Critically analyse the potential risks and opportunities AI presents to Indian IT services.

Opportunities: Enhanced productivity through automation of routine tasksNew business avenues as AI orchestration partnersStrategic advantage for firms with strong data engineering and AI integration capabilities Risks: Potential reduction in entry-level jobs and employment pressureFirms that fail to adapt may lose competitivenessOverreliance on AI tools without contextual integration could disrupt service quality Analysis: AI represents both a tailwind and a potential structural challenge. Firms that integrate AI thoughtfully, align frameworks with governance standards, and reskill their workforce can convert disruption into a growth opportunity. Conversely, complacency or inadequate adaptation could exacerbate social and operational risks.

In a scenario where a large bank adopts agentic AI for core operations, what role will Indian IT services firms play?

Scenario Overview: A large bank decides to deploy an AI tool like Claude Co-work for credit processing and customer service automation. Role of IT Services Firms: Assess existing infrastructure and legacy systems compatibility with AIClean, structure, and prepare data for AI integrationEmbed AI workflows while ensuring regulatory compliance and cybersecurityMonitor outputs, tune models, and provide governance oversight Outcome: The AI tool alone cannot deliver enterprise-level efficiency. Indian IT services firms act as orchestration partners, ensuring that AI adoption translates into operational gains while mitigating risks. This illustrates the evolving, value-added role of services firms in AI-driven environments.

AI's Limitations in Capturing Deep Enterprise Context

Q: What is the impact of agentic AI tools like Anthropic’s Claude Co-work on the Indian IT services sector?

Overview: Agentic AI tools such as Claude Co-work represent a significant advancement in AI capabilities. They have moved beyond being mere coding assistants to orchestrating complex workflows and knowledge-intensive tasks. These tools have demonstrated remarkable productivity gains, reducing the time and effort required for certain programming and operational tasks. Enterprise Context: Despite these advancements, most Indian IT services firms operate in environments with deeply embedded legacy systems, regulatory requirements, fragmented databases, and cybersecurity constraints. The real challenge lies in integrating AI into these complex ecosystems rather than replacing traditional services outright. Implications: While AI can enhance productivity, it cannot fully substitute the contextual expertise required for system integration, modernization, and orchestration. Indian IT services firms continue to add value by managing complex enterprise environments and ensuring smooth interoperability across diverse technologies. Example: Large banks or pharmaceutical companies rely on decades of legacy data and compliance structures. An AI tool alone cannot restructure or manage these systems effectively; human-led orchestration remains essential.

Q: Why are concerns about AI replacing traditional IT services considered exaggerated?

Complexity of Enterprise Systems: Most enterprises have highly complex IT landscapes, including legacy infrastructure, multiple integrated platforms, and strict regulatory obligations. These environments require specialized knowledge that AI tools cannot fully replicate. Historical Precedent: The ERP wave provides a useful analogy. When platforms like Oracle and SAP became mainstream, experts feared that system integrators would become redundant. In reality, the demand for integration, customization, and maintenance services increased fivefold, highlighting that technology adoption often strengthens rather than weakens services demand. Forward-looking Perspective: AI is likely to evolve the role of IT services firms rather than eliminate them. Firms will transition from traditional implementation to AI orchestration, embedding AI frameworks into enterprise workflows, managing governance, and ensuring regulatory compliance. This ensures continued relevance and even potential growth for well-adapted firms.

Q: How will the role of IT services firms evolve in the AI-driven landscape?

From Implementation to Orchestration: Traditionally, IT services firms focused on implementing software and coding solutions. In an AI-driven environment, their role will shift toward orchestrating AI frameworks across enterprise systems, ensuring seamless integration, and embedding AI tools into complex workflows. Data Readiness and Governance: AI adoption requires clean, structured, and compliant datasets. Firms with capabilities in data engineering, platform management, and AI governance will become critical partners for enterprises, helping prepare data and integrating AI responsibly within operational and regulatory constraints. Partnerships and Strategic Advisory: IT firms will also advise on which AI frameworks to adopt, evaluate competing solutions, manage hyperscaler partnerships, and align AI initiatives with broader business objectives. This ensures that AI adoption translates into tangible productivity gains while maintaining enterprise stability.

Q: What are the structural shifts visible in the IT services sector due to AI, and why do they matter?

