
What governance controls do Canadian businesses need before deploying AI?
Before deploying AI, document the permitted uses and data, each vendor’s responsibilities, and the legal authority for processing personal information, with consent where it is required. In Microsoft 365, check access permissions, sensitivity labels, data-loss prevention, logging and retention against the workflow and the licences you hold, and test that those controls work before rollout.
Are AI tools safe for regulated industries like healthcare and legal?
It depends on the use. Microsoft states that prompts, responses and data accessed through Microsoft Graph are not used to train its foundation models, and that Copilot only shows a user what that user can already open. Before a clinic or law firm uses Copilot with patient or client information, assess the workflow, the legal authority for the processing, the vendor terms and who has access. Sensitivity labels, audit logging and access reviews support that assessment.
What is the most common AI mistake Canadian SMBs make?
Buying a per-user license for every employee before identifying the workflows where AI actually saves time. The result is low engagement, dormant licenses, and a perception that AI did not deliver. The fix is the inverse: pilot with a 5 to 10 user cohort in a high-volume document or email workflow, measure time saved, then scale based on actual usage data.
