How India’s IT Firms Are Turning AI Into Revenue

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India’s largest IT services companies are beginning to talk about artificial intelligence in a way investors have been waiting to hear for several years: as a live business driver shaping revenue streams, deal structures, workforce deployment, and operating priorities, even as global markets debate whether the current wave of AI spending will translate into durable returns.

The shift is uneven. Some companies have begun to explicitly separate artificial intelligence-related revenue and deal momentum in their disclosures. Others still speak about AI as a horizontal embedded across services, without carving out a clean financial line. 

But taken together, the latest round of quarterly results suggests that AI is increasingly shaping how these firms pitch themselves to clients, how they organize delivery, and how they explain growth to shareholders.

TCS: Industrial Scale AI

Among the large firms, Tata Consultancy Services Ltd (TCS) is the clearest in demarcating AI as a business line. In its third-quarter results for the period ended December  2025, the company disclosed $1.8 billion in annualized AI services revenue, growing 17.3% quarter-on-quarter in constant currency terms, alongside total quarterly revenue of ₹67,087 crore. 

The company has begun positioning AI as a full-stack offering, spanning infrastructure, platforms, and applications, rather than a layer sitting on top of cloud services.

In its earnings call and press release, TCS management repeatedly tied AI growth to long-duration contracts and infrastructure commitments, including its HyperVault AI data center business and deeper partnerships with hyperscalers and enterprise software providers.

Chief executive K. Krithivasan told analysts that the company was “seeing clients move from experimentation to scaled adoption, where AI is embedded into core operations rather than treated as a side initiative.”

What stands out in TCS’s disclosures is the language. Management spoke about industrializing AI delivery, embedding it into operations, and using scale, workforce depth, and long-term contracts as competitive advantages.

TCS also disclosed that more than 217,000 employees have been trained in advanced AI skills, underscoring that its approach is labor-intensive and execution-heavy, rather than purely platform-led. 

Infosys: Enterprise Platforms

Infosys has taken a different route. Unlike TCS, it has not broken out AI revenue as a separate financial line. Instead, it has focused on describing AI as a driver of large deals, margin resilience, and client stickiness. 

In its 14 January earnings call, the company highlighted $4.8 billion in large deal wins during the quarter, with management repeatedly pointing to its Topaz AI platform as a differentiator in winning enterprise contracts.

Chief executive Salil Parekh said Infosys was “working with AI across the core of our clients’ businesses, where reliability, security, and repeatability matter more than speed alone.”

Parekh told analysts that Infosys is working with 90% of its top 200 clients on AI-led programs and is currently executing 4,600 AI projects, supported by thousands of forward-deployed engineers embedded at client sites.

The company introduced Topaz Fabric, an agent-ready architecture designed to help enterprises manage AI deployment across data, applications, and workflows. 

Rather than emphasizing speed or experimentation, Infosys has leaned heavily on repeatability, safety, and suitability for regulated environments, including healthcare, financial services, and public-sector engagements such as its work with the UK National Health Service.

HCLTech: Applied AI

HCLTech sits somewhere between these two approaches, and its disclosures reflect that. The company reported $146 million in advanced AI revenue for the quarter, up nearly 20%, alongside $3 billion in new bookings.

Unlike Infosys, HCLTech has begun explicitly monetizing AI as a near-term growth lever, and unlike TCS, it has pushed beyond software and services into what it calls “physical AI” use cases.

Chief executive C. Vijayakumar said on the earnings call that the company was “seeing faster monetization where AI is applied to real-world operations, not just digital workflows.”

In its filings, HCLTech highlighted applications of AI in industrial inspection, mining, laboratory operations, and engineering workflows. Its AI Force platform is positioned as a way to embed AI across software development and IT operations, while its growing proprietary software portfolio, with annual recurring revenue exceeding $1 billion, allows the company to bundle AI services with products.

The tone of HCLTech’s disclosures suggested a focus on speed to revenue and differentiation, rather than building a long-cycle infrastructure moat. 

Wipro: Governed Deployment

Wipro’s commentary has been more cautious, and more focused on governance and operational reliability. The company has not separated AI revenue in its financial statements. Instead, management has emphasized large, multi-year deals where AI is embedded into core business processes, particularly in regulated sectors such as healthcare, insurance, and digital platforms.

In its mid-January investor interaction, Wipro chief executive Srinivas Pallia said the company was prioritizing “AI systems that are predictable, auditable, and scalable in production environments, where trust and compliance are non-negotiable.”

The company highlighted its PayerAI platform in healthcare and insurance, and its role in trust and safety operations for global technology firms, where thousands of specialists are deployed to train and supervise machine learning systems aligned with content and regulatory requirements.

Wipro’s messaging positions AI less as a growth spike and more as an operational layer that must work predictably at scale, particularly in environments where errors carry regulatory or reputational risk. 

Tech Mahindra: Embedded Execution

Tech Mahindra’s disclosures reflect a company in transition. It has not reported AI revenue as a separate category, but its latest earnings presentation and board filings indicate a sharper focus on embedding AI into telecom, enterprise operations, and internal delivery models.

The company highlighted a large multi-year telecom deal in Europe and detailed its progress in training more than 80,000 employees in AI and generative AI, with a majority of its sales and support workforce now AI-enabled.

Chief executive Mohit Joshi told analysts that AI was “being built into how we deliver and sell, not positioned as a standalone solution.”

Tech Mahindra has also tied its AI strategy to ecosystem partnerships, particularly with hyperscalers, and to domestic initiatives under India’s AI Mission, including work on local-language large language models.

The message to investors is that AI is being treated as a core execution engine rather than a standalone offering, even if the financial impact is not yet cleanly visible in reported numbers. 

Different Paths, Same Test

What emerges from the latest disclosures by IT services firms is a clearer segmentation of approaches across India’s IT sector. 

While TCS is using scale, infrastructure, and explicit revenue disclosure to anchor its AI narrative, Infosys is betting on platforms and enterprise safety to drive large deals. 

HCLTech is pushing fast monetization and physical-world applications, while Wipro is emphasizing trust, governance, and operational AI in regulated environments, even as Tech Mahindra is repositioning itself around AI-enabled execution and ecosystem leverage.

With the exception of TCS and HCLTech, most firms are still reluctant to isolate AI as a standalone revenue stream. Productivity gains from AI are acknowledged, but often described cautiously, with management noting that efficiency benefits may compress legacy revenue even as new value pools open up.

For investors, the harder question now is how consistently AI-driven work can be converted into durable revenue, how much of it replaces existing billing rather than adds to it, and which firms can move beyond storytelling to sustained financial disclosure.