ILO Working Paper, Disruption without Dividend? warns that the digital divide is creating a global split in how Generative AI (GenAI) reshapes labor markets. While High-Income Countries (HICs) face significant automation risks — with over 30% of employment exposed — developing nations like India, classified as lower-middle income, show lower overall exposure but face a unique "white-collar bypass" risk.
For Lower-Middle-Income Countries (LMICs) like India — where the analysis is grounded in 2022 labor force data for over 1.6 lakh observations — the impact is complicated by the fact that workers in automation-vulnerable roles often have internet access, while those in augmentation-prone roles frequently lack the digital infrastructure to realise productivity gains.
This imbalance reflects growth in displacement risks for connected workers in poorer settings, while the potential for GenAI to act as a functional prerequisite for upward mobility remains hindered by structural task differences and infrastructure gaps.
Key Findings on Global GenAI Exposure
The Digital Infrastructure Gap: In Low-Income Countries (LICs), less than 10% of employment is exposed to GenAI, and those who are often lack the connectivity to benefit from augmentation.
Demographic Vulnerabilities: Exposure is notably higher among younger, female, and educated workers, particularly those in office-based and clerical roles.
Task Composition Differences: Workers in developing economies perform more manual and fewer non-routine analytical tasks, which plays a role in limiting the transformative potential of GenAI compared to HICs.
Automation vs. Augmentation: While automation risks are concentrated in wealthier nations, the potential for task augmentation is more evenly distributed, provided digital barriers are removed.
Concentrated Risk: Sectors like financial services and commerce show high exposure globally, but in developing nations, this is often concentrated in retail and wholesale.
Productivity Concentration: Without targeted policies, GenAI’s benefits are supported by existing wealth, likely concentrating gains in richer economies while poorer settings face displacement.
What is the "White-Collar Bypass"? The "white-collar bypass" refers to a developmental risk where the office-based jobs that historically facilitated the transition to high-income status and gender equality in advanced economies may never fully emerge in developing countries. This phenomenon plays a role in slowing structural transformation, as GenAI may automate these "entry-level" professional roles before developing nations can establish a robust service-led middle class. This risk is supported by the digital divide, where the lack of widespread internet access prevents workers from using GenAI to augment their skills, potentially trapping the labor force in manual or routine roles while high-value service work is consolidated in digitally advanced regions.
Policy Relevance: Navigating the AI Transition in India
Bridging the Augmentation Gap: For India, the findings reflect growth in the urgency to expand digital infrastructure to "augmentation-prone" rural and semi-urban sectors to ensure productivity dividends are not lost.
Protecting Vulnerable Segments: Since exposure is higher among female and educated youth, the report plays a role in highlighting the need for the Ministry of Labour to tailor reskilling programs for the service and retail sectors.
Internalising Task Differences: Recognising that Indian occupations involve more manual tasks contributes to a more realistic assessment of GenAI’s immediate disruption, allowing for a phased transition.
Supporting Equitable Adoption: Tailored labor-market policies are supported by the need to prevent GenAI from exacerbating existing income inequalities between connected urban hubs and the rest of the country.
Follow the Full Working Paper Here: ILO Working Paper 166: Disruption without Dividend? GenAI’s Global Impact (2026)
