AI Just Became Capital Risk

On 24 June, the Reserve Bank of India dropped a draft that does not look like an AI regulation at first read. It is titled Guidance on Regulatory Principles for Model Risk Management, 2026, and the language is pure prudential supervision: board-approved frameworks, three lines of defence, independent validation, risk tiering, model inventories retained for ten years after decommissioning. Spend twenty minutes with it and a sharper thought lands. India's central bank has just pulled AI into the family of supervisory tools normally reserved for capital and liquidity. The principle that a regulated entity remains fully accountable for the outcomes of every model it uses, including those sourced from third-party vendors, quietly closes a loophole that the entire fintech-banking AI stack had been leaning on.

What the draft actually says

The reach is wide. Eleven categories of regulated entities — commercial banks, small finance and payments banks, urban and rural co-operatives, NBFCs across all layers, AIFIs, ARCs, credit information companies — are inside the perimeter. The definition of “model” is wider still. It covers not just neural networks and generative AI but also algorithms, analytics tools, decision rules, even spreadsheet-based tools where they materially influence credit, pricing or risk decisions. Every such model must sit under a Board-approved Model Risk Management Framework. Every high-risk model must be cleared by the Risk Management Committee of the Board before deployment. Every customer-facing AI interface must disclose that it is AI, list its limitations, and offer the user a way to switch to a human. And every deployed AI system needs a working kill switch — a one-button deactivation if the outputs go wrong. Comments are invited until 24 July.

The deep idea: models as a new line on the risk ledger

This is the consequential part, and most early commentary is missing it. When a regulator says you cannot offload accountability to a vendor's API, it is doing for AI what Basel did for credit risk in the early 1990s. Models become a class of exposure. They have to be inventoried, tiered by materiality, validated independently, monitored for drift, and signed off at the highest level. That is the grammar of capital, not the grammar of code.

It is also the only grammar that scales. The RBI's own FREE-AI Committee survey last year found that nearly 21% of regulated entities were already deploying AI in production — across credit underwriting, cybersecurity, customer support, sales — and 67% wanted to go deeper. At that level of penetration, telling boards “your tech team has this” is not serious supervision. Every credit cycle eventually meets its model failure mode. A regulator that waits to discover the failure is a regulator already too late.

The kill switch rewrites the vendor market

Look at the supply side and the picture sharpens. A small set of global tech firms quietly supplies a disproportionate share of the AI models running inside Indian financial services. The draft flags this concentration explicitly as a systemic supply-chain risk. Combine it with the third-party-is-no-defence clause and the direction of travel is unmistakable: a serious push toward indigenous, auditable, swap-out-able model stacks. The new market is not the bank's market. It is a RegTech market — model validation firms, bias-testing labs, explainability auditors, red-teaming shops. A compliance officer reading this draft on Monday morning is realising that the next critical hire is not another data scientist; it is an independent validator who can sign off on someone else's data scientist.

Why this logic will travel beyond banking

I have spent enough time inside a national administration deploying AI on citizen-facing systems to read this draft as a template, not an end-point. Tax, customs, social security, urban service delivery, public health records — every government body running models on millions of files faces the same accountability question the RBI is now putting to banks. Who signs off on the model that decides a refund? A scrutiny notice? An eligibility threshold? The answer cannot be “the algorithm”. It has to be a named human, with a department behind them, with an institutional framework above them.

Three things from this draft will, I think, become the standard public-sector discipline within two budget cycles:

• the inventory rule — every active and decommissioned model on a register, with a ten-year tail;
• the explainability threshold — outputs interpretable to the extent the business process actually requires;
• the kill switch as a non-negotiable product feature, not a nice-to-have.

The trade-off, and the right side of it

The cost is real. Industry analysts already estimate a 50 to 100 basis point rise in IT spending for serious adopters. Smaller NBFCs and co-operative banks will feel it the most. Time-to-market for AI-driven products will lengthen. None of this is free, and the smaller institutions will need a transition window the draft does not yet promise.

