AI and the Future of India’s Workforce

This editorial is based on “Reimagining work with AI-ready workforce” published in the Financial Express on 15/09/2026. The article highlights that blending AI with manufacturing requires parallel workforce upskilling, indigenous data infrastructure, and ethical safeguards to bridge the deployment-impact gap and ensure human-centric economic growth in India.

Despite 56% of surveyed global manufacturing executives using AI agents and 37% deploying over ten (per a Google Cloud–National Research Group survey), a “deployment–impact gap” exists. Installing AI software alone doesn’t raise productivity. Transforming Indian manufacturing requires simultaneous factory investments in machines, reliable data, redesigned workflows, worker participation, and continuous skilling.

How is AI Reshaping and Diversifying the Modern Workforce?

Creation of New-Age Occupational Categories: AI has generated specialised roles across the technology value chain, including machine-learning engineers, AI product managers, MLOps (Machine Learning Operations) specialists, data annotators, prompt designers, model auditors , red-teamers and AI-ethics officers. The World Economic Forum ’s Future of Jobs Report 2025 projects 170 million new jobs and displacement of 92 million jobs globally by 2030—a net addition of 78 million , with AI and machine-learning specialists among the fastest-growing occupations. India already had approximately 4.2 lakh AI professionals in 2024; the domestic AI market was projected to reach USD 17 billion by 2027 , expanding demand for engineering as well as governance and business roles.

Expansion of Employment Beyond the IT Sector: AI employment is spreading into agriculture, healthcare, manufacturing, finance, education, logistics, climate services and public administration , reducing the identification of digital employment exclusively with software companies. E.g., CropIn provides AI-based agricultural intelligence, while Wadhwani AI’s cotton-pest advisory systems combine agronomy, machine vision and rural extension. Indian health-tech company Qure.ai uses AI-supported interpretation of X-rays and CT scans, enabling radiologists and frontline health workers to jointly screen diseases. Thus, AI creates interdisciplinary roles such as agri-data analyst, clinical-AI validator, climate-risk modeller and industrial-automation technician.

Geographical Diversification and Distributed Work: Cloud-based AI tools allow firms to separate employment from physical headquarters, encouraging remote work, distributed teams and talent utilisation in Tier-II and Tier-III cities . Employees in cities such as Coimbatore, Indore, Bhubaneswar, Jaipur and Kochi can participate in software testing, analytics, customer support and AI operations without migrating permanently to major metropolitan centres. Location-independent AI platforms also enable Indian specialists to provide translation, design, coding and consulting services to global clients, producing a geographically dispersed digital-services workforce .

Linguistic Democratisation of Digital Employment: Natural-language processing, speech recognition and machine translation reduce the English-language barrier that previously excluded many Indians from technology-enabled occupations. Voice-based AI can help artisans, farmers, delivery workers and micro-entrepreneurs access market information and digital services in their preferred language. This creates demand for linguists, dialect experts, voice-data collectors and local-language model evaluators , integrating humanities graduates and regional communities into the AI economy.

Greater Inclusion of Persons with Disabilities: AI-based assistive technologies can convert disability-related barriers into workplace accommodations. Speech-to-text supports employees with hearing impairment; text-to-speech and computer vision assist visually impaired workers; predictive text and cognitive assistants help persons with dyslexia or certain neurodivergent conditions. Since approximately 1.3 billion people—around 16% of the global population —experience significant disability, accessible AI can greatly widen the available talent pool.

Skills-Based Entry and Career Mobility: AI is weakening rigid degree-based occupational boundaries by allowing workers to demonstrate micro-credentials, portfolios and task-specific capabilities . A commerce graduate can move into business analytics, a mechanical technician into predictive maintenance, and a language graduate into conversational-AI testing after modular training. The WEF estimates that 39% of workers’ existing skills will be transformed or become outdated by 2030 , making reskilling, lifelong learning and skills-first hiring central to workforce mobility.

Democratisation of Entrepreneurship and Creative Work: Generative AI allows small teams and individuals to perform functions that earlier required separate departments, encouraging micro-enterprises, creator entrepreneurship and AI-enabled MSMEs . A rural enterprise can use AI for product design, regional-language advertising, demand forecasting and inventory management; independent creators can undertake editing, animation, translation and audience analytics. This produces “micro-multinationals” i.e., small firms capable of serving wider markets.

