Agentic AI and the Future of Indian IT: Disruption or Recalibration?

I. Disruption Narrative vs Strategic Recalibration 

The AI Impact Summit 2026 in New Delhi unfolded under the long shadow of disruption. Weeks earlier, Anthropic’s release of its agentic system, Claude Cowork, intensified global anxiety about autonomous AI executing complex workflows — coding, analytics, integration, compliance tasks — with minimal human input. Commentators projected that up to 40% of knowledge work in outsourcing-heavy economies could be automated over time. For India’s $280 billion IT services sector, the implication was blunt: if execution is automated, the intermediary collapses.

This “Anthropic-triggered” narrative was amplified precisely because agentic AI differs from earlier automation waves. These systems do not merely assist; they decompose problems, call APIs, iterate against databases, and complete end-to-end workflows. The threat is not incremental efficiency — it is value-chain compression.

Yet at the Summit, India’s political and industry leaders have articulated a markedly different view. Prime Minister Narendra Modi framed AI as a transformer rather than a destroyer of the IT sector. Leaders from Infosys, Wipro, and TCS emphasized orchestration, integration, and contextual deployment as growth vectors. 

More importantly, the announcements in and around the Summit were structural signals: Anthropic partnering with Infosys and Cognizant; OpenAI partnering with TCS for enterprise deployment, AI-optimized data infrastructure, and industry-specific services.

This divergence is not "optimism versus pessimism". The real question is strategic:

Is Indian IT about to be disintermediated — or is it recalibrating itself into the AI execution control layer?

II. What Agentic AI Actually Changes: Compression of Execution

To answer that question, the threat must be acknowledged clearly.

Agentic AI compresses the value of standardized execution. L1/L2 coding, testing, ticket resolution, and routine integration workflows are structurally exposed. If enterprises can deploy autonomous systems capable of chaining tasks across tools and databases, the historical labor arbitrage model weakens.

The value chain shifts:

Old model:

Enterprise → IT Services → Software Tools

Emerging model:

Enterprise → Foundation Models → Orchestration Layer → Domain Execution

The contest is over the orchestration layer — the control interface between general-purpose AI models and complex enterprise systems.

If hyperscalers internalize orchestration, service intermediaries shrink. If integrators retain governance authority, the model providers remain upstream infrastructure suppliers.

The Summit partnerships suggest that Indian IT companies are attempting the latter path.

III. Strategic Pivot #1: From Labour Scale to Human-AI Orchestration

The future of Indian IT does not lie in defending execution-heavy roles. It lies in supervising AI-executed workflows.

Agentic systems amplify output but introduce new risks: hallucinations, regulatory breaches, audit gaps, security exposure, bias propagation. In regulated sectors — finance, telecom, healthcare, energy, mining, etc — enterprises cannot delegate accountability to black-box autonomy.

This creates demand—not for mass coding—but for supervisory architecture:

Validation frameworks

Compliance monitoring layers

Audit trails and explainability integration 

Domain-aware prompt engineering

Human override systems

The workforce implication is structural. Execution roles contract; oversight and governance roles expand. The competitive advantage shifts toward producing  professionals with T-shaped skillset: deep domain expertise combined with broad AI workflow management familiarity.

India’s historical trust capital in enterprise integration becomes strategically relevant here. For decades, Indian IT companies have operated inside global companies’ ERP, CRM, risk, and compliance stacks. That embedded familiarity positions them not as displaced labour, but as mediators between enterprise complexity and foundation models.

The “AI job apocalypse” narrative assumes a binary: humans or machines. The strategic reality is different: enterprises will demand governed autonomy. The firms that design that governance layer will capture durable relevance.

IV. Strategic Pivot #2: Compute Sovereignty as Structural Leverage

A few months back, TCS and TPG formed a joint venture through TCS's HyperVault subsidiary to build a GW-scale AI-ready data-centre network in India. The partnership involves a combined investment of up to ₹18,000 crore over the next few years. HyperVault focuses on liquid-cooled, high-density facilities for AI training, inference, hyperscalers, and enterprises, with energy efficiency and connectivity across cloud regions. Tata Group Chairman Natarajan Chandrasekaran emphasized building world-class AI infrastructure to position TCS as the world's top AI-led tech services company. 