But picture the alternative. A black-box model, bought from a vendor, denying loans to a cluster of small borrowers in a particular district, operating under no one's clear authority. Someone eventually discovers it — they always do. The regulator pays, the bank pays, the customer pays, and trust in AI itself pays the heaviest tax of all. The cost of the RBI's draft is the cost of preventing that discovery.

The cleaner way to say it: in a regulated industry, AI stops being a technology decision and becomes a capital decision. Capital decisions are made in boardrooms, not in model repositories. The window closes on 24 July. The more interesting question is not whether this framework is right for finance — it broadly is — but how quickly the rest of the public-private edge of AI adoption catches up. The RBI has, almost without saying so, written the first chapter of an Indian AI accountability code. Other regulators will write the rest, or they will inherit the failures of not having done so.

#RBI #AIGovernance #ModelRisk #BankingRegulation #IndianEconomy #AIAccountability #PublicFinance #RegTech

Government Isn't A Hyperscaler

On Monday, Alphabet fell about 5%, dragging the communication services sector down with it. Memory chip stocks plunged in Asia overnight and the selling crossed the Pacific by Tuesday morning. Beneath the price action, the cause was specific and revealing: investors are starting to ask whether the enormous sums being poured into artificial intelligence will earn their keep, and reports of senior talent leaving Alphabet's AI teams added to the unease.

That question — will the AI capex pay off — is the right question for shareholders. It is the wrong question for a government.

What the market wobble actually says

The sell-off was narrow, not broad. The Russell 2000 closed above 3,000 for the first time even as the big tech names tumbled. That tells you the market is rotating, not collapsing. The doubt is concentrated in a handful of names whose valuations had assumed the AI trade would compound for several more years without a pause.

The question being asked is simple. Amazon, Microsoft, Alphabet and Meta have collectively guided to roughly 25 billion in capital expenditure for 2026. Their combined free cash flow is forecast to fall to about billion in the third quarter — a decade low. At that ratio, the cash being spent is no longer comfortably backed by the cash being earned. That is what the nervous side of the market is pricing.

Why this question doesn't translate to government

Public administration does not sell tokens. It does not have shareholders. Its “revenue” is taxes and its “product” is rights, services, and the slow accretion of public trust. The whole logic of equity-style ROI — free cash flow divided by capex — is structurally absent.

And yet, watch any AI tender written by any government this year and you will see private-sector language smuggled in. Pilots. Use cases. Productivity gains. Cost savings per FTE. We are evaluating public AI investments in the only vocabulary the consultants brought to the room, and the result is that everyone ends up arguing over a metric nobody in the citizen-service chain actually cares about.

From inside a national tax administration, having worked on conversational AI for citizens at scale, I can say something specific. The right number was never “queries handled per rupee of compute.” It was something closer to: did the next confused taxpayer get an accurate answer, in the language she actually speaks, in time to act on it, without having to travel to a counter? That is an outcome question. It is not a capex question.

An outcomes-first lens

If the market is pricing AI on cash flow, the government should price AI on three different things:

• Service latency. How much faster does a citizen get an answer, a refund, or a decision after the system is deployed?
• Service reach. How many more citizens, in how many more languages and pin codes, can the same service touch without adding counters?
• Officer leverage. How much higher-order work can the same officer do because the system has absorbed the routine?

None of these are radical. They are simply restatements of what the public sector exists to do. The discipline is to write them into the procurement document before the vendor walks in, not after the dashboard is built.

A proposal: separate the two stacks

Here is a concrete proposal. A government should split its AI estate into two stacks and evaluate them by two completely different rules.

The internal stack — drafting, summarisation, case retrieval, file review, internal search across decades of orders — can be evaluated commercially. Hours saved, error rates, contractor-substitution ratios. That is fair, because the work being replaced was already priced.

