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TCS claims to have trained over 550,000 employees in basic digital skills and 100,000 in advanced capabilities. Yet the company still eliminated 19,755 positions citing “skill mismatch” as workers cannot transition to AI-heavy technical roles. The disconnect between stated reskilling investments and actual workforce retention reveals a hidden crisis: training programs designed for incremental skill evolution prove inadequate when technological transformation occurs at revolutionary pace.
CEO K. Krithivasan’s explanation cuts to the heart of the problem. “Some people find it difficult to transition to tech heavy roles,” he told media, acknowledging that not all employees possess capacity to make the leap regardless of training availability. This admission challenges the narrative that reskilling programs can universally prevent displacement. The skills demanded by AI-centric work—advanced mathematics, machine learning algorithms, neural network architecture, large language model optimization—require foundational competencies that corporate training programs cannot easily impart.
According to Storyboard18’s reporting on union responses to TCS layoffs, the Nascent Information Technology Employees Senate (NITES) accused TCS of shirking responsibility by framing layoffs as skill deficiencies rather than acknowledging corporate failures. “This is not restructuring. This is a mass sacking dressed in corporate jargon,” NITES stated, arguing the decision jeopardizes thousands of employees emotionally, psychologically, and financially.
The timeline disparity compounds the crisis. AI capabilities evolve monthly while traditional corporate training programs operate on annual cycles. When TCS deploys new AI-powered services to clients, the learning curve for existing employees often exceeds project timelines. Companies face binary choices: hire externally for AI expertise (abandoning incumbent workers) or lose contracts to competitors with AI-ready talent pools.
Chief HR Officer Sudeep Kunnumul’s explanation reveals additional constraints. TCS now requires employees to maintain minimum 225 billable days annually—a policy change that intensifies pressure on workers whose skills no longer align with client demands.
Understanding India’s Largest IT Company Admits Its Workers Can’t Keep Up: Why TCS’s 20,000 Layoffs Signal the End of Traditional Outsourcing provides crucial context for how reskilling failures manifest in workforce displacement.
The broader TWITCH pattern illustrates industry-wide reskilling failures. Wipro, Infosys, Tech Mahindra, Cognizant, and HCLTech all face similar challenges matching workforce capabilities to client requirements.
As Tech.co’s analysis of Accenture’s layoffs demonstrates, even Western consulting firms face identical reskilling challenges. Accenture eliminated over 11,000 positions despite training initiatives, suggesting the problem extends beyond Indian IT into fundamental questions about workforce adaptability during periods of accelerating technological change.
Examining Why American Corporations Are Cutting IT Outsourcing Contracts as AI Replaces Offshore Labor reveals how client-side AI adoption creates urgency that overwhelms traditional training timelines.
For affected workers, the crisis proves particularly acute for mid-career professionals. Entry-level employees possess academic foundations closer to AI requirements. Senior executives shape strategy rather than execute technical work. Mid-level workers—those with 10-20 years of experience in traditional IT services—face worst positioning: too experienced for entry-level AI roles, too technically focused for strategic positions.
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Works Cited
“TCS misses labour meeting on layoffs, defers onboarding ‘temporarily’, alleges NITES.” Storyboard18, 1 Aug. 2025, www.storyboard18.com/how-it-works/tcs-misses-labour-meeting-on-layoffs-defers-onboarding-temporarily-alleges-nites-78071.htm. Accessed 14 Nov. 2025.
“Accenture Makes Room for AI With Thousands of Layoffs.” Tech.co, 27 Sept. 2025, tech.co/news/accenture-layoffs-ai-pivot. Accessed 14 Nov. 2025.
