Level-Set on AI in Manufacturing: Key Trends, Challenges, and Opportunities

Summary: Tariff pressure, supply chain uncertainty, and labour shortages are pushing Canadian manufacturers to rethink how they deploy their people and capital. Ottawa has responded by tilting the tax system to encourage businesses to invest more in innovation and productivity through Bill C-15, the Budget Implementation Act, 2025 No. 1, which received Royal Assent on March 26, 2026.¹

The measures now in law include a significantly expanded SR&ED program, with higher credit limits and broader access, and a Productivity Super-Deduction that lets you deduct more of the cost of eligible capital investments faster.²

What follows cuts through the artificial intelligence (AI) hype to focus on where the technology creates real value, where projects stall, and how to build and fund a financial case, including tax incentives that can improve the economics.

Why AI Is on Every Canadian Manufacturer’s Agenda Now

In just two years, the share of Canadian businesses using AI to produce goods or deliver services more than tripled, from 6.1% in the second quarter of 2024 to 19.2% in the same period in 2026.³ AI is no longer confined to technology companies. It is finding its way into how businesses analyse information, manage inventory, forecast demand, and improve operations.

For manufacturing leaders, the real case for AI starts when those gains translate into measurable results on the production line. Take Quebec-based Maya HTT,⁴ whose more than 200 industrial and physical AI deployments increased production throughput by an average of 17% while creating or maintaining more than 1,000 jobs.

With advanced technologies increasingly shaping how manufacturers compete and grow, Canada’s manufacturing sector has targeted growth in sales by 50% to $1 trillion and in exports by 50% to $540 billion by 2030. Getting there will require sharper productivity and competitiveness across the sector, making AI worth examining wherever it can improve output, efficiency, or cost.

The Trends: Where AI Is Actually Working on the Floor

AI can help manufacturers improve product quality, plan production more effectivelyand free employees to focus on higher-value work.Some of the most practical AI use cases include:

• Predictive maintenance: Sensors and AI models monitor equipment data for changes that may signal wear or failure. Predictive equipment maintenance is already recognized as an advanced-manufacturing application in Canada.⁵ Its biggest contribution is uptime. Instead of relying only on fixed maintenance schedules or waiting for equipment to fail, you can direct maintenance toward machines showing signs of trouble, helping reduce unexpected shutdowns and unnecessary servicing.
• Machine-vision inspection: AI-enabled cameras can inspect parts and processes for defects or inconsistencies that may be difficult to catch quickly and consistently by eye. Computer vision and image understanding are established areas of Canadian advanced-manufacturing research.⁶ The main contribution is quality control, helping catch problems earlier, reduce scrap and rework, and make inspection more consistent.
• Forecasting and inventory optimization: AI can analyse demand, inventory, purchasing, and production data together to support better planning. One Scale AI-funded Canadian project uses predictive modelling to support procurement, production flow, inventory management, and order fulfilment.⁷ The key contribution is better use of working capital and production capacity, with less excess stock, fewer shortages, and output that more closely follows expected demand.
• Robotics, cobots, and generative AI: AI can make robots more adaptable, while collaborative robots, or cobots, can take on repetitive or physically demanding work alongside employees. Canadian advanced-manufacturing research includes adaptive robotics, machine vision, and human-robot collaboration.⁶ Their most immediate contribution is labour productivity, allowing people to focus on work that requires judgement, skill, or problem-solving. Generative AI is a different type of tool and is better assessed against specific information or workflow needs rather than treated as a substitute for industrial automation.

These technologies are part of the broader Industry 4.0 shift, but creating value does not require a plant-wide transformation.⁸ A focused application that solves one costly production problem can be a more practical place to start.

The Challenges: Why AI Projects Stall

Canada’s SME AI Adoption Blueprint notes that manufacturing and other less-digitized sectors have lower AI adoption rates, with barriers including legacy systems, limited digital maturity, and shortages of internal AI expertise.⁹ The barriers tend to emerge in five areas:

• Data and integration: AI is only as useful as the information feeding it. For an older plant, connecting equipment, cleaning up years of inconsistent records, and linking fragmented systems can become a project in itself. Getting the data right takes longer than the AI implementation. ⁹
• Skills and change management: You need people who can operate, interpret, and trust the system. The federal blueprint cites shortages of technical and cross-disciplinary talent, while an OECD survey cited in the report found that 50% of small and medium-sized enterprises surveyed across four G7 countries said employees lacked the skills to use generative AI.⁹ Training and operator involvement therefore matter as much as installation.
• Cybersecurity and operational-technology risk: Connecting more plant systems can also create new routes for disruption. The Canadian Centre for Cyber Security notes that industrial control and operational-technology systems often have long lifecycles, require high availability, and can become harder to secure as components age.¹⁰ Cybersecurity therefore needs to be considered alongside reliability and production continuity, not added after deployment.
• Budget constraints: Smaller businesses often face tighter budgets and fewer skilled workers when adopting AI. Beyond the technology itself, the Business Development Bank of Canada (BDC) advises factoring in implementation costs, including training, maintenance, support, upgrades, and short-term productivity losses during the transition.¹¹ Build the full cost of AI adoption into your budget, not just the cost of the technology itself.
• Scaling too quickly: A successful experiment still has to work reliably in day-to-day operations. The federal blueprint recommends tightly defined use cases, phased implementation, and pilots to test value and reduce the risk of scaling. ⁹ Start with one costly problem, set a baseline and success measure, then expand only when the operational and financial results support it.

Making the Financial Case: ROI and Capital Planning

The financial case for AI starts with a baseline: what is the problem costing you today, and what would improve if the project worked?

