Ottawa wants 60% of Canadian businesses using AI by 2034. Here is what the June 2026 national AI strategy contains, what is still uncertain, and a practical action plan for owners and finance leaders.
On June 4, 2026, the federal government launched AI for All, a five-year national strategy intended to make Canada a leader in responsible AI development and, just as importantly, in AI adoption. For business owners, one number stands out: the government wants business AI adoption to reach 60% by 2034.
That is an ambitious goal. Statistics Canada reports that 19.2% of Canadian businesses used AI to produce goods or deliver services in the second quarter of 2026, up from 6.1% in the same quarter of 2024. Adoption has tripled in two years, but most businesses, and especially most small ones, are still on the sidelines.
This article explains what the strategy contains, what it means for small and mid-sized businesses, what remains uncertain, and how to act now without waiting for every detail to be finalised.
Key takeaways
- AI for All is a five-year federal strategy launched June 4, 2026, with a target of 60% business AI adoption by 2034.
- For SMEs, the most relevant announced measures include BDC’s $500 million LIFT program to help finance AI integration and an additional $700 million for affordable compute access.
- Canada still has no standalone AI law. The Artificial Intelligence and Data Act died in early 2025; the government is pursuing privacy reform, including a new privacy bill, instead.
- Existing laws already apply to AI use: privacy, human rights, consumer protection, employment and sector rules.
- The smart move for SMBs is to start small, document how AI is used, and prepare for funding and privacy changes rather than waiting.
The starting point: where Canadian businesses are today
Statistics Canada’s survey for the second quarter of 2026 paints a picture of fast but uneven adoption.
| Measure | Q2 2026 |
|---|---|
| Businesses using AI in the past 12 months | 19.2% (12.2% in Q2 2025; 6.1% in Q2 2024) |
| Businesses saying AI is not relevant to them | 40.0% |
| Highest-adopting industries | Information and cultural industries (42.3%), finance and insurance (40.4%), professional, scientific and technical services (32.4%) |
| Lowest-adopting industries | Agriculture, forestry, fishing and hunting (4.5%), wholesale trade (7.9%), construction (9.2%) |
| Urban vs rural businesses | 21.0% vs 9.9% |
| Businesses with 100+ employees | 27.8% use AI |
| Businesses with 1 to 4 employees | 19.9% use AI; 41.4% say it is not relevant |
The most common uses among businesses that have adopted AI were data analytics (36.6%), text analytics (34.5%), virtual agents or chatbots (28.2%), natural language processing (27.0%) and large language models (24.8%).
The most frequently cited barrier was cybersecurity or privacy concerns (13.4%), followed by cost (10.6%). These are exactly the issues the new strategy tries to address.
What is in AI for All
The strategy is organised around six pillars:
- Protecting Canadians and democracy.
- Empowering Canadians through skills.
- Accelerating adoption, especially among small and medium-sized enterprises.
- Building sovereign AI infrastructure.
- Scaling Canadian AI companies.
- Building trusted international partnerships.
It also describes a “build, partner, buy” approach to key AI capabilities: build where Canada has strengths, partner where it makes sense, and buy where it does not.
Measures most relevant to SMBs
| Measure | What has been announced | Why it matters to SMBs |
|---|---|---|
| BDC LIFT program | A $500 million Business Development Bank of Canada initiative to help SMEs finance AI integration | Addresses cost, the second most common barrier |
| Compute Access Fund expansion | An additional $700 million for affordable sovereign compute for Canadian SMEs | Most relevant to businesses building or fine-tuning their own AI |
| National AI Literacy Initiative | Part of a broader skills and reskilling push | Helps with the skills gap many firms report |
| Canadian AI Safety Institute | $50 million under the protection pillar | Signals a focus on trustworthy AI |
| Canada Trusted AI Certification (proposed) | A proposed certification program | Could become a procurement signal for vendors |
| Canadian Tech Growth Fund (proposed) | A proposed $500 million to address scale-up capital for AI companies | Relevant to Canadian AI and SaaS start-ups |
Details such as eligibility rules, application processes and timelines for several of these programs were not fully set out when the strategy launched. Treat the amounts above as announced intentions and check the program pages before planning around them.
The regulatory picture: no AI Act, but rules still apply
Many business owners assume Canada has an AI law. It does not.
- The Artificial Intelligence and Data Act (AIDA), part of Bill C-27, died in early 2025 when Parliament was prorogued.
- Instead, the government is pursuing privacy reform, online safety legislation, funding and adoption supports.
- After the strategy’s release, the government introduced Bill C-36, the Protecting Privacy and Consumer Data Act, and Bill C-34, the Safe Social Media Act. The strategy’s privacy proposals include enshrining a fundamental right to privacy, strengthening individual control over personal data, protecting children’s information, and addressing deepfakes and surveillance pricing. Check the bills’ current status and final text before relying on any specific provision.
Laws that already apply to your AI use
| Area | What it means in practice |
|---|---|
| Privacy (PIPEDA federally; Quebec’s Law 25 for Quebec residents; provincial laws in some sectors) | You need a lawful basis and transparency for personal information processed by AI tools, and safeguards for where data goes |
| Human rights | Automated decisions affecting hiring, pricing, credit or service access must not discriminate |
| Consumer protection and advertising | Claims made by chatbots or AI-generated marketing are still your claims |
| Employment | Monitoring employees with AI tools raises consent and policy issues |
| Intellectual property and contracts | Check who owns AI outputs and whether vendors can use your data |
| Sector rules | Financial services, health and other regulated sectors have additional obligations |
The practical message: you do not need to wait for an AI law to adopt AI responsibly, and you cannot ignore the laws that already exist.
