AI may not take your job, but it could remove the first rung of your career ladder

AI may not be causing mass unemployment yet, but it could be removing the entry-level jobs through which young workers traditionally build expertise. With India rapidly adopting workplace AI, the authors argue apprenticeships must be redesigned and embedded into higher education to create a new career ladder.

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AI may not take your job, but it could remove the first rung of your career ladder

AI may not take your job, but it could remove the first rung of your career ladder

By Amit Kapoor and Mohammad Saad, ET CONTRIBUTORSLast Updated: Sep 06, 2026, 02:16:00 PM IST

AI may not be causing mass unemployment yet, but it could be removing the entry-level jobs through which young workers traditionally build expertise. With India rapidly adopting workplace AI, the authors argue apprenticeships must be redesigned and embedded into higher education to create a new career ladder.

Apocalyptic predictions of mass AI-driven unemployment are yet to materialize, but that does not mean the labour market is unaffected. As AI becomes capable of performing routine tasks, entry-level roles that largely comprise such work are coming under pressure. As a result, value addition may lie not in automatable tasks, but in those requiring expertise, judgment and domain knowledge. Yet by reducing the need for entry-level workers, AI could remove the first step of the professional ladder, creating a shortage of the very expertise that becomes more valuable.

For most professionals, routine work is a normal part of the early stages of their careers. An IT professional, for instance, does not develop system-level architecture or establish engineering standards. Early tasks instead revolve around writing simple functions, straightforward debugging and documentation. Once proficient, workers move to more advanced roles requiring higher-level skills. Importantly, routine tasks provide the technical foundation for more complicated work and help build broader expertise. However, AI can alter this progression by replacing entry-level work at a cost-effective rate. For well-defined tasks, AI may even surpass human capabilities, giving firms an incentive to automate entry-level roles.

These dynamics are not empirically unfounded. According to a 2026 study on the US labour market by Stanford researchers, young workers in AI-exposed occupations are increasingly falling behind their less-exposed peers. Employment among workers aged 22–25 in highly AI-exposed occupations is about 19% below where it would have been had it kept pace with similarly aged workers in less-exposed occupations. Experienced workers show no comparable gap, because their skill sets comprise expert-level judgment and domain knowledge in addition to routine proficiency. For such individuals, AI is a tool that complements their capabilities rather than replacing them.

While this evidence is based on US labour market data, similar developments could follow in India given its growing AI adoption. According to Microsoft’s Work Trend Index 2026, India is among the fastest adopters of workplace AI, with 34% organisations having agent workflows, compared with 26% globally.

Given this context, policymakers face a unique challenge where AI is not merely threatening youth employment. Instead, it is denying young workers the first step in their professional journey and thus threatening the future of expertise itself.

However, solving the problem is not straightforward. Developing sufficient capacity to shield the workforce from the effects of AI would require significant resources and coordination among a range of stakeholders. Even before that, policymakers would need to deliberate on how curricula, skilling mechanisms and hiring policies should be adapted to evolving labour market realities. A permanent solution, therefore, would only be possible in the long run.

But an immediate solution is nonetheless necessary, and apprenticeship programmes could be a suitable way to equip graduates with skills that allow them to move beyond the routine work for which their degrees prepare them. However, existing apprenticeship models cannot simply be scaled up because AI is distorting firms’ incentives to hire apprentices. If the work an apprentice is trained to perform can ultimately be substituted by AI, firms have little incentive to hire them in the first place. This is particularly problematic because traditional apprenticeships were designed around a labour market where routine, entry-level work was readily available. Apprentices typically work on real-world problems to build entry-level skills. As AI reduces demand for such tasks, this pathway into the labour market is becoming weaker.

Even if the traditional apprenticeship model is embraced, India’s current apprenticeship landscape already faces considerable challenges. The country has two major national apprenticeship systems: the National Apprenticeship Promotion Scheme (NAPS) and National Apprenticeship Training Scheme (NATS). On conventional metrics, the system has improved substantially. Apprentices engaged under NAPS increased from 3.08 lakh in 2020–21 to 9.86 lakh in 2024–25. However, NITI Aayog’s 2026 assessment shows that completion rates are declining.

NITI Aayog notes that apprenticeship completion rates from FY 2018–19 to FY 2023–24 show a consistent decline. In FY 2018–19, 92.14% of male and 82.33% of female apprentices engaged under NAPS successfully completed their training. By FY 2023–24, these rates had fallen to 59.6% and 60.8%, respectively.

Additionally, apprenticeship is still not a normal part of operation for most Indian firms. According to the Economic Survey 2025–26, roughly 6,100 enterprises are actively engaged in apprenticeship training, a small fraction of the 5.2 lakh registered small and medium enterprises in the country.

If India is to use apprenticeship to train workers beyond routine tasks and preserve expertise for the future, it must address these existing challenges while modifying the apprenticeship model and reinforcing it with reforms within the graduate education system.

Firstly, apprenticeship needs to be significantly broadened beyond its traditional focus on manual and vocational occupations to include cognitive disciplines. Rather than being treated primarily as a post-graduation pathway into employment, apprenticeships should be integrated into graduate education itself. The objective should be for students to acquire substantial workplace experience before they graduate, so that they enter the labour market having already developed proficiency in foundational and routine tasks and can devote their later years to developing expertise and judgment.