Key takeaways

Artificial intelligence (AI) and robotics have the potential to mitigate the macroeconomic consequences of an ageing workforce. However, the capacity to automate jobs is contingent upon which specific industries are experiencing the most significant ageing. AI and robots are found to substitute most effectively for roles in industries with younger workforce profiles, such as finance and insurance. Conversely, older, high-employment sectors like health and agriculture currently possess less scope for automation. A new ageing-automation scorecard for more than 130 economies indicates that some of the fastest-ageing jurisdictions may face substantial challenges in alleviating demographic pressures through technological means.

Population and workforce ageing across jurisdictions and industries

Populations globally are ageing at rapid rates, which carries significant macroeconomic implications. A smaller, older workforce tends to weigh on aggregate supply, productivity, and growth, while shifting spending towards healthcare and long-term care. This demographic shift also strains pension systems and public finances. In advanced economies, the share of the population aged 65 and over has increased by approximately two-thirds since 1990, reaching over 20%. This figure is projected to rise to 28% by 2050. Emerging Europe and Central Asia show similar trends, while regions such as sub-Saharan Africa remain comparatively young.

Workforce ageing proceeds at varying speeds across different sectors. Globally, the employment-weighted share of workers aged 55 and over in the agricultural sector rose from 24% in 2010 to 33% in 2024. Real estate saw an increase from 27% to 34%, while health and social work rose from 18% to 25%. Industries also differ significantly in their overall size and economic importance. Trade represents the highest median employment share at 14.9%, followed by agriculture at 14.2%. Agriculture remains the largest global employer, with over 700 million workers. Manufacturing also serves as a major source of employment, maintaining a median share of roughly 10%.

Industry exposure to automation through AI and robots

To evaluate which industries can substitute retiring workers with technology, the report utilises an “AI-robot exposure” indicator. This measure combines AI capabilities with specific task requirements and industry-level robot density data. AI exposure is currently highest in occupations involving cognitively demanding tasks, particularly in finance, professional and technical services, education, and information technology. In contrast, robot exposure is heavily concentrated in the manufacturing sector, reflecting the ability of industrial robots to substitute for repetitive physical tasks in factories.

The analysis finds that industries with a higher share of workers aged 55 and over tend to have lower exposure to AI and robots. For instance, agriculture has one of the highest shares of older workers but very low automation exposure. Health and social work also present a challenge; while the sector is large and ageing, it is only weakly exposed to AI and robots because many care and interpersonal tasks remain difficult to automate. These patterns suggest that in many sectors where labour shortages are most likely to bind as populations age, the scope for technological offset is currently limited.

Where can automation offset workforce ageing?

The “ageing-automation overlap” index measures the extent to which a jurisdiction’s workforce is ageing in industries amenable to automation. The index is higher when older workers are concentrated in sectors where AI or robots can relieve shortages. Results vary widely across jurisdictions. The overlap is most favourable in North America, parts of northern and western Europe, and Australia. Conversely, it is low across much of southern and Southeast Asia and parts of the Balkans and Caucasus. Most economies in Latin America and sub-Saharan Africa fall in the middle of the distribution.

On average, richer economies are better positioned to automate their ageing workforces, with a positive correlation between the overlap index and GDP per capita. Higher-income nations tend to experience ageing in comparatively automatable sectors. However, there are notable exceptions among advanced economies where automation capacity is heavily concentrated in manufacturing—a sector that typically has a younger workforce. The global ageing-automation mismatch has worsened in many jurisdictions since the 2010-2013 period, with the overlap index declining in over half of the economies sampled, showing a median change of –0.09.

Conclusion and policy implications

The research demonstrates that while automation can cushion ageing-related labour shortages, this benefit is restricted to jurisdictions where older workers are concentrated in high-potential industries. Projected demographic developments in many advanced economies are likely to amplify macroeconomic pressures due to adverse overlaps. Being a global leader in AI or robotics adoption does not automatically ensure a jurisdiction is well-placed to handle an ageing workforce.

Policy responses must reflect the specific industry composition of ageing. In economies where ageing is concentrated in sectors with limited automation potential, authorities may need to focus more heavily on increasing labour force participation, promoting labour mobility, or encouraging immigration to sustain economic growth. Worryingly, countries with lower automation potential currently tend to have smaller migrant stocks. Improving labour mobility is critical to helping workers switch occupations as technology evolves. Even in economies with low overlap, the adoption of AI and robots can still enhance overall productivity and output.