Forget the AI layoff panic. India has a deeper employment problem

The question that actually matters for India, he argues, is how the country manages the productivity AI unlocks, and who ends up better off because of it.

Job counts move up and down every quarter for a wide array of reasons and not just AI. But the question of how AI led productivity gains will be distributed between capital and labour remains persistent and it’s the one this piece is built around.

Headline numbers offer only half the picture

Tracking headline IT numbers reveals just how non-linear this transition will be. Industry data from tech staffing specialist Xpheno showed that IT fresher hiring had declined roughly 80% industry-wide from FY22 to FY25. Xpheno projects IT fresher offers dropping further from roughly 44,000 in FY26 to a planned 25,000 for FY27.

However, recent Q1 FY27 results from Indian IT majors have complicated the picture with some IT majors such as TCS making a substantial addition to their workforce, revealing a deeply fragmented corporate strategy rather than a straight-line decline.

Company	Q1 FY27 Net Headcount Change	Strategic Takeaway
TCS	+9,279 employees	Onboarded 14,000 freshers in its strongest quarterly headcount addition in nearly four years; CEO K Krithivasan pushed back on fears that AI will shrink the workforce.
HCLTech	-3,292 employees	The steepest sequential drop in two years; revenue per employee rose 3.3% YoY as automation and AI infusion allowed them to deliver work with fewer people.

This divergence shows that higher productivity and employment growth do not have a simple linear relationship. The story of AI reshaping the Indian job market is one that comes packed with several caveats, concerns and promises. 

As we begin to untangle the same, it becomes important for us to look at previous technology based job market disruptions to learn how people have traditionally reacted to such change.

What history tells us about technology shocks

“Every technological wave so far has created more anxiety than unemployment, because we are better at predicting which jobs will be affected than predicting which jobs will be created,” Sabharwal says. 

He’s watched this play out before during the automation of manufacturing, through the 2008 financial crisis, through the hiring freeze that came with Covid and the digitisation led overhiring spree that came after. 

 “The difference this time is that AI touches cognitive work rather than physical work, so the educated feel personally and particularly vulnerable. But history suggests technology destroys certain tasks much faster than it destroys whole occupations.” 

This distinction is important. Jobs are not single activities. They are bundles of routine and non-routine tasks. Technology can remove some components of a job without eliminating the occupation itself.

An earlier Financial Express analysis of Anthropic’s Economic Index found that routine work attached to junior roles such as basic customer support, research, IT support and documentation were the most exposed to automation. As a result, junior roles at large corporate roles were being more heavily re-designed than senior ones. 

The evidence pointed less towards the overnight disappearance of entire professions and more towards a redistribution of tasks between workers and AI systems. 

Not unemployment: The most worrying AI-linked Indian job market problem

Sabharwal places the AI debate within a much older weakness in India’s economy. “India’s problem is not unemployment, it has been 4-8% since 1947 but employer poverty,” he said. 

“Most Indians work, but too many work in enterprises that are too small, too informal and have very low productivity to offer steadily rising wages, structured learning or meaningful career mobility. AI on its own doesn’t fundamentally change this diagnosis. If anything, it reinforces it,” he explained.

As per data posted by the Ministry of Statistics and Programme Implementation (MoSPI), India hosted an estimated 7.92 crore non-agricultural establishments employing 12.81 crore workers in 2025. 

These include small manufacturers, retailers, repair shops, restaurants, transport operators and other businesses that form the everyday base of India’s informal economy. While these microbusiness businesses are vital for immediate livelihoods, their sub-scale nature keeps wages low and career progression minimal.

How to solve employer poverty ?

If employer poverty is the underlying weakness, data suggests that higher productivity can likely be part of the cure. At its simplest, productivity means producing more value with the same amount of labour, capital and time. 

“Countries become prosperous not because they protect existing jobs but because they continuously create higher-productivity work. AI is another productivity technology,” Sabharwal told Financial Express. Historically, higher productivity technologies have reduced costs, expanded markets and created occupations that did not previously exist. 

Though basic digital access has improved with the share of unincorporated enterprises utilising the internet for business purposes rising to 40%, the Economic Survey of India indicates that wider productivity and job creation gains remain unrealised. 

Cheap AI tools can help small retailers generate product descriptions, translate customer messages, answer routine queries, and employ modest social media marketing capabilities  to reach buyers beyond their neighbourhood.

However it becomes important for us to note here that access to a chatbot does not automatically translate into organisational adoption. 

