Agentic AI and Indian IT: From Fear to Forward-Integration

When Y Combinator partner Tom Blomfield posted on X (on 1 March) that "the entire Accenture workforce is about to be outperformed by a 24-year-old who learned Claude Code last Tuesday", he was doing what Silicon Valley does best: collapsing a complex structural question into a punchy provocation. 

The post sparked widespread debate about AI disrupting IT, consultancy, and other white-collar jobs. It was covered by multiple outlets in India (where Accenture and similar companies have massive workforces) feeding an already anxious conversation about AI's threat to India's $290 billion IT services sector. It deserves a serious response — not a defensive one, but a strategic one.

The Disruption Narrative Gets the Diagnosis Wrong

The fear driving headlines like Blomfield's rests on a simple substitution logic: if AI can do what IT workers do, IT workers become redundant. This logic is not entirely wrong — it correctly identifies that agentic AI compresses the value of standardized execution. Routine coding, testing, ticket resolution, and low-level integration work are structurally exposed. The labour arbitrage model that built Indian IT's first four decades is genuinely eroding.

But substitution logic breaks down when it encounters enterprise complexity. The real world of large enterprises — sprawling ERP systems, multi-country compliance requirements, fragmented legacy infrastructure, decades of accumulated technical debt — is not a clean environment where a capable AI agent can simply be pointed at a problem and trusted to solve it. 

The evidence bears this out. Beyond Key says that 88 percent of companies claim to have used AI, but most have not moved beyond pilot testing. Forrester expects 2026 to be a year of correction rather than acceleration, with meaningful enterprise-wide deployment still 3-5 years away, particularly in regulated and legacy-heavy sectors. Gartner notes that firms with strong platform engineering and product operating models deploy faster — a description that fits Indian IT's accumulated capability precisely. 

Nandan Nilekani has named this the "deployment gap" — the widening distance between what AI models can do and what enterprises can actually operationalize. That gap is not primarily technological. It is organisational: disconnected data ownership, unclear governance, misalignment between IT systems and business operations. Closing it requires exactly the kind of embedded, longitudinal, contextual knowledge that Indian IT companies have built over decades inside the world's most complex enterprises. A 24-year-old with Claude Code, however talented, cannot replicate thirty years of institutional trust.

Moving Up the Value Chain—and the Thought Chain

Recognising that the threat is overstated is not, however, a strategy. The more important question is what Indian IT should become, not merely what it still is. And here the sector faces a genuine fork in the road.

The conventional answer is to move up the value chain — from execution to orchestration, from writing code to governing AI workflows, from implementation to transformation. That answer is correct but incomplete. There is a deeper hierarchy at work, which I call the thought chain: the ladder of cognitive contribution to enterprise outcomes, running from execution through optimisation through orchestration to strategy. Indian IT companies have historically occupied the bottom rungs. Management consultancy firms — McKinsey, BCG, Bain, Kearney, etc — have owned the top.

What is striking is that these consultancy firms have recognised the vulnerability of owning strategy without owning delivery, and have spent the last decade backward integrating into IT implementation. McKinsey built QuantumBlack. BCG built BCG X. Deloitte and PwC expanded massively into technology execution. They understood that insight without implementation is losing its premium.

The mirror move — IT companies forward-integrating into strategy — is the logical and largely unmade response. Indian IT companies are uniquely positioned to make it, because they possess something no consulting firm can replicate: embedded, multi-decade knowledge of how enterprises actually function, not as observed from the outside through interviews and benchmarks, but as lived from the inside through system migrations, workflow transformations, and compliance implementations. That is a richer empirical base for strategic advice than any consulting methodology.

The need for this move is sharpening. According to a BusinessLine report (published on 8 March), a persistent disconnect exists between how enterprises measure AI success — model accuracy, tool adoption, localised efficiency — and what their boards actually want: revenue growth and margin impact. Bridging that gap requires someone who can translate operational AI capability into strategic business outcomes. 

That is not a delivery competency. It is a strategy competency. And the firms that build it will command premium value in a market currently served by consultancy firms which can advise but cannot deliver, and IT companies which can deliver but have not yet learned to advise.

Two Models of Expansion

A strategic pivot is already beginning, unevenly, across the sector. Two distinct models are emerging, shaped by each firm's structural position.

Conglomerate-backed companies are pursuing what might be called inward expansion — using the capital, assets, and industrial depth of their parent groups to own the physical infrastructure of AI delivery. TCS is the clearest example. Through its HyperVault subsidiary, in a joint venture with TPG, it is building a 1 gigawatt AI-ready data centre network — equivalent to India's entire data centre capacity just a year ago. Paired with investments in custom AI chiplets designed to run specific workloads 40 percent more efficiently than generic hardware, TCS is executing a transition from labour arbitrage to infrastructure arbitrage. The competitive moat here is physical and capital-intensive — difficult to replicate precisely because it requires the kind of cross-group resource mobilisation that only a conglomerate like Tata Group can enable.

Standalone companies, lacking that conglomerate backing, are pursuing outward expansion — partnering with established hardware and platform players to co-develop IP rather than building it unilaterally. Infosys's expanding strategic tie-up with Intel, centred on Topaz Fabric — a purpose-built agentic services suite integrating infrastructure, models, data, and workflows into a composable enterprise AI ecosystem — is the clearest example. Rather than owning silicon, Infosys is embedding its orchestration capability into Intel's compute stack, creating a jointly defensible platform offering. 

Neither of the above paths is inherently superior. Together they illustrate that the era of pure labour arbitrage is ending, and Indian IT's leading companies are searching, in different ways, for the asset-backed models that will replace it.

The Sociological Binding Constraint

What neither model has yet fully addressed is the deeper organisational transformation that the thought chain ambition requires. The technology exists. The market opportunity is visible and growing. The structural conditions — India's heterogeneous, rapidly industrializing domestic economy, the Microsoft and Google investment commitments, the Anthropic partnerships with Infosys and Cognizant, etc — are more favourable than they have ever been.

The binding constraint is sociological. Indian IT's investor base, board composition, and management culture were all formed during the outsourcing boom of the 1990s and 2000s. That era rewarded headcount scaling, margin discipline, and revenue predictability. It systematically penalised the long-horizon, high-uncertainty bets that product-building and strategic repositioning require. The mental models, incentive structures, and institutional habits of that era persist well beyond the conditions that produced them — a classic case of structural lag.

Wipro is perhaps the starkest illustration. It possessed genuine product-building DNA — among India's earliest PC manufacturers, with industrial and consumer businesses that could have served as captive deployment environments for technology products. It chose the safer path of IT services, and has spent four decades accumulating exactly the kind of missed opportunity that the current moment demands reversal.

Conclusion

The clock is running. The deployment gap that currently makes Indian IT seem indispensable will close over time, as AI systems become more self-configuring. At the same time, GCCs expanding across India are beginning to occupy the embedded, domain-rich positions that Indian IT has been slow to claim. The strategic window is real but not permanent.

Companies that move up the thought chain — from delivery to orchestration to strategy — and back that move with defensible platform assets, will survive and lead. Companies that optimize the returns from past decades' framework at the expense of the next decades', will find Blomfield's provocation, however glib today, becoming a prophecy by default. The choice, as always, belongs to the investors and boards who hold the mandate—and the willpower—to make it.