AI is growing rapidly in India, but are the workers standing strong?

 The artificial intelligence (AI) revolution, characterised by machine learning algorithms, automation, and data-driven decision-making, is surging across the nation. From IT hubs in Bengaluru and Hyderabad to manufacturing floors in Pune and service centers in Gurugram, AI promises unprecedented efficiency, innovation, and global competitiveness. The government’s “AI for All” strategy, a booming startup ecosystem, and significant investments from global tech giants paint a picture of a nation leaping into a tech-powered future. Yet, beneath this shiny narrative lies a more complex and urgent question: as AI grows rapidly, are India’s workers—its vast and diverse human capital—standing strong?

The evidence suggests the ground is shifting, and many workers are struggling to keep their footing. The challenge is multifaceted, rooted in the structure of India’s labour market itself. The nation’s workforce is bifurcated: a small, highly skilled elite and a massive base of semi-skilled or unskilled workers. For the elite—AI researchers, data scientists, and engineers in top-tier firms—the boom is a golden era. They are in global demand, command soaring salaries, and are the architects of this new world. Their strength is undeniable. However, for the vast majority, the picture is less certain. AI’s impact is being felt through two primary channels: automation of routine tasks and the creation of a demanding skills gap. 1. The Automation Anxiety: India has long been the “back office of the world,” employing millions in IT services, business process outsourcing (BPO), and support roles. These jobs, often involving data entry, basic code testing, transaction processing, and customer service, are precisely the tasks AI and robotic process automation (RPA) are adept at replacing. A 2023 report by the World Bank indicated that automation could affect a significant portion of routine cognitive jobs in India’s services sector. While total job apocalypse predictions are often overstated, the reality is a gradual but steady erosion of entry-level IT/ITES positions, which have been a traditional ladder for upward mobility for India’s middle class. Similarly, in manufacturing, collaborative robots and AI-driven quality control are changing the nature of factory work, demanding fewer but more technically adept workers. 2. The Chasm of the Skills Gap: The new jobs AI creates—in data annotation, AI model maintenance, cybersecurity for AI systems, and AI-augmented design—require a skill set that the current education and training systems are struggling to impart at scale. There is a critical mismatch: millions of graduates possess theoretical knowledge but lack the applied, practical skills in machine learning operations (MLOps), data literacy, or even advanced digital fluency. This gap leaves a large pool of workers “structurally unemployed”—present in the workforce but not equipped for its demands. Upskilling initiatives, while growing, are fragmented and often inaccessible to those outside major cities or premier institutions. Where Workers Are Finding Strength: Resilience and Adaptation Despite these tectonic shifts, to say Indian workers are merely passive victims would be a profound misconception. Their strength is manifesting not in static resistance, but in remarkable adaptation and resilience, visible in several key trends:

1. The Gig and Platform Economy Surge: Faced with uncertain traditional employment, millions are turning to platform work. From Zomato delivery riders and Urban Company beauticians to Upwork freelancers doing digital marketing, the gig economy is providing a crucial shock absorber. It offers flexibility and immediate income, though often at the cost of job security, benefits, and labour protections. The strength here is entrepreneurial—workers are leveraging digital platforms to create their own micro-enterprises. 2. Self-Upskilling and the Online Learning Revolution: Indian professionals are among the world’s most prolific consumers of online learning. Platforms like Coursera, Udemy, and homegrown NPTEL see massive enrollment from Indian learners seeking courses in data science, programming, and AI fundamentals. This represents a powerful, self-driven strength—a collective hunger to adapt and remain relevant. Workers are investing their own time and resources to bridge the skills gap, often while employed. 3. Strategic Integration, Not Replacement, in Key Sectors: In sectors like healthcare, agriculture, and education, AI is often less about replacing workers and more about augmenting their capabilities. For example, a radiologist using an AI tool for faster TB detection in X-rays becomes more productive, not redundant. Similarly, farmers using AI-driven advisories on soil health or irrigation can improve yields. Here, the worker’s strength is enhanced by the tool, suggesting a collaborative future if the transition is managed well. 4. Unionisation and Advocacy in New Forms: While traditional union power has waned, new forms of collective advocacy are emerging. The All India Gig Workers’ Union is fighting for better pay and conditions for app-based workers. Tech professionals are increasingly vocal about ethical AI development and the societal impacts of their work. This evolving consciousness is a nascent but vital form of strength, pushing the discourse beyond pure efficiency to include worker welfare. The Crucial Supports That Are Missing For this inherent resilience of Indian workers to translate into genuine, widespread strength, systemic support is critically lacking. The current landscape is one of individual struggle against systemic odds. Policy Lag: Labour laws, crafted for a different era, are ill-suited for the platform and AI-driven economy. Social security, minimum wage guarantees, and benefits remain largely tied to formal employment, leaving gig and contract workers vulnerable. Education System Inertia: The formal education system—from schools to many universities—is too slow to reform curricula. There is an urgent need to integrate critical thinking, data literacy, and computational skills from an early age, moving beyond rote learning. Corporate Responsibility Gap: While large corporations invest in AI, their investment in reskilling their own existing workforce often lags. A more stakeholder-centric approach, where profits from AI efficiency are partially reinvested in employee transitions, is needed. Digital Divide: The AI opportunity is geographically concentrated. Workers in smaller towns and rural areas, women returning to the workforce, and older employees face higher barriers to accessing upskilling and new opportunities, risking a deepening of inequality. Conclusion: Standing, but on Shifting Sands Indian workers are standing, but often on ground made uncertain by rapid technological change. Their strength is evident in their agility, entrepreneurial hustle, and relentless self-improvement. They are not a collapsing wall but a river, finding new paths as old ones block. However, standing strong—with security, dignity, and confidence in the future—requires a foundation that is currently being eroded. True strength will not come from workers adapting alone, but from a societal compact that matches their resilience with robust support. This demands proactive, forward-looking policy (like universal social security and future-skills mandates), radical educational reform, and a corporate culture that views its workforce not as a cost to be optimised by AI, but as the ultimate asset to be invested in. The AI race in India is not just about building the smartest algorithms; it is about building the smartest, most inclusive transition. The workers are showing up with remarkable adaptability. The question now is whether India’s institutions will build the ground firm enough for them to stand truly strong. The answer will determine whether the AI revolution becomes a tide that lifts all boats or a wave that washes away the vulnerable.