Skilling programs lag AI; students must prioritize aspiration and depth
Why does the gap between corporate skilling programs and the pace of AI matter right now? While companies pour resources into short‑term certifications, students and developers sit in workshops hearing the same warning: the tools are evolving faster than the curricula. Here’s the thing—panelists at a recent business‑startup forum argued that merely ticking off a list of software competencies won’t cut it.
The discussion, hosted under the banner “Skilling Programmes Can’t Keep Up with AI, So What Can Students, Developers Do?” featured Rex Jesu Das, head of edge and industrial AI at LTIMindtree, among others. He and his fellow speakers repeatedly pointed to a shift from “tool‑centric” training toward deeper, domain‑led capability. As the conversation unfolded, a clear pattern emerged: when knowledge is a click away, what truly sets a professional apart is not the badge on the wall but the ambition to understand why the technology works and how it can be applied.
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, describe.
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.
The summit made clear that AI adoption is outpacing most training efforts. Yet, as the panel agreed, the real bottleneck lies in skilling, not in the technology itself. Rex Jesu Das of LTIMindtree warned that programs must evolve quickly if they are to stay relevant, and that the only lasting advantage will be “aspiration and depth of understanding.” That sentiment echoed throughout the discussion, where leaders stressed a move away from purely tool‑centric curricula toward domain‑led capability.
Short, intensive courses may fill immediate gaps, but longer‑term depth remains uncertain; it is not yet known which specific capabilities will stay indispensable as algorithms grow more powerful. Stakeholders left with a practical question: how to redesign curricula fast enough without sacrificing the depth that the panel deemed essential? The answer, according to the participants, will require both industry‑wide coordination and a willingness to prioritize depth over speed, a balance that remains to be proven in practice.
Further Reading
- Papers with Code - Latest NLP Research - Papers with Code
- Hugging Face Daily Papers - Hugging Face
- ArXiv CS.CL (Computation and Language) - ArXiv
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Common Questions Answered
Why do corporate skilling programs struggle to keep up with the rapid evolution of AI, according to the panel discussion?
The panel highlighted that many corporate skilling programs focus on short‑term certifications and tool‑centric curricula, which cannot match the speed at which AI tools are released and improved. As a result, students and developers quickly outpace the knowledge delivered in these programs, creating a widening skills gap.
What did Rex Jesu Das of LTIMindtree say about the role of aspiration and depth of understanding in AI‑driven workplaces?
Rex Jesu Das argued that in an era where information is instantly accessible, only aspiration and deep domain knowledge can provide a sustainable competitive edge. He emphasized that moving away from merely ticking off software competencies toward domain‑led capability is essential for lasting relevance.
How did the digital twin platform example illustrate the uneven pace of AI transformation between digital‑native firms and legacy industrial companies?
Das described his team’s digital twin platform for a manufacturing client, showing that digital‑native firms can rapidly adopt AI‑enabled solutions while legacy industrial companies lag behind due to slower skilling and integration processes. This contrast underscored the need for faster, domain‑focused training to bridge the gap.
According to the summit, what is identified as the real bottleneck to AI adoption: the technology itself or the skilling programs?
The summit concluded that the primary bottleneck is not the AI technology but the inadequacy of current skilling programs, which fail to evolve quickly enough. Leaders stressed that without updated, aspiration‑driven curricula, organizations will struggle to fully leverage AI advancements.