What are the Key AI-Related Developments Posing Risk to the Workforce?

Generative AI Automating Cognitive and White-Collar Tasks: Unlike earlier automation, which primarily replaced manual routines, Large Language Models (LLMs) can draft reports, write code, translate documents, process claims and handle customer queries. The IMF estimates that AI could affect nearly 40% of global employment and approximately 60% of jobs in advanced economies , potentially reducing labour demand and wages where substitution dominates. In India, customer support, manual software testing and routine coding are particularly vulnerable because they constitute major components of the labour-intensive outsourcing model.

Agentic AI and End-to-End Workflow Automation: The transition from assistive chatbots to autonomous AI agents allows software to plan, execute and monitor multi-step business processes with limited human intervention. AI agents can undertake invoice processing, recruitment screening, inventory management , compliance reporting and customer-service resolution, threatening entire workflows rather than isolated tasks. This produces workforce compression , where one AI-enabled employee supervises work previously undertaken by several junior employees.

Erosion of Entry-Level Jobs and the “Broken Career Ladder”: AI increasingly performs precisely those repetitive tasks through which young workers traditionally acquire professional experience—basic coding, documentation, market research and preliminary legal or financial analysis. A Stanford-linked study using payroll data found that employment among 22–25-year-olds declined by about 13% in the most AI-exposed occupations , while experienced workers remained comparatively protected. Reduced recruitment of trainees and junior analysts can create a “missing-middle problem” : firms may later lack experienced professionals because beginners were denied opportunities for workplace learning.

Rapid Skill Obsolescence and Mid-Career Displacement: Continuous improvement in AI models shortens the economic life of existing skills, creating a persistent reskilling treadmill . Workers proficient in legacy programming, manual testing, content production or traditional back-office processing may face displacement before affordable retraining becomes available. E.g., TCS announced the reduction of over 12,000 positions—about 2% of its workforce—in 2025 , citing skill mismatches amid an AI-led restructuring of India’s USD 283-billion outsourcing industry.

Skill-Biased Technological Change and Wage Polarisation: AI disproportionately rewards workers possessing advanced digital skills, proprietary data or managerial authority , while reducing the bargaining power of routine-task workers. Highly skilled professionals may receive an AI productivity premium , whereas clerical, support and middle-skill employees face wage stagnation or redundancy—producing a barbell labour market concentrated at high- and low-wage ends. Women may be disproportionately affected because they are overrepresented in clerical, administrative and customer-service occupations with high generative-AI exposure.

Algorithmic Management and Platform-Worker Precarity: AI is increasingly being used not only to perform work but also to assign, monitor, evaluate and discipline workers . Ride-hailing and delivery platforms use algorithms to determine task allocation, routes, incentives, ratings and account suspension, often without explaining the decision to workers. Such algorithmic control creates information asymmetry. Platforms know how workers are scored, while workers cannot meaningfully contest falling ratings or automated deactivation .

Bias in AI-Based Recruitment and Performance Evaluation: Resume-screening tools can indirectly discriminate through proxies such as address, career gaps, language style or educational institution, disadvantaging women, persons with disabilities and candidates from marginalised regions . Automated video interviews may inaccurately interpret accents, facial expressions or eye contact, creating an “automation bias” in which recruiters unquestioningly accept machine-generated scores. Because many models operate as black-box systems , rejected applicants may receive neither an intelligible reason nor an effective opportunity to appeal.

Workplace Surveillance, De-skilling and Psychosocial Stress: AI-enabled cameras, keystroke monitoring, biometric attendance, productivity scoring and sentiment analysis are creating the “quantified worker.” Excessive dependence on AI recommendations may cause cognitive deskilling , as doctors, engineers, teachers and administrators gradually lose independent diagnostic or decision-making abilities. OECD research covering more than 6,000 firms across six countries found algorithmic management sufficiently widespread to require stronger workplace governance.

Concentration of AI Ownership and Unequal Distribution of Productivity Gains: AI development depends upon expensive computing infrastructure, proprietary datasets, semiconductor capacity and foundation models , favouring a small number of corporations and countries. Firms owning AI systems can capture productivity gains while workers experience layoffs, wage pressure or intensified output targets—creating a capital–labour asymmetry . This creates an emerging AI value-chain hierarchy : a small technological elite owns models and intellectual property, whereas an invisible workforce performs poorly protected data and moderation labour.

