Artificial intelligence (AI) is transforming productivity across industries, promising efficiency, speed, and lower operational costs. However, when AI adoption is primarily used to replace low-skilled labour, it creates hidden economic and social costs. In India—where a large share of employment is informal, routine, and low-skilled—AI-driven automation disproportionately impacts bottom-of-the-pyramid workers. This article illustrates how early AI adoption in India is accelerating job displacement among low-skilled workers, weakening labour mobility, consumption, and long-term economic stability.
What Are Bottom-of-the-Pyramid Jobs in India?
Bottom-of-the-pyramid jobs in India are concentrated in low-skilled, routine, and manual work, often within informal or semi-formal employment structures. These roles typically offer low wages, limited job security, weak social protection, and minimal pathways for skill progression.
This segment of the workforce is especially vulnerable to automation. The World Bank estimates that up to 69% of jobs in India are at risk of automation, with risk concentrated in repetitive and low-skilled occupations. At the same time, India’s gig and platform workforce—an important absorber of low-skilled labour—has expanded rapidly. According to NITI Aayog, gig workers numbered around 7.7 million in 2020–21 and are projected to reach 23.5 million by 2029–30.
While manufacturing is often seen as a solution for large-scale job creation, it has not absorbed labour at the required pace. Formal manufacturing employment rose to around 1.85 crore workers in FY 2022–23, but the sector’s overall employment share remains limited. As a result, a large proportion of India’s workforce remains clustered in low-productivity, high-risk roles with few buffers against AI-driven displacement.
Economic and Social Costs of AI-Driven Labour Substitution
AI-led automation delivers short-term efficiency gains for firms, but its broader costs—especially when applied to low-skilled labour—are often underestimated. Job displacement is the most immediate consequence. As AI systems take over routine tasks, low-skilled workers face layoffs, reduced hours, or wage compression. Many also experience skills obsolescence, as routine roles offer limited opportunities for reskilling into higher-value work.
Weak social protection further magnifies these risks. Informal and gig workers typically lack severance, unemployment insurance, or retraining support. Displaced workers often move into lower-paid and more precarious employment, increasing income inequality. Over time, job losses tend to concentrate in particular regions and industries—such as garments, assembly-line manufacturing, logistics, and retail—leading to pockets of acute local economic distress.
For organisations, aggressive automation can lead to long-term risks. Large-scale displacement reduces household income and consumption, weakening demand for goods and services. Research shows that layoffs negatively affect productivity, morale, and long-term growth, not just displaced workers.
Firms also risk losing tacit knowledge embedded in experienced workers when automation replaces labour without knowledge transfer. Additionally, AI adoption brings infrastructure and environmental costs. Data centres and digital systems require heavy investment and energy consumption, creating externalities that offset some efficiency gains.
Early Signs of AI-Driven Job Displacement in India
Evidence of AI’s uneven impact is already visible across key Indian industries, particularly those reliant on routine and rules-based tasks. India’s IT services sector has historically employed large numbers of entry and mid-level workers performing repetitive tasks such as software testing, application maintenance, basic coding, and data processing. With the rise of generative AI and robotic process automation, these functions are increasingly automated.
Major IT firms have announced significant layoffs in recent years, with analysts linking workforce reductions to AI-enabled automation in basic coding, testing, and customer support. Surveys also show that IT leads other sectors in advanced AI adoption, making low-complexity roles the earliest targets for substitution.
In manufacturing, especially labour-intensive sectors such as textiles and footwear, automation is reshaping shop floors without delivering large-scale job growth. In retail and frontline services, self-checkout kiosks, chatbots, automated warehouses, and emerging delivery technologies are already replacing entry-level roles. These developments indicate that displacement pressures are occurring well before AI adoption becomes fully mature, particularly for low-skilled and frontline workers.
Why AI Disproportionately Affects Low-Skilled Workers and the Gig Economy
The current wave of AI adoption targets tasks that are structured, repetitive, and rule-based—precisely the characteristics of low-skilled work. In contrast, jobs requiring creativity, judgement, and complex human interaction are more resistant to automation. Organisations often prioritise automating low-skilled roles to achieve faster returns on investment, especially in back-office, customer support, and frontline operations. In India, weak labour protections for informal and contract workers further lower the cost of replacement. India’s labour market thus faces a “double vulnerability”: a high concentration of automation-prone jobs combined with limited AI preparedness and reskilling capacity.
India’s rapidly expanding gig economy faces particularly high automation risk. Gig roles—such as delivery, micro-tasks, and platform-based services—are highly standardised and data-driven. AI already performs key gig functions, including route optimisation, automated customer communication, and performance monitoring. With India’s gig workforce projected to reach 23.5 million by 2029–30, the scale of potential displacement is substantial. As platforms increasingly automate task allocation and delivery, many gig workers may find their roles reduced or eliminated altogether.
Managing AI Adoption Without Deepening Inequality
AI adoption in India is not inherently harmful, but its current trajectory risks deepening inequality by disproportionately displacing bottom-of-the-pyramid workers. When automation focuses narrowly on cost reduction through labour substitution, it creates hidden costs—job losses, wage precarity, weakened consumption, and regional economic stress.
For employers, excessive reliance on AI can erode human capital and long-term demand. For workers, routine-heavy roles face escalating risk, making upskilling and transitions into human-centric work increasingly urgent. For policymakers, the priority is clear: AI-driven displacement is already underway and requires intentional deployment, stronger social protections, and sustained investment in skills and mobility. Without these measures, the promise of AI-led growth may come at the expense of inclusive and sustainable development.