In this context, the OpenAI-TCS infrastructure collaboration, announced the day before yesterday at the AI Impact Summit, is not a symbolic gesture; it signals a deeper shift. AI economics are increasingly dominated by compute intensity and energy cost. Sovereign infrastructure reduces exposure to external cloud pricing power, data residency conflicts, and geopolitical friction. For policymakers, domestic AI-optimized data centers strengthen regulatory credibility. For enterprises, localized infrastructure reduces latency and compliance risk.

Compute sovereignty, therefore, is more than capacity building. It is leverage.

If Indian IT companies help anchor AI execution on Indian soil — aligned with local data laws and sectoral regulation — they shift from being contractors on global platforms to operators of execution infrastructure.

This is infrastructure arbitrage replacing labor arbitrage.

In a world where model providers concentrate intellectual property upstream, control over deployment infrastructure downstream becomes the counterweight.

V. Strategic Pivot #3: India-Scale IP and Platform Ownership

India's domestic market, I argue, is not a fallback; it is the proving ground.

India’s massive-scale in industries like e-commerce, fintech, renewable energy, automobiles, highways, railways, aviation, healthcare (alongwith emerging industrial clusters) creates non-trivial deployment complexity. AI solutions tested and applied in this industrial environment — multilingual, regulation-heavy, cost-sensitive — become exportable templates to other emerging markets.

The risk for Indian IT is remaining integration-heavy but IP-light. 

The opportunity is transforming domestic deployments into platform assets:

AI-enabled grid optimization systems

EV fleet orchestration frameworks

Industrial predictive maintenance platforms

Compliance-first financial AI stacks

Revenue models then shift from billing hours to licensing, co-creation, and revenue sharing.

Thus, a domestic pivot could therefore serve two purposes:

Deepening India’s AI adoption.

Generating exportable IP rooted in real complexity.

That is the pathway from service vendor to platform co-creator.

VI. Industrial Friction as Advantage

Much of the automation anxiety is megapolitan and office-centric. Physical industries present a different terrain.

Electronics factories, renewable energy systems, logistics corridors, and industrial clusters operate within safety constraints, legacy systems, fragmented data environments, and regulatory oversight. Integrating agentic AI into SCADA systems, manufacturing execution platforms, and energy management networks requires contextual knowledge, compliance alignment, and on-site operational literacy.

Agentic AI does not self-deploy itself into such environments. It must be embedded, validated, stress-tested, and monitored.

Industrial friction is not a weakness; it is a strategic moat.

As India expands manufacturing capacity, industrial corridors, renewable energy infrastructure, etc, the demand for contextual AI integration would scale alongside. This would sustain relevance for firms positioned as orchestrators rather than simply executors.

VII. The Risk Map

The strategy is plausible, but not guaranteed.

Three risks stand out:

Hyperscaler Encroachment: If foundation model providers forward-integrate to orchestration layers and offer turnkey enterprise AI stacks, integrators lose leverage.

IP Deficit: If Indian IT remains dependent on foreign models without building exportable domestic platforms, margin compression would become inevitable.

Skill Polarization: If execution displacement happens faster than supervisory upskilling, workforce disruption could outpace role reconfiguration.

Therefore, strategic repositioning would require disciplined execution on all three fronts.

VIII. The Conditional $400 Billion Path

The projection that India’s IT industry could hit $400 billion in annual revenue by 2030 is not implausible — but it is conditional.

It depends on four transitions succeeding simultaneously:

- Migration from execution labor to orchestration governance.

- Expansion of sovereign compute infrastructure.

- Conversion of domestic deployments into exportable IP platforms.

- Workforce transformation aligned with AI supervisory roles.

If these hold, Indian IT companies would evolve upward in the value chain. If they stall, value-capture would shift upstream to model providers.

IX. From Back Office to Control Layer

India’s historical role in global technology was the back office — executing workflows designed elsewhere.

Agentic AI now executes workflows.

The strategic opportunity is therefore neither defensive nor nostalgic. It is positional.

India can occupy the control layer between foundation models and real-world enterprise complexity — governing, contextualizing, and 

operationalizing AI at scale.

The AI moment is not about resisting disruption. It is about deciding who controls the interface between autonomous systems and human institutions.

This interface — not raw code, not raw models — is where enduring value will reside.