Why AI Adoption Remains a Struggle for Manufacturing SMEs in Canada

A closer look at Ontario’s factories and the roadblocks to transformation

Artificial Intelligence (AI) has the power to dramatically improve productivity, reduce waste, and unlock new efficiencies for manufacturers. But for Canada’s small and medium-sized manufacturers (SMEs)—especially those based in Ontario—the path to AI adoption remains filled with friction.

Based on recent reports and on-the-ground surveys conducted since mid-2023, here are the four most common barriers holding back AI deployment in Ontario’s manufacturing sector:

🚧 1. Imagination Gap: “We don’t know where to begin”

One of the biggest blockers is surprisingly basic—most SMEs can’t clearly visualize how AI applies to their operations. They may have heard about AI in theory, but they struggle to identify use-cases that are low-risk and high-reward.

🔹 In MNP’s 2025 roundtables with southern Ontario manufacturers, leaders frequently said they “know AI is important but don’t see the first project that won’t disrupt operations.”

🔹 According to the Business Development Bank of Canada (BDC), only 12% of Canadian firms have implemented AI in production settings.

💸 2. Up-Front Cost and Uncertain ROI

AI investments aren’t just about software. For many SMEs, it also means upgrading equipment, hiring outside help, and training staff—all of which adds up.

🔹 A 2024 survey by NGen and EMC found high technology cost was the most frequently cited barrier by manufacturers.

🔹 According to BDC, 58% of small businesses that want to modernize cite “financing demand” as a primary obstacle.

In some cases, small Ontario firms have shelved promising projects simply because they couldn’t justify the cost without a guaranteed return.

👷 3. Skills Gap and Workforce Readiness

AI doesn’t run on its own. Successful adoption requires operators, engineers, and managers who can use, interpret, and improve intelligent systems. But that talent is scarce—and expensive.

🔹 Only 1 in 3 Ontario manufacturers invest more than $100 per employee annually on technical upskilling.

🔹 SMEs often lack access to in-house data scientists or controls engineers, leading to stalled or abandoned projects.

🧱 4. Data Quality and Legacy Systems

AI needs data—and good data at that. But many manufacturing SMEs are running on legacy equipment that doesn’t generate usable data. Or, the data they do have is siloed, outdated, or mislabeled.

🔹 One Guelph-based manufacturer spent 60% of its AI pilot budget just cleaning and restructuring equipment data.

🔹 Many Ontario firms lack MES/SCADA infrastructure, making it difficult to stream data for predictive analytics.

🔍 Quick Recap

Barrier	Key Stats
Imagination Gap	Only 12% of firms use AI in production (BDC)
Up-front Costs	58% cite lack of financing as key obstacle (BDC)
Skills Shortage	< 33% invest in workforce training (StatsCan)
Data/Legacy Issues	60% of pilot cost = data readiness (MNP roundtable)

🛠️ What SMEs Can Do Next

To start breaking through these barriers, here are a few low-friction ideas:

Start with a 6-week pilot project that shows ROI quickly (e.g., using AI for visual inspection or scrap rate reduction).
Perform a data readiness audit to map current sensor/PLC data and identify gaps.
Leverage funding like the NGen AI for Manufacturing Challenge or FedDev Ontario programs with support from external consultants.
Use a fractional AI team model—partnering with AI consultants and systems integrators rather than hiring full-time staff.

💬 Final Thoughts

Ontario’s manufacturing SMEs know that AI is coming. But they need more than hype—they need tools, funding, and guidance to get moving. The good news? The ecosystem is maturing, funding programs are in place, and consultants like AdventX can help lower the barrier to entry.

Want help mapping your AI path? Reach out to start with a no-obligation AI readiness assessment at hello@adventx.co.

📖 Further Reading

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