The Portable Worker, How AI Can Transform India’s Social Security from Fragmented Schemes to Seamless Entitlement
Nearly 90 per cent of India’s workforce operates in informal employment arrangements. They are gig workers delivering food through aggregator platforms, construction labourers moving between states for seasonal work, home-based artisans selling through e-commerce intermediaries, and domestic workers serving multiple households across a city. Their work is real, their contribution to the economy is immense, and their vulnerability is profound. Yet, for decades, India’s social security architecture has been designed for a different kind of worker—one with a permanent establishment, a continuous employment history, and a single, stable geographical location. The result is a fundamental mismatch: a formal, static system attempting to serve an informal, dynamic workforce.
The operationalisation of the Social Security Code, 2020, and the subsequent draft rules placed in the public domain, represent a deliberate and long-overdue correction of this mismatch. By formally recognising gig workers, platform labourers, and the unorganised sector within a unified legal framework, the Code shifts the conceptual anchor of social security from the contract to the worker. It acknowledges that employment in contemporary India is not a single, lifelong relationship with one employer but a portfolio of engagements across multiple sectors, geographies, and platforms. The worker is the only constant; the system must therefore organise itself around the worker, not the job.
This is a transformative legislative intent. But intent is not implementation. The task now confronting India’s social security administration is the translation of legal recognition into lived protection for nearly 94 crore beneficiaries—a population larger than that of any country except India itself. This task is not merely administrative; it is conceptual, technological, and federal. It requires reimagining the relationship between citizens and the state, between data and rights, and between the Union government and the States. And it requires, at its core, a capability that India has already demonstrated at population scale but must now deploy with unprecedented sophistication: the capacity to track, connect, and serve individuals as they move across the vast, complex terrain of India’s labour markets.
The Scale of the Challenge: From 19% to 45% and Beyond
The sheer magnitude of India’s social security expansion defies easy comprehension. According to ILOSTAT’s SDG indicators, social security coverage has grown from 19 per cent of the population in 2014 to nearly 45 per cent today. In absolute terms, this represents approximately 94 crore beneficiaries—more than the entire population of Europe. India now operates one of the world’s largest social security systems, not on paper but in live, transactional reality.
This expansion has been enabled by institutions such as the Employees’ Provident Fund Organisation (EPFO) and the Employees’ State Insurance Corporation (ESIC), which collectively process crores of contribution, compliance, and benefit transactions annually across establishments, sectors, and states. These are not survey-based estimates or modelled projections; they are administrative records generated by the daily operations of the economy. Every contribution deducted from a salary, every employer compliance filing, every benefit disbursed to a retired worker or hospitalised employee—each is a data point in a living map of India’s labour markets.
The availability of such live, transactional data fundamentally changes what policy delivery can realistically aim for. When social security administration was conducted through paper records and periodic surveys, the state operated in a fog of retrospective approximation. It knew, roughly, how many workers had been covered, approximately how much had been contributed, and broadly where gaps existed. Today, the state knows—or could know, if its systems were intelligently connected—exactly who is contributing, for how long, on what wage base, and with what employment history. The fog has lifted. What remains is the task of connecting the vantage points.
The Legacy Problem: Siloed Systems and Administrative Resets
India’s social security architecture, however impressive in its aggregate scale, suffers from a structural fragmentation inherited from decades of scheme-specific delivery. Each programme—EPFO, ESIC, e-Shram, PMSYM, state-level welfare boards, and scores of others—evolved independently, with its own beneficiary registries, contribution records, eligibility criteria, and benefit delivery mechanisms. A worker moving from formal employment covered by EPFO to gig work registered on e-Shram does not carry her provident fund history with her; she starts afresh, her prior contributions and entitlements effectively reset to zero for the purposes of the new scheme.
This is not a technical problem; it is a design problem. The systems were not designed for mobility because the workforce they were originally intended to serve was not mobile. A permanent employee of a formal manufacturing firm in the 1970s could reasonably expect to remain with that firm, in that location, for the entirety of her career. Her provident fund account was a single, continuous record with a single employer. Today’s worker, by contrast, may have half a dozen employment spells across multiple sectors and states before she reaches thirty. Each spell generates contributions to some scheme or combination of schemes. Each scheme maintains its own ledger. And when the worker moves on, the ledger is archived and effectively forgotten.
