Karsten Wenzlaff, Advisor
August 26th, 2025
Karsten Wenzlaff is a Crowdfunding and Fintech Expert from Germany. He is Secretary-General of Digit[...]
Canada’s AI Productivity Test Is Execution
May 13, 2026 | NCFA Insight | Artificial Intelligence And Data, Capital Markets And Funding, Risk Compliance And Regtech
Canada’s AI Productivity Test Is Execution
On May 13, 2026, Bank of Canada External Deputy Governor Michelle Alexopoulos delivered a speech on AI and productivity at the Ottawa Economics Association and Canadian Association for Business Economics Spring Policy Conference. Her message was direct. AI can help Canada grow faster, but only if firms turn adoption into workflow gains, owned IP, stronger investment, and real operating results.
Michelle Alexopoulos, External Deputy Governor, Bank of Canada:
“To put it simply, the Bank of Canada cares about AI because of its potential to significantly affect productivity, economic growth, employment and inflation.”
Adoption numbers show progress, but also a gap:
- Canadian business AI use rose from 3% in 2022 to about 12% in 2025
- Finance and insurance lead many sectors, with adoption above 30% (good for fintech but adoption alone doesn't create productivity)
- Accommodation and food services sit near 1.5%
Statistics Canada gives the upside a useful range. AI could raise Canada’s annual labour productivity growth by 0.4 to 1.1 percentage points over the next decade. Its April 2026 analysis also found that Canadian firms that adopted AI were 16.8% more productive than firms that did not. Those numbers are encouraging but they aren't automatic.
Adoption Isn't The Finish Line
The first wave of AI in many companies has been useful but shallow. Staff use tools to draft, summarize, search, analyze, and code faster. That saves some time but it doesn't always change the business or lead to large productivity increases. The harder work (and benefits) starts when AI enters high value workflows such as onboarding, fraud review, lending files, advisor support, treasury, payments, and compliance testing.
See: Canada At A Productivity Crossroads, Bank Of Canada Warns
That's where fintechs and financial institutions should focus.
Better AI execution should show up in operating numbers. Faster approvals. Lower error rates. Stronger fraud detection. Lower cost per file. Cleaner compliance evidence. If a firm cannot measure the workflow gain, it has not found the productivity gain.
The Bank is also using AI in its own work. AI helps forecast inflation and economic activity, track sentiment, analyze household and business data, review earnings call transcripts, and monitor financial stability. AI is already entering regulated analysis, but the Bank is clear that AI doesn't make monetary policy decisions. It use AI to sharpen judgment, not replace accountability.
That same control point now runs through governed AI workflows in finance. The value isn't just a quicker answer, but a workflow that leaves evidence, keeps humans responsible, and gives risk teams something they can inspect.
Compute Is Now Core Infrastructure
The compute point is hard to ignore. Top U.S. technology firms like Alphabet, Microsoft, Meta, Amazon and Oracle spent roughly US$200 billion on AI investment in 2024. That figure doubled to about US$400 billion in 2025. The Bank also noted that AI data centres are expanding so quickly that power generation is struggling to keep up. Compute is no longer a back office technology cost. It's now industrial, economic and national security infrastructure.
Canada has started to respond. The federal AI Compute Access Fund helps Canadian SMEs access compute for AI projects, with project compute costs ranging from $100,000 to $5 million. That funding helps some companies get beyond small pilots, but it doesn't solve the whole problem. If Canadian companies cannot sustainably access enough affordable compute, the country risks training talent here while building value somewhere else.
For fintech operators, compute affects competitiveness. AI in fraud, risk, underwriting, compliance, markets, and customer support needs secure data pipelines, model testing, and monitoring. Firms that cannot fund compute and controls will stay stuck in trials. While companies that can fund and execute both have a better chance of turning AI into operating advantage.
Jobs Are Changing Before They Disappear
The Bank’s labour message is more balanced than the public debate. There is no evidence yet that AI is replacing workers on a large scale. About 90% of Canadian businesses that adopted AI reported no staffing effect. Roughly 4% reported job creation, while about 6% reported employment decreases linked to AI use.
See: Real Time Rail Puts Canada’s Productivity Test In Focus
The reality is reported job data can lag, and i t doesn't mean the AI labour risk narrative fake. The Bank noted weak hiring in AI exposed roles such as entry level coding and customer service. It also flagged younger workers as a group to watch. This connects directly to recent evidence on AI spending and workforce redesign and AI’s hidden workforce costs. The question is how companies are redesigning workflows in the age of AI. Will they break training channels, judgment, supervision, and customer trust?
The time savings are real. The Bank cited Indeed research showing that 57% of Canadians who use AI at work save one to two hours a day, while 22% save three to five hours. The value depends on what happens next.
If workers use the time for better service, stronger analysis, and tighter controls, then productivity can improve. If companies only cut junior roles, then they might lose the next generation of trained operators.
