AI Governance and Future of Work

 

My Speech at AI-DPI – 26 Conference organised by NCEAR

Let me begin with a simple observation that I think frames everything we're about to discuss.

In the last few centuries, we witnessed multiple technological disruptions ranging from printing press to industrial revolution to computers to internet. It restructured society what work meant, where people lived, what skills had value, what governments needed to do. The economic and social ripple effects played out over decades.

Today, we are living through a transformation that is much more profound but the ripples are moving in months, not decades. And that gap between the speed of technological change and the capacity of our institutions to respond is precisely why conversations like this one matter.

Welcome to what I hope will be a frank, insightful, and perhaps uncomfortable conversation about AI, governance, and the future of work.

 Let me set the scene.

In the last three years, artificial intelligence has crossed a threshold that surprised even its creators. Large language models can now draft contracts, write code, analyse medical scans, counsel customers, generate creative content, and conduct research, tasks that, until recently, defined the upper tier of knowledge work.

We are no longer talking about AI that automates the routine. We are talking about AI that can perform the cognitive. That is a qualitatively different kind of disruption, and it demands a qualitatively different kind of response.

Three tensions sit at the heart of today's discussion.

First tension is on Governance

When it comes to governance of AI key questions that arise are

Who governs AI? Who benefits? Who bears the cost of disruption? These are political and moral questions, not just technical ones.

There lies the tension between speed and safety. AI development is moving at a pace that regulatory frameworks were simply not designed to match. The EU AI Act took years to negotiate and is already facing questions about whether its risk categories reflect the technology as it exists today, let alone as it will exist in three years. India is developing its own digital governance frameworks, and the choices made here, given the scale of this country's workforce and its digital ambitions, will matter not just domestically but globally.

The core challenge for governance is this: if you regulate too slowly, you cede the field to actors, corporate or national, who face no constraints. If you regulate too quickly, you risk encoding today's assumptions into law and stifling the innovation that could actually solve problems. There is no comfortable middle ground. There is only the hard work of trying to get it roughly right, fast enough to matter.

Here the tension is also between innovation and accountability. The companies building the most powerful AI systems are, understandably, advocates for an environment that allows rapid development. Many also, to their credit, genuinely grapple with questions of safety and responsibility. But the incentive structures of competitive markets are not naturally aligned with the kind of careful, transparent, accountable development that the stakes of this technology require.

Governance, at its best, creates the conditions under which accountability becomes not a constraint on innovation but a foundation for the trust that allows innovation to scale. We do not have that governance architecture yet. Building it nationally and internationally is one of the defining challenges of this decade.

Next tension in in he "Future of Work" that is Already Here

There are three competing narratives on this paradigm shift

• Displacement: AI replaces human jobs at scale
• Augmentation: AI makes workers more productive and valuable
• Transformation: New categories of work emerge that we can't yet name

Here lies the tension   between productivity and dignity. Every study that examines AI's impact on knowledge work shows significant productivity gains. Legal researchers, coders, financial analysts, writers when well-supported by AI tools, they produce more, faster, and often at higher quality. This is genuinely good news.

But productivity gains do not automatically translate into widely shared prosperity. The history of technological disruption is also a history of transition costs  borne disproportionately by workers who lack the resources, the retraining opportunities, or the institutional support to adapt. The question is not whether AI will transform work. It will. The question is whether that transformation will be something we navigate together or something that happens to millions of people who had no voice in shaping it.

 What does this mean for India, specifically?

India is not a passive observer in this story. It is one of the central actors.

This country has one of the world's youngest and most rapidly digitising workforces. It has a technology sector that has spent decades building the global knowledge economy's operational backbone. It has a government that has shown real ambition in digital public infrastructure, from UPI to Aadhaar to the Open Network for Digital Commerce.

And it faces a specific, urgent challenge. A significant proportion of India's IT and BPO workforce, millions of skilled, educated, middle-class workers are employed in precisely the categories of knowledge work that generative AI most directly disrupts. Customer support, document processing, software testing, data annotation, back-office operations. These jobs are not going away tomorrow. But the trajectory is clear, and the window for preparation is not infinite.

At the same time, India has something that not every country has in this moment: scale as an asset. The diversity and volume of India's linguistic, cultural, and domain-specific data; the depth of its technical talent; its position as a potential standard-setter for the Global South in AI governance, these are genuine opportunities, if they are seized with intention.

 So what do we actually need?

I'll offer three propositions to anchor our panel discussion.

First: governance must be adaptive, not just reactive. We need regulatory frameworks that are designed to evolve, that build in review cycles, that involve multistakeholder input, that distinguish between the risks of different applications rather than treating AI as a monolithic category. A diagnostic AI in a hospital has different risk parameters than a recommendation algorithm on a social platform. Governance that treats them identically will either over-regulate the beneficial or under-regulate the harmful.

Second: the future of work requires active investment, not just passive optimism. It is not enough to say that new technologies create new jobs, historically, they often do. What matters is the transition: whether workers have access to retraining, whether institutions like schools and universities adapt their curricula in time, whether social safety nets are designed for an economy where the nature of employment is changing. This is a policy challenge, not just a market one.

Third: the voices in the room must expand. The conversations that shape AI governance tend to happen in a relatively small number of rooms, boardrooms, regulatory agencies, international standards bodies, academic conferences. The people whose working lives will be most directly transformed are rarely in those rooms. That needs to change , not as a matter of procedural fairness, but because the decisions will be better if the inputs are broader.

Let me close with this.

I am neither a pessimist nor an optimist about AI. I am a realist who believes that the outcomes of this transformation are genuinely open that they will be determined not by the technology alone, but by the choices we make about how to develop it, deploy it, govern it, and distribute its benefits.

The future of work is not written. It is being written right now, in the decisions being made in companies, in legislatures, in classrooms, and in conversations like this one.

My hope for today is that we leave this room with sharper questions, not just comfortable answers and perhaps with a clearer sense of where action, not just analysis, is required.

 "AI gives us leverage, ethics gives us direction."