This “administrative reset” imposes significant costs. For the worker, it means loss of continuity: contributions made in one phase of employment do not count towards eligibility thresholds in the next. For the employer, it means duplicative compliance: registering a worker who has already been registered elsewhere requires repeating processes that should be portable. For the state, it means fragmented visibility: it knows the worker existed in multiple schemes but cannot aggregate those fragments into a coherent employment history. The cumulative effect is exclusion: workers who move frequently, as informal and gig workers inevitably do, find that their social security entitlements do not move with them.
The Portability Imperative: From Fragments to Continuity
The design imperative arising from this diagnosis is unambiguous: portability. Social security coverage, contributions, and benefits must move seamlessly with the worker across districts, states, sectors, and employment types. The worker’s entitlement must be attached to her person, not to her job. Her contribution history must be a single, cumulative record, not a collection of disconnected fragments.
India has already laid the foundational infrastructure for such portability. The Universal Account Number (UAN) under EPFO, now extended into platforms like e-Shram, provides a unique, lifelong identifier for workers that can, in principle, link their various employment and contribution records across schemes and jurisdictions. The Aadhaar ecosystem provides biometric authentication that can verify worker identity with high assurance. The India Stack provides interoperable digital infrastructure that can facilitate seamless data exchange between disparate systems.
What has been missing is the intelligent connective tissue that can actually perform the linking—that can recognise, in real time or near-real time, that the worker who has just registered for e-Shram is the same worker who contributed to EPFO for three years, and that those contributions should be aggregated with future contributions to determine her eligibility for pensions, insurance, and other benefits. This is where artificial intelligence becomes not a speculative future capability but an immediate governance necessity.
The AI Opportunity: Connection, Not Prediction
The article’s authors—senior officials and technologists with direct responsibility for India’s social security systems—offer a crucial reframing of AI’s role in governance. “The value of AI,” they write, “lies not in prediction alone, but in connection.” This is not the AI of science fiction, autonomously making decisions about who deserves benefits. It is the AI of pattern recognition, entity resolution, and process optimisation—applied not to replace human judgement but to enable systems that currently operate in isolation to function as an integrated network.
Consider the concrete use cases. When a worker moves from construction employment in Maharashtra to platform-based gig work in Karnataka, her EPFO contributions cease and her e-Shram registration begins. In the current fragmented system, this transition is invisible to the administration. The worker’s cumulative contribution history is interrupted; her eligibility for benefits that require continuous contribution periods may be jeopardised; her entitlement to portability—theoretically guaranteed—is practically denied.
An AI system trained on historical patterns of employment transitions can identify this movement with high confidence. It can flag the contribution lapse not as an enforcement failure but as a transition event, triggering automated verification of the worker’s new employment status and seamless linkage of her pre-transition and post-transition contribution records. It can generate a personalised notification to the worker, explaining that her social security coverage has been maintained and advising her of any additional steps required. It can provide the Karnataka gig platform with verified information about the worker’s prior contributions, reducing their compliance burden and incentivising formal registration.
This is not speculative. Every component of this workflow already exists in India’s digital infrastructure. The UAN provides the persistent identifier. Aadhaar provides the authentication. EPFO and e-Shram provide the transactional data. What is missing is the intelligent orchestration layer that connects these components dynamically, in response to worker mobility rather than static administrative schedules.
The Federal Dimension: AI as Enabler, Not Intrusion
India’s federal structure, with labour on the Concurrent List, adds complexity to any unified social security architecture. States are not merely implementing agencies; they are constitutional partners in the delivery of welfare, with their own priorities, capacities, and political accountabilities. Any system that appears to centralise control or displace State autonomy will meet resistance, regardless of its technical sophistication.
The authors’ framing of AI’s federal role is therefore strategically significant. They emphasise that national digital systems should provide “common foundations through shared identifiers and interoperable standards,” while States remain “closest to workers and employers, shaping inspections, enforcement priorities, and welfare delivery.” AI, in this conception, is not an instrument of centralisation but an enabler of cooperative federalism. It allows States to retain their distinctive approaches to welfare delivery while participating in a national ecosystem of portable entitlements.
