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The adoption of artificial intelligence (AI) continues to accelerate among Canadian businesses at an increasingly rapid rate, according to Statistics Canada, which shows 19.2% of them using it to produce goods or deliver services in 2026 representing a 12.2% hike over 2025 and a significant 13% increase compared to 2024.

But despite the uptick, experts say the adoption level of AI in Canada is not universal, something the Diversity Institute’s Dr. Wendy Cukier, one of Canada's leading experts in disruptive technologies, innovation processes and diversity, says highlights a clear paradox.

“We’re (Canada) globally recognized for our leadership in the development of AI tools, and we know there’s been a huge investment in AI R&D,” she said. “But we’re low in terms of both the use of AI as well as our commitments to AI literacy and training, and some of this is the structure of our economy.”

Canada ranks 44th out of 47 countries in AI training and literacy, and businesses often rely on existing staff for its implementation. 

Dr. Cukier outlined issues surrounding the adoption of AI in business during a recent webinar hosted through the Ontario Chamber of Commerce’s Skills Bridge program entitled ‘Building an AI-Ready Workforce’.

During her address, she outlined data pertaining to AI’s use in businesses, including the readiness among Canadian SMEs (small and medium-sized enterprises). The data she presented indicated that younger businesses have higher AI adoption rates (78%) versus their more mature counterparts (48%), and that SMEs in larger urban areas (78%) are more AI prepared than those in rural settings (55%). As well, larger SMEs – those with more than 100 employees – are more likely to be ‘enthusiastic’ AI users. 

AI literacy important

“We are a country of small businesses, and small businesses typically don’t have the resources or sometimes the expertise needed to move things forward with new technologies,” said Dr. Cukier, noting that improving productivity is a key driver for SMEs when it comes to implementing AI. “Large corporations, for the most part, are investing in AI in order to reduce costs, and that often includes reducing headcount. What we see with small to medium enterprises, in contrast, is they’re trying to do more with less.”

Many businesses are already using AI for data analytics, text analytics, chatbots and natural-language processing. Stats show that data analytics was the most common AI application being used by 36.6% of Canadian businesses in 2026 who have adopted artificial intelligence in effort to save employees time and allow them to focus on more complex or creative work.

However, when it comes to AI adoption in businesses, Dr. Cukier explained the importance of not only having a certain level of literacy about these new technologies, but a clear understanding of how they will be of benefit to the business.

“When you’re thinking about the AI innovation process in your organization, the first step is not, ‘Here’s technology, it’s cool, let’s use it’. It’s what are the business problems that are a priority or what are you actually trying to accomplish, so that you identify where the opportunities are and assess whether or not the technology makes sense.”

She warned about the potential pitfalls of allowing tech experts to dictate AI adoption for businesses.

“One of the dangers of leaving AI adoption to the technologists is often they love the technology for its own sake rather than really thinking about what are the corporate objectives or goals that we’re trying to accomplish,” said Dr. Cukier, explaining there are certain questions that should be considered. “What do we know about our organizational culture that will either drive or impede adoption? What is it we need to do to get people to start using this technology?”

She described an ‘AI Competency Framework’ devised in 2025 by the Diversity Institute divided into three layers:

• Basic AI Literacy: Knowledge of core AI concepts, along with familiarity with generative AI tools, and AI in value chain awareness of key ethical considerations.
• AI Innovation Skills: An understanding of how AI is used in different industries and having competence in using AI technologies for content generation and analysis, plus mid-level expertise working with datasets.
• Deep AI Skills: Knowledge of AI development frameworks and models and advanced knowledge of AI applications in specific fields, as well as a deep understanding of ethical AI development.

“We think the real emphasis needs to be for small and medium enterprises is the middle layer, what we’re calling ‘AI Innovation Skills’,” said Dr. Cukier, explaining this layer provides many opportunities for people across disciplines and different parts of the organization. “You have to think about processes, and behaviours and you have to think about policies and so on. It’s really important to recognize that this requires some technology knowledge for sure, but also change management expertise.”

Practical AI training tips 

Start with the “why”

Explain how AI can help employees save time, improve productivity, analyze information, and make better decisions. Employees are more likely to adopt AI when they understand its business value.

Teach AI fundamentals

Make sure employees understand what AI can and cannot do, including concepts such as generative AI, hallucinations, bias, and limitations.

Provide hands-on training

Don't rely exclusively on presentations. Give employees real tasks—such as drafting emails, summarizing documents, brainstorming ideas, or analyzing data—and let them practice using AI.

Teach effective prompting

Show employees how to give AI clear instructions, provide context, specify the desired format, and refine responses. Prompting should be treated as a practical workplace skill.

Create clear AI policies

Employees should know what information they are permitted to enter into AI tools. Establish rules around confidential information, customer data, intellectual property, and sensitive company information.

Emphasize human oversight

AI-generated content should not automatically be considered accurate. Train employees to fact-check important information, review calculations, and use professional judgment before acting on AI recommendations.

Train by job function

A salesperson, accountant, HR professional, and software developer will use AI differently. Provide general AI training first, followed by role-specific examples and workflows.

Identify AI champions

Select employees who are enthusiastic about AI and give them additional training. They can help colleagues experiment with tools, share successful use cases, and encourage responsible adoption.

Measure results

Track whether AI training improves productivity, quality, employee satisfaction, or customer outcomes. Use those results to determine what training should be expanded or changed.

Make training continuous

AI tools and capabilities change rapidly. Instead of treating AI education as a one-time course, provide ongoing workshops, refresher sessions, internal examples, and opportunities for employees to share what they're learning.