Training employees on AI: what Canadian companies are doing in 2026

The executive committee approved the AI licenses in the spring. Six months later, a handful of employees are actually using them, each in their own way, and no one has been trained. The tool budget is spent. The skills budget does not exist.

Figures released in June 2026 by Statistics Canada put a name to this imbalance. AI adoption has tripled in the country over two years. Skills, however, are barely keeping pace.

Among Canadian businesses using AI, 44.4% have modified their training or staffing practices as a result of this usage, according to Statistics Canada (June 2026).

Why has training employees in AI become the priority for 2026?

Because the tools have already entered the workplace, but the skills have not followed. In the second quarter of 2026, 19.2% of Canadian businesses were using AI to produce goods or provide services, three times more than in 2024, according to Statistics Canada.

The same survey introduced questions about training and staffing for the first time. This is no coincidence. The question in 2024 was "should we use AI?" The question in 2026 is "who here 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 training plans rather than one-off sessions.

For a complete picture of the Canadian figures, our analysis of AI adoption in Canadian businesses details the gaps by sector and size.

What are the companies that take AI training seriously doing?

They train their current employees first, then their managers, and they do so all the more as they grow in size. Statistics Canada data (June 2026) outlines a clear action plan.

Key takeaways among businesses using AI:

• 44.4% have modified their training or staffing practices due to AI;
• 32.0% offer AI-related training to their existing employees and 21.6% to their managers;
• among companies with 100 or more employees, these proportions rise to 68.1% for employees and 51.7% for managers;
• 32.8% of large companies have hired employees with AI skills and 30.2% have engaged external consultants or vendors;
• in parallel, only 39% of AI users said they had received training from their employer, according to the 2024 Microsoft and LinkedIn Work Trend Index.

Quick read: large companies have understood that skills are built in-house. Elsewhere, employees are still learning on their own, often with tools that no one is overseeing. Our article on shadow AI shows where that leads.

Why does generic AI training yield so little?

Because training detached from real tasks evaporates in a few weeks. A lecture on large language models does not change how a clerk applies payments, nor how a project manager writes their progress reports.

At PlanAxion, we see the same pitfall in our ERP mandates and AI workshops: when a company talks to us about training, they have almost always purchased the session before choosing the process. The reverse order works better. You first choose a process that causes pain every week, then you train the people who execute it, using their own data.

AI proficiency isn't built in a conference room. It’s built into a process that the employee executes every week.

Lasting deployments are built on this logic. The 2026 Google Cloud report cites the case of the Canadian company Telus, where more than 57,000 employees use AI regularly as part of a structured program. This is a vendor figure, to be read as an order of magnitude, not a guaranteed average.

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

Inventory real-world usage, choose a priority process, train within the workflow, and then measure what has changed. Four steps, in that order.

The inventory comes first. A declared amnesty (tell us what you’re using, no one will be blamed) produces a map of usage in two weeks that is more accurate than any technical audit.

The choice of process comes next. Each candidate passes the same filter: problem to solve, expected value, available data, required effort. Only one process at a time—the one that costs hours every week.

Training within the workflow. The people who execute the process learn on their own files, not on fictional examples. Managers, for their part, learn how to supervise: what to check, what to approve, and what to reject.

Measurement comes last. Hours recovered, error rates, processing times. Without a quantified starting point, training will remain an expense that is impossible to defend in the next budget.

This is the logic behind our 4-week AI workshop: prepare, identify, prioritize, validate data, decide, and deliver. Team training is tied to the selected use case rather than the other way around. The investment varies according to scope and is confirmed during a short discovery call.

Where should you start before the end of 2026?

Not with a course catalogue. Start with an honest list of what your teams are already doing with AI, a process chosen for its value, and training provided within that process. Large Canadian organizations are already training more than two-thirds of their user employees. Waiting a year means letting this 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 figures describe your specific situation.

Frequently asked questions about AI employee training

How many companies in Canada are training their employees in AI?

According to Statistics Canada (June 2026), 32.0% of companies that use AI offer AI-related training to their existing employees, and 21.6% to their managers. Among companies with 100 employees or more, these proportions reach 68.1% and 51.7%. Training increases significantly with company size.

Where should you start when training employees in AI?

Start with a no-blame inventory of real-world usage to find out who is already using what. Then, choose a priority process based on value and available data, train the people who execute it within their workflow, and measure the hours recovered and errors avoided.

Should you train managers or employees first?

Both, but not in the same way. Employees learn to execute with AI on their own files. Managers learn to supervise, which means deciding what to check, what to approve, and what to reject. Large Canadian companies are already training 51.7% of their managers, according to Statistics Canada.

How much does AI employee training cost?

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

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

Training develops team skills for specific tasks. An AI workshop is a decision-making process: inventorying use cases, prioritizing based on value and data, and creating a quantified roadmap. The workshop ideally precedes training, as it determines which process should be equipped first.