Canadian small and medium-sized businesses are no longer sitting on the sidelines of digital transformation. According to new research from BDC, 96% of Canadian SMEs invested in digital technologies in 2025, up from 91% in 2021. Three in ten now use generative AI. Nearly all have some form of electronic data. More than half now use data often or always to make business decisions, more than double the rate reported five years ago.

On the surface, this looks like a decisive shift. Canadian entrepreneurs are buying tools, testing AI, digitizing operations, and recognizing that technology is no longer optional. BDC’s study also shows clear benefits: 43% of SMEs say technology investment improved operational efficiency, 34% say it improved customer experience, and 33% say it improved business performance.

The deeper story is more complicated, and far more important for Canada’s economy. Adoption is rising, but maturity remains low. BDC created a digital maturity score based on technology use, data management, corporate culture, strategy, and AI intensity. Only 8% of Canadian SMEs achieved a very high score, while 15% reached a high score. That means fewer than one-quarter of Canadian SMEs are operating at a high or very high level of digital maturity. Most remain moderate, low, or very low.

This distinction matters because the productivity upside is enormous. BDC estimates that if all Canadian SMEs reached a very high level of digital maturity, SME productivity could rise by nearly 38%. Under certain conditions, that could translate into a 14% increase in Canadian GDP, or roughly $350 billion. For a country that has spent years worrying about weak productivity, sluggish business investment, and declining competitiveness, this is one of the clearest economic opportunities on the table.

Canada’s productivity problem is well known. BDC notes that Canada has one of the lowest productivity levels in the G7 per hour worked, ahead of Japan but behind the United States, Germany, France, the United Kingdom and Italy. Closing that gap requires more than exhorting businesses to “innovate.” It requires understanding what actually separates a company that has bought technology from one that uses it to transform performance.

AI is now central to that question.

BDC found that generative AI is already used by 30% of SMEs, while other forms of AI, whether embedded in software or used independently, are used by 15%. The most common applications are sales and marketing, administrative tasks, management, finance, and HR. Fewer businesses are using AI in production, distribution, logistics, inventory, or the actual delivery of goods and services.

That pattern is understandable. Generative AI is easy to begin using in support functions. A business owner can test it for emails, summaries, proposals, customer responses, marketing copy, HR templates, or internal documentation with little upfront cost. These uses can save time and build familiarity. They also carry lower implementation risk than integrating AI into production workflows, inventory systems, predictive maintenance, supply chain planning, or operational decision-making.

The productivity question is whether SMEs can move beyond scattered use and into structured implementation.

BDC’s data suggests that planning, training, and internal data quality are the dividing lines. Among SMEs using AI, 78% are satisfied with the return on investment. That is a strong signal that AI is already producing value. The satisfaction rate rises to 85% among businesses with a formal technology adoption plan that includes AI, compared with 66% among those using AI without a plan. It rises to 86% among SMEs that train their staff in AI, compared with 53% among those that use AI without training. It is also higher among firms whose AI tools incorporate internal data.

This should shape how Canadian businesses think about AI strategy. The issue is not simply whether an SME has access to ChatGPT, Copilot, Gemini, Claude, or AI features inside existing software. The issue is whether employees know how to use those tools appropriately, whether the business has identified the processes where AI can create measurable value, and whether the company’s own data is organized well enough to support useful outputs.

Data is emerging as one of the central constraints. BDC found that 96% of SMEs have electronic data, with customer and sales data the most common categories. Yet almost half of SMEs with digital data have data that is not integrated at all or only partly integrated. This limits what AI can do. A business may technically have years of customer, inventory, sales, accounting, and operational information, but if those records are scattered across spreadsheets, inboxes, accounting platforms, CRM systems, and disconnected databases, the business cannot easily convert that information into intelligence.

The result is a gap between AI enthusiasm and AI capability. Generative AI can help individuals work faster, but business-level productivity gains require systems. They require structured data, clear workflows, internal knowledge, staff training, and governance. In practical terms, an SME that wants to use AI well may need to do less chasing of shiny tools and more foundational work: clean up records, document processes, integrate systems, define use cases, and decide what outcomes matter.

Cybersecurity is another major drag on adoption. BDC found that 45% of SMEs experienced a cyberattack or attempted cyberattack in the previous 12 months, up sharply from 17% in 2021. Even excluding phishing attempts, the rate was 31%. Cybersecurity is now the second biggest barrier to digital adoption, behind cost. This is especially important because every step toward digital maturity also increases exposure. More data, more software, more cloud tools, more integrations, and more AI systems all expand the attack surface.

This creates a difficult reality for SMEs. The same technologies that can raise productivity also require new defensive capacity. BDC reports that 95% of SMEs now have at least one cybersecurity measure in place, such as firewalls, backups, two-factor authentication, anti-malware software, encryption, or training. That is encouraging, but the rising incidence of attacks shows that cybersecurity cannot be treated as an afterthought. It is part of digital maturity.

The policy implications are significant. Canada does not just need more AI companies or more AI research. It needs stronger adoption capacity across the real economy. That means helping SMEs buy, implement, secure, and govern technology in ways that produce measurable gains. It also means recognizing that the smallest businesses face the steepest barriers. Microbusinesses are less likely to have internal AI talent, less likely to provide training, and less able to absorb the cost and risk of complex implementation.

For B2B companies selling into the SME market, the BDC findings are a map of opportunity. Entrepreneurs need practical tools, implementation support, cybersecurity services, training, data integration, workflow automation, and Canadian solutions that reduce risk rather than add complexity. The winners will be companies that understand SME constraints and package AI around business outcomes, not abstract capability.

For SMEs, the message is equally clear. AI adoption is not the finish line. It is the beginning of a more demanding phase of digital transformation. The businesses that benefit most will be those that pair AI with planning, training, internal data, cybersecurity, and process discipline.

Canada’s productivity problem has many causes, but this report makes one thing difficult to ignore: a meaningful part of the solution is already available. The question is whether Canadian SMEs can move from digital adoption to digital maturity quickly enough to capture it.