NITI Aayog on October 8 released a pioneering study, AI for Inclusive Societal Development. The roadmap proposes a national mission "Digital ShramSetu" that leverages AI and frontier technologies to overcome systemic barriers faced by informal workers and can be harnessed to transform the lives and livelihoods of India’s informal workers. The five key components of roadmap:
- Develop a national blueprint
- Coordinate fragmented stakeholders
- Catalyse strategic partnerships
- Translate innovation into impact
- Provide policy and regulatory support
Ecosystem
India has one of the largest informal economies in the world, with about 90% of the workforce employed under informal arrangements, contributing nearly half (around 45-50%) of the country's GDP having 490 million informal workers. The informal sector includes unregistered enterprises, self-employed workers, casual laborers, domestic workers, and informal service providers, often lacking social security benefits. India's e-Shram portal, launched in August 2021 to create a National Database of Unorganised Workers (NDUW), has registered over 30.98 crore unorganised workers as of August 2025.
Migration and urban informal work are intertwined, with informal jobs. The informal sector poses challenges like poor working conditions, job insecurity, and exploitation, especially for migrant workers. Indian MSMEs employing informal workers also suffer on a competitive scale is the quality of talent. Businesses compensate for inferior quality labour with depressed wages which in turn creates an unattractive career pathway; hinders upward mobility; and disincentivizes talent.
Additional Reading: Why do rural enterprises remain small ?
Challenges
1. Harassment of MSMEs by labour inspectors is a reported issue in India, reflecting concerns over misuse of power, frequent inspections, and arbitrary penalties. The complex regulatory environment and multiple overlapping laws cause delays and create opportunities for rent-seeking behaviors from officials.
2. Workers with limited digital literacy become more dependent on intermediaries (officials, cybercafe operators, CSC operators) who can extract rents. This will create new rent-seeking opportunities. Local officials could charge fees for "faster processing" of digital IDs or demand bribes
3.Bureaucrats resist change, preferring to maintain their power and scope. Incentives encourage expanding departments and budgets rather than achieving efficiency. The administrative state centralizes power among unelected officials. The same bureaucrats who struggle with existing schemes will be tasked with implementing AI-powered verifiable credentials and smart contracts.
4. Drawing from James C. Scott’s work, the discussion delves into how increased state legibility—enabled by systems like Aadhaar and UPI— have enabled government to operate from 2009 to 2024 without Privacy Law. The 15-year gap since Aadhaar’s launch without a privacy framework underscores systemic neglect. India currently lacks a fully enacted constitutional act specifically dedicated to AI regulation akin to the European AI Act.
5. Even if there is motivation in the government at top tiers, there is not always capacity to understand complex technological systems by frontline user. India's DPI success (UPI, Aadhaar) succeeded because they involved standardized, high-volume transactions with limited discretionary implementation. Digital ShramSetu requires complex, discretionary decision-making at the local level—exactly where Indian state capacity is weakest and most corrupt.
6. Like poverty status, the classification of workers as formal or informal is fluid. Workers may shift between informal and formal employment due to job transitions, gig economy roles, and contractual changes. This fluidity complicates policy design, social protection coverage, and statistical measurements, demanding adaptive, inclusive frameworks.
7. India's skill development ecosystem reveals a systematic corruption pattern that AI implementation could either amplify or mitigate, depending on design choices.
Suggestions
1. AI algorithms can be used to match registered workers with job opportunities in their skill areas and geographic locations, optimizing employment pathways and reducing informality and underemployment. This can be initiated from Polytechniques and ITIs in the initial phase and gradually used for unorganized workers.
2. e-Shram portal must provide AI-facilitated interoperability with other government benefits like UDYAM, e-Pension, post office and healthcare schemes can offer a seamless experience for workers, facilitating holistic social protection.
3. When an informal worker registered on e-Shram secures formal sector employment, their verified credentials and employment history can be linked to EPF enrollment processes, helping with identity verification, tracking contributions, and ensuring portability of social security benefits.
4. The roadmap assumes informal workers want to transition to formal systems. Application of the technology must necessarily be accompanied by design of transparent processes. AI can be used for self-certification, digitization of compliance to reduce physical inspections, and stronger grievance redressal mechanisms to protect MSMEs from excessive or unfair enforcement. This is important to create pathways for the informal worker to initiate the journey into an entrepreneur integrated into formal economy.
5. Labour courts and dispute resolution mechanisms are increasingly exploring the use of AI to improve efficiency, reduce backlogs, and enhance fairness in labour law enforcement. AI can analyze large volumes of workplace cases, assess precedents, and suggest outcomes based on legal principles, helping resolve disputes like wrongful termination more systematically.
6. Rather than voluntary adoption, India can consider sector-by-sector mandatory digitization starting with high-impact areas like contractual workers of PSUs and PM Vishwakarma beneficiaries.
7. Last but not least, India must separate policymaking, implementation, and oversight functions. There must be creation of an independent ombudsman systems for digital services and platform involved in gig economy.
8. The mission should operate in true mission mode: establishing autonomous implementation units at state level with direct resource allocation, hiring authority, and performance accountability, bypassing traditional bureaucratic hierarchies that create implementation bottlenecks.
Global Lessons
Estonia’s government ministries are required to appoint AI officers and create AI implementation plans, effectively making AI adoption in public sector organizations a regulated requirement. In summary, Estonia mandates AI adoption and implementation plan within defined sectors such as education and government administration. Yet, Estonia's digital success required complete administrative restructuring before technology deployment.
Inside Amsterdam’s high-stakes experiment to create fair welfare AI: Even though Netherland Government worked hard to build a fair AI system to detect welfare fraud, the algorithm still showed bias against people with non-Dutch speaking migrants and those with lower incomes. Ethical AI needs ongoing human oversight, community involvement, and understanding that automation has limits when dealing with complex social fairness issue.
Conclusion
India's Digital ShramSetu mission confronts a fundamental paradox: it requires sophisticated state capacity to implement solutions for populations that exist precisely because of weak state capacity. India's Digital ShramSetu mission could indeed be transformative, but success requires acknowledging current limitations rather than assuming technological solutions will overcome social and economic realities. The Digital ShramSetu mission's success depends on recognizing that technology is a governance multiplier, not a governance substitute