AI Tests India’s Tech Model and Policy Vision

A quiet anxiety has long shadowed India’s technology corridors, though it has mostly been obscured by narratives of boundless opportunity and perpetual reinvention. Multiple surveys have quantified this unease. Microsoft’s Work Trend Index 2023 found that nearly three‑quarters of Indian workers feared AI could threaten their roles. The government’s Economic Survey for 2024–25 echoed similar concerns, acknowledging that the rapid acceleration of AI has intensified apprehensions about the labour market’s future. 

Yet, in public discourse, sectoral leaders and industry bodies have largely preferred a rhetoric of inclusive progress and digital empowerment, seldom addressing the disruptive force AI might truly represent.

That silence has been finally broken, and tellingly not by design. Tata Consultancy Services, India’s largest IT services exporter and an emblem of the sector’s global ascendancy, has announced plans to lay off approximately 12,000 employees worldwide or roughly 2% of its workforce. This follows two other cost‑containment measures within the same quarter: deferred wage hikes in April and new restrictions on non‑billable periods announced in June. 

Surely, it will not be easy for the TCS CEO to manage large‑scale job reductions within India, where political scrutiny and policy interventions are likely to arrive at his office in short order. Yet he is fulfilling a core obligation of any steward of a publicly-listed enterprise of such systemic significance: to preserve shareholder value while seeking, as far as possible, to mitigate the human cost of those whose roles have become structurally redundant. In essence, confronting legacy inefficiencies and releasing roles misaligned with strategic relevance is an act of governance that many across this sector, and beyond, may increasingly be required to embrace.

The company has positioned these layoffs as a function of skill mismatches and redeployment challenges, explicitly distancing them from AI‑driven automation. Yet, to view these developments purely through the prism of workforce alignment would be to overlook the deeper systemic tremors at play.

For over three decades, India’s IT and IT‑enabled services industry has underpinned national aspirations of middle‑class mobility and economic modernisation. Employing millions directly and many more through allied services, it has been a consistent contributor to foreign exchange reserves, a favourite of domestic capital markets, and among the few sectors where Indian firms earned global brand recognition. Its corporate governance standards too have often exceeded domestic norms, shaped by exposure to stringent international compliance regimes and multinational clients.

Yet the architecture of this success has rested predominantly on labour‑arbitrage and scale. Indian firms could offer high‑quality, English‑speaking engineering talent at a fraction of the cost prevalent in developed markets. But generative AI now threatens to compress these value chains. It can replicate or accelerate tasks once delegated to large offshore teams — from coding and testing to documentation and routine project management. As global clients pivot from staff‑augmentation contracts to AI‑enabled co‑creation models and outcome‑linked engagements, the sector’s cost‑efficiency narrative risks obsolescence. The very technological advances once championed as force multipliers now question the relevance of the business architecture itself.

It is telling that industry associations and sector leaders have remained hesitant to articulate this structural vulnerability. Their PR machinery and regularly churned research reports to present a cosy future opportunities still centre on reskilling, digital transformation and the promise of AI, as well as other emerging technologies, as an enabler, rarely confronting its role as a displacer. Meanwhile, the stagnation of entry‑level salaries over nearly two decades and persistent concerns around graduate employability underscore deeper frictions between academic output and industry demand. The conversation about how AI compounds these inequalities and skills gaps has yet to receive the candour it warrants.

Compounding the challenge is the limited appetite within Indian industry for foundational research and development. The sector’s R&D intensity remains markedly low by international standards, reflecting a preference for near‑term service delivery over deep intellectual property creation. Calls to industry and academia to bridge skilling gaps have become ritualistic, repeated each year with limited structural change. Indian graduates continue to emerge from universities misaligned with evolving technological demands, as much as any other functional knowledge. The ecosystem often conflates short training interventions with true capability building, leaving it fragile in the face of technological shocks.