The citizen-facing stack — multilingual chatbots, eligibility navigation, guidance on a new statute, status tracking on a refund — must be evaluated as service delivery. The metric is not unit economics. The metric is whether a person previously locked out of a service is now inside it.

Confuse the two stacks and the second one will always lose to the first on a finance committee's spreadsheet. It will be quietly defunded the moment an AI valuation cycle turns. And, as Monday's tape made plain, the AI valuation cycle will turn.

The public sector organisations point

Professor Michael Ting's course on the Analysis of Public Sector Organizations at SIPA made an argument that has aged into something close to a law. Public agencies serve multiple principals — legislatures, ministers, courts, auditors, citizens — and the metrics they adopt silently decide which principal they end up serving. Adopt private-sector ROI metrics for public AI and the system will end up optimising for the auditor's spreadsheet, not the citizen at the window. That is not a hypothetical risk. It is the default trajectory.

What to do this quarter

Three moves, for any department about to sign an AI contract:

• Write the outcome metrics before the price negotiation, not after.
• Build at least one citizen-facing metric whose headline a non-technical minister can defend in question hour.
• Keep funding the citizen stack even when the headline AI trade in the markets is going through one of its periodic doubts. Especially then.

The market is reminding everyone this week that the private bet on AI is a bet. Government is not making the same bet. It is deploying a tool that should be judged by whether the queue moved. The discipline is to keep saying that out loud while the news cycle is busy saying everything else.

#AIinGovernment #PublicSectorAI #DigitalGovernance #AICapex #IndiaAI #Governance #PublicFinance

When GeM Learns Tamil

A handloom weaver in Karur. A carpenter in Bhubaneswar. A metal-fabricator in Rohtak. None of them has ever sold to a government department through GeM. Not because their work is poor; because the tender document is in English.

Last week, on 15 June, the Digital India BHASHINI Division and the Government e-Marketplace signed an MoU to integrate AI-powered language technologies into GeM, letting buyers and sellers transact across the platform in 22 Indian languages, with voice as a first-class input. The press release was polite and technocratic. Underneath it sits one of the more consequential pivots in Indian e-governance in years.

What was actually signed

GeM is the national procurement portal under the Ministry of Commerce. Government buyers, from ministries down to municipal corporations, use it to purchase goods and services from registered sellers. The transaction volumes are not small. Until last week, the platform spoke fluent English with Hindi as a polite afterthought. The new MoU plumbs the marketplace with BHASHINI's translation APIs, voice bots, domain-specific language models, and a voice-first interaction layer.

This is not a one-off. In recent weeks BHASHINI has signed similar agreements with DPIIT for the industrial ecosystem, with the Ayush Ministry for traditional health knowledge, with the Centre for Railway Information Systems, and with Kathmandu University to extend Indian-language AI into South Asia. The portfolio matters more than any single MoU. Together, they amount to a slow, deliberate plumbing of the Indian state with a shared language layer.

Language is infrastructure, not a feature

Many departments still treat language support as a checkbox. Get the portal translated to Hindi, drop in a few Tamil PDFs, declare victory in the next presentation. That mental model is wrong. Language is not paint applied at the end of a project. It is the medium in which citizens think, decide and act. A self-employed entrepreneur in Madurai will not navigate a fourteen-page English tender to bid on a six-lakh contract, even if a translation button exists somewhere.

What BHASHINI is building is something different: a horizontal language layer of the Indian state. The platform supports 36 Indian languages for text translation, 23 for voice recognition and speech synthesis, and reportedly processes more than 15 million AI inferences a day. Once that layer exists, any new government service, from a procurement portal to a hospital appointment system to a tax helpline, can become multilingual at the speed of an API call rather than at the speed of a fresh translation tender.

This is how digital public infrastructure should be built. UPI did it for payments. Aadhaar did it for identity. DigiLocker did it for documents. Language was the last big primitive that every department was hand-rolling badly for itself. It now has a shared standard.

