In offices across India, generative AI has stopped being a future concept and started becoming routine. Employees now use AI tools to draft emails, summarise meetings, translate between Indian languages, generate code snippets, create first drafts of marketing copy, and automate parts of customer support and back office operations. These changes are not experimental pilots anymore. They are embedded into everyday workflows.
As adoption accelerates, the question dominating conversations is no longer whether generative AI will change work. It is whether the change represents a threat to jobs, an opportunity for better work, or a deeper evolution of how employment itself is structured. The answer, according to data and early evidence, appears to be all three at once.
Global institutions have tried to quantify what is happening. The International Monetary Fund estimated in 2024 that around 40 percent of jobs worldwide could be affected by artificial intelligence. Importantly, affected does not mean eliminated. It includes roles where tasks are automated, reshaped, or augmented. The International Labour Organization has repeatedly emphasised that generative AI is more likely to transform tasks within jobs than destroy entire occupations, with clerical and administrative functions among the most exposed.
India’s situation adds another layer of complexity. A large informal workforce limits immediate exposure in some manual roles, while digitally intensive sectors such as IT services, BPOs, finance, media, and government facing services are seeing much faster adoption. At the same time, these very sectors are also where India has the strongest opportunity to build AI enabled services for global markets.
Ashwini Vaishnaw, India’s Minister for Electronics and IT, has publicly acknowledged this duality, saying that AI will create new jobs while also displacing some existing ones. The challenge, he has noted, lies in preparing the workforce for transition rather than resisting technology altogether.
Employer expectations reflect a similar tension. The World Economic Forum’s Future of Jobs Report projects that by 2027, about 83 million jobs could be eliminated globally while 69 million new roles may be created, resulting in short term churn. Looking further ahead to 2030, the same framework projects net job creation as new categories of work scale. These projections are based on employer surveys and depend heavily on assumptions about reskilling and policy support.
In India, generative AI is landing first where work is already digitised. In IT services, AI copilots assist developers with writing and testing code, drafting documentation, and resolving bugs. GitHub’s research shows that developers using AI coding tools can complete tasks significantly faster than those without them. For large Indian IT firms, this productivity gain has not yet translated into mass layoffs. Instead, teams are being restructured, with engineers expected to handle more complex integration, domain specific problem solving, and client advisory roles.
Roshni Nadar Malhotra, Chairperson of HCLTech, has described AI as a productivity amplifier that allows professionals to move away from repetitive execution and focus on higher value work. Her view reflects how many large employers currently position AI internally, as an assistant rather than a replacement.
Customer service offers another revealing case. A peer reviewed study by economists Erik Brynjolfsson, Danielle Li, and Lindsey Raymond found that generative AI tools increased productivity in call centres by about 14 percent, with the biggest gains among less experienced workers. AI helped standardise responses, surface relevant information faster, and improve service quality. At the same time, if fewer agents are needed to handle the same volume of queries, hiring patterns inevitably change.
Manufacturing experiences generative AI differently. Traditional automation has long reshaped factories, but generative AI’s role is more indirect. It supports design optimisation, maintenance diagnostics, reporting, and supply chain analysis rather than replacing physical labour outright. Industry studies suggest predictive maintenance powered by AI can reduce downtime by up to 30 percent and maintenance costs by up to 20 percent. This shifts demand toward supervisors and technicians who can work with data and systems rather than perform repetitive inspections.
Healthcare is another sector seeing task level change. Hospitals and diagnostic chains are piloting AI tools to assist with clinical documentation, coding, audits, and administrative workflows. These systems do not make medical decisions, but they reduce time spent on paperwork. The implication for jobs is not fewer doctors, but different allocation of time and skills within healthcare teams.
Education sits at a sensitive intersection. Generative AI can help teachers with lesson planning and assessments, but it also raises concerns around academic integrity. Universities and schools are revising evaluation methods to emphasise analytical and project based learning. The labour impact here is gradual, but it requires educators to adapt pedagogy rather than resist tools students are already using.
Creative industries perhaps feel the tension most acutely. Generative AI can produce first drafts of scripts, images, and marketing material quickly and cheaply. The Writers Guild of America strike in 2023 brought global attention to how creative labour seeks guardrails rather than outright bans. Writers negotiated protections ensuring AI would not replace credited human authors. Similar conversations are emerging in India’s advertising, media, and design sectors, where low end production work is most exposed while creative direction and intellectual property management grow in importance.
The gig economy adds another layer. NITI Aayog estimates that India’s gig workforce could reach over 20 million by the end of the decade. Generative AI can create micro work such as data labelling, content moderation, and evaluation tasks, but it can also intensify algorithmic management and income volatility. Here, AI neither clearly destroys nor clearly creates stable employment. It reshapes how work is organised and compensated.
Wages remain an unresolved question. Productivity gains do not automatically translate into higher pay. Kristalina Georgieva, Managing Director of the IMF, has warned that AI could widen income inequality if gains disproportionately benefit highly skilled workers and capital owners. Without policy intervention, productivity improvements may lead to job compression rather than job enrichment.
Reskilling therefore becomes the central variable. The World Economic Forum estimates that nearly half of all employees globally will need some form of reskilling by 2027. In India, public and private initiatives have expanded AI training programs, but scale and accessibility remain challenges. Training a clerical worker to use AI tools safely requires weeks, while preparing an engineer to govern and integrate AI systems may take months.
Christy Hoffman, General Secretary of UNI Global Union, has cautioned that AI is often framed as efficiency, but without worker protections it can result in doing more work with fewer people. Her position reflects labour concerns globally that productivity should not come at the cost of job security and dignity.
India’s policy framework attempts to balance innovation and protection. The Digital Personal Data Protection Act sets boundaries on data use. The IndiaAI Mission signals government investment in compute infrastructure, datasets, and skilling. MeitY advisories on AI use point toward accountability and risk mitigation rather than blanket restrictions. Each of these creates not only constraints but also new roles in compliance, governance, and auditing.
The debate between AI led development and pro human positioning often presents a false choice. Evidence suggests outcomes depend less on the technology itself and more on how institutions deploy it. If generative AI is used primarily to cut costs, displacement dominates. If it is used to expand services, improve quality, and invest in human capability, evolution becomes the dominant narrative.
For India, the stakes are particularly high. With a young workforce and ambitions to become a global AI hub, the country must navigate between speed of adoption and social stability. The period from 2023 to 2030 is likely to be marked by uneven transitions. Some roles will disappear. Others will be redesigned. Entirely new categories will emerge around data governance, AI evaluation, safety, and integration.
The job market impact of generative AI is therefore not a single story. It is a layered one, unfolding differently across sectors, skills, and regions. Threat, opportunity, and evolution are not competing outcomes. They are happening simultaneously.
The real question is not whether to support AI led development or pro human approaches. The question is how to design systems where both can coexist. The answer will shape not just jobs, but the future of work itself in India.
Disclaimer: All data points and statistics are attributed to published research studies and verified market research. All quotes are either sourced directly or attributed to public statements.