Artificial Intelligence (AI) now performs over 40 per cent of software development tasks, yet the technology is far from being a standalone solution, often of fering incomplete or low-quality results that demand strict human intervention. While 20-40 per cent of workloads across technology organisations are being done by AI, the surge in automation has reinforced the need for skilled professionals with concerns regarding the quality of AI outputs. While machines are scaling productivity, they still lack the reliability required for autonomous decision-making.
These findings came to the fore in a new Nasscom–Indeed Future of Work Report 2025, titled ‘Work Reimagined: The Rise of Human–AI Collaboration’, that released recently and draws on insights from over 120 HR leaders across IT services, BPM, ER&D, Global Capability Centres (GCCs) and product companies, along with indepth interviews with senior industry leaders. The report highlights a decisive shift in how work will be structured over the next two years. Nearly 97 per cent of HR leaders said they expect the future of work by 2027 to be shaped by humans working alongside AI on a daily basis, rather than using it occasionally as a support tool. This marks a transition from AI as a productivity enhancer to an embedded collaborator influencing workflows, decisions and outcomes.
Rather than eliminating jobs, the report argues that AI is reshaping the nature of roles themselves. Higher-order activities such as scope definition, system architecture, and data model design are emerging as areas where human–AI collaboration is proving most effective. In contrast, routine and repeatable tasks—including boilerplate code generation and unit test creation—are expected to be increasingly automated over the next two to three years.
Rapid Adoption
The report also points to the rapid adoption of Agentic AI, with over 95 per cent of respondents saying they are already using or planning to use AI agents. Among them, more than 65 per cent believe AI agents outperform humans in tasks requiring high-volume data processing and speed. However, this shift is creating uneven pressure on job seekers. To stay relevant, individuals are being encouraged to focus on continuous learning, adaptability, and capability building, including proficiency in AI tools, critical evaluation of AI outputs, multidisciplinary skill development, and moving up the value chain—from coding to architecture-level thinking.
Risk to Reinvention
Despite strong momentum, employers cited several barriers to AI implementation. Security and privacy risks topped the list at 77 per cent, followed by integration with legacy systems (61 per cent), ethical and governance concerns (59 per cent), and resistance to change (58 per cent). Around 40 per cent of HR leaders also acknowledged a lack of workforce readiness for AI-era skills. In response, organisations are investing heavily in transformation. Seven in ten HR leaders are focusing on upskilling, while 79 per cent prioritise internal reskilling as their primary AI strategy, 83 per cent of HR leaders said they are actively redesigning jobs to shift humans toward judgment, creativity and accountability, while AI handles scale and speed. Speaking on the findings, Ketaki Karnik, head of research, Nasscom, said, “AI is no longer a future consideration for the technology industry and is already shaping how work gets done. The real opportunity now lies in preparing people to work effectively alongside AI. As AI adoption deepens, skilling and capability building will be central to ensuring that talent continues to move up the value chain and delivers meaningful outcomes for businesses.”
Fundamental Shift As AI changes task composition within roles, the report notes a fundamental shift in hiring practices 85 per cent of hiring managers reported an increase in skills-based hiring, while 98 per cent highlighted the growing demand for hybrid and multidisciplinary skills. For entry level talent, organisations are seeking job-ready candidates, with assessments increasingly focused on live projects, hackathons, case-based evaluations, portfolios, and measurable outcomes rather than academic credentials alone. For midand senior-level roles, hiring decisions are being driven by end-to-end ownership, decision-making under ambiguity, and demonstrated impact across past projects.
The report concludes that while fears of mass job displacement are overstated, the challenge lies in roles evolving faster than hiring frameworks and education systems can adapt. For institutions and learners alike, aligning skills, curricula and assessments with this Human + AI reality is emerging as a priority.