As micro businesses still require reliable connectivity, usable data, working capital, digitally capable employees and managers willing to change established processes to successfully unlock AI led economic growth. Without these foundations, AI may remain a convenient tool rather than become a genuine productivity engine.

Research by the OECD and the Competition Commission of India on the benefits and challenges of adoption of AI in work points in the same direction. As per researchers behind these institutes, this structural divide sets up two wildly different economic paths for the country:

The Inclusive Path: In the more inclusive scenario, access to AI, finance and relevant training allows smaller informal and mid-sized businesses to improve productivity, reach wider markets and grow into capable employers. 

The Concentration Path: In the alternative scenario, the gains remain concentrated among large companies with the capital, data and managerial capacity to reorganise entire businesses around AI. These firms may expand output and market share considerably without producing a proportionate increase in employment.

India’s productivity challenge is therefore not only about making individual workers faster. It is about creating more employers capable of converting technology into scale.

Need to strike the right balance between productivity and jobs

AI can help companies produce more with the same workforce or maintain output with fewer people. Whether India gains better jobs or simply leaner payrolls will depend on how businesses use that productivity dividend. 

Researchers at the World Economic Forum have forecasted for 92 million jobs to be eliminated because of AI by 2030 and 170 million can to be created which leaves a net gain of 78 million.

But the employment gains do not come automatically or painlessly. As per research conducted by World Economic Forum businesses must actively experiment and rebuild workflows to unlock meaningful gains. This can be best understood through the following example.

When an engineer writes code faster or an analyst processes data rapidly, a firm gains surplus productive capacity that it can deploy in one of two ways: 

Market Expansion (Case 1): Firms can leverage their boosted productivity to focus on growth by entering new markets, launching new products, improving their service quality and creating more strategic new jobs in the process as AI takes over routine work.  

Payroll Pruning (Case 2): Companies choose to maintain the exact same output as before, but with significantly fewer people, focusing simply on cost reduction.

While organisation productivity rises in both cases, the effect on employment remains completely different. This is why researchers behind the economic survey of India have argued that the question of artificial intelligence’s impact on work cannot be reduced to whether AI improves efficiency. It clearly can. The more important question is where the gains from that efficiency go. 

It is the scale and method at which enterprises choose to go forward with either one of the pathways that will shape how AI affects India’s labour market. 

What does industry data suggest?

Early corporate data suggests that productivity and employment growth do not have to move in opposite directions. PwC’s 2026 Global AI Jobs Barometer found companies in highly AI-exposed sectors recorded a 34% rise in workforce productivity between 2018 and 2025 (versus 24% in the least-exposed sectors), alongside higher headcount growth.

The results do not mean AI will automatically produce more jobs. They indicate that companies capable of converting new technology into additional products and revenue can raise productivity without necessarily shrinking employment.

However, it becomes important to note here that this dual productivity remains incredibly difficult to reach. In PwC’s global survey of 4,454 CEOs across industries, only 12% reported achieving both higher headcount and higher revenue from AI investment in 2025. The other 88% saw either higher revenue with higher costs, or cost cuts with marginal revenue impact.

As per PwC researchers, the stark gap highlights that giving employees AI tools is not the same as redesigning a business around them — though 56% of those CEOs still expect the structural investment to pay off over a longer horizon.

The takeaway from research conducted by the World Economic Forum and PwC is clear: re-designing business workflows to complement AI capabilities takes time and experimentation. Furthermore, in a lot of cases these transitions may not immediately translate into productivity and economic gains. 

Workforce planning is moving from ladders to portfolios

While we have previously talked about how AI has substantially raised the employability bar for fresher roles, now requiring proof of work (internships, projects) alongside AI literacy and human skills such as stakeholder management and decision making. 

Here we focus on AI’s impact on workforce planning and organisational design as a whole.  

Employment began shifting from being a lifetime contract to a taxi cab relationship a long time ago. AI has accelerated that trend but did not start it,” Sabharwal told Financial Express.

For decades, more customers generally required more employees, more layers of supervision and larger support functions. AI weakens that connection between economic growth and employee headcount. 

A smaller team can now draw upon automated research, coding, customer support and administrative capacity to support a growing customer base.

Staffing agencies expect companies to soon organise themselves around a smaller permanent core, supported by fixed-term employees, consultants, apprentices, freelancers, project workers and AI systems. 