What this means for different kinds of businesses
Micro and small businesses (1 to 19 employees)
Two in five say AI is “not relevant”. Often that is because AI has been presented as a big technology project. In reality, the first wins are usually small: drafting and summarising, answering common customer questions, bookkeeping assistance, and simple automations between the tools you already use. The LIFT program, if its terms fit, could make modest integration projects more affordable.
Mid-sized businesses (20 to 99 employees)
Statistics Canada found that 34.3% of businesses this size still consider AI not relevant, and cost is a more common barrier for them than for the smallest firms. This group has the most to gain from process automation and AI agents in finance, operations and customer service, where volumes are large enough for time savings to add up.
Larger businesses (100+ employees)
These firms are already more likely to use AI and to invest in training and external help. Among businesses this size using AI, 68.1% trained employees and 30.2% used external consultants or vendors. The next step for them is governance: inventorying AI use, setting policies, and preparing for privacy reform and possible certification schemes.
Rural businesses
Rural adoption (9.9%) is less than half the urban rate. Remote-friendly AI tools for scheduling, customer communication and bookkeeping can matter more where hiring is harder.
A practical 6-step action plan
1. Take an inventory
List every AI tool already in use, including the “shadow AI” employees use on their own: chat assistants, writing tools, meeting note-takers, AI features inside your accounting or CRM software. For each, note what data goes in.
2. Pick two or three high-value use cases
Look for frequent, time-consuming tasks with clear inputs and outputs. Common starting points:
- Customer enquiries and appointment booking.
- Invoice processing and collections follow-up.
- Proposal, quote and report drafting.
- Sales lead research and qualification.
- Internal knowledge search across policies and past work.
3. Write a one-page AI use policy
Cover which tools are approved, what data must never be entered, when human review is required, and who to ask. Keep it short enough that people will read it.
4. Check your privacy basics
Update your privacy policy to reflect AI use where relevant, choose vendor plans that do not train on your data, and confirm where data is stored. If you serve Quebec residents, review Law 25 obligations, including transparency about automated decisions.
5. Build the business case, then look for funding
Estimate time saved, error reduction and revenue impact for each use case. A clear business case makes financing conversations, including with BDC under the LIFT program or with other programs, much easier.
6. Train your team
The strategy emphasises AI literacy for a reason. Short, practical sessions on prompting, checking outputs and data hygiene often deliver more value than any single tool.
Preparing for financing and support programs
Whether you approach BDC under the LIFT program, another lender, or a provincial or federal support program, a well-prepared AI project is easier to fund. Most funders want to see the same things.
| What funders look for | What to prepare |
|---|---|
| A clear business problem | One page describing the process, its cost today and why it matters |
| A defined scope | The specific use cases, systems involved and what is out of scope |
| Expected benefits | Hours saved, error reduction, revenue impact, with your assumptions shown |
| Realistic costs | Software, implementation, training, internal time and ongoing costs |
| Delivery capability | Who will implement it, their experience, and your internal project owner |
| Risk management | How you will handle privacy, security and change management |
| Measurement | The metrics you will track and how often |
A finance-led business case also protects you. If the expected return does not justify the cost, it is better to discover that before you borrow.
Five AI use cases with strong returns for Canadian SMBs
Based on the most common AI applications Statistics Canada reports, and on what tends to work for growing businesses, these five use cases are good candidates for a first project:
- Customer enquiries and appointment booking through a website assistant grounded in your own content, with handoff to staff.
- Accounts payable and receivable automation, including invoice capture, matching, coding suggestions and collections reminders, with human approval.
- Sales and proposal support, drafting proposals, summarising discovery calls and researching prospects.
- Management reporting, combining accounting, CRM and operations data into dashboards with plain-language summaries.
- Internal knowledge search, letting staff ask questions of your policies, procedures and past work.
Each can start small, show results within a quarter, and expand once value is proven.
What remains uncertain
Be honest with your team and board about what is not yet known:
- The final content and timing of privacy reform.
- Eligibility and application details for some announced funding programs.
- Whether and how a “Canada Trusted AI Certification” will work, and whether customers or governments will require it.
- How the strategy’s measures will be funded and implemented over five years.
None of these are reasons to wait. They are reasons to build adoption on sound foundations (clear policies, good data practices and documented decisions) that will hold up whatever the final rules look like.
Frequently asked questions
No. Canada has no standalone AI statute. AIDA died in early 2025. Existing privacy, human rights, consumer protection, employment and sector laws apply to AI use, and the government is pursuing privacy reform.
It was announced as a $500 million Business Development Bank of Canada initiative to help small and medium-sized enterprises finance the integration of AI tools. Check BDC for current eligibility and terms.
It is a national goal, not an obligation. It does signal that government programs, procurement and support will increasingly favour businesses that adopt AI responsibly.
It can be, if you know where the data goes, use business-grade tools with appropriate data protections, limit access, keep humans in the loop for consequential decisions, and comply with privacy law.
Start with an inventory of current AI use, then one or two high-volume, low-risk workflows such as customer enquiries or invoice processing. Measure the results before expanding.
The bottom line
AI for All tells Canadian businesses where the country is heading: more AI adoption, more support for SMEs, and more attention to privacy and trust. The businesses that benefit most will not be the ones that wait for every rule to be final. They will be the ones that start with clear use cases, sensible guardrails and measurable results.
Netxeno helps Canadian businesses turn AI strategy into working systems, from AI strategy and consulting to SMB digital transformation. With a finance-led approach, every recommendation starts with a business case. Book a free AI-readiness audit.
This article is general information, not legal or financial advice. Check program pages and consult qualified advisors for your situation.
