Macro Markets and Machines_The Economic and Market Transformation Driven by AI_GWM report - Flipbook - Page 21
Macro, Markets, and Machines
November 2025
Exhibit 13 – Share of Jobs Exposed to AI Across Economies
60%
42%
26%
Advanced economies
Emerging economies
Low-income countries
Source: IMF; Scotia Wealth Management.
In advanced economies where employment is concentrated in service-oriented and knowledge-intensive work
(i.e., skilled labour), AI exposure is high. Jobs in fields such as software development, customer service, legal, and
healthcare diagnostics could be materially impacted, to name a few. This creates both a greater risk of
disruption, along with greater potential for productivity gains. In comparison, emerging markets and low-income
countries have larger shares of the workforce in industries like resource extraction and construction, where tasks
are less automatable, at least for now with current technology. As a result, these economies may see slower
near-term adoption, but also fewer labour market disruptions.
Exposure may also differ across countries. In the United States, the Stanford Digital Economy Lab’s paper
(Brynjolfsson et al., 2025) documented AI-related displacement pressures in software development roles,
particularly for young workers. The study found that since October 2022, employment for the 22-25 age cohort
fell by nearly 20% versus its late-2022 peak. Employment was lower for the age 26-30 cohort as well, though by
a significantly lower margin, while hiring for those aged 31 and older continued to rise.
The study expanded this analysis by observing employment trends across other levels of AI exposure for various
professions. Marketing and sales managers for instance, which rank in the fourth quintile of AI exposure, saw
employment decline for young workers, similar to the trend observed for software developers. Conversely,
occupations less exposed to AI, such as stock clerks and health aides, continued to see growth in employment
since late 2022 across all age cohorts. Unlike the United States, countries with stronger labour protection laws,
like those in Europe, could see a slower pace of labour market disruption but also a slower realization of
productivity benefits. In countries like India, exposure may be high (and concentrated) in IT services, which may
face risks from automation but also have opportunities to export AI-based services.
Preparedness: Even if an economy has a high exposure score, its ability to integrate AI effectively depends on
readiness. The IMF’s preparedness index ranks the United States highest at 0.77. Advanced economies have an
average preparedness score of 0.73, while emerging markets and low-income countries score 0.50 and 0.35,
respectively (Exhibit 14). A high preparedness score suggests that the economy has the physical and human
capital to integrate AI quickly and efficiently. This could mean having robust broadband networks, data
infrastructure, a skilled and educated workforce, and access to funding. By contrast, emerging markets and
low-income countries could face bottlenecks in this area, meaning productivity gains may be limited or slow
to materialize.
Scotia Wealth Management
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