There’s an industry-wide rush to adopt artificial intelligence and automation, but the real battle is on skilling. It’s all about which programmes suit the workforce, how fast they must evolve, and which capabilities will remain indispensable even as algorithms grow more powerful.
At Tamil Nadu’s technology and innovation summit UmagineTN 2026, technology leaders, enterprise executives and founders debated how skilling must change in an AI-saturated economy.
At a panel discussion titled ‘Ahead of Algorithms: Winning in a Rapidly Digital World’, Cecil Sunder, director of cloud and AI platforms at Microsoft, argued that the very definition of skill has been permanently altered. Intelligence, he said, has become a commodity. With generative AI models now capable of producing code comparable to that of top-tier global programmers, traditional markers such as proficiency in C++ or Python no longer differentiate talent.
“The question is no longer whether you can code,” Sunder said. What matters instead is domain mastery and passion—whether in marketing, accounting, biotechnology or drug research. The foundations of these domains do not change, even as the tools do. In a world where knowledge is instantly accessible, he said, aspiration and depth of understanding are the only sustainable differentiators.
That shift from tool-centric skills to domain-led capability was echoed across the panel.
Rex Jesu Das, head of edge and industrial AI at LTIMindtree, described how his team built a digital twin platform for a manufacturing client, highlighting the uneven pace of transformation between digital-native firms and legacy industrial companies. Algorithms, he noted, are only one component of a much larger system that includes data pipelines, AI agents, factory design, and process reengineering. In that complexity, skilling cannot be reduced to learning AI models alone.
“Human-in-the-loop is here to stay,” Das affirmed, pushing back against fears of mass job displacement. Humans, he argued, provide the emotion, energy and contextual judgement that AI systems lack, making continuous reskilling essential rather than optional.
From a macro perspective, Devkant Aggarwal, regional head at IBM India, noted that algorithms already shape daily life invisibly, from consumption patterns to economic leadership. Countries such as the US, China and India, he said, are pulling ahead precisely because of how effectively they deploy algorithmic systems.
Yet even in an algorithm-led economy, Aggarwal stressed that human skills such as negotiation, relationship-building and problem-solving remain critical. These capabilities allow individuals and organisations to “ace” digital transformation rather than merely automate processes.
He gave the example of IBM’s Naan Mudhalvan programme, the result of collaboration with the Tamil Nadu Skill Development Corporation and Anna University. The initiative focuses on upskilling students in emerging technologies by enabling them to work on real-world problem statements through project-based learning.
The experience revealed constraints that technology alone could not solve—from language preferences to the importance of women mentors for female students. These barriers, Aggarwal said, required human intervention, not chatbots, even as technology acted as an enabler.
It’s the startups under investor pressure that feel the gap between technology’s promise and practical execution hit hardest.
Dinesh Arjun, co-founder and CEO of electric motorcycle maker Raptee.HV, said his 150-person, digitally native organisation—with an average employee age of 24—had never known traditional ways of working. While that brought speed and flexibility, it also created challenges in aligning tools and workflows across teams, especially with limited capital.
Unable to afford the expensive enterprise systems used by large original equipment manufacturers, Raptee.HV’s engineers built an internal stack covering everything from product lifecycle management and ERP to inventory and testing—without top-down direction. The result, Arjun said, was a company able to operate like a much larger manufacturer with a fraction of the resources, enabling both survival and innovation.
That experience shapes how he evaluates talent. Certifications and narrow tool expertise, Arjun warned, often produce candidates whose understanding is confined to specific modules—precisely the kind of work AI will increasingly automate. What his company values instead is agility: the ability to achieve outcomes even without sophisticated tools, an area where Indian talent can still create disproportionate value.
The discussion identified adaptability as the core skill of the future.
Sunder argued that asking which specific AI techniques to learn—such as retrieval-augmented generation (RAG), fine-tuning, model context protocols (MCP) or agent-to-agent (A2A) systems—makes limited sense in a landscape evolving faster than skilling programmes can keep up.
With no option to opt out of learning, he urged students to stay deeply engaged with emerging technologies, follow their rapid evolution, and use them to translate ideas into real-world outcomes rather than treating any single technique as an end in itself.
Even infrastructure constraints, such as GPU shortages and limited cloud capacity, are transitional challenges rather than structural barriers. Demand for AI is outstripping supply, Sunder said, but that imbalance only reinforces the need to accelerate learning and experimentation.
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