Decoupling of Revenue and Headcount: One key shift is that revenue growth is no longer tightly linked to employee growth. AI-driven productivity enables higher output without proportional increases in workforce, particularly at entry levels. Implications for Employment: This trend necessitates a focus on efficiency and skill-intensive roles. Entry-level hiring pressures may decrease, which can impact urban employment patterns and necessitate reskilling initiatives. Strategic Significance: The sector must navigate these structural shifts carefully. Policies for workforce upskilling, AI governance, and operational efficiency are critical to ensuring that productivity gains do not translate into social or employment stress. Gradual adaptation over the next 2–5 years is essential for sustainable growth.

Q: Can you give an example of a historical technology shift similar to AI in IT services?

ERP Adoption Wave: In the 1990s and early 2000s, the adoption of ERP platforms such as Oracle and SAP raised fears that custom coding and system integration roles would disappear. Outcome: Contrary to expectations, demand for system integrators increased significantly. Services firms delivered far greater value than ERP vendors themselves through integration, customization, and ongoing maintenance, highlighting that platform adoption often increases the need for human-led services. Lesson for AI: AI adoption may similarly augment rather than replace IT services. While agentic AI can automate coding and workflows, the orchestration of complex enterprise systems, compliance management, and strategic alignment will continue to require human expertise.

Nasscom chief Rajesh Nambiar discusses the impacts of AI tools on the IT services industry amid declining growth rates.

pocketias team

•February 16, 2026•7 mins read

Indian IT Services in Transition: Navigating AI Integration, Enterprise Complexity, and the Shift from Implementation to Orchestration

Not Started

Indian IT Services Sector in the Age of Agentic AI

1. Sell-off in IT Stocks and the AI Disruption Debate

Indian IT services stocks have witnessed one of their sharpest recent sell-offs amid concerns of slowing growth and rapid advancements in generative and agentic AI tools such as Anthropic’s Claude Co-work. Industry growth has slipped into single digits, triggering fears about structural disruption.

The anxiety stems from the increasing capability of AI systems to move beyond code assistance toward orchestrating complex workflows and knowledge-intensive tasks. As AI productivity gains become more visible, questions are being raised about whether traditional services models remain viable.

This debate has larger implications for India’s growth model. The IT services sector is a major contributor to exports, employment generation, and foreign exchange earnings. Any structural disruption could impact macroeconomic stability, urban employment, and India’s position in global value chains.

If misinterpreted, short-term technological transitions could lead to underinvestment, talent flight, and strategic hesitation in a sector that remains central to India’s economic architecture.

"While these tools are powerful, they cannot fully substitute for the intricate enterprise context in which services firms operate." — Rajesh Nambiar

The core governance concern is whether AI represents cyclical technological evolution or structural obsolescence. Misreading this transition could distort policy support, workforce planning, and capital allocation.

2. Nature of AI Advancements: Productivity vs. Enterprise Complexity

Agentic AI tools have demonstrated significant productivity improvements. They can now generate code, automate tasks, and coordinate workflows at unprecedented speed, moving beyond traditional coding assistants.

However, enterprise IT environments differ substantially from isolated development settings. Large organisations operate with legacy systems, fragmented databases, cybersecurity constraints, regulatory compliance obligations, and deeply embedded operational dependencies.

Indian IT services firms operate precisely in these complex, context-rich environments. Their value lies not merely in writing code but in integrating systems, modernising infrastructure, building microservices, and managing cross-platform interoperability.

The assumption that AI tools can “plug and play” into such environments underestimates the institutional and architectural complexity of enterprise ecosystems.

"You cannot simply ask an AI tool to build a complete enterprise system overnight." — Rajesh Nambiar

The development logic is clear: AI may automate tasks, but enterprise transformation requires contextual integration. Ignoring this distinction risks conflating tool capability with systemic deployment capacity.

3. Historical Analogy: ERP Wave and Lessons for AI Transition

A comparable moment occurred during the ERP (Enterprise Resource Planning) wave, when platforms like Oracle and SAP became mainstream. At the time, there was concern that standardised software platforms would eliminate custom coding and make services firms redundant.

Instead, demand for system integrators increased significantly. Services firms created greater value through implementation, integration, customisation, and long-term maintenance than ERP vendors did during deployment cycles.

Although generative AI differs in scale and autonomy, the historical analogy highlights an important pattern: technological standardisation often increases demand for integration rather than eliminating it.

ERP adoption led to a fivefold increase in demand for system integrators (as per industry observation in the interview context).

The AI cycle may evolve differently, especially since agentic systems can directly generate code. Nevertheless, the integration layer remains critical.

"At the time, there was a strong belief that full-blown ERP systems would eliminate custom coding altogether. In reality, the opposite happened." — Rajesh Nambiar

The governance takeaway is that technological platforms often shift value chains rather than destroy them. Failure to internalise this lesson could result in defensive policymaking instead of adaptive reform.

