AI Training for Employees: What Canadian Businesses Actually Do in 2026

The executive committee approved the AI licences in the spring. Six months later, a handful of employees actually use them, each in their own way, and nobody has been trained. The tool budget is spent. The skills budget does not exist.

The figures Statistics Canada released in June 2026 put a name on that imbalance. AI adoption has tripled in two years across the country. The skills are barely keeping up.

Among Canadian businesses that use AI, 44.4% made changes to their training or staffing practices because of that use, according to Statistics Canada (June 2026).

Why has AI training for employees become the 2026 priority?

Because the tools are already inside the business, and the skills have not followed. In the second quarter of 2026, 19.2% of Canadian businesses used AI to produce goods or deliver services, triple the 2024 rate, according to Statistics Canada.

The same survey introduces questions on training and staffing for the first time. That is no accident. The 2024 question was "should we use AI?". The 2026 question is "who here actually knows how to use it?".

Google Cloud reaches the same conclusion in its AI Agent Trends 2026 report: the fifth trend of the year is not a tool. It is building an AI-ready workforce, with continuous learning plans rather than one-off sessions.

For the full Canadian picture, our analysis of AI adoption in business in Canada breaks the numbers down by sector and company size.

What do companies that take AI training seriously actually do?

They train their existing employees first, then their executives, and the larger they are, the more they do it. The Statistics Canada data (June 2026) reads like an action plan.

The benchmarks worth keeping, among businesses that use AI:

• 44.4% changed their training or staffing practices because of AI;
• 32.0% provide AI-related training to existing employees and 21.6% to existing executives;
• among businesses with 100 or more employees, those proportions climb to 68.1% for employees and 51.7% for executives;
• 32.8% of large businesses hired employees with AI-related skills and 30.2% brought in external consultants or vendors;
• meanwhile, only 39% of AI users said they had received training from their employer, according to the 2024 Microsoft and LinkedIn Work Trend Index.

The quick read: large companies have understood that the skill gets built internally. Elsewhere, employees still learn alone, often on tools nobody oversees. Our article on shadow AI shows where that leads.

Why does generic AI training deliver so little?

Because training detached from real tasks evaporates within weeks. A conference on large language models does not change how a clerk applies payments, or how a project manager writes status reports.

At PlanAxion, we see the same trap in our ERP mandates and AI workshops: when a company asks us about training, it has almost always bought the session before choosing the process. The reverse order works better. Pick a process that hurts every week first, then train the people who run it, on their own data.

AI skills do not take root in a conference room. They take root in a process the employee runs every week.

The deployments that last are built on that logic. Google Cloud's 2026 report cites the Canadian company Telus, where more than 57,000 team members use AI regularly as part of a structured program. A vendor figure, to be read as an order of magnitude, not a guaranteed average.

How do you structure AI training for your teams in four steps?

Inventory real usage, pick one priority process, train in the flow of work, then measure what changed. Four steps, in that order.

The inventory first. A declared amnesty (tell us what you use, nobody gets blamed) produces a truer map of usage in two weeks than any technical audit.

Then the process choice. Every candidate passes the same filter: the problem to solve, the expected value, the available data, the required effort. One process at a time, the one costing hours every week.

Training in the flow of work. The people who run the process learn on their own files, not on made-up examples. Executives learn to supervise: what to check, what to approve, what to reject.

Measurement last. Hours recovered, error rates, processing delays. Without a numbered starting point, training stays an expense impossible to defend at the next budget round.

That is the logic of our four-week AI workshop: prepare, identify, prioritize, validate the data, then decide and deliver. Team training gets anchored to the selected use case rather than the other way around. The investment varies with scope and is confirmed during a short exploratory call.

Where should you start before the end of 2026?

Not with a course catalogue. With an honest list of what your teams already do with AI, one process chosen for its value, and training delivered inside that process. Large Canadian organizations already train more than two thirds of their AI-using employees. Waiting a year lets that gap become structural.

Transparency note: the figures cited come from public surveys and reports (Statistics Canada, Microsoft and LinkedIn, Google Cloud). Scopes and definitions vary from one study to another, and none of these numbers describes your specific situation.

Frequently asked questions about AI training for employees

How many businesses train their employees on AI in Canada?

According to Statistics Canada (June 2026), 32.0% of businesses that use AI provide AI-related training to existing employees and 21.6% to existing executives. Among businesses with 100 or more employees, those proportions reach 68.1% and 51.7%. Training rises sharply with company size.

Where should a company start with AI training for employees?

Start with a no-blame inventory of real usage to learn who already uses what. Then pick one priority process based on value and available data, train the people who run it inside their flow of work, and measure the hours recovered and the errors avoided.

Should you train executives or employees first?

Both, but not the same way. Employees learn to execute with AI on their own files. Executives learn to supervise, meaning they decide what to check, what to approve and what to reject. Large Canadian businesses already train 51.7% of their executives, according to Statistics Canada.

How much does AI training for employees cost?

The cost depends on headcount, the target process and the depth of the program. An approach anchored to one specific use case generally costs less than a generic catalogue, because it targets fewer people and produces a measurable result. The investment is confirmed during a short exploratory call.

What is the difference between AI training and an AI workshop?

Training builds team skills on specific tasks. An AI workshop is a decision process: a use case inventory, a prioritization based on value and data, and a costed roadmap. The workshop ideally comes before the training, because it determines which process deserves to be equipped first.