The voice part, which most commentary will miss

The MoU foregrounds voice. Translation is the obvious win; voice is the deeper one. A welder in Latur with a smartphone and modest literacy will not type a procurement query into a form. He will speak it. Voice agents that understand Marathi, Telugu, Bengali and Bhojpuri properly are how the next two hundred million Indians will actually interact with the state.

From inside an administration that serves taxpayers at population scale, I can say this with some conviction: the bottleneck is rarely the legal text. It is the gap between the citizen's spoken question and the legal text's English answer. Closing that gap, in real time, across 22 languages, is harder than building any specific service. It is also more transformative.

Platform thinking, not product thinking

I have argued before, and still believe, that public-sector AI strategy which begins and ends with “let us build a chatbot” misses the point. A chatbot is a user interface. An interface alone solves nothing if the underlying service, data and language plumbing is broken. The BHASHINI-GeM partnership matters precisely because it inverts the usual order. It does not ship a flashy procurement assistant. It plumbs the marketplace with multilingual capability so that any future interface, whether chat, voice, IVR or mobile app, can speak the citizen's language without each department rebuilding the linguistics from scratch. That is platform thinking, and it is the right thinking for a state with thousands of services and finite budgets.

Three things that must follow

If this is to scale beyond a press release, three things should be insisted upon. One, every central ministry should publish a short, public roadmap for plugging at least one citizen-facing service into BHASHINI within six months; pick the highest-volume interaction and start there. Two, language quality cannot be self-certified by the platform that builds it; an external panel of native speakers, including from non-scheduled languages, should publish quarterly benchmarks the way RBI publishes inflation prints. Three, the income tax, GST and welfare-delivery systems, which together touch nearly every Indian, must move early. That is where the political payoff is largest, and where the harm of getting it wrong is also greatest.

Two final observations. The Eighth Schedule lists 22 languages, but India actually speaks well over a hundred; treating BHASHINI as the floor, not the ceiling, will matter. And this kind of infrastructure rarely makes news on the day it launches. It shows up two years later, when a self-employed seamstress in Tirupur wins her first government order in Tamil and does not remember a time it was otherwise.

#BHASHINI #GeM #DigitalIndia #AIGovernance #PublicProcurement #MultilingualAI #VoiceFirst #DPI

Stop Building Chatbots.

Eighty-two percent. That is the share of inbound citizen queries an AI agent called Bobbi resolved in its first week across three English police forces, without a single hand-off to a human officer. The number matters less for policing than for what it telegraphs to the rest of government: the chatbot era, finally, is ending.

I say this with some scepticism about my own past. Anyone who has helped stand up a citizen-facing assistant inside a large national department knows the temptation to call any conversational widget a chatbot and declare modernisation complete. It is not. Bobbi, alongside Singapore's GovTech work and Estonia's interoperable agent network, signals that the next layer of public-sector AI will look very different from what most administrations are currently buying.

Chatbots answer. Agents act.

The technical distinction is sharper than the marketing suggests. A chatbot replies to a prompt. An agentic system is handed a goal — process this permit renewal — plans the steps, calls the databases, validates against the rules and executes the transaction. One produces text. The other completes a workflow.

This is why a recent World Economic Forum and Capgemini exercise mapping seventy core government functions sorts them more cleanly by workflow than by department. Eligibility assessment, document processing, fraud detection, permit issuance — these cut across ministries; they are the natural unit of analysis for an agent, not an org chart. Agents do not need to break silos. They operate outside them.

The chatbot habit is the trap.

The single most common mistake I see, both in India and abroad, is treating an agentic deployment as a chatbot upgrade. Same procurement template, same vendor, same knowledge base, a slightly cleverer model bolted on the back. That is not what the technology is for. The Capgemini survey of 350 public-sector organisations finds that ninety percent intend to explore or deploy agentic AI within two to three years, while Gartner forecasts that more than forty percent of those projects could be cancelled by 2027 — usually because the agency moved before understanding where the actual value sits.