4. Structural Evolution of the IT Services Model

The role of services firms is expected to evolve from “implementation partners” to “AI orchestration partners.” Instead of merely deploying software, firms will integrate external AI frameworks into enterprise workflows, ensure governance compliance, and align systems with business objectives.

A key emerging domain is data readiness. Enterprise databases have evolved over decades, often lacking standardisation. Preparing this data for AI use—cleaning, structuring, securing, and ensuring regulatory compliance—is a complex and labour-intensive process.

Moreover, enterprises must evaluate competing AI frameworks, determine integration pathways, and ensure adherence to cybersecurity and regulatory standards. These functions require contextual and sector-specific expertise.

Emerging Focus Areas:

Data engineering and governance
AI integration with legacy systems
Regulatory compliance management
Partnerships with hyperscalers
Enterprise architecture redesign

"The real question is our ability to harness [AI] power within enterprise systems. In fact, this could be more of a tailwind than a headwind." — Rajesh Nambiar

Therefore, while task-level automation may reduce routine work, system-level complexity sustains the relevance of services organisations.

The structural shift is not from relevance to redundancy, but from labour-intensive execution to high-value orchestration. If firms fail to upgrade capabilities, however, displacement risks will materialise.

5. Employment and Growth: Decoupling of Revenue and Headcount

One visible structural trend is the divergence between revenue growth and employee growth. Historically, Indian IT expansion was closely tied to large-scale hiring, particularly at the entry level.

With AI-driven productivity gains, revenue may grow without proportional increases in headcount. This signals a shift toward efficiency-driven models and higher skill intensity.

The industry anticipates a two-phase transition:

Phase 1 (Next 2 years):

Stabilisation and adjustment of operating models
Embedding AI into workflows
Managing efficiency pressures
Phase 2 (Following 3 years):
- Emergence of a new growth trajectory
- AI-enabled service expansion

This decoupling has implications for:

Urban employment patterns (GS1: Urbanisation and migration)
Skill development policy (GS2: Education and skilling)
Digital economy and innovation (GS3: Science & Technology)

"The expertise within services firms will remain critical, especially in helping enterprises meaningfully leverage AI within their broader technology landscape." — Rajesh Nambiar

If not managed through reskilling and workforce transition strategies, entry-level job compression could exacerbate youth employment challenges.

The governance logic demands anticipatory skilling policies. Ignoring workforce restructuring could convert technological opportunity into social stress.

6. Long-Term Outlook: Gradual Evolution, Not Abrupt Displacement

Over the next 10–15 years, the transition is likely to be evolutionary rather than disruptive. Enterprise systems are deeply embedded, interconnected, and regulated. Interface contracts, operational dependencies, and compliance obligations limit abrupt technological replacement.

The AI wave is foundational and powerful, yet its real impact depends on how effectively enterprises harness it within their broader technology landscape.

For India, this moment presents both risk and opportunity:

Risk of complacency in upgrading talent and service offerings
Opportunity to reposition as a global AI integration and governance hub

"Over the next 10–15 years, what we are likely to see is gradual evolution, not abrupt displacement." — Rajesh Nambiar

Thus, rather than an existential crisis, the present moment may represent a strategic inflection point.

Technological disruption rewards adaptive ecosystems. The critical variable is not AI capability alone, but institutional preparedness to integrate and govern it.

Conclusion

The rapid rise of agentic AI has triggered legitimate concerns about the future of India’s IT services sector. However, enterprise complexity, data readiness challenges, regulatory constraints, and integration demands continue to sustain the relevance of services firms.

The sector is entering a phase of structural evolution marked by productivity gains, workforce restructuring, and role transformation toward AI orchestration. Policy emphasis on reskilling, digital governance, and innovation partnerships will determine whether this transition becomes a headwind or a tailwind for India’s growth trajectory.

"Companies that have built strong data engineering, AI, and platform capabilities, particularly those with partnerships with hyperscalers, will continue to thrive." — Rajesh Nambiar

In the long run, the resilience of India’s IT services industry will depend not on resisting AI, but on embedding it strategically within complex enterprise ecosystems.

Quick Q&A

Everything you need to know

Overview: Agentic AI tools such as Claude Co-work represent a significant advancement in AI capabilities. They have moved beyond being mere coding assistants to orchestrating complex workflows and knowledge-intensive tasks. These tools have demonstrated remarkable productivity gains, reducing the time and effort required for certain programming and operational tasks.

