AI Threatens Pipeline of Future Managers, CEA Nageswaran Warns

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Artificial intelligence risks hollowing out the entry-level work through which companies have traditionally trained future managers, creating a longer-term problem for businesses even if automation delivers immediate productivity gains, India’s Chief Economic Adviser V. Anantha Nageswaran has warned.

As companies use AI to handle research, analysis, drafting and other routine tasks once assigned to junior employees, they could inadvertently remove the first stages of the career ladder where young workers learn how organizations operate and develop professional judgement.

“The trouble is AI now precisely does this junior work. The bottom rungs of the ladder are being sawn off,” Nageswaran said at the 16th Indian Management Conclave at IIM Lucknow’s Noida campus on Friday, 18 September.

“The routine tasks that once taught the young their trade are the first to be automated.”

His warning shifts the debate over AI and employment beyond the number of jobs that might disappear. It raises a different question for companies and business schools: if machines take over much of the basic work performed by new employees, where will organizations develop the experienced managers they will need a decade or two later?

“For a century, we learn judgement on the job, slowly through apprenticeship,” Nageswaran said. “And if AI removes the apprenticeship, where will the next generation of judgement be?”

Entry-level Work Under Pressure

There is growing evidence that AI is affecting younger workers differently from their more experienced colleagues, although research has yet to establish a broad collapse in employment caused by the technology.

A Stanford Digital Economy Lab study published in August found no evidence of widespread economy-wide job displacement from AI in the US. The pattern was markedly different among younger workers.

Employment among 22- to 25-year-olds in jobs highly exposed to AI was about 19% lower than it would have been if it had grown at the same pace as employment in less AI-exposed jobs, according to the study, which used payroll records covering millions of US workers through June.

The gap had widened from 15% in the researchers’ earlier data. Crucially, the change appeared to be occurring mainly because companies were hiring fewer young workers rather than firing existing employees.

Experienced workers in the same occupations showed no comparable decline. The researchers also found that employment weakness was concentrated in occupations where AI tended to “automate” tasks. Jobs in which AI was used to complement employees rather than replace parts of their work performed better.

That distinction closely reflects Nageswaran’s concern. A company can automate a junior employee’s spreadsheet work, basic research or first draft without eliminating the manager who reviews those tasks. But the junior employee previously learned by performing them, seeing corrections and gradually taking responsibility for harder decisions.

Removing that work can improve productivity today while weakening the pipeline from which companies select experienced employees later.

India Shows Signs of the Same Squeeze

Evidence from India is more mixed, making it difficult to isolate AI from slower hiring, economic conditions and changes in the technology sector. LinkedIn data covering India and several major Western economies found entry-level hiring remained weak in 2026, but said the slowdown appeared broader than AI alone.

The gap was more visible in occupations where AI can augment workers, including software engineering, while junior hiring in jobs either insulated from AI or more directly disrupted by it broadly tracked overall hiring trends.

India’s technology sector nevertheless offers signs of the change Nageswaran described. The country’s high-growth software-as-a-service companies have been increasing revenue without adding workers at the rates seen earlier.

The top 20 high-growth Indian SaaS unicorns employed slightly more than 37,000 people but increased their combined headcount by less than 1% in the year to March 2026, according to data from staffing firm Xpheno reported by Moneycontrol.

Companies have increasingly concentrated hiring on AI, data and specialized engineering positions while automating some quality-assurance, support and other routine work. The effect does not necessarily mean fewer opportunities for graduates across the economy.

LinkedIn’s 2026 graduate employment data identified AI specialist and generative AI engineer among India’s fastest-growing entry-level occupations, alongside growth in areas including human resources and consulting.

The emerging pattern is therefore less a disappearance of junior work than a change in what companies are willing to pay junior workers to do.

The Management Pipeline Problem

Nageswaran argued that this creates a particular problem for management education. Business schools have traditionally taught finance, accounting, strategy, marketing and other disciplines before graduates entered organizations and developed judgement through experience. AI can increasingly perform parts of the analytical work at the beginning of that process.

Adding an AI course to an MBA curriculum will not solve the resulting problem, Nageswaran said.

“Adding one AI elective will not be enough,” he said.

Business schools would instead need to “teach judgement, not just technique.”

If an AI system can produce a financial model, analyze a market or prepare a strategy document, managers still need to decide whether the assumptions are sensible, whether important information is missing and whether the output can be trusted. Those abilities typically depend partly on experience.

Nageswaran’s argument is that companies risk automating the work through which that experience has historically been accumulated. The concern is already appearing inside individual industries.

HDFC Asset Management Chief Executive Navneet Munot made a similar argument this month when discussing the effect of AI on investment management.

Munot said asset managers could use AI to accelerate research but warned against assuming that this meant they no longer needed junior analysts.

“I keep thinking that if I don’t hire an associate or an analyst today, how will I create that fund manager who has a deep sense of purpose, a deep sense of trusteeship, and has this contrarian thinking, this character and conviction 20 years later?” Munot said.

“Anybody who’s thinking that I won’t hire the juniors or I will reduce the number of people, that firm will have trouble 20 years later.”

The parallel is significant because investment management is one of the professions where junior employees traditionally spend years collecting information, building models and analyzing companies before receiving responsibility for investment decisions.

AI can perform increasingly large parts of the first stage. It cannot automatically reproduce the years of experience that were accumulated while doing it.

Productivity Creates its Own Incentive

Companies nevertheless have strong reasons to automate junior work. Generative AI can reduce the amount of time required for coding, document preparation, research, customer support and data analysis. That allows experienced employees to handle more work with smaller teams.

For employers looking at quarterly costs and productivity, replacing repetitive junior assignments with software can therefore be economically attractive. The longer-term cost is harder to measure. Companies may discover years later that they have fewer workers who have progressed through enough stages of the business to take senior roles.

That tension is particularly relevant to India, where a large technology and business-services industry was built partly around companies employing large numbers of graduates to perform relatively standardized work before some advanced into more specialized and managerial positions.

As AI takes over a larger share of those tasks, the industry’s traditional pyramid-shaped staffing model is already beginning to change. At the same time, demand is moving toward workers with deeper domain knowledge and AI skills. The result could be a narrower entry point into professional careers rather than the wholesale disappearance of work.

No Simple Evidence of an AI Jobs Collapse

The available research also argues against treating every decline in graduate hiring as evidence of AI displacement. Stanford researchers explicitly found no economy-wide employment decline attributable to AI and cautioned that the effects so far are concentrated among particular age groups and occupations.

LinkedIn’s analysis similarly suggests economic uncertainty and broader hiring conditions are contributing to weakness in entry-level recruitment. The distinction matters for India, where the labor market is simultaneously producing fewer openings in some traditional technology roles and rapidly expanding demand for workers with AI-related capabilities.

Nageswaran has previously argued that India should adopt AI in ways that support mass employment rather than treating maximum automation as an end in itself. At the India AI Impact Summit in February, he called for continuous reskilling as AI alters the nature of work and argued that technological adoption needed to be reconciled with India’s employment requirements.

His latest warning extends that argument from the number of jobs available today to the quality of the workforce companies will have years from now. If employers remove the routine assignments that once functioned as professional apprenticeships, Nageswaran said, management schools and companies will have to find another way of giving young employees the experience needed to exercise judgement.