Tony Frost and Christian Dippel are associate professors of business economics and public policy at Western University’s Ivey Business School.
Can artificial intelligence be the silver bullet for Canada’s chronic productivity woes? Techno-optimists think so; policymakers certainly hope so. But the answer will not be decided by research labs, frontier models or the handful of large corporations that dominate many sectors of the Canadian economy. It will be decided by whether small and medium-sized businesses can actually adopt these tools – and use them to modernize operations, reach new markets and innovate.
Small and medium-sized enterprises (SME) employ roughly two-thirds of Canada’s private-sector workforce and generate close to half of private-sector GDP. If productivity does not rise in this segment, it will not rise nationally.
Canada’s current AI conversation feels misplaced: Productivity gains that will actually move the needle will come from the ordinary businesses that make up the bulk of the economy: manufacturers, distributors, construction companies, professional service providers, and retailers – many of which have never hired a data scientist or built an AI capability in-house.
Firms in the 10-to-300 employee range may be the most important targets for AI transformation. Many are sophisticated in their domains – embedded in North American supply chains, meeting demanding quality standards and operating with real discipline. Yet they are often data-rich but systems-poor. They sit on years of sales and production data, inventory records, purchasing histories, compliance logs and customer information, but much of it resides on outdated platforms and is used mainly for record-keeping and transaction processing. AI promises the possibility of turning that stranded data into something far more valuable.
As is the case with countless struggling SMEs, operational knowledge is trapped in emails, spreadsheets and in a handful of veteran employees’ heads. Quoting, procurement and scheduling often rely on manual workarounds, making it difficult to scale and harder to sell beyond its core customer base and geographic region. Without a way to systematically draw on historical data, these firms struggle to craft bespoke customer solutions.
AI’s promise for firms like this isn’t just efficiency. It is the ability to operate like a much larger company without building a massive back office. Instead of relying on a few experts to price complex custom orders, often at a snail’s pace, an AI-enabled quoting tool can draw instantly on past contracts, supplier inputs and production constraints – allowing the firm to respond faster, win more deals, and expand confidently into new product and geographic markets. Applied effectively, AI can lower the fixed costs of growth.
Yet the firms that stand to benefit most from AI are often the most hesitant to adopt it. That hesitation is often misread as conservatism or lack of ambition. In our experience, it is usually a rational response to uncertainty and downside risk. Unlike large firms, most SMEs do not have internal teams to experiment, absorb failure or separate real technical progress from impressive-sounding vendor claims. For them, adopting AI does not feel like a series of small bets. It feels like a leap into the unknown.
A related challenge resembles the classic “lemons problem” in economics. A firm hiring its 10th software engineer already knows what good looks like. But a firm hiring its first has none of that. It may not know how to screen candidates, define the right role or even evaluate whether the output is genuinely useful. One bad hire can derail the entire effort.
And the problem isn’t only risk. Many SMEs don’t yet have a clear picture of what AI could actually enable in their business, so it gets framed too narrowly as a back-office efficiency tool rather than as a lever for redesigning how the firm quotes, schedules, sells, serves customers, develops products and expands into new markets.
The predictable result is delay. Many SMEs tell us they are waiting for AI tools to become cheaper, simpler or more “proven.” Others adopt simple off-the-shelf solutions and conclude they have “done AI.”
What SMEs need is not a national AI strategy, but practical on-ramps that reduce the risk of getting started. In most cases, the first step is not an advanced application. It is getting the basics right: understanding what data the firm already has, where it sits and which operational decisions it could realistically improve. That kind of groundwork is not glamorous, but it is what turns AI from a buzzword into a usable capability.
The harder problem is that many SMEs cannot de-risk this transition on their own. They lack the expertise to scope pilots, evaluate vendors, hire technical talent and translate AI tools into a coherent operational or growth strategy. For them, AI adoption does not feel like a manageable sequence of experiments. It feels like a single, high-stakes bet.
Paradoxically, at the very moment SMEs are struggling with meaningful AI adoption, the entry-level market for junior software and AI talent has softened as large employers and tech giants tighten hiring and AI begins to displace some traditional junior work. Young engineers are eager for real problems and responsibility and SMEs have such problems in abundance. But the bridge between them is missing.
In the end, Canada’s AI future will be won on shop floors, in warehouses and in the operational back offices of the hundreds of thousands of small and medium-sized firms that make up the backbone of the Canadian economy.
This column is part of Globe Careers’ Leadership Lab series, where executives and experts share their views and advice about the world of work. Find all Leadership Lab stories at tgam.ca/leadershiplab and guidelines for how to contribute to the column here.