TeamLease’s own data shows an increase in companies’ appetite for contract workers.”Every employer balances three risks — business uncertainty, technological uncertainty and regulatory uncertainty. Flexible staffing helps manage all three,” the co-founder of teamlease told Financial Express.

His broader claim is that the ladder metaphor for careers no longer fits. 

“Careers are becoming portfolios rather than ladders. Workers will increasingly move between permanent employment, fixed-term work, consulting, project work, apprenticeships, freelancing and entrepreneurship over their working lives.” 

As per Sabharwal the important question to look at this time is not just the increase in contract work in comparison to permanent postings but to understand whether workers continue to acquire skills and remain employable. 

“Employability is the only real job security,” he told Financial Express.

The reality of this shift is already visible in LinkedIn’s Work Change Report, which notes that professionals entering the workforce today are on pace to hold twice as many jobs over their careers compared to a generation ago.

How should India look at preparing its workforce for an AI led Industry

For policymakers and educators, the immediate challenge is ensuring that India’s massive youth demographic moves with the technology rather than being left behind by it. 

Sabharwal argues that India’s current educational model requires a structural re-design.

“For decades, we’ve operated on the assumption that education comes first and work comes later. That made sense when knowledge changed slowly. Today, knowledge is depreciating much faster than degrees. AI has shortened the half-life of technical skills.” 

The solution in his view is not merely adding an AI subject to the curriculum, but structurally integrating learning with active work. 

Sabahwal also pointed to education models of countries with low employment rates such as Germany, Switzerland, and Austria as an example of what India can look to change in India’s education system. 

Germany, Switzerland and Austria hold well established apprenticeship based learning models that interlock classrooms with workplaces where students get to contribute to the activities in the modern workplace much earlier than in India. 

Sabharwal argues that graduates in these settings don’t become employable because they’ve spent four years in classrooms. They become employable because they’ve repeatedly solved real problems with colleagues and customers which also helped them pick up soft skills that are becoming increasingly valuable today.

The benefits of Sabharwal’s solution for the campus to corporate pipeline are not theoretical and have been backed by the latest Economic survey of India.

The latest economic survey that came out before the budget this year estimated that broader labour reforms, paired with vocational training and better worker mobility could add 1.25 percentage points to GDP by 2030. Offering an official, numeric version of Sabharwal’s own prescription of apprenticeships, mobility and continuous skilling.

Sabharwal has spent more than two decades building TeamLease into one of India’s largest staffing and workforce-solutions firms. Alongside his distinguished corporate career, he has previously also served on  RBI’s Central Board and several government committees on skills, education and employment.

What job seekers should do to improve employability ?

Asked for one takeaway for readers navigating an AI-led, hypercompetitive job market, Sabharwal offered a single idea: don’t optimise your career around today’s tools; optimise it around tomorrow’s adaptability.

Every major technological revolution has eliminated some tasks, changed many others and created entirely new occupations that nobody could predict beforehand. AI will be no different. 

As per Sabharwal, the graduates who thrive won’t necessarily be the best coders or the best prompt engineers. They’ll be the people who combine technical fluency with enduring human capabilities like curiosity, judgment, innovation and ability to learn continuously.  

He distills this personal architecture into three foundational ideas for youngsters to follow: fitrat (understanding one’s instincts), riyaaz (sustained effort), and taalim (continuous learning).

Translating this into a career path requires a shift in how professionals evaluate opportunities. Sabharwal recommends candidates entering the job market today to prioritise growth over legacy labels. 

As per the Teamlease founder,  young graduates entering the market today should not decide their first job using traditional filters like manufacturing versus services or starting salary instead they should try to work for high-growth companies with dynamic managers where they can absorb critical soft skills. 

“Sometimes I think a big job in a small company is better learning than a small job in a big company,” Sabharwal told Financial Express while emphasising on the importance of early career exposure to capabilities like decision making and leadership.

The takeaway from Sabharwal’s insights and WEF data was clear: As tools and technologies get reinvented every few years, the best long term career strategy remains the one focused on building character, judgment, and learning agility which compound for decades.

The policy challenge

For India, protecting every existing task is not a sustainable employment strategy. Neither is assuming that markets will automatically create better work for every person displaced.

The policy challenge is to accelerate the creation of higher-productivity employers while helping workers move with technology. Sabharwal places better education, apprenticeships, labour mobility and continuous learning at the centre of this transition.

AI will almost certainly allow India to produce more with less time and fewer routine inputs. Whether that becomes a solution to the productivity problem or an aggravation of the jobs problem depends on what happens next.