Karsten Wenzlaff, Advisor
August 26th, 2025
Karsten Wenzlaff is a Crowdfunding and Fintech Expert from Germany. He is Secretary-General of Digit[...]
Bank of Canada Finds Hiring Weakness In AI Exposed Jobs
August 20, 2026 | NCFA Insight | Artificial Intelligence And Data, Public Sector Policy And Industrial Strategy
Weaker Hiring In Jobs With Greater AI Exposure
On August 20, 2026, Bank of Canada research on AI and Canadian hiring shows that people coming from occupations with greater artificial intelligence exposure are having a harder time finding work than people coming from less exposed occupations. During 2015 to 2019, the estimated job finding rate at the fully exposed end of the Bank's model was 2.2 percentage points lower than at the unexposed end. In 2025, it was 13.9 points lower. The comparable difference in job separation rates barely changed.
The Bank isn't saying AI alone caused the gap. The pandemic, immigration, trade changes and weaker labour market conditions also affected hiring. What stands out is where the difference appears. People in more exposed occupations aren't leaving or losing jobs much faster, but those trying to find work are having more difficulty getting hired.
Getting Hired May Weaken Before Jobs Disappear
The occupations near the top of the Bank's exposure ranking are heavy on information work. Data entry clerks, receptionists, payroll administrators and accounting clerks, banking and insurance clerks, records management staff, customer service representatives and office support workers all rank highly. Jobs that depend more on physical work, specialized human skills or judgment generally rank lower.
The Bank estimates the relationship using Statistics Canada Labour Force Survey data and occupation level AI exposure scores. No workers in the data sit at exactly 0% or 100% exposure, so those endpoints are estimates rather than two observed groups of workers. The Bank describes the comparison as an upper bound.
Statistics Canada research on AI and employment provides an important check. Employment generally grew from November 2022 through December 2025 across occupations with different levels of potential AI exposure. Vacancies in highly exposed occupations where AI may replace more tasks also fell at a similar rate to vacancies in occupations with lower exposure.
See AI Usage Data Shows Early Labour Market Strain
Those findings can coexist. Overall employment can hold up while people trying to enter or reenter some occupations take longer to get hired. The Bank also finds that younger workers are more concentrated than older workers in several occupations with moderate or high AI exposure. That puts more attention on entry points into the labour market, not just on whether established workers are being laid off.
Companies Can Reduce Hiring Without Large Layoffs
A company doesn't need a large round of layoffs to use less labour. It can replace fewer people who leave, open fewer junior positions or use the same team to handle more work. The Bank's August data show why layoff announcements alone are a poor measure of the employment effect.
A separate Bank of Canada survey of Canadian firms points in the same direction. Firms expected AI to have little effect on employment over the following year but modest net negative effects over three years. They expected the impact to build over time rather than arrive as an immediate employment shock.
New Bank of Canada evidence on business AI adoption adds another layer. More than two-thirds of surveyed business leaders said they personally use AI in a typical work week, but only 8% of businesses reported significant AI use in core operations. Over the next three years, 23% expect AI to reduce employment while 11% expect a positive employment effect. That suggests hiring effects could emerge before broad operational transformation is complete, leaving a sizeable gap between using AI tools and redesigning businesses around them.
If AI lets companies produce more with existing teams, labour demand can weaken first through vacancies, replacement hiring and junior recruitment. If those measures deteriorate in the occupations where AI use is rising fastest, the case for an AI related employment effect gets stronger. If they recover with the rest of the labour market, it gets weaker.
See Can Headline Inflation Hide AI Job Losses?
The Bank of Canada July AI employment paper approached the issue through an economic model rather than observed labour market outcomes. It separates AI that helps workers produce more from automation that transfers tasks away from workers. Both reduce labour demand in the model, with the larger effect coming when machines take over tasks. The August research adds observed Canadian labour data without proving that AI caused the hiring gap.
Finance Shows How The Job Mix Can Change
Finance is a useful place to watch because AI use is already high and several financial jobs rank among the Bank's more exposed occupations. Statistics Canada found that 40.4% of finance and insurance businesses used AI to produce goods or deliver services during the previous 12 months, more than twice the 19.2% Canadian business average.
The Bank's 2026 Financial System Survey shows a similar pattern among major financial organizations. Nearly all 54 respondents reported using AI, although most still described adoption as limited or moderate. They generally use it to complete existing tasks faster while keeping people responsible for critical decisions carrying financial, legal or reputational consequences.
Financial firms also have an implementation problem. In the Bank survey, 58% of respondents reported difficulty integrating AI into existing systems and workflows. Another 56% cited weak AI literacy among current employees or difficulty hiring and retaining people with specific AI expertise.
Governed financial AI workflows show why both things can happen at once. Software can collect information, compare records, prepare research, identify accounting breaks and assemble know your customer files before a person reviews the work or makes the decision. A firm may need less manual work around a process while placing more value on employees who understand the business well enough to challenge the output.
That becomes especially important for junior roles. Employees have traditionally learned finance by preparing files, reconciling records, reviewing documents, gathering evidence and completing first pass analysis before taking responsibility for harder decisions. AI's hidden costs in replacing junior workers include weakening some of those early career training routes. If AI removes more of that routine work, firms may eventually need fewer junior hires while still competing for experienced analysts, operators, compliance professionals and risk managers.
Current evidence doesn't show that this has happened across Canadian finance. It does show high AI use, exposed information work and shortages of people with the skills to implement and oversee the technology. For founders, financial institutions and investors, the employment question is therefore bigger than how many jobs AI eliminates. It's also about which jobs companies stop adding, which skills become more valuable and how firms build experienced people when some of the work that trained them is automated.
Talking Point
If AI reduces the number of people companies need to hire before it reduces existing headcount, how quickly will Canada's employment data show the change?
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