Capital Is Part Of The AI Productivity Stack
Execution takes money. AI firms and AI adopting fintechs need a lot of investment to compete and the middle stage is expensive. Canada has strong research and strong founders, but too many companies hit a capital wall before they become global platforms.
Budget 2025 recognized part of the gap. It proposed $750 million to support Canadian firms facing early growth stage funding gaps, with details expected in 2026. It also proposed $1 billion for BDC to launch the Venture and Growth Capital Catalyst Initiative. This is good but allocation matters. Capital needs to reach firms when compute, enterprise sales, compliance, and global distribution become expensive.
See: Fintech’s Role in Canada’s Productivity Revival
Beyond just announcements, Canada needs a fuller capital stack with more domestic lead investors, growth equity, private credit, venture debt, angel capital, compliant investment crowdfunding, strategic corporate capital, and better public market routes for quality scaleups. Capital should help productivity companies scale from Canada, not push them to sell early or move the value elsewhere. CVCA reported $56.5B in Canadian private equity investment across 483 transactions in the first nine months of 2025, the strongest nine month period on record. More of that capital needs to back productivity firms that can scale from Canada and keep IP, customers, and senior talent here.
Fintech investment is concentrating into fewer larger deals, which makes scale-up capital more important. Otherwise, Canadian companies may build and test the prototype in Canada but scale the value somewhere else. That’s the leakage problem. That is where fintech’s role in Canada’s productivity revival becomes practical. Better access to capital, faster technology adoption, and stronger business investment need to show up in firm level execution.
IP Decides Who Captures The Value
The OECD’s 2025 Canada survey lays out the structural problem clearly. Canada’s productivity performance has lagged peers, and limited investment in intellectual property and digital technologies has held back growth. That is the bridge between AI use and AI value.
In a recent Financial Post op ed on Canada’s IP gap, Louis Carbonneau argues that Canadian founders often build strong technology but lack the literacy, capital discipline, and enforcement capacity needed to own and extract value from it. He points to weak IP diligence in venture funding, limited IP education, thin enforcement culture, and policy support that often helps companies file a first patent without helping them turn it into a defensible business asset.
If Canadian companies use imported AI tools but don't own proprietary workflows, data layers, patents, models, or distribution channels, the productivity gap can widen and value continue to leak away. IP strategy shouldn't be a legal afterthought. It needs to be part of the productivity and growth plan.
Budget 2025 proposed new IP support, including $84.4 million over four years to extend Elevate IP, $22.5 million over three years to renew support for the Innovation Asset Collective’s Patent Collective, and $75 million over three years to extend the National Research Council’s IP Assist Program. That support can help, but only if it's tied to business strategy, and follow through after the first filing.
The IP Canada Report 2025 shows an eye popping statistic. In 2024, nearly 86,500 patents, trademarks, and industrial designs were filed in Canada by non residents. In 2023, Canadian residents filed about 44,500 IP rights abroad. Although Canada participates in global IP markets, participation is not the same as owning the most valuable parts of AI enabled productivity.
What Canada Should Do Next
Canada needs to treat AI execution like an economic buildout and not another software trend. Adoption is still early. Compute is expensive. Jobs are changing. Capital is thin at the scale up stage. IP decides who keeps the value.
- Fintechs and financial institutions should start with work that can be measured. Pick a workflow where AI can cut time, cost, or risk. Prove the gain. Keep humans accountable where judgment matters. Then protect the data, process, and product layer that creates the advantage.
- Policymakers should focus on the bottlenecks. Fund compute access. Back growth capital. Use procurement to give Canadian firms real reference customers. Make IP support practical, not just educational. Keep AI rules clear enough that smaller firms can comply without getting buried. Funding cannot stop at firms. Canada also needs broad AI upskilling, retraining, and adoption support for workers, founders, small businesses, nonprofits, and community organizations. These groups are often closest to the real productivity problems. They help people use new tools safely, understand risk, and apply AI in daily work. A rising tide only lifts all boats if the support reaches beyond the biggest institutions.
See: Why SME Loan Competition In Canada Is Under Review
- Investors and boards should ask what the company owns. Ask where AI creates measurable operating leverage. Ask whether the firm can keep its customers, data assets, senior talent, and IP in Canada long enough to become an anchor company.
- Canada should make IP diligence part of public innovation funding, embed IP strategy in business and engineering education, build stronger advisory support for SMEs, help founders enforce rights when it matters, and push public backed investors to ask harder IP questions before they fund companies. That also connects to Canada’s need to turn IP into capital, because protected ideas only matter if firms can finance, defend, and commercialize them.
That's how AI adoption improves becomes Canadian productivity and competitiveness. Not through more pilots. Not through more research reports. Through financed, governed, IP protected companies, skilled workers, and community capacity that can turn AI into practical gains.
Talking Point
Can Canada turn AI adoption into owned productivity gains, or will the biggest value flow to foreign platforms that provide the tools, compute, capital, and distribution?
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