Skip to main content
Student studies AI code on a laptop, symbolizing the gap between skilling programs and AI advancements. [kdesign.co](https://

Editorial illustration for AI Skills Gap Widens: Aspiration and Depth Now Key to Student Success

AI Skills Gap Widens: Strategic Learning Becomes Critical

Skilling programs lag AI; students must prioritize aspiration and depth

Updated: 3 min read

The skilling programs built to future-proof careers are already relics. They’re obsolete by lunch. For anyone building a professional life, the only viable path forward is to stop trying to out-code the machine. Start cultivating what it lacks: human judgment.

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.

This is a brutal demand for a different rigor. Consider Rex Jesu Das at LTIMindtree: his factory-floor digital twin runs on data pipelines and process redesign. The human is the core logic.

Devkant Aggarwal of IBM sees nations winning through algorithms, but people win within them by mastering negotiation and relationship-building. His own Naan Mudhalvan program in Tamil Nadu hit walls that weren't technical—language preferences, the need for women mentors. The hard parts are stubbornly human.

So the advice isn’t to learn Python faster. It’s to learn a domain—manufacturing, medicine, logistics—so deeply you can tell the AI what to do. And when it’s wrong.

Aspiration fuels that depth. Without it, you’re just maintaining the pipeline for a model that will soon maintain itself.

Common Questions Answered

How are AI skills evolving beyond traditional technical training?

AI skills are shifting from tool-centric approaches to domain-led capabilities that emphasize deeper understanding and strategic thinking. Students now need to develop more than just technical proficiency, focusing instead on aspiration and genuine comprehension of AI technologies.

What makes aspiration and depth of understanding critical in the current AI skills landscape?

In an era of instantly accessible knowledge, aspiration and depth of understanding have become the primary differentiators for professionals. These qualities allow individuals to move beyond surface-level tool usage and develop more meaningful, strategic approaches to AI implementation.

How are digital-native firms and legacy industries experiencing different AI transformations?

The pace of AI transformation varies significantly between digital-native firms and legacy industrial companies, creating an uneven technological landscape. Examples like LTIMindtree's digital twin platform for manufacturing demonstrate how some companies are more adept at integrating advanced AI solutions than others.

LIVE03:21OpenAI's Miles Wang in Talks for USD 2B AI Drug Discovery Startup