To decide whether the investment earns its keep, compare the full cost of implementing it with the productivity and cash-flow gains it is expected to generate, then determine how quickly those gains can repay the investment. For an AI or automation project, build your case around these four areas:

1. Annual measurable benefit: Put dollars on downtime avoided, lower scrap and rework, labour capacity freed up, and additional throughput. Use your own plant economics. For example, if avoiding 20 hours of downtime annually preserves $4,000 in contribution margin per production hour, the potential annual benefit is $80,000 before other savings. Avoid double-counting benefits that overlap.
2. Total cost of ownership: Include hardware, sensors, software, integration, data preparation, cybersecurity, training, financing, and ongoing support. Separate upfront capital from recurring operating costs and consider how changes in labour, software, and maintenance flow through cost of goods sold, overhead, and margins.
3. Payback period: Estimate how long net cash benefits will take to recover the initial investment. Test base, upside, and downside cases, including slower implementation or smaller-than-expected productivity gains.
4. Cash and funding: Time the project against your cash flow and cost of capital. If your project qualifies, refundable SR&ED investment tax credits can support cash flow, but eligibility should be confirmed before including them in the business case.¹³

For smaller manufacturers, the investment does not need to begin with a large-scale transformation. Start with one high-value predictive-maintenance or machine-vision pilot, measure the result, and scale only when the numbers support it.

Funding It: SR&ED, the Productivity Super-Deduction, and Provincial Credits

Tax incentives can improve the after-tax economics of an AI or automation investment, but the programs do different jobs.

The SR&ED program is Canada’s main federal incentive for R&D investment, and it recently became significantly more valuable. For tax years beginning after December 15, 2024, most CCPCs and eligible Canadian public corporations can earn an enhanced 35% investment tax credit on up to $6 million of qualifying expenditures. That can generate up to $2.1 million in investment tax credits on qualifying current expenditures, with the credit 100% refundable up to the expenditure limit.¹³

For CCPCs, the taxable-capital phase-out now runs from $15 million to $75 million. Eligible Canadian public corporations can also access the enhanced rate, and qualifying capital expenditures are once again eligible. Credits earned on qualifying capital expenditures are 40% refundable.¹³

Simply installing a proven AI system generally will not qualify. Development work may qualify when you are pursuing a technological advancement and resolving a genuine technological uncertainty through systematic experimentation or analysis.¹⁴ Certain depreciable equipment that meets the SR&ED-use requirements may also qualify.¹⁵

For capital investment, the Productivity Super-Deduction lets you write off 100% of the cost of qualifying manufacturing and processing machinery and equipment, provided it is acquired after January 1, 2025, and is available for use before 2030.¹⁶ Certain productivity-enhancing assets also qualify for immediate expensing, with timing rules that vary by asset class.

Provincial incentives may add another layer. In Ontario, the Made Manufacturing Investment Tax Credit provides a 15% credit on up to $20 million of qualifying annual investment, for a maximum of $3 million, on eligible investments made after May 14, 2025, and before 2030.¹⁷ The credit is refundable for qualifying CCPCs.

These incentives can support different parts of the same investment, but not every expenditure can receive every benefit. For example, an expenditure claimed as an SR&ED capital expenditure cannot also be included for capital cost allowance.¹⁵ Have our Aprio team review eligibility, timing, and sequencing before you build tax savings into the investment case.

Does Our AI Project Qualify for SR&ED?

Before including SR&ED in your business case, work through the Canada Revenue Agency’s (CRA) current eligibility framework.¹⁴ Eligible work must be conducted in Canada and meet two core requirements: it must seek a scientific or technological advancement and involve systematic investigation or search through experiment or analysis.

1. Is the work being carried out in Canada?
   SR&ED work must be conducted in Canada. A broader AI project may involve activities elsewhere, but only eligible work carried out in Canada can be included in the SR&ED claim.
2. Are you trying to achieve a technological advancement?
   There must be more than a business improvement. You must be trying to gain new technological knowledge because the knowledge available at the start of the work cannot readily resolve the problem. For an AI project, this could arise when existing approaches cannot meet the required accuracy, speed, reliability, integration, or operating conditions.
3. Are you resolving that uncertainty through systematic experiment or analysis?
   The CRA requires more than ordinary troubleshooting. You should be able to identify the problem, develop a hypothesis, test it through experiment or analysis, and draw logical conclusions from the results. The project does not have to succeed. Failed tests can still produce the technological knowledge SR&ED is intended to support.
4. Which supporting activities are directly tied to that work?
   Engineering, design, operations research, mathematical analysis, computer programming, data collection, and testing can qualify when they are directly in support of eligible SR&ED work and are carried out to the extent needed for it.¹⁴
5. Can you separate SR&ED from routine work?
   Routine quality control, commercial production, routine data collection, training, and simply acquiring or applying existing know-how are excluded. Keep evidence of your hypotheses, tests, results, and conclusions as the work progresses so you can support the claim.

The takeaway: An AI project does not qualify simply because the technology is advanced. The potential SR&ED claim lies in the eligible experimental work you undertake to overcome a genuine technological uncertainty.

Final Thoughts: Make Your Next AI Investment Count

The best AI investment is not necessarily the one with the most advanced technology. It is the one that solves a valuable operating problem, clears your financial hurdle, and can be implemented with the right controls.

Before committing capital, validate the numbers, confirm eligibility for available incentives, and plan how the technology will scale. Aprio can help you assess the opportunity and build a practical path from pilot to payback confidently.