India AI Summit Flags Urgent Reset in Skills Model

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Artificial intelligence is forcing a rethink of how India educates, trains and employs its workforce, with business and academic leaders at the India AI Impact Summit 2026 warning that degrees and narrow technical skills are losing currency faster than institutions can adapt.

Across four high-impact sessions, from skilling and higher education to employability and leadership, speakers discussed with a shared concern: in the AI age, degrees may fade in importance, but human capability will not.

From Degrees to ‘Quality at Scale’

In the session on ‘AI and the Future of Skilling,’ M.S. Vijay Kumar, a professor affiliated with the Massachusetts Institute of Technology, set the tone by reframing the conversation.

“As futurists,” he said, “we do not predict the future, we help make preferred futures possible.”

Rather than focusing on AI as a standalone force, he placed it within a longer arc of educational change. The real challenge, he argued, is understanding what must remain invariant amid rapid innovation.

Working on a forthcoming book, titled ‘Invariance and Innovation,’ he is exploring a central question: What anchors education when everything else is changing?

He pointed to MIT’s 1999 decision to launch MIT OpenCourseWare, a bold experiment that made course materials freely available worldwide. At the time, MIT confronted a core dilemma: What is the true value of an MIT education?

The answer lay not in content alone, but in active learning, “minds-on, hands-on” engagement, and analytical rigor blended with real-world problem solving.

He said, “Education is both a contact sport and a team sport.”

Back then, scale meant documentation and distribution. Today, scale means something far more complex.

“Scale is not scalar, it’s a vector,” he said. It has magnitude and direction. Learners are no longer just 17-year-olds entering university. They are mid-career professionals, entrepreneurs, and workers navigating reinvention.

To address this shift, he said, MIT has launched a modular, AI-enabled learning platform designed to provide foundational AI courses followed by domain-specific applications. 

The ambition? 

To reach a billion learners.

The new model of scale, he argued, rests on three pillars:

• Learning science — understanding how people actually learn and forget
• AI-enabled personalization — formative assessment and guided pathways
• Ecosystems and partnerships — collaboration across academia and industry

Decoding the Hardest Skill

During the leadership talk on ‘Harnessing AI for the Future of Learning and Work,’ Sanjay Jain of Google offered a more grounded reflection.

“It’s not that people don’t want to learn new things,” he said. “But change doesn’t come naturally to us.”

For professors who have built decades of expertise, adopting AI tools can feel disruptive. A 25-year-old may experiment freely; a 50-year-old may hesitate. understandably.

Yet Jain has seen inspiring counterexamples. He cited a professor at IIT Tirupati who is using NotebookLM to teach research skills from the very first class. The opportunity, he suggested, lies in reframing AI not as a threat, but as relief.

“These tools can reduce the time spent on mundane tasks and give teachers more time to engage meaningfully with students.”

Ultimately, he distilled adaptation into two requirements:

• The willingness to learn.
• The discipline to do the harder work — answering the complex questions students now ask.

And those questions, he emphasized, are only getting tougher.

The Employability Shift

If the earlier sessions focused on education, ‘The Future of Employability in the Age of AI’ confronted the economic consequences head-on. Vineet Nayar of Sampark Foundation reframed AI through history.

The Industrial Age, he argued, created value not just through machines but through a management idea: break complex processes into smaller sub-processes. This “de-skilling” enabled mass employment. Education systems adapted to produce knowledge workers trained in narrow, task-specific sub-skills.

That model powered India’s IT boom, employing millions. Now AI is automating those very sub-skills. 

“The crisis emerges there,” he said.

While global technology investment is accelerating, automation is reshaping the kind of employability that matters. In the AI age, macroskills, creativity, problem-solving, and reimagination become the new currency.

“AI is not merely technology,” he emphasized. “It becomes powerful only when applied to meaningful use cases.”

Teaching children AI tools as a technical subject is insufficient. What they must learn is imagination.

At Sampark Foundation, a non profit working with state governments to improve primary education, students are sometimes given the beginning of a story and asked to complete it. The exercise is designed to build structured thinking and creativity alongside core subjects such as mathematics.

Nayar also issued a strategic warning about data sovereignty. As global large language models expand, Indian data is becoming fuel for foreign systems. Without domestic AI capability, India risks repeating what happened in the early software era: losing long-term competitive advantage.

“If we give away our data without building our own capabilities,” he cautioned, “we may celebrate short-term benefits but lose strategically.”

The choice before India, he argued, is stark: adapt education, encourage entrepreneurship, and build indigenous AI, or risk being left behind.

Smita Prakash of ANI brought the debate into the newsroom. She said, AI is a stress test for journalism.

Students are already using AI tools, sometimes quietly, even as parts of the education system resist recognizing AI as foundational literacy. In her view, AI learning should begin as early as middle school.

But she also described a growing problem: identical AI-generated resumes flooding inboxes. Candidates polish profiles using tools like OpenAI’s systems, yet lack the underlying skill when interviewed.

There is a deeper challenge. Budget constraints are pushing news organizations to rely heavily on AI-generated content rather than on-ground reporting.

“This is not just disruption,” she warned. “It is an existential challenge.”

She raised concerns about intellectual property as global tech firms scrape Indian media content without proportional compensation. Without policy safeguards, she warned, traditional journalism may struggle to survive.

The future of media, she suggested, may involve AI-generated anchors, personalized tone selection, and algorithmically curated narratives, raising profound questions about authenticity and sustainability.

Building AI Champions

Yet another session, ‘Empowering the Human Edge,’ shifted the lens to enterprise.

Aparna Ganesh of Tata Sons emphasized that AI transformation must begin at the top. “How do we design specialized programs for senior leadership so they truly understand the art of the possible?” she asked.

Leaders must anticipate disruption, not merely respond to it.

Within Tata, the focus is on building AI champions, business managers who constantly “reimagine” operations, value creation, and outcomes. Alongside leadership reimagination, organizations must continuously update technical talent.

But therein lies the challenge: the pace of AI evolution.

“By the time you complete one round of upskilling,” she noted, “the landscape has already shifted.”

The Bigger Question

Across sessions, the common theme that emerged is that AI is accelerating automation, compressing skill lifecycles, and blurring boundaries between disciplines.

But it is also exposing the fragility of systems built for another era.

A key takeaway from the summit was not technological, but philosophical: Degrees alone will not define the future, micro-skills alone will not sustain employment, and tool literacy alone will not guarantee relevance.

What will matter are macroskills: imagination, adaptability, judgment, and the ability to collaborate with machines without surrendering human agency.

The debate ultimately turned on alignment. Without coordination between education, enterprise and policy, AI risks widening gaps. With it, the technology could expand opportunity.