Yet these structural weaknesses are compounded by a deeper national failing that has persisted for decades: India’s chronically misaligned and outdated education system. Successive reforms have tinkered at the margins, but the core architecture — rote learning, exam‑centred evaluation and insufficient emphasis on analytical and design thinking — remains largely unchanged. As global value chains move from low‑cost assembly to knowledge‑intensive production, and as technology creation demands interdisciplinary depth, India’s graduates often arrive with degrees but not capabilities. This educational inertia now constrains the country’s ability to compete not only in the higher‑value segments of technology services, but also in advanced manufacturing, deep tech and the emerging domains of AI, quantum computing and biotechnology. What was once an internal developmental challenge has become an external competitive liability.

The implications ripple far beyond corporate balance sheets. TCS is part of the Tata Group, one of India’s largest and most systemically significant conglomerates. Its consistent profitability and cash flows have historically supported group‑wide investments and strategic pivots. Any volatility in TCS’s earnings trajectory or talent strategy, therefore, has consequences that extend into the broader imagery and capital allocation of the group itself. The optics of large‑scale workforce reductions at such a flagship employer also influence public sentiment and investor confidence in the wider economy.

If sentiment around the IT and ITES sector turns markedly negative, the effects may not remain confined to stock valuations alone. If confidence in what has long been India’s most dependable engine of white‑collar employment erodes, it could temper purchase sentiment across housing, automobiles, consumer durables and discretionary services. In a domestic economy where middle‑class optimism fuels demand, even the perception of prolonged uncertainty in this flagship sector could cast a longer shadow on broader consumption and investment appetite.

Globally, peer economies have begun to respond with deliberate statecraft and industrial strategy. The United States has channelled billions into AI and semiconductor ecosystems through initiatives like the CHIPS and Science Act. The European Union has advanced the AI Act to establish guardrails, while simultaneously investing in AI research clusters. China has pursued a state‑led AI strategy that seeks both technological sovereignty and global leadership. Against this backdrop, India’s approach, heavily reliant on private enterprise to absorb disruption and fund innovation appears reactive rather than strategic.

Beyond employment metrics, AI also tests the governance frameworks that once distinguished Indian IT. Legacy models built to ensure data security, client confidentiality and regulatory compliance are now insufficient for algorithmic accountability, explainability and data localisation. Firms must pivot from measuring human effort to governing AI‑driven decisions that directly shape commercial and ethical outcomes. This transition demands new oversight models, skill sets in leadership and rethinking the fiduciary responsibilities of boards.

The senior management cohort within the sector illustrates another under‑examined vulnerability. Many who achieved professional success in previous technology cycles have not reinvested in adapting their skills for an AI‑augmented future. In an industry where the half‑life of expertise shortens each year, the reluctance or inability to reskill at the top layers constrains organisational agility and deepens the risk of structural inertia.

These developments show a harsher reality: the model of competing through abundant, cost‑effective human capital, once dubbed the “body shoppers” or “cyber‑coolie” paradigm is encountering fundamental limits. For decades, the strategy of scaling human effort delivered foreign exchange, urban employment and political legitimacy. AI compresses these pathways, demanding a pivot to productisation, proprietary platforms and innovation‑driven competitiveness.

This is not to diminish AI’s transformative promise. But the industry must move beyond placatory narratives of frictionless transformation to confront the uneven and often painful redistribution of work, value and opportunity that AI entails. Avoiding this conversation risks eroding trust among employees, investors, policymakers and society at large.

TCS’s decision may be framed as a tactical realignment. Yet, in substance, it is a sentinel event that signals disruption has moved from forecast to fact. For policymakers, regulators and strategic thinkers, the imperative is clear. 

AI cannot be treated as a peripheral enabler of an unchanged model. It must be seen as a structural force demanding new institutions, new governance frameworks and sustained investment in human and intellectual capital. Transformation is not merely what AI enables. It is what it dismantles, what it demands to be rebuilt and how societies choose to navigate the turbulence it brings.