Enterprise Context: Despite these advancements, most Indian IT services firms operate in environments with deeply embedded legacy systems, regulatory requirements, fragmented databases, and cybersecurity constraints. The real challenge lies in integrating AI into these complex ecosystems rather than replacing traditional services outright.

Implications: While AI can enhance productivity, it cannot fully substitute the contextual expertise required for system integration, modernization, and orchestration. Indian IT services firms continue to add value by managing complex enterprise environments and ensuring smooth interoperability across diverse technologies.

Example: Large banks or pharmaceutical companies rely on decades of legacy data and compliance structures. An AI tool alone cannot restructure or manage these systems effectively; human-led orchestration remains essential.

Complexity of Enterprise Systems: Most enterprises have highly complex IT landscapes, including legacy infrastructure, multiple integrated platforms, and strict regulatory obligations. These environments require specialized knowledge that AI tools cannot fully replicate.

Historical Precedent: The ERP wave provides a useful analogy. When platforms like Oracle and SAP became mainstream, experts feared that system integrators would become redundant. In reality, the demand for integration, customization, and maintenance services increased fivefold, highlighting that technology adoption often strengthens rather than weakens services demand.

Forward-looking Perspective: AI is likely to evolve the role of IT services firms rather than eliminate them. Firms will transition from traditional implementation to AI orchestration, embedding AI frameworks into enterprise workflows, managing governance, and ensuring regulatory compliance. This ensures continued relevance and even potential growth for well-adapted firms.

From Implementation to Orchestration: Traditionally, IT services firms focused on implementing software and coding solutions. In an AI-driven environment, their role will shift toward orchestrating AI frameworks across enterprise systems, ensuring seamless integration, and embedding AI tools into complex workflows.

Data Readiness and Governance: AI adoption requires clean, structured, and compliant datasets. Firms with capabilities in data engineering, platform management, and AI governance will become critical partners for enterprises, helping prepare data and integrating AI responsibly within operational and regulatory constraints.

Partnerships and Strategic Advisory: IT firms will also advise on which AI frameworks to adopt, evaluate competing solutions, manage hyperscaler partnerships, and align AI initiatives with broader business objectives. This ensures that AI adoption translates into tangible productivity gains while maintaining enterprise stability.

Decoupling of Revenue and Headcount: One key shift is that revenue growth is no longer tightly linked to employee growth. AI-driven productivity enables higher output without proportional increases in workforce, particularly at entry levels.

Implications for Employment: This trend necessitates a focus on efficiency and skill-intensive roles. Entry-level hiring pressures may decrease, which can impact urban employment patterns and necessitate reskilling initiatives.

Strategic Significance: The sector must navigate these structural shifts carefully. Policies for workforce upskilling, AI governance, and operational efficiency are critical to ensuring that productivity gains do not translate into social or employment stress. Gradual adaptation over the next 2–5 years is essential for sustainable growth.

ERP Adoption Wave: In the 1990s and early 2000s, the adoption of ERP platforms such as Oracle and SAP raised fears that custom coding and system integration roles would disappear.

Outcome: Contrary to expectations, demand for system integrators increased significantly. Services firms delivered far greater value than ERP vendors themselves through integration, customization, and ongoing maintenance, highlighting that platform adoption often increases the need for human-led services.

Lesson for AI: AI adoption may similarly augment rather than replace IT services. While agentic AI can automate coding and workflows, the orchestration of complex enterprise systems, compliance management, and strategic alignment will continue to require human expertise.

Opportunities:

Enhanced productivity through automation of routine tasks
New business avenues as AI orchestration partners
Strategic advantage for firms with strong data engineering and AI integration capabilities

Risks:

Potential reduction in entry-level jobs and employment pressure
Firms that fail to adapt may lose competitiveness
Overreliance on AI tools without contextual integration could disrupt service quality

Analysis: AI represents both a tailwind and a potential structural challenge. Firms that integrate AI thoughtfully, align frameworks with governance standards, and reskill their workforce can convert disruption into a growth opportunity. Conversely, complacency or inadequate adaptation could exacerbate social and operational risks.

Scenario Overview: A large bank decides to deploy an AI tool like Claude Co-work for credit processing and customer service automation.

Role of IT Services Firms:

Assess existing infrastructure and legacy systems compatibility with AI
Clean, structure, and prepare data for AI integration
Embed AI workflows while ensuring regulatory compliance and cybersecurity
Monitor outputs, tune models, and provide governance oversight

Outcome: The AI tool alone cannot deliver enterprise-level efficiency. Indian IT services firms act as orchestration partners, ensuring that AI adoption translates into operational gains while mitigating risks. This illustrates the evolving, value-added role of services firms in AI-driven environments.