India's First Global AI Summit

The first-ever global AI summit hosted in the Global South just kicked off in Delhi. And I was there on the expo floor yesterday.

Bharat Mandapam was buzzing — 300+ exhibitors from over 110 countries, 20+ Heads of State converging later this week, and Sundar Pichai landing in Delhi for a keynote. The India AI Impact Summit 2026 is massive by any measure.

Which of these AI tools can actually survive contact with Indian government reality?

Not the pitch deck reality. The ground reality. The 10,000-user, legacy-system, compliance-heavy, can't-afford-downtime reality.

With that lens, I walked the expo floor for hours. And seven solutions stood out — not because they had the flashiest booths, but because they addressed problems I deal with every single day.

Here's what I found.

1. Deloitte PRAGYA — When Consulting Meets AI at Scale

Deloitte's PRAGYA platform wasn't just another enterprise AI dashboard. What caught my attention was how it bridges the gap between strategic advisory and operational execution.

In government, we've seen no shortage of consulting reports that gather dust on shelves. What we need are tools that take a recommendation and help you implement it — across hundreds of offices, with varying levels of digital maturity, in real time.

PRAGYA seems to be built for exactly that kind of complexity. For large-scale government transformation programs — the kind where you're implementing a new Act across an entire department — this is the type of AI-assisted project intelligence that could change how we manage reform.

2. LKS In-House Litigation Management — This One Hit Home

If you've ever managed litigation portfolios in government, you know the pain. Thousands of cases. Multiple courts. Overlapping deadlines. Paper trails that would fill a warehouse.

I've spent years in ITAT litigation and transfer pricing disputes both in Mumbai and Amritsar, including cases worth hundreds of crores. The single biggest bottleneck isn't legal strategy — it's tracking, coordination, and institutional memory.

LKS has built an AI-powered litigation management system designed for in-house legal teams (as of now). Automated case tracking, deadline management, outcome analysis, and pattern recognition across your case portfolio.

For the Income Tax Department — which handles lakhs of cases across the country at any given time — a system like this isn't a luxury. It's an operational necessity. The question isn't whether we need it. It's how fast we can adapt it to our scale.

3. Smoothtalk AI — Virtual Calling That Could Transform Citizen Services

Picture this: a taxpayer in a Tier 3 city has a query about their assessment. Today, they call a helpline, wait, get transferred, explain their problem three times, and maybe — maybe — get a resolution.

Smoothtalk AI is building virtual calling agents that can handle these interactions with natural, human-like conversation. Not the robotic IVR menus we've all grown to hate. Actual contextual dialogue.

For any government department that handles millions of citizen queries — and the Income Tax Department certainly does — this technology could fundamentally reshape the service delivery experience. Imagine every taxpayer getting an intelligent, patient, context-aware agent on the other end of the line, available 24/7, in multiple languages.

We're not there yet. But the demo I saw suggests we're closer than most people think.

4. Government AI — A UK Perspective on Sovereign Deployment

This one was fascinating for a different reason. Government AI is a UK-based platform built specifically for public sector use cases.

What made me stop and engage wasn't just the product. It was the philosophy. They've clearly thought through the unique constraints of government — compliance requirements, audit trails, data sensitivity, and the fact that "move fast and break things" is not an acceptable operating principle when you're dealing with citizens' data and rights.

Seeing how another country approaches sovereign AI deployment gives useful comparative perspective. At CBDT, as we think about integrating AI into tax administration, understanding global best practices — not just Silicon Valley practices — is essential. The UK's approach to government-specific AI platforms is worth studying closely. They infact have their own courses on iGOT platform.

5. ACTUALITY — On-Premise Deployment for Data That Can't Leave the Building

This is the unsexy but critical conversation that most AI summits skip.

Every AI vendor will tell you their cloud solution is secure. And maybe it is — for a private company. But when you're dealing with taxpayer data, national security information, or sensitive government records, "trust our cloud" is not sufficient.

ACTUALITY offers on-premise AI deployment. Your data stays on your servers. Your models run on your infrastructure. Full control, full compliance.