A worker registered with the Kerala Welfare Fund for construction workers who moves to Tamil Nadu does not need to surrender her Kerala entitlements and begin anew. The shared identifiers and interoperable standards enable Tamil Nadu’s systems to recognise her prior contributions, apply State-specific eligibility rules, and ensure continuity of protection. The worker experiences seamless portability; the States retain policy autonomy; the national system provides connective infrastructure without imposing substantive uniformity.
This is the institutional role for AI that the authors advocate: not a replacement for federal negotiation but a tool that makes federalism work better by reducing the transaction costs of inter-State cooperation. It is a vision of technology as constitutional lubricant rather than constitutional disruptor.
The Cautionary Note: Digitisation Is Not Reform
The authors are emphatic, however, that AI is “not a shortcut to universal social security.” They draw on hard-won lessons from earlier phases of digitisation, which too often consisted of layering new technology over old pathologies. Digitising a poorly designed procedure does not fix it; it amplifies its inefficiencies at scale. AI layered over unclear roles, inconsistent data, or weak accountability will not produce intelligent governance; it will produce intelligently automated dysfunction.
The sequencing imperative is therefore critical. Initial AI deployments should focus on low-risk, high-impact use cases that build confidence and demonstrate value without exposing vulnerable populations to algorithmic error. Cleaning registries of duplicate or dormant entries. Clearing backlogs of unprocessed claims. Detecting compliance gaps through pattern analysis rather than random inspection. Reducing the repetitive administrative work that consumes frontline officials’ time and energy.
Only once these foundational capabilities are in place should AI be extended to more consequential domains—and even then, only within sandboxed environments where errors can be contained and corrected without systemic harm. The authors’ caution is not Luddism; it is the prudence of practitioners who have seen technology fail when divorced from institutional context.
Trust as Infrastructure
The article’s closing emphasis on trust is not rhetorical flourish; it is operational necessity. Employers need predictability in their compliance obligations. Workers and unions expect transparency in how their data is used and their entitlements determined. State governments must see national digital systems as enabling rather than intrusive. Each of these constituencies has legitimate concerns that must be addressed through visible, verifiable system design.
AI systems built with shared dashboards, common standards, and clear audit trails can reduce discretion and its attendant risks of arbitrariness and corruption. When eligibility determinations are based on transparent rules applied consistently across cases, when contribution histories are verifiable by workers themselves, when enforcement priorities are derived from objective data rather than subjective suspicion—in each of these dimensions, AI can strengrather than weaken the trust that sustains large-scale welfare systems.
But this outcome is not automatic. It requires deliberate choices: to prioritise transparency over proprietary secrecy, to design for auditability rather than black-box optimisation, to subject algorithmic systems to the same constitutional scrutiny applied to human decision-makers. The technology does not determine its own governance; governance determines the technology’s impact.
Conclusion: From Vision to Execution
The Social Security Code embodies a clear and welcome statement of intent: that India’s social protection system will reflect how Indians actually work, not how they worked in the mid-20th century. The expansion of coverage from 19 per cent to 45 per cent of the population demonstrates that India can scale social protection when it commits to doing so. The digital infrastructure—UAN, e-Shram, Aadhaar, India Stack—provides the foundational layers on which portable, worker-centric social security can be built.
What remains is the execution challenge: converting this intent, this demonstrated capacity, and this infrastructure into seamless, reliable protection for the 94 crore workers who currently hold entitlements in fragmented, disconnected silos. This is not primarily a technological challenge; the technology exists and India has already demonstrated mastery of population-scale digital systems. It is an institutional challenge: reconfiguring administrative routines that have hardened over decades, aligning incentives across federal partners, and building the trust that enables workers to claim their rights and employers to fulfil their obligations.
AI, deployed judiciously and anchored in institutional strength, can help meet this challenge. It can connect systems that currently operate in isolation, identify transitions that currently go unremarked, and reduce the transaction costs that currently exclude mobile workers from the protection they have earned. It can make government not merely efficient but “deliberately human”—responsive to the actual rhythms of working life rather than the administrative categories of a vanished era.
