India finds itself at a unique inflection point in its technology journey. With over 700 million smartphone users, a thriving digital public infrastructure, and open-source initiatives like AI4Bharat, which has curated more than 1,800 hours of vernacular audio data, the country has the tools to lead global AI adoption. Speaking at the Global Technology Summit in April 2025, Nandan Nilekani outlined a compelling vision: lowering the cost of AI inference to just ₹1, making artificial intelligence not only scalable but accessible across the social spectrum.

But as India advances on the infrastructure front, a deeper question arises—will this AI wave create inclusive job opportunities or deepen existing workforce divides?

AI’s dual effect on employment

The rise of generative AI is shifting the foundation of work, but not in uniform ways. The reality is two-fold:

• On one side, Skill-Biased Technological Change (SBTC) is amplifying demand for AI-literate professionals—those who can design, deploy, or work in tandem with these systems. These roles come with higher wages, more autonomy, and global relevance.

• On the other side, Routine-Biased Technological Change (RBTC) is quietly automating tasks that are repetitive and rule-based—functions that were once considered stable. And this automation is now creeping into mid-level white-collar jobs, not just factory floors.

Together, these trends explain why the AI revolution is as much about re-skilling as it is about innovation. The NASSCOM–BCG 2024 report estimates that while AI could displace certain roles, it also has the potential to create 97 million new jobs globally, many of which hinge on the ability to work with AI, not around it.

Learning Agility and AI Bilingualism: The Skills that Matter Now

In such a dynamic landscape, the capability that separates those who thrive from those who stall is not merely technical—it’s learning agility. This means the ability to adapt quickly, shed outdated practices, and embrace AI-enhanced workflows.

A complementary edge is what we might call AI bilingualism—the ability to combine domain knowledge with AI fluency. It’s not about everyone becoming a data scientist; it’s about professionals understanding how AI affects their roles and using that knowledge to remain relevant.

We see examples of this mindset in action. Amazon has integrated over 750,000 robots into its operations but isn’t eliminating jobs—instead, it has invested over $1.2 billion to reskill 350,000 employees, preparing them for AI-augmented tasks in 2019 itself. In healthcare, Johnson & Johnson has introduced GenAI training for more than 56,000 employees, enabling smoother collaboration between humans and machines.

Closer to home, TCS shared in its April 2025 earnings call that its AI-led offerings are driving spectacular growth. The company is actively hiring across Ai roles all while investing heavily in internal capability-building to support its global AI delivery mandates.

Policy and Public Sector Initiatives

India’s policymakers have responded with a framework that balances ambition with responsibility. The National Strategy for Artificial Intelligence (NSAI), spearheaded by NITI Aayog, sets the tone by embedding AI in sectors that matter—healthcare, agriculture, and education—while reinforcing the need for data protection and algorithmic accountability.

The IndiaAI Mission is a step further, with investments focused on making cutting-edge compute resources available to startups, researchers, and educational institutions. Efforts like AI4Bharat serve not just as technical platforms, but as equalizers—making AI accessible in Indian languages, lowering participation barriers, and powering solutions for the next billion users.

India’s model acknowledges its diversity and scale. It reflects a belief that democratizing access to AI must go hand in hand with building digital and cognitive skills at scale.

A Roadmap Rooted in Inclusion and Competence

India’s AI moment is not just about leapfrogging global peers—it’s about doing so on its own terms. With the right mix of low-cost access, vernacular capability, and policy intent, the country is crafting a model that prioritizes people alongside platforms.

As Nandan Nilekani rightly emphasized, India has the ingredients—digital infrastructure, affordable devices, and public innovation—to lead the AI century. But leadership in AI won't be defined by compute power alone; it will be measured by how many people are carried forward by the wave, not left behind.

AI can either deepen inequality or drive broad-based progress. The deciding factor lies in whether businesses commit to reskilling, whether individuals embrace continuous learning, and whether policymakers sustain their focus on inclusive innovation.

If all three move in step, we will realise India’s ‘AI for ALL’ mission through ‘AI by All’.