For Indian government agencies bound by data localization requirements and handling some of the most sensitive personal data in the country, on-premise deployment isn't optional. It's the baseline requirement for any serious AI adoption. ACTUALITY understands this, and that alone puts them ahead of a lot of flashier competitors who haven't thought through the government procurement and compliance lens.

6. Sovereign AI — Local LLMs for Government Solutions

If ACTUALITY addresses where the data lives, Sovereign AI addresses something equally important: where the intelligence comes from.

Large Language Models trained on Western internet data don't inherently understand Indian tax law, Hindi administrative procedures, or the nuances of how a CBDT circular differs from a notification. They can approximate. They can't natively operate in our context.

Sovereign AI is building local LLMs designed for government use — models trained on local data, in local languages, for local administrative contexts. Data stays within borders. Models understand the operating environment they're deployed in.

This is the future of public sector AI, and India — with its scale, linguistic diversity, and digital infrastructure ambitions — should be leading this charge. The IndiaAI Mission's focus on building indigenous AI capacity aligns perfectly with what Sovereign AI is demonstrating.

I can tell you: the gap between a generic LLM and one that understands Section 148A of the Income Tax Act 1961 is not a nice-to-have. It's the difference between a tool that helps and one that creates more problems than it solves.

7. FUSKI.ai — Training Your Workforce Before You Deploy Your AI

Here's a truth that doesn't get enough airtime at AI summits: the biggest bottleneck to AI adoption in government isn't technology. It's people.

You can have the most sophisticated AI system in the world, but if the 50,000 officers who are supposed to use it don't understand it, don't trust it, and haven't been trained on it — you've just bought a very expensive piece of software that nobody opens.

FUSKI.ai builds AI-powered training modules for workforce upskilling. Custom learning paths. Adaptive difficulty. Progress tracking. The kind of structured capability building that large organizations need before they can meaningfully adopt AI tools.

When I think about our challenge at CBDT — implementing a new Act across 700+ offices with staff at wildly different levels of digital comfort — this is exactly the gap that needs filling. You can't just send a circular saying "use AI now." You need a systematic, scalable training infrastructure.

FUSKI.ai could be that infrastructure.

The Bigger Picture

Walking the expo floor, what struck me most wasn't any individual product. It was the shift in the overall conversation.

Three years ago, the question at these events was: "Should government use AI?"

Two years ago, it became: "Can government use AI responsibly?"

Yesterday at Bharat Mandapam, the question had evolved to: "How fast can we deploy AI, and what's stopping us?"

That's a seismic shift. And it tells you something about where India — and the Global South more broadly — stands in the AI landscape. We're not spectators in this revolution. We're not waiting for Silicon Valley to build solutions and then adapting them for our context.

We're building. We're deploying. We're setting the terms.

The India AI Impact Summit 2026 — anchored in the three Sutras of People, Planet, and Progress — isn't just a diplomatic gathering. It's a statement of intent. India is positioning itself as a global convenor for responsible, inclusive AI. And based on what I saw on the expo floor, the ecosystem is rising to match that ambition.

The summit continues through February 20th (in fact extended to 21st Feb for general public), with PM Modi's inaugural address tomorrow setting the tone for the main event. I'll be watching closely.

But if the expo is any indication, the future of AI in governance isn't coming.

It's already here. Walking the floor at Bharat Mandapam.

Teaching AI to Global Tax Officials

NADT had invited me to deliver two sessions as part of their ITEC programme on "Innovations in Tax Administration: Building Capacity for the Future." The morning session was about opportunity — how generative AI is reshaping everything from taxpayer services to complex cross-border investigations. The afternoon was about the guardrails — data privacy, algorithmic bias, ethical governance, and the cautionary tales of what happens when governments deploy AI without asking the right questions first.

On paper, these are two separate topics. In practice, they're inseparable. You can't talk about the transformative potential of AI in tax administration without immediately confronting the question: at what cost, and to whom?

That tension — between innovation and protection, between speed and fairness — became the throughline of my entire day in Nagpur.

The Room That Changed My Perspective

Here's what I wasn't fully prepared for: the quality of the questions.