The value sits in outcomes, not interactions. The right question is not can the bot answer this but what is the citizen actually trying to finish, and how many steps can we collapse into one supervised flow. An estimated one hundred and forty billion dollars in US federal benefits goes unclaimed each year because the application paths are too fragmented for the people who most need them. Most large administrations, India included, will find similar pockets if they look honestly: schemes whose stated reach far exceeds their actual delivery, not for want of policy but for want of plumbing.

What conversational AI for citizens actually teaches.

Citizen-facing conversational systems at national scale teach two unfashionable lessons. First, the volume of routine queries is far larger than any budget anticipated, and far more repetitive: a small set of questions accounts for most of the load. Second, when the citizen needs to complete something rather than learn something, the conversational layer hits a wall. The handover to forms, portals and back-office staff is where the experience breaks.

Agents are precisely the technology for that gap. In a direct-taxes context, an agent can read a notice, retrieve the relevant return, pre-fill a form against rule sets, cross-check historical filings and route only the genuinely ambiguous cases to a human officer. The administrator stays the architect. The agent does the clerical labour that nobody, on either side of the counter, particularly enjoys.

Bounded autonomy, glass-box defaults.

The discipline the field is converging on is bounded autonomy: being deliberate about what an agent is allowed to do, keeping a human meaningfully in the loop, and making every step auditable. The phrase that fits a public-sector context is glass-box governance. A chatbot answers, and the trail is shallow. An agent acts, and each action — every rule consulted, every database queried, every form submitted — should leave a perfect record. Used well, this is more accountable than the human-only system it replaces, not less.

The procurement template should change accordingly. Stop buying chatbots. Buy a workflow agent that ships with an audit log by default, with explicit, narrow permissions per task and a hard escalation rule for any case touching rights or material amounts. Bounded autonomy belongs in the contract, not in a vendor promise.

Where to start.

The most useful first move for any Indian department is unglamorous: pick one workflow that already exists end-to-end on paper or in disconnected portals — refund issuance, grievance redressal, a single notice-and-reply cycle — and rebuild it as an agentic flow with a human checkpoint at the decision. Measure completions, not interactions. Compare cost not to the chatbot it replaces but to the staff hours it returns. That number is what budget committees will eventually demand, and it is the only one that matters.

The chatbot was a useful detour. It taught millions of citizens that they could speak to the state in plain language and get a sensible answer. The agent is what turns that conversation into a completed transaction. The departments that grasp the difference now, and procure for it accordingly, will spend the next decade doing more with steadier headcount. The rest will spend it explaining why their bot still cannot actually do the thing.

#AgenticAI #PublicSectorAI #DigitalGovernance #GovTech #CitizenServices #AIinGovernance #IndiaAI

Three Cents On The Dollar

Three cents.

That is roughly what one extra dollar of AI model spend is replacing, in routine knowledge work, at the firms most exposed to the shift. Among the most AI-exposed companies, each $1 drop in spending on online labour marketplaces corresponded to only about three cents in added AI model spend by the third quarter of 2025. The ratio comes from firm-level payments data analysed by Ryan Stevens of Ramp, and it has been doing the rounds in public-sector commentary this week.

If that number holds, the implication is not that AI is ‘cheaper’ in the comforting, marginal way most agency notes describe it. The implication is that the market price of a meaningful slice of first-pass, reviewable knowledge work has collapsed by roughly an order of magnitude. That is not a procurement story. It is a public finance story, and we should treat it as such.

What the 33-to-1 ratio actually says

A 33-to-1 substitution ratio is not, strictly, a substitution ratio. It is a ratio between two budget lines, captured at one moment, in one slice of the economy, by one provider’s payments data. Read with care, it tells you three things.