This is how India can show the way. Not through technological triumphalism, treating AI as a magical solution to deep-seated structural problems. But through institutional pragmatism: identifying where technology can enable human judgement, where data can illuminate policy choices, and where digital infrastructure can strengthen rather than supplant the federal compact. The Social Security Code offers the legal framework. India’s administrative systems offer the operational scale. The task now is to bring them together in a system that travels with the worker, from job to job, from State to State, from vulnerability to security. That is the promise of portable social security. And that is the work ahead.
Q&A Section
Q1: What is the fundamental mismatch between India’s traditional social security architecture and its contemporary workforce?
A1: The fundamental mismatch is between a static, formal system designed for permanent, geographically stable employees and a dynamic, informal workforce characterised by mobility, multiple employment spells, and platform-based work. Traditional social security (EPFO, ESIC) assumed a worker would remain with a single employer, in a single location, for most of their career. Benefits and contributions were attached to the job, not the person. Contemporary Indian workers, however, frequently move across districts, states, sectors, and employment types (formal, informal, gig, platform). Each move in the legacy system triggers an administrative reset: the worker’s prior contribution history is not automatically recognised by the new scheme, resulting in loss of continuity, fragmented records, and frequent exclusion from benefits requiring continuous contribution periods. The Social Security Code’s recognition of gig and platform workers is a legislative response to this mismatch.
Q2: What does the article mean by “administrative reset,” and why is it a problem?
A2: “Administrative reset” refers to the fragmentation of a worker’s social security history when they move between schemes, employers, or jurisdictions. Because India’s social security systems evolved in silos—each with its own beneficiary registry, contribution records, and eligibility criteria—a worker’s prior contributions are not automatically recognised when they register with a new scheme. Their EPFO history does not travel to e-Shram; their state welfare board contributions do not count towards central scheme eligibility. The worker must effectively start from zero each time, even though they have contributed continuously across their working life. This is problematic because it raises transaction costs (workers must navigate multiple registration processes), risks exclusion (workers who move frequently may never meet continuous contribution thresholds), and undermines the very concept of social security as an earned entitlement rather than a series of disconnected, easily forfeited claims.
Q3: How does the article reframe the value of AI in governance, and why is this reframing significant?
A3: The article reframes AI’s value from prediction to connection. In popular discourse and much policy discussion, AI is associated with forecasting—predicting which workers will quit, which firms will default, which regions will experience unemployment. The authors, all practitioners, argue that AI’s more immediate and consequential value lies in connecting existing systems that currently operate in isolation. This reframing is significant because it democratises AI: it does not require exotic capabilities, massive computational infrastructure, or the replacement of human decision-makers. It requires pattern recognition, entity resolution, and process optimisation—applied not to speculate about the future but to make the present system function coherently. It positions AI as a tool for institutional integration rather than institutional disruption, and as an enabler of federal cooperation rather than central control.
Q4: What are the “clear lessons” from earlier phases of digitisation that the authors caution must be heeded?
A4: The authors identify two clear lessons. First, avoid digitising poorly designed procedures. Layering technology over unclear roles, inconsistent data, or weak accountability does not fix these underlying problems; it amplifies inefficiencies at scale. Business process redesign, rule clarification, and data cleaning must precede technological intervention. Second, move beyond administrative silos. AI draws its value from identifying patterns across systems. If systems remain deliberately disconnected—if EPFO, ESIC, e-Shram, and state welfare boards continue to operate as independent fiefdoms—AI cannot perform its connective function. The technological challenge is inseparable from the institutional challenge of breaking down bureaucratic boundaries that have hardened over decades.
Q5: How does the article conceptualise AI’s role within India’s federal structure, and why is this conceptualisation strategically important?
A5: The article conceptualises AI as an enabler of cooperative federalism, not an instrument of centralisation. National digital systems provide “common foundations”—shared identifiers (UAN, Aadhaar), interoperable standards, and connective infrastructure—while States retain autonomy over inspections, enforcement priorities, and welfare delivery. AI enables this division of labour by reducing the transaction costs of inter-State coordination. A worker moving from Kerala to Tamil Nadu can have her prior contributions recognised by Tamil Nadu’s systems without requiring either State to surrender its policy autonomy. This conceptualisation is strategically important because it addresses the legitimate concerns of State governments that national digital systems might be mechanisms of encroachment on their constitutional domain. By framing AI as a tool that strengthens State capacity without centralising control, the authors position technological reform as compatible with, indeed supportive of, India’s federal compact.