I walked in expecting to explain foundational concepts. What is a large language model? What does RAG architecture mean? How does prompt engineering work? And yes, some of that was necessary. But the participants — officers from developing economies across Asia, Africa, and beyond — weren't just absorbing information passively. They were interrogating it.

One officer asked how you prevent an AI-assisted audit system from encoding historical biases in taxpayer selection. Another wanted to know how her country, with limited digital infrastructure, could adopt AI without deepening the digital divide. A third pushed back on the idea that AI could meaningfully assist in transfer pricing without understanding the specific regulatory nuances of his jurisdiction.

These weren't theoretical objections. They were the questions of people who would actually have to implement these systems back home, with real constraints and real consequences.

That shift — from "should we adopt AI?" to "how do we adopt it responsibly?" — told me something important. The global conversation about AI in government has moved past the hype phase. What people want now is practical, honest guidance.

What I Showed Them (And What I Learned Showing It)

In the morning session, I walked through real examples rather than hypotheticals. India's NUDGE campaign, where AI-powered behavioural nudges led to 24,678 taxpayers voluntarily revising their returns and disclosing ₹29,208 crore in foreign assets — with zero litigation. Singapore's IRAS chatbot, which saved over 11,000 officer-hours. The CBDT's ₹3,000 crore data analytics project with LTIMindtree that's building predictive capabilities at a national scale.

I also got personal. I showed them the time-savings analysis from my own transfer pricing work — how document review that used to take eight weeks can now be compressed to two, how comparability analysis that consumed a month now takes three days. The room went quiet when I put up the numbers. Not because the technology was surprising, but because the implications were immediate. Every officer in that room was mentally mapping those efficiency gains onto their own caseload.

But the moment that stayed with me came in the afternoon.

The Cautionary Tales That Matter

When I brought up Australia's Robodebt scandal — where an automated debt recovery system wrongly targeted hundreds of thousands of welfare recipients — the energy in the room shifted. This wasn't abstract anymore. These were real governments, using real algorithms, causing real harm to real people.

The Netherlands childcare benefits crisis, where an AI system flagged families — disproportionately those with dual nationalities — for fraud based on flawed data and biased algorithms, hit even harder. Some participants came from countries with similar demographic complexities. The lesson wasn't subtle: if wealthy, technologically advanced nations can get this catastrophically wrong, what does that mean for countries with fewer resources to build safeguards?

I made a point that I believe deeply: these failures weren't caused by bad people. They were caused by good people who asked "Can we automate this?" before asking "Should we automate this?" Who asked "How much will this save?" before asking "Who might this harm?"

The room didn't just nod along. They debated. They shared examples from their own contexts. They pushed me to be more specific about what "human oversight" actually looks like in practice when you're understaffed and under-resourced.

What I'm Taking Away

Three things crystallised for me during that day in Nagpur.

First, the demand for practical AI literacy in government is enormous and largely unmet. Officers don't need more keynote speeches about the "fourth industrial revolution." They need to sit down with a tool, try a prompt, see what works, understand what fails, and build intuition through practice. The most engaged moments in my sessions weren't during the slides — they were during the live demonstrations, when participants could see AI drafting a tax notice or analysing a scenario in real time. I gave a live demonstration of how we give a context in a prompt and how does it lead to a customised notice generation.

Second, developing economies have a genuine opportunity to leapfrog. They're not burdened by legacy systems the way some advanced administrations are. If they build thoughtfully — with ethical frameworks baked in from day one rather than bolted on after a scandal — they can set a global standard. That's not wishful thinking. It's a strategic possibility, and it requires exactly the kind of cross-country learning that programmes like ITEC enable.

The Question I Keep Coming Back To

Tax administration isn't glamorous. It doesn't make headlines unless something goes wrong. But it's the infrastructure that funds schools, hospitals, roads, and defence. When we get it right, societies function. When we get it wrong — through bias, opacity, or carelessness — the most vulnerable bear the cost.

So the question isn't whether AI will transform tax administration. It already is. The question is whether we — the people in the room that day, and the thousands like us around the world — will shape that transformation with the care it demands.

I left Nagpur cautiously optimistic that we will.