[This article is contributed by Priti Shetty, Chief People and Culture Officer, WeWork India]
The conversation around artificial intelligence and employment has largely focused on one question: will AI create more jobs than it displaces? For business and HR leaders, however, a more important question is emerging. What happens to the talent pipeline when the work through which people traditionally gained practical experience and built knowledge and skills begins to change? As organisations automate more routine work, they also risk changing the very pathways through which young professionals have historically built the experience needed to take on more complex roles.
India is already seeing strong demand for AI capabilities. According to our recent report with Redseer, AI and the Future of Workspace, 85% of firms have increased their AI and ML headcount. Yet only 18% of AI roles in 2025 were for professionals with 0 to 3 years of experience, compared with 28% for those with 4 to 6 years of experience and 25% for those with 7 to 10 years. Demand is growing, but organisations are increasingly looking for professionals who already have meaningful experience.
That creates a paradox. If organisations need more experienced talent, where will that experience come from when the traditional entry-level pathway is itself being reshaped by AI? Entry-level work has traditionally served two purposes. It helped organisations deliver business outcomes while giving employees an opportunity to learn. Routine assignments helped young professionals understand customers, processes and organisational dynamics. Working alongside experienced colleagues allowed them to observe how decisions were made, ask questions and gradually take on greater responsibility. AI is changing this model by automating some of the routine and execution-focused work that formed an important part of early-career experience.
This does not make early-career talent less relevant. It makes early-career development more intentional. If AI can complete repetitive tasks more efficiently, employees have an opportunity to move towards higher-value activities earlier in their careers – analysing information, solving problems, working with customers and understanding the business context behind decisions. The objective should not be to preserve every traditional entry-level task. It should be to preserve the learning and capability-building that those tasks enabled.
For decades, organisations could largely rely on experience accumulating naturally as employees progressed through their careers. AI is challenging that assumption. Experience will increasingly need to be created deliberately rather than acquired simply through tenure. That means strengthening mentorship, creating meaningful project opportunities, encouraging cross-functional exposure and designing early-career roles that give employees greater exposure to decision-making and real business problems.
It also changes what organisations should value in early-career talent. Technical skills will remain important, but adaptability, critical thinking, communication, curiosity and the ability to work effectively with technology will become increasingly valuable. As the shelf life of specific technical skills shortens, the ability to learn, unlearn and apply knowledge in new contexts will matter just as much as what an individual already knows. More importantly, building AI capability cannot be limited to hiring people into specialised AI roles. As AI becomes embedded across organisations, professionals across finance, HR, marketing, sales and operations will need to understand how to apply technology within their own domains. The future workforce will not simply need more AI specialists; it will need more people who can combine domain expertise with technology fluency.
At the same time, automation does not necessarily mean work becomes more individualised. Our research also found that 93% of firms report increased collaboration intensity as AI-generated work creates a greater need for human validation, alignment and exception handling. As AI takes on more execution, people increasingly need to challenge assumptions, interpret context and determine what action should follow. Human value, in many roles, will increasingly lie not simply in producing an output, but in applying judgement to it.
For early-career employees, these capabilities often develop through interaction with experienced colleagues – observing decisions, asking questions, receiving feedback and working through complex situations together. They are difficult to develop through formal training alone. Ironically, therefore, the more technology changes individual execution, the more important the human and social dimensions of learning may become.
This is where the role of HR becomes more strategic. Talent strategy cannot sit downstream of technology decisions. If AI changes the work employees perform, HR leaders need to be part of the conversation about how roles are redesigned, what employees need to learn and how career pathways evolve. The question is not simply which tasks can be automated, but what happens to the learning, exposure and experience of those tasks once created.
The changing nature of work also has implications for the workplace. If judgement, collaboration and knowledge-sharing become more important, organisations need environments that enable them. For early-career professionals, some of the most valuable learning happens informally – observing how a senior colleague approaches a difficult situation, participating in discussions beyond their immediate role or receiving feedback in real time. In this context, the workplace becomes part of an organisation’s learning architecture, supporting not just productivity but capability development.
The AI talent paradox is ultimately not about the disappearance of employment. It is about the transformation of the pathways through which people enter and progress within the workforce. Organisations will need to become as deliberate about creating experience as they are about deploying technology. AI can automate tasks and create capacity for employees to focus on more meaningful and complex work. Whether that translates into stronger talent development will depend on how organisations redesign jobs, learning and career progression. The traditional entry-level pathway may be changing, but the need to build talent is not. The organisations that recognise this early will be better positioned to turn AI adoption into sustainable workforce capability.