AI Reskilling Becomes the Key for the IT Industry: What Skills Are Needed in the AI Era?

The number 23,000 sounds small compared to Tata Consultancy Services' total employees, which reaches half a million. But that is the number of employees laid off by the Indian IT giant throughout fiscal year 2026. 

According to Business Standard—most of this was triggered by the accelerated AI adoption in the technology services industry. Together with the four other largest IT companies (Infosys, HCL Tech, Wipro, Tech Mahindra), the combined workforce contraction reached nearly 7,000 people, reversing the hiring trend from the previous year.

This situation has pushed AI reskilling to a new level—from just a topic discussed by HR departments to an urgent necessity across all industries. The question is simple: what AI skills are actually needed so that IT workers don't get crushed by this wave of automation?

Key Takeaways

• NITI Aayog projects India's technology jobs could drop by up to a quarter by 2031, with 60% of formal sector jobs vulnerable to automation.
• AI reskilling is most needed in high-level cognitive skills—analysis, AI workflow design, and agent supervision—not just basic technical abilities.
• Companies that are already scaling AI, such as TCS, Infosys, and Wipro, prove that rapid adoption actually opens up new roles: agent orchestrator, AI workflow designer, and forward-deployed engineer.

Why AI Reskilling is Urgent in the IT Industry

The debate about AI's impact on jobs is no longer a long-term prediction—the numbers are already visible. According to Business Standard, NITI Aayog (the Indian government's think tank) projects that technology jobs could drop from 7.5-8 million in 2023 to 6 million by 2031. 

Meanwhile, customer experience jobs are projected to fall from 2-2.5 million to 1.8 million in the same period. Around 60% of the country's formal sector jobs are considered vulnerable to automation.

Bhaskar Chakravorti, dean of global business at The Fletcher School, Tufts University, estimates that around 2 million net jobs will be lost in India over the next five years, after accounting for new jobs created—equivalent to $12 billion in lost wages per year. 

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An even sharper warning comes from Shiv Shivakumar, operating partner at Advent International, who estimates that 25-40% of jobs will be threatened by AI within the next 36 months. Interestingly, the speed of AI reskilling correlates directly with how much benefit organizations feel from AI. 

Data from an interview with Puneet Chandok, President of Microsoft India & South Asia, shows that TCS, Infosys, and Wipro have each scaled Microsoft 365 Copilot to 100,000 employees—a total of 300,000 seats in six months, with daily usage rates above 85% at Infosys and TCS.

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Illustration: Generated-AI image

The Most In-Demand AI Skills in the Workplace

It's not just about being able to use a chatbot AI—the skills companies are really looking for now are far more specific. Chandok mentions roles such as agent orchestrator, AI workflow designer, and forward deployed engineer as positions that didn't even exist internally at Microsoft 12 months ago.

Data from the Microsoft Work Trend Index, as quoted by Business Standard, also shows an interesting shift: 49% of jobs using Copilot are now categorized as cognitive work—analysis, critical thinking, problem-solving—not just administrative tasks. A total of 58% of users admit they can now do things they couldn't do 12 months earlier.

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Venkatesh Bala, former visiting lecturer at Columbia University and president of Bala Consulting, has a more specific view on the direction of this new demand for skills. 

He mentions that demand for IT services will actually grow strongly as companies convert individual-level AI productivity into effectiveness at the enterprise level—with new needs in cybersecurity, data engineering, AI governance, and AI agent supervision. Some of the most relevant skill areas to learn now:

• AI governance and agent supervision — ensuring AI agents work according to policy and don't deviate from business objectives.
• Data engineering and token infrastructure — managing data quality and AI model usage costs at the organizational level.
• Workflow design and agent orchestration — designing multi-agent workflows, not just using one AI tool in isolation.
• AI-based cybersecurity — both securing the AI system itself and using AI to strengthen cyber defenses.

Chandok himself emphasizes that the AI model is ultimately not the main differentiator—what matters more is the intelligence of the organization and the ability of workers to use the model effectively. This is why AI reskilling is more important than simply subscribing to the most expensive AI tools.

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Challenges and Realities of Reskilling: Not an Instant Solution

Unfortunately, AI reskilling is not as easy as taking an online course and being immediately ready for work. The "State of AI in Indian Enterprise" report from CIO & Leader, based on interviews with over 300 technology leaders from May-June 2026, found that more than 60% of companies are still in the pilot phase even after two to three years of investment. 

Only 12% see a significant measurable return on investment, while the remaining 57% have no measurable ROI at all or cannot even confirm it.

An Icrier survey of 651 IT companies supported by OpenAI in February 2026 did conclude that there is no strong evidence yet of mass layoffs due to AI. 

But there is an important caveat: 55% of companies confirmed a decline in entry-level job openings over the past two years, and 68% cited high reskilling costs with uncertain investment returns as the main obstacle.

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Shiv Shivakumar spoke candidly about the business logic behind this: it makes sense to reskill if a company is growing rapidly, but if AI is only driving 10-12% growth, the business case becomes weak. 

Chakravorti added that companies tend to train employees they are keeping, not those they will let go—and AI eats most jobs in the upper-middle-income bracket, making it difficult to retrain mid-career employees.

A Nomura study published this month actually offers a more balanced picture: India added 83,100 AI-related jobs, while those lost in AI roles numbered 32,921 from 2023 to August 2026. This means that new job creation is still larger than those lost—as long as workers are willing and able to transition to relevant skills.

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Conclusion

AI reskilling is no longer an option for IT workers—data from NITI Aayog, CIO & Leader, and Icrier all show real pressure on technology jobs, even though the scale is still debated. 

What is clear is that the most sought-after skills are not just basic technical abilities, but high-level cognitive capacity: managing AI agents, designing workflows, maintaining governance, and using AI as a thinking tool, not a replacement for thinking. 

Companies that have successfully scaled AI prove that new opportunities are indeed opening up, but only for those willing to invest seriously in skill transitions, not just follow the trend.

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FAQ

What is AI reskilling? 

AI reskilling is the process of retraining workers' skills so they can work alongside AI systems, including the ability to manage, supervise, and maximize the output of AI tools in the workplace.

What AI skills are most in demand in the IT industry right now? 

The most sought-after skills include AI agent supervision, multi-agent workflow design, AI governance, and data engineering—not just basic abilities to use a chatbot AI.

Does reskilling guarantee that workers won't be laid off? 

There is no absolute guarantee, but data shows that companies prefer to retrain employees they still consider relevant rather than those whose roles are completely replaced by automation.

How big is the impact of AI on IT jobs in India? 

NITI Aayog projects a drop in technology jobs of up to a quarter by 2031, although other studies like Nomura note that new job creation still outpaces losses as of August 2026.

How do I start learning AI for work? 

Start by understanding how AI agents and automation workflows work in your own field, then deepen your knowledge in specific areas like AI governance or data engineering according to industry needs.