Artificial Intelligence (AI) technology could boost productivity for India’s 46 million workers in complementary industries, far exceeding the IT sector's job creation to date. This growth can, in turn, trigger economic ripple effects across industries via backward and forward linkages. At the same time, entry-level jobs in basic coding, software testing, record processing and customer support give young workers opportunities to acquire practical experience through routine tasks. As AI automates more of these tasks, opportunities to develop the skills and judgement needed for higher-value employment may narrow. Firms could consequently face shortages of experienced talent even as fewer opportunities remain for new entrants to acquire that experience.
Routine Work Remains a Larger Challenge
In 2025, 11.4 percent of India’s employed population aged 15–64, or 69.9 million workers, were in occupations facing AI-related substitution pressure. A further 21 percent, or 127.5 million workers, were in occupations with low AI exposure but high routine-task intensity, where conventional automation remains relevant. By contrast, 7.6 percent, or 46.3 million workers, were in occupations where AI is more likely to complement human expertise and raise productivity.
The share of employment in AI-complementary occupations increased from 5.3 percent in 2012 to 7.6 percent in 2025, while the combined share of the two high-routine categories rose from 27.6 percent to 32.4 percent. These estimates describe occupational exposure to automation, rather than actual job losses, but indicate the scale and composition of employment that may be affected.
These shifts point to two priorities: expanding access to more productive, AI-complementary work and ensuring that workers can acquire the experience needed to progress into it. India's response must address both changing skill requirements and the routes through which workers develop competence.
Different Tasks Face Different Pressures
The composition of employment matters as much as overall AI exposure. Sales workers, clerks and customer-service staff feature among occupations facing AI-related substitution pressure. Machine operators, cleaners and helpers, food-processing and garment trades, and construction and manufacturing labourers are concentrated in low-AI-exposure occupations with high routine-task intensity. These jobs face automation pressures that extend beyond AI.
By contrast, doctors, teachers, scientists, engineers and managers are among those in AI-complementary occupations, where technology is more likely to support human expertise and raise productivity. The distinction matters for policy: workers facing possible task substitution require different forms of support from those whose productivity could improve through AI adoption.
The First Rung Is Harder to Climb
Young workers face a particular challenge because they are more concentrated in occupations with high routine-task content. In 2025, 39.4 percent of employed Indians aged 15–24 worked in high-routine occupations, compared with 29 percent among those aged 45–54 and 24.2 percent among those aged 55–64. Among urban workers aged 15–24, the share reached 55 percent. The youngest cohort also had the lowest share of AI-complementary employment within AI-exposed work, at 22.4 percent.
This concentration raises concerns about how young workers will acquire practical experience as routine tasks become automated. Firms may benefit from automation while continuing to need employees capable of handling complex assignments, resolving exceptions and exercising independent judgement. Sustaining career progression will require opportunities for supervised learning and the gradual assumption of greater responsibility.
Gender differences reveal another dimension of the transition. Nationally, AI-complementary occupations accounted for 7.7 percent of male employment and 7.4 percent of female employment. In urban areas, however, women had a higher share of employment in AI-complementary occupations than men, at 19.5 percent against 14.5 percent. This creates potential for productivity gains among employed urban women, although occupational exposure alone does not establish whether these gains will translate into better earnings or career advancement.
Exposure Does Not Guarantee Opportunity
AI-exposed occupations accounted for 48.3 percent of urban employment in 2025, compared with 15 percent in rural areas. Yet 26.5 percent of rural workers were in high-routine occupations, while only 4.3 percent were in AI-complementary work. Rural India faces automation and mechanisation pressures alongside limited participation in occupations where AI could directly support human expertise and raise productivity.
State-level differences further illustrate why exposure alone is an incomplete measure of opportunity. The share of employment in AI-exposed occupations ranged from 42 percent in Kerala to 16.8 percent in Madhya Pradesh. Himachal Pradesh, despite relatively modest overall exposure, had the highest complementary share of AI-exposed employment among major states. In Bihar, 61.5 percent of AI-exposed employment was in the substitution-pressure category, while 24.1 percent was in complementary work.
These differences call for responses suited to local employment structures. States with larger AI-complementary workforces may have greater scope to improve productivity through technology adoption. States with fewer such opportunities will also need to expand access to skilled occupations and support workers moving out of routine-intensive employment. Training strategies must therefore respond to the kinds of jobs available and the opportunities for workers to progress within them.
Building Pathways from Training to Expertise
India needs clearer pathways linking education and training to supervised work, recognised competence and progression into more complex roles. AI literacy should form part of foundational training, alongside domain knowledge and the ability to verify outputs, exercise independent judgement, resolve exceptions and take responsibility for decisions. Workers will increasingly need opportunities to practise these capabilities on real workplace problems.
Engineering programmes, industrial training institutes, polytechnics and apprenticeships should assess competence through practical assignments and workplace projects. In occupations undergoing automation, employers should design learning opportunities that allow workers to progress from routine execution towards verification, problem-solving, oversight and client engagement. Supervised workplace experience will be particularly important where automation reduces the volume of simpler tasks traditionally assigned to new entrants.
Credential systems should recognise capabilities acquired through formal education, vocational training and workplace experience. Modular qualifications, verified workplace experience and portable records of competence could allow workers to accumulate recognised skills as their responsibilities change. Workers should be able to update specific capabilities without repeating entire qualifications, with workplace learning receiving recognition alongside formal credentials.
Employment pathways also need to extend into occupations with sustained demand for human expertise. Healthcare, education, elderly care and skilled trades offer potential avenues because they rely substantially on human interaction, practical judgement or physical presence. Expanding opportunities in these sectors will depend on workforce demand, investment, training capacity and the availability of viable employment conditions.
Public incentives for hiring and training should encourage employers to provide supervised learning, apprenticeships and credible routes into higher-value roles. Support could be linked to demonstrated competencies, verified workplace experience and progression into sustained employment. Social protection should remain accessible as workers move between employers, platform-based work and self-employment.
Employers will need to create opportunities for learning and progression, educational institutions must prepare workers for changing task requirements, and governments should support training finance, credible assessment and recognised credentials. Policy success should be measured by whether workers enter employment, demonstrate competence and advance into more productive roles, rather than by training enrolment or certification alone.
Over the coming decade, India's challenge will be to combine the productivity gains from automation with opportunities for workers to develop expertise and advance into more complex roles. As routine tasks become less central to employment, education systems and employers will need to create alternative routes through which workers acquire practical experience, demonstrate competence and assume greater responsibility. The success of this transition will depend on whether technological progress expands productive employment while keeping career progression accessible to new entrants.