One: buyers have found a class of output where the first pass is now an AI deliverable rather than a human one, and the first pass is the part they used to outsource. Two: the unit cost of producing that first pass has fallen far enough that even a sceptical CFO is rewriting last year’s contractor budget. Three, and most important: the saved spend is not, mostly, being reinvested in a more expensive in-house model. It is leaving the books altogether.

For a government department, that last point matters most. In the private sector, ‘leaving the books’ eventually shows up as somebody’s lost job. In the public sector it shows up - or refuses to show up - as a backlog that does not grow, a clearance cycle that does not lengthen, a sanctioned post quietly left vacant. The change is invisible until you go looking for it.

Why government cannot just copy the ratio

It would be a mistake to read this number and rush to swap people for tokens inside a tax administration. Public-sector value is not what the private sector buys when it buys ‘first-pass output’. From inside a national tax administration, one learns quickly that the value of a routine notice is not in its first draft. It is in the fact that the draft is recorded, attributable, reviewable and defensible six years later under cross-examination.

None of that vanishes if AI does the drafting. But it changes who is accountable for what, and at which moment in the workflow. Records-management law, natural justice, statutory limitation, the discipline of audi alteram partem - these are not procurement constraints. They are the architecture of the trust the department holds with the citizen. Any honest AI strategy in this domain begins there and works backwards to the model, not the other way around.

The serious question for the head of any large administration is therefore not ‘where can I deploy a model?’ It is: where in my workflow is the first pass currently a bottleneck, who reviews it, and what does that review actually verify? If the review is genuine - a senior officer reading, weighing, signing - AI is a gift; the bottleneck moves to where it belongs, which is judgment. If the review is rubber-stamping, AI will reprice that work to zero and the institution will not notice until the courts do.

Where the repricing is already real

The categories where this repricing is real will be familiar to anyone who has worked the field. Drafting of routine intimations and standard show-cause notices. Cross-referencing returns against third-party information. Summarising voluminous assessment records before a hearing. Preparing first-pass replies to grievance petitions. Tagging and classifying taxpayer correspondence. Translating dense statutory text into plain-language guidance for citizens.

Each is a place where the marginal cost of a first pass has fallen sharply. Each is also a place where a human signature still ought to mean something. The design problem is precisely how to keep the signature serious while the draft becomes cheap.

Three operating shifts that would actually help

• Measure the bottleneck, not the licence. An honest dashboard tracks clearance cycle times, refund-issue latency, time-to-first-response on grievances. If those are not moving, the model is decorative.
• Promote the reviewer. Where AI does the first pass, the human downstream should be more senior, not less. The saving is bought by elevating the reviewer’s standard, and paying her for judgment rather than throughput.
• Budget for the audit trail before the model. Every AI-assisted output in a tax administration must carry a verifiable record of what the model saw, what it produced, what the officer changed, and why. The audit trail is the primary product. The draft is secondary.

The budget question we should be asking

Michael Ting’s course on public sector organisations at Columbia used to insist that the deepest puzzle in bureaucratic design is not how to motivate effort but how to allocate scarce attention. The Ramp number, read honestly, is a story about attention, not effort. Cheap first passes free attention; what an institution does with that freed attention is the whole game.

The danger a department most needs to guard against is the most boring one: cashing the saving without rebuilding the work. If sanctioned posts are quietly left vacant, if the freed hours are absorbed by more of the same routine work, and if the audit trail is patched on at the end, then the 33-to-1 ratio will become the public sector’s number too - and we will have repriced our own knowledge work without ever asking what we wanted to do with the difference.

The better view, I think, is to treat this moment as a budgeting exercise, not a tooling exercise. Ask, for every freed officer-hour, what higher-value question we now want that officer to be asking. Then build the model around the answer. The cheapest first pass in history will be wasted on the same old second pass.

