What Does Employability Mean When AI Does the Work?
At IndiaAI Impact Summit 2026, industry leaders argue that employability in the AI era depends less on coding and more on reimagining work itself.
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On Day 1 of the IndiaAI Impact Summit 2026, the conversation turned to a question that feels both urgent and unsettling: In an age where AI can write, code, analyze, and decide, what does it really mean to be employable? On the panel, industry leaders and academics grappled with how skills, credentials, and even career paths are being rewritten in real time.
The session, “The Future of Employability in the Age of AI,” part of the IndiaAI Impact series, brought together voices from business, technology, healthcare and media to unpack the anxieties—and opportunities—emerging from rapid automation.
Opening the conversation, Info Edge founder Sanjeev Bikhchandani urged the audience to view AI not as an existential threat but as a productivity engine. Recalling the resistance to computerisation in Indian banks during the 1980s, he argued that fears of job losses often accompany technological shifts but do not always materialise. “When new technology comes in, there are great fears of job loss. But very often productivity goes up,” he said, noting how AI tools in his own company are being used to serve previously unviable customer segments rather than replace employees.
For Bikhchandani, the message to young professionals was pragmatic: learn to use AI tools before they are used on you. “If you don’t do AI, AI will be done to you,” he remarked, urging students to set personal targets for mastering multiple platforms within months.
Sateesh Seetharamiah, EdgeVerve, framed AI as a “capability multiplier” rather than a job eliminator. Drawing on enterprise software development, he said AI has delivered 70–80 percent productivity gains within product teams. The near future, he suggested, may see agile teams composed of “three humans and five agents,” with humans defining problems and overseeing AI systems. “Human beings are not going anywhere. Jobs are not going anywhere. The nature of jobs will change,” he said, underscoring lifelong learnability as the most critical skill in an era of accelerating change.
Healthcare offered a contrasting lens. Dr. Anurag Mairal of Stanford University of Medicine argued that AI could be a net job creator in health systems, particularly in countries like India, where millions lack access to care. With 5 billion people globally lacking meaningful access to healthcare, AI-enabled diagnostics, care navigation, and digital health systems could create entirely new categories of employment. “We should flip the narrative,” he said, pointing to emerging roles such as care navigators for high-risk patients. Rather than catching up, he urged India to leapfrog in AI-enabled healthcare delivery.
Yet the optimism was tempered by warnings from media veteran Smita Prakash, who described AI as an existential threat to journalism. She noted a growing trend of applicants using generative AI to produce indistinguishable resumes, as well as newsrooms relying on AI-generated summaries instead of ground reporting. “The old order has broken down,” she said, warning that scraping of Indian media content by global AI firms without adequate compensation poses intellectual property risks. In a world of algorithmically generated news feeds, she cautioned, long-form journalism and on-the-ground reporting face shrinking attention and revenue.
Vineet Nayar, former CEO of HCL Technologies and founder of the Sampark Foundation, offered a structural analysis. Industrial-era management broke complex processes into sub-skills, creating mass employment through specialised, repeatable tasks. AI, he argued, is now automating precisely those sub-skills. The crisis, therefore, is not technology itself but the obsolescence of narrowly defined micro-skills. The future belongs to what he called “macro skills”—the ability to reimagine, solve problems, and design solutions that machines cannot independently conceive. Teaching children to code AI tools, he suggested, is less important than teaching them to think, question, and re-engineer systems.
Across sectors, a common thread emerged: automation will intensify, restructuring workforces in phases—from process automation to workforce reconfiguration and eventually business model transformation. The challenge is not whether jobs will disappear, but whether workers and institutions can pivot fast enough.
As the discussion closed, one theme resonated: India’s demographic dividend could become either a liability or a global advantage. With the right reskilling push and a focus on problem-solving over rote knowledge, the country could compete internationally for the next generation of AI-driven roles.
