The Indian stock market may have lost some of its shine in recent months as foreign institutional investors pull money out, in part because global capital is increasingly gravitating toward economies more directly tied to the artificial intelligence infrastructure boom.
Yet when it comes to the adoption of AI in the workplace, India is emerging as one of the world’s standout performers.
India has become one of the strongest markets globally for workplace AI adoption, with employees reporting some of the highest levels of productivity gains, job satisfaction and optimism about the future role of AI agents, according to Boston Consulting Group’s AI at Work 2026 report.
Among frontline employees, 95% of Indian respondents said they use AI at least several times a week, making India the highest-ranked market surveyed. The global average stood at 74%.
The gap was equally striking among managers and business leaders. India topped all surveyed markets, with 97% reporting regular AI use, compared with a global average of 90%.
At the same time, employers are grappling with a persistent talent shortage.
According to ManpowerGroup’s Global Talent Shortage Survey, 82% of Indian employers are struggling to fill open positions in 2026, significantly higher than the global average of 72%.
For the first time, AI-related skills have overtaken all other competencies as the hardest for employers to find, surpassing traditional engineering and IT expertise.
AI literacy and AI model development are now among the most sought-after yet scarce skills in the job market.
The combination of accelerating AI adoption and a widening skills gap is pushing companies to rethink how they develop talent internally.
Arushree Agarwal, CEO of upGrad Enterprise, which partners with organisations on workforce upskilling and corporate training programmes, points to data showing that 83% of employees now view AI skills as essential across job levels, while 70% of employers prioritise AI capabilities during the hiring process.
In an interview with the publication, Agarwal also weighs in on some of the more pressing questions emerging around AI and the labour market as the technology seeps deeper into our offices and lives.
“AI fluency assessments and practical skill demonstrations will find their way into most hiring processes — not because formal credentials are losing value, but because they are no longer sufficient on their own,” she tells Invezz.
Excerpts:
Invezz: Do you think there is enough institutional eagerness to take a step towards training employees in AI skills, or is it something that employees are largely doing on their own? Which sectors, and within them, which functions are seeing the greatest demand for reskilling in AI?
Arushree Agarwal: The institutional eagerness is unambiguous.
upGrad Enterprise’s Workforce Wishlist report 2025 tells a clear story: 83% of employees see AI skills as essential across job levels, and 70% of employers are now prioritising AI skills at the point of hiring.
That is not a soft signal. That is a mandate.
But eagerness alone does not build capability, and this is where the structure matters enormously.
At upGrad Enterprise, we think about AI capability building through three lenses: AI for Leaders, AI for Users, and AI for Builders.
These are not just audience segments — they represent three distinct failure points if any one of them is neglected.
A leadership layer without a strategy will deploy AI without direction. Builders without depth will build the wrong things.
And users who do not know how to actually apply these tools day-to-day will ensure that even the best-built solutions go underutilised.
All three have to move together, or the investment does not compound.
In terms of where demand is sharpest — yes, engineering and digital delivery functions are seeing intense pressure.
But the more interesting shift is the breadth.
Marketing teams want AI-led personalisation, finance teams are looking at automation, HR is using AI for talent analytics, and operations is driving efficiency through AI implementation.
The mandate has moved from building AI capability in tech to building AI fluency across the organisation.
AI is no longer a specialist skill.
It is becoming an organisational capability — and the organisations that treat it that way are the ones pulling ahead.
Invezz: According to data, entry-level jobs and junior white-collar jobs are most susceptible to AI disruption. Do you think companies will eventually prefer AI-verified skill assessments over college credentials?
Arushree Agarwal: It will not be a replacement — it will be a combination, and that combination is already taking shape.
Formal education builds the foundational thinking, the structured reasoning, the communication and problem-solving capabilities that remain genuinely important.
That does not go away.
What is changing is the weight of proof of work alongside those credentials.
When 83% of professionals rank AI as the most in-demand skill, and that demand cuts across every job level, it follows that the ability to demonstrate actual AI fluency — not just claim it — becomes part of nearly every hiring conversation.
A degree tells you someone can learn. A portfolio of real projects, a demonstrated ability to work with AI tools, a track record of self-driven applications — that tells you someone already has.
The fresher who walks in with a GitHub repository or a Kaggle project is making a different kind of case than the one who walks in with a certificate alone.
The honest answer is that AI fluency assessments and practical skill demonstrations will find their way into most hiring processes — not because formal credentials are losing value, but because they are no longer sufficient on their own.
The benchmark has simply moved. And for individuals, that is not a threat — it is an opening.
The ones who are proactively building that proof of work, with or without institutional sponsorship, are the ones who will move up the value chain faster.
Invezz: The upgrades in AI itself are happening at light speed. How will upskilling keep pace?
Arushree Agarwal: A part of upskilling in AI is learning to adapt and move with this light speed — so being agile with AI is itself a skill that good learning programmes need to build.
The goal is not to teach people every tool or every model update.
The goal is to build a mindset and a methodology that allows people to continuously learn, unlearn, and relearn as the technology evolves.
This is why the quality of the learning architecture matters so much.
Programmes grounded in first principles and scientific thinking give learners a durable foundation.
When you understand the underlying logic of how AI works, adapting to new tools becomes far more intuitive.
Content alone cannot do this. Structured, outcome-oriented learning that builds genuine capability is what makes the difference.