The Three-Cent Government Hour

A small number in a payments-data paper has been bothering me. Among firms most exposed to generative AI, every $1 fall in spending on online labour marketplaces by the third quarter of 2025 was matched by roughly three cents of added AI model spending. Three cents where a dollar used to sit. That is not a productivity nudge. It is a repricing of a whole category of work.

What the ratio actually says

The number comes from a Ramp analysis picked up in commentary on US federal AI policy. The exposed firms were not abandoning the work. They were buying the same first-pass output — drafts, summaries, light code, document review — at a fraction of the prior price. A slice of knowledge work has moved from a labour line item to a software line item, with the ratio between the two collapsing by more than an order of magnitude.

For private firms, this plays out through hiring and margins. For governments, it plays out through almost everything: workforce composition, training pipelines, procurement rules, and the implicit deal that lets young officers grow into senior ones.

Why tax administrations sit in the bullseye

Walk through any direct-tax office and ask what the work actually is. Reading. Drafting. Summarising. Comparing one provision against another. Translating dense statute into plainer language. Spotting the inconsistencies between a return, a third-party report and a bank statement. Almost the entire stack is language-heavy. That is exactly where the three-cent ratio bites hardest.

I have watched this from the inside. Mapping an old direct-tax statute against a new one — tracking which old section migrates where, what is dropped, what is reorganised — is genuinely difficult professional work. A few years ago that effort would consume dozens of officers for months. A current-generation model, given the right corpus and a careful prompt, will now produce a competent first draft of much of it in an afternoon. Not the final word. But a credible first pass.

The same is true of taxpayer-facing communication. Building an assistant that can field lakhs of routine questions on a new law — what the slabs are, how to elect a regime, what to fill where — was, until recently, a serious capital and talent project. Today the floor for that capability has fallen sharply. The hard part is no longer building the bot. The hard part is governance: what it is allowed to say, how its mistakes are caught, how a taxpayer appeals an answer that turned out to be wrong.

The junior officer problem

A Stanford working paper this year found a 16 per cent relative employment decline for workers aged 22 to 25 in the occupations most exposed to generative AI. Read that and a managerial instinct should fire. In any large department, the junior cadre is not just there to do work. It is there to learn. The years spent reading scrutiny files, drafting orders, sitting through hearings — those years are how a tax officer becomes a tax officer.

If the model does the first draft, what does the junior do? The wrong answer is: nothing, until they are senior enough to “supervise” the model. A supervisor who has never written the draft cannot meaningfully review one. Within a decade we would be running an administration whose middle ranks know how to prompt AI for a note but cannot tell when the note is quietly wrong on a point of law.

What the junior role should become

I think the redesign is closer to this. The junior officer is no longer the drafter. They are the evaluator, the contester, the case-builder. From day one they are taught to interrogate a machine-generated draft: where is the citation, is the authority current, does the inference survive cross-examination by a contrary view. They are taught to construct the hard cases the model gets wrong, and to document them. They become the institution’s quality control function rather than its typing pool. That is more demanding work, not less. It suits the calibre of people the service actually recruits.

A proposal worth piloting

If a tax administration wants a concrete way in, here is one. Pick a single, well-bounded workflow — say, drafting routine rectification orders, or first-level responses to grievance petitions. Build a model-assisted pipeline with three deliberate seams: a machine first draft, a structured human evaluation against a checklist, and a logged audit trail of every override. Track three numbers — time saved per case, override rate, and downstream litigation outcomes for the cases that went out. Run it for a year. Publish the numbers.

This is unfashionable advice in a moment that prefers headline pilots and grand strategies. The three-cent ratio is not going to wait for a strategy document. It is already changing what a dollar of knowledge work buys in the market. A serious department asks what that means for its own internal economics, builds the governance to capture the gain safely, and quietly redesigns its junior roles before the redesign happens to it.

The departments that get this right will not be the ones that bought the most expensive tools. They will be the ones whose officers learned, early, to argue with a machine and win.