Steps Taken by India to Ensure AI Readiness IndiaAI Mission as the National AI Architecture: Implemented by the IndiaAI Independent Business Division under MeitY , it operates through 7 pillars i.e., Compute Capacity, Innovation Centre, Datasets Platform, Application Development, FutureSkills, Startup Financing, and Safe & Trusted AI. The architecture links researchers, start-ups, academia, industry and government departments, advancing the objective of “Making AI in India and Making AI Work for India.”

Democratisation of High-End Compute Capacity: India is building a publicly supported AI compute commons so that access to GPUs does not remain restricted to large technology corporations. By February 2026, more than 38,000 GPUs had been onboarded under the IndiaAI common compute facility for use by start-ups, universities and public agencies at subsidised rates.

Development of Sovereign Foundation Models: India is supporting indigenous Large Language Models and multimodal models trained on Indian languages, datasets and socio-cultural contexts. The government had shortlisted 12 teams by February 2026 to develop indigenous foundation models, reducing dependence on foreign proprietary systems. Projects involving Sarvam AI , BharatGen and other Indian research consortia focus on multilingual text, speech and image capabilities.

Creation of AIKosha and India-Specific Data Infrastructure: Since reliable datasets are the raw material of AI, the government has established AIKosha —the IndiaAI Datasets Platform . It provides access to curated datasets, models, toolkits and use cases for researchers and start-ups within a secure environment.

Mission-Oriented Research through AI Centres of Excellence: Three AI Centres of Excellence in Healthcare, Agriculture and Sustainable Cities were approved with an allocation of ₹990 crore , led by consortia involving institutions such as AIIMS, IIT Delhi, IIT Ropar and IIT Kanpur. The Union Budget 2025–26 announced another Centre of Excellence in AI for Education with ₹500 crore , aimed at personalised learning, intelligent tutoring and administrative efficiency.

Building an AI-Ready Talent Pipeline: The IndiaAI FutureSkills pillar seeks to expand advanced AI education beyond a small group of elite institutions. By February 2026, government support covered more than 8,000 undergraduate students, 5,000 postgraduate students and 500 PhD scholars in AI-related fields. Data and AI Labs in smaller cities are intended to address the Tier-I–Tier-II skills divide, while FutureSkills PRIME , implemented by MeitY and NASSCOM, provides industry-aligned training in AI, big data, cloud computing and cybersecurity.

Supporting Start-ups and Public-Interest AI Applications: Through the IndiaAI Startup Financing and Application Development pillars , the government is addressing the early-stage funding and market-access barriers faced by Indian AI firms. By February 2026, 30 India-specific AI applications had been approved in areas such as healthcare, agriculture, climate resilience and governance. MeitY platforms—including Technology Incubation and Development of Entrepreneurs, SAMRIDH and MeitY Startup Hub —provide incubation, investment linkages and access to testing facilities.

Building a Safe, Trusted and Globally Connected AI Ecosystem: The Safe & Trusted AI pillar supports solutions for bias mitigation, explainability, privacy-enhancing technologies, deepfake detection, model auditing and ethical certification; responsible-AI tools are being hosted through AIKosha. The Digital Personal Data Protection Act, 2023 , MeitY’s responsible-AI work and the RBI’s FREE-AI framework for financial services seek to embed consent, human oversight and risk-based model governance. Internationally, India’s participation in the Global Partnership on AI , its G20 advocacy of “Responsible AI for All” and hosting of the India AI Impact Summit 2026 strengthen cooperation on interoperable standards and inclusive AI governance.

What Measures can India Adopt to Capitalise AI for Workforce Strengthening?

Establish a National AI–Labour Market Observatory: Create a real-time institution that combines PLFS , EPFO, ESIC , e-Shram , National Career Service and enterprise-vacancy data to identify occupations being augmented, transformed or displaced by AI. Publish six-monthly skills obsolescence alerts for vulnerable sectors such as BPO, banking operations, retail, logistics and media. The Observatory should translate forecasts into training seats, migration assistance and local employment planning, replacing reactive reskilling with anticipatory workforce governance .

