AI risks creating a gap in India's professional career ladder

The task vs. job distinction

A new white paper from the IIT Madras Centre for Responsible AI (CeRAI) explores how artificial intelligence is reshaping the Indian work landscape. The researchers argue that it is important to distinguish between individual tasks, specific jobs, and broader occupations. A system that can perform a single task, such as reading a medical scan, does not automatically make an entire profession redundant.

The paper identifies two primary patterns of AI integration in the workplace. In the first pattern, AI handles the majority of routine tasks while humans are left to validate the output or manage exceptions. In the second pattern, humans remain responsible for the core work, but they use AI as a tool to increase their personal productivity.

The impact of these patterns depends on several variables, including the specific industry, the rules governing the work, and the cost of using AI. For example, a customer service agent might no longer handle password resets, but they will still manage callers with complex problems and short tempers. As the report notes, most jobs are made of tasks, and only some of those tasks can be performed by AI.

Impact on the service economy

India's cost-sensitive service economy is already seeing the first pattern of AI adoption. Routine processes like manufacturing quality checks, invoice processing, and GST bookkeeping are being automated using relatively inexpensive, task-specific systems. These applications do not necessarily require the most advanced AI models available today.

For businesses operating on thin profit margins, the decision to adopt AI often comes down to a simple cost comparison. If the cost of an automated system is lower than the cost of human labor, the business is likely to move forward with the technology. This makes automation particularly attractive for high-volume, repetitive tasks that do not require complex human intervention.

However, the paper emphasizes that the distribution of these changes is as significant as the technology itself. While some tasks vanish, new roles are being created to support the new infrastructure. These include positions such as AI trainers, conversation designers, robot operators, and escalation specialists. The challenge lies in ensuring that the workers displaced by automation are the same ones who find these new opportunities.

The vanishing career ladder

The most significant concern raised by the IIT Madras report is the vulnerability of entry-level workers. Many junior roles consist of repetitive tasks that serve as essential training grounds for more experienced professionals. When AI handles these basic tasks, it removes the first steps of the career ladder that many people use to learn a profession.

In software engineering, for instance, AI coding assistants can generate routine code, while senior engineers focus on architecture and complex debugging. Similarly, junior lawyers might spend less time on document review if AI handles the research, even though senior lawyers still manage the litigation strategy. This creates a potential gap where the basic work used to build experience is no longer available to newcomers.

If organizations automate the foundational work, they must develop new methods to cultivate the judgment required for senior roles. The paper suggests that entry-level roles might shift toward checking AI outputs, resolving escalated cases, or evaluating system performance. The goal is to ensure that productivity gains do not come at the cost of an entire generation losing their first opportunity to learn a trade.

Policy and research needs

The researchers acknowledge that their current taxonomy is a structured hypothesis rather than a settled finding. Much of the evidence currently comes from practitioners and vendors, and India currently lacks rigorous research on specific skill bundles. To address this, the paper calls for more India-specific labor market research to understand how these shifts are actually playing out.

The report suggests three specific prescriptions for moving forward. First, it encourages the use of affordable, narrow AI for micro, small, and medium enterprises (MSMEs). Second, it calls for funding dedicated to labor research. Third, it recommends building structured transition pathways and liability rules before AI adoption accelerates further across the country.

Training for the workforce must be linked to realistic alternative roles rather than abstract goals. The paper stresses the need for clarity on who pays for reskilling, how long the transition will take, and whether workers can maintain their earnings during the process. Ultimately, the focus must be on how to maintain a pipeline of experienced professionals in a world where machines do the junior work.