After Aadhaar, the Productivity Test

On 28 April 2026, NITI Aayog and the Frontier Technology Hub released a roadmap called DPI@2047. The Chief Economic Adviser described India's next phase of digital public infrastructure as a total factor productivity engine. That phrase is doing more work than it looks. It marks the end of one era and the beginning of a harder one.

Reach was the easy part

The first decade of DPI was a reach problem. Could we give 1.4 billion people a verifiable identity? Could we route real-time payments at population scale? Could we plug welfare into a single pipe? JAM, UPI, DigiLocker and GeM answered yes. DPI@2047 quietly concedes that the welfare-delivery question is settled, and sets a different one. The new task is not to reach the citizen; it is to lift what the citizen, the small firm and the small farm can actually produce.

That is a different test, and most departments are not yet measuring themselves against it. The roadmap proposes eight sectoral transformations across MSMEs, agriculture, education, health, credit, energy and social protection, with a state-led, district-executed model and pilots from 2026-27. Notice the language: district-executed. The unit of accountability is shifting downwards. Productivity, unlike inclusion, cannot be claimed in a press release; it has to show up in somebody's actual output.

Why most departments will misread this

The instinctive reading of DPI 2.0 inside government will be: more APIs, more dashboards, more apps. That misreads the brief.

Phase one of DPI worked because it disentangled the rails, identity, payments and data, from the application layer. Phase two is being asked to do something subtler. It must disentangle existing work from existing process. A welfare benefit can move through a new pipe without changing what the benefit is. Productivity gains demand the opposite. The pipe is uninteresting; what changes is the work itself. Deloitte's Government Trends 2026 puts it crisply: the biggest gains come not from automating old processes but from redesigning the work itself. A UK trial of over 20,000 civil servants using generative AI for three months saved an average of 26 minutes a day per person, nearly two working weeks a year. That is not because the AI replaced anybody; it is because the work was finally allowed to be done differently.

Inside Indian government, I have watched well-meaning officers turn a transformative tool into a faster version of the form it was meant to replace. The form persists, the discretion persists, the file persists. Faster, but unchanged. DPI 2.0 will succeed or fail on whether departments are willing to give that comfort up.

Three moves a department should make immediately

Stop digitising forms; redesign the file. The unit of bureaucratic work in India is the file. Every project I have seen that put a digital wrapper around an unchanged file reproduced the same delays in colour. Pick three high-volume work-streams, write down what an ideal file looks like with AI-assisted drafting embedded in it, and re-engineer backwards. The form is downstream; the file is upstream.

Build agents that draft, not bots that retrieve. Most public-sector AI in India today is a chatbot that finds a circular. That was the right starting point. It is now the ceiling. The gain lies in agents that draft an order, prepare a notice, summarise a representation, and present a ready-to-sign output. The officer reviews and decides; the typing is gone. We are using a five-times leverage tool as a 1.2-times search tool, and calling it transformation.

Measure officer-minutes, not transactions. If DPI 2.0 is about productivity, the metric must be productivity. Most dashboards still count transactions: files moved, returns filed, calls answered. None of that tells you whether the work got lighter. The metric that should matter, and that nobody is asked to report, is officer-minutes saved per case. A department that reports this number will, within two quarters, look very different from one that does not.

The harder test is institutional will

The CEA said something else at the launch that has not been quoted enough: India has strong design capabilities, but success will depend on sustained institutional will to move from strategy to execution. That is the polite version of the real problem. Indian bureaucracy is excellent at announcing platforms and indifferent at reorganising work around them. DPI 1.0 succeeded partly because it was built outside the line department, on rails the rest of government had to either ride or be left behind by. DPI 2.0 is being handed to the line departments themselves.

So the productivity test is, in the end, a leadership test. Which Secretaries will decide that their teams write fewer pages, sign fewer files and answer fewer queries by the end of next year? Those are the departments where DPI 2.0 will arrive. The rest will get a new portal.