Introduce Individual Learning Accounts and AI Transition Credits: Every worker should receive a portable Lifelong Learning Account , jointly financed by government, employers and sectoral skill councils. Introduce paid reskilling leave and larger transition credits for women returning to employment, informal workers and employees in high-exposure occupations. Payments to training institutions should be linked to employment, wage and retention outcomes , preventing certificate inflation and low-quality “AI course” proliferation.

Protect the Entry-Level Career Ladder through AI Apprenticeships: Companies receiving public contracts or technology incentives should maintain structured graduate, diploma and ITI apprenticeship pathways. Apprentices should learn to verify AI output, detect hallucinations, interpret models and exercise domain judgment, rather than merely operate software. A Human Capability Clause in incentive agreements could require firms to disclose how automation affects junior recruitment, mentoring and internal promotion.

Shift Incentives from Labour Replacement to Worker Augmentation: Tax benefits and production incentives should reward AI adoption only where it produces higher wages, safer work, reduced drudgery or improved worker productivity. Before deploying high-impact AI, large enterprises should undertake an Algorithmic Job-Impact Assessment covering displacement, deskilling, gender implications and retraining needs. An Augmentation Tax Credit could support cobots for hazardous manufacturing, AI diagnostic assistance for health workers and predictive-safety tools for miners.

Create AI Extension Services for MSMEs and Worker Cooperatives: Most MSMEs cannot employ data scientists or assess expensive proprietary systems; India should therefore establish cluster-based Shared AI Service Centres . Sector-specific open tools could be shared through common facility centres , preventing each small firm from purchasing separate infrastructure. Producer organisations and worker cooperatives should receive data-ownership and collective-licensing rights , ensuring that platforms do not appropriate their commercial knowledge without compensation.

Enact an Algorithmic Rights Charter for Workers: Employees and platform workers require enforceable rights when AI influences recruitment, wages, ratings, work allocation or termination. The Charter should guarantee prior notification, meaningful explanation, independent audit , human review and a right to appeal automated decisions. India can adapt the United Kingdom’s Algorithmic Transparency Recording Standard , which requires public bodies to disclose significant algorithmic tools, while extending similar principles to large private employers.

Build an AI Transition Security System: Since, conventional unemployment protection is inadequate for repeated occupational transitions; India needs portable, worker-centric transition insurance . Link e-Shram/UAN accounts with wage insurance, retraining stipends, career counselling and relocation assistance for workers displaced by verified technological restructuring. Establish a sectoral Automation Adjustment Fund , financed through employer contributions and a small share of productivity gains from large-scale AI deployment.

Conclusion

The future of Indian manufacturing will not be determined by whether factories adopt AI, but by how AI is designed, governed and shared. Technology must augment human capability, reduce dangerous and repetitive work and help workers move into safer, better-paid and judgement-intensive roles. India must therefore replace a narrow automation strategy with a human-centred industrial transformation built on skills, worker participation, interoperable data, MSME access, social protection and accountable algorithms. India’s competitive advantage cannot rest on cheap labour competing against intelligent machines; it must rest on an empowered workforce using intelligent machines more productively, safely and equitably.

Drishti Mains Question "India’s competitive advantage must rest on an empowered workforce using intelligent machines equitably." In this context, evaluate India's preparedness for skills-based technological shifts.

Frequently Asked Questions (FAQs) 1. What is the “deployment–impact gap” in AI?

It refers to the gap between adopting AI technologies and achieving actual productivity gains, which depend on data quality, workflow redesign, worker participation and skilling. 2. What does the WEF Future of Jobs Report 2025 project about AI and employment?

It projects 170 million jobs created and 92 million displaced globally by 2030, resulting in a net increase of 78 million jobs. 3. What are the seven pillars of IndiaAI Mission?

They are Compute Capacity, Innovation Centre, Datasets Platform, Application Development, FutureSkills, Startup Financing, and Safe & Trusted AI. 4. How can AI contribute to workforce diversification in India?

AI is creating new occupations, expanding employment across non-IT sectors, enabling distributed work, supporting regional languages, and improving inclusion of persons with disabilities. 5. What are the major workforce risks associated with AI?

Key risks include job displacement, entry-level job erosion, skill obsolescence, wage polarisation, algorithmic management, workplace surveillance and recruitment bias.