AI Panic vs Enterprise Reality: What’s Really Happening in Indian IT?

Since late January, tech and IT stocks have faced strong selling pressure amid concerns about AI disruption. Software and SaaS companies worldwide were affected as investors grew concerned that advanced AI tools could threaten traditional software revenue streams.

By January 27th, the Nifty IT index had fallen about 20% in the past month. Major IT stocks such as Infosys, TCS, and HCL Technologies dropped by 16% to 22% during this period.

This article relies on expert commentary and company conference calls and announcements to analyse the situation for Indian IT companies.

Let’s start by looking at what these companies do. Take a big retailer like Walmart. Walmart needs strong, often custom software to handle ordering goods and billing customers. It also needs cybersecurity to protect both the customer and the company’s financial data. Data analytics help track which products sell quickly on certain days and what types of customers buy specific items. Usually, Walmart outsources this work to Indian IT companies through long-term contracts. These companies use a global delivery model, with a small team working onsite with Walmart, while most of the work happens in India. Engineers in India build and maintain the software and provide support as needed. The main point is that IT companies earn money based on the number of hours their engineers work.

So how could AI change this? Many believe that with tools like generative AI coding assistants and automated testing systems, it’s possible to write code, fix bugs, create and test scenarios, and document software with much less human effort. What once required 10 engineers might now require fewer engineers with AI’s help. This could hurt IT companies’ revenue. As mentioned earlier, these companies also maintain software and provide support. But as AI gets better at spotting problems and handling basic support through chatbots, even their steady support income could be at risk.

These worries deepened after a US-based AI firm, Anthropic, announced new AI capabilities that can autonomously perform complex tasks across diverse functions, such as legal analysis, financial modelling, marketing workflows, and data operations. This announcement has led to the perception that AI can no longer be viewed solely as a support layer that improves efficiency, but as a system that performs billable work.

What do industry experts say about this, though?

– Tech adoption follows the S-curve: S-curve adoption explains how new tech spreads over time. Growth starts slowly because there are few adopters and costs are high. Over time, as the value of the tech becomes evident, its adoption increases, and as the market matures, growth slows. This pattern of a slow start, a quick rise, and gradual slowing creates the S shape. Experts opine that while AI capabilities are advancing quickly, there are major integration and regulatory hurdles to be addressed. They posit that major technology platform shifts typically take 15-20 years to mature fully.

– Enterprise environments are too complex for probabilistic AI models: Most current AI models, especially large language models, are probabilistic. This means that the outputs are generated probabilistically, not via rule-based logic. While this is good for generating code drafts, writing emails, etc., their direct applicability in mission-critical business systems is currently limited. Large business software systems must be repeatable, error-free and auditable, and the current AI crop cannot yet handle this.

– Incumbents are gatekeepers: IT companies control the system that stores the clients’ data and runs their daily operations. Because they already manage these workflows and customer relationships, they have an advantage when AI is added to the platform. ServiceNow, a cloud-based platform that provides business solutions, has indicated that when AI features are built into its solutions, about 90% of the revenue goes to the company. Only the remaining amount goes to the underlying AI model provider, such as OpenAI or Anthropic.  

– Expansion of the total addressable market: Experts opine that AI is creating massive new high-value opportunities for IT services across areas like modernising legacy codes powering global finance systems, integrating AI into robotics and cyber-physical systems, etc.  

– AI as a complement and not a substitute: History shows that technology tends to alter the composition of tasks rather than eliminating the need for human input; a case in point is the introduction of Microsoft Office. According to this report, despite fears of displacement, job postings for software engineers have risen by 11% annually in early 2026.

The expert view points towards transformation rather than immediate termination, which is what the market is fearing. While AI will surely compress low-value tasks, it will also likely open new revenue streams via modernisation, integration, and platform-led services.

What are Indian IT companies saying about AI disruption and adoption?

At its AI Day 2026, Infosys described artificial intelligence as a major technology shift, like moving from mainframes to PCs or from on-premises systems to the cloud. While foundational AI models are advancing rapidly, business adoption is slow due to the complexity of legacy systems and limited data readiness. The majority of the IT budget is still allocated to maintenance, and Infosys believes modernising legacy systems is necessary to scale AI. The company does not see AI replacing workers, but as an opportunity for high-value modernisation and process redesign. It also launched the Topaz Fabric platform, which allows integration of various AI models and cloud systems. This allows clients to quickly move from pilot projects to full production deployment.

Tata Consultancy Services (TCS) is seeing improvement in AI adoption and has reported an annualised AI services revenue of $1.8 billion during the 3rd quarter of FY26. Management highlighted the shift from pilot projects to ROI-driven implementations in key markets like North America. The company is witnessing active integration of AI to improve customer experience and enhance business models in sectors like retail banking and software. The company has set up dedicated AI labs in India for US financial clients and is deepening partnerships with Nvidia, Google, etc.  TCS reported quarterly deal TCV of $9.3 billion and expressed confidence in a strong CY26, supported by sustained AI- and data-led demand.

HCL Technologies reported annualised AI services revenue of ~$590 million and an improvement in realisation, mostly driven by advanced AI offerings. The company has also observed a shift from experimentation and pilot programs toward broader enterprise-wide reimagination of business processes.  Like peers, HCLT is also seeing targeted discretionary spending specifically in AI infrastructure and agentic AI use cases. The company has secured meaningful AI-led wins, including a $473 million modernisation deal leveraging its AI Force 2.0 platform and a large GenAI-powered vendor consolidation deal with a US insurer.  

The recent sell-off does indicate real concerns about how AI will affect the traditional manpower-led IT service model. Productivity gains could reduce low-value tasks, potentially negatively affecting current billing models. However, expert opinion and management insights indicate that while businesses are adopting AI, the pace remains slow. Integration complexity, the need to modernise legacy systems, regulatory hurdles, and data readiness are all hurdles to rapid adoption and to protecting against immediate disruption of business models. AI is also creating several high-value opportunities in areas such as modernisation, data engineering, etc. So to conclude, AI will certainly disrupt Indian IT; however, the likelihood of that leading to lower demand is likely to remain low, and there will most likely be a shift in value. The evidence certainly points to evolution and not extinction.  Which companies will win depends on the pace of technology adoption, pricing power and how these companies manage to capture value.

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