LTIMindtree CEO: AI Fears Overblown, IT Stable

At a time when artificial intelligence is raising concerns about the future of IT services, LTIMindtree’s CEO, Venu Lambu, believes the fears are overstated.

Lambu has dismissed suggestions that AI could trigger a structural slowdown in the industry, calling such concerns “overblown or exaggerated.” He made it clear that he does not see any long-term structural decline in the IT sector. Even as AI reshapes how services are delivered, he remains optimistic.

According to him, artificial intelligence will undoubtedly transform traditional service models. Some conventional tasks may see reduced demand as automation improves efficiency. At the same time, he sees larger opportunities emerging in building new capabilities around AI platforms, tools and models.

Lambu compared the current shift to earlier technology transitions that the industry has successfully navigated. However, the key difference now, he noted, is the faster pace of innovation. This requires companies to respond more quickly. Additionally, they must adapt at a faster pace.

In the near term, AI-led productivity gains are already affecting revenue dynamics. Some of LTIMindtree’s largest clients, who are adopting AI more rapidly, are seeing efficiency benefits. The company has been passing on these productivity gains to customers since the fourth quarter of last year. As a result, this has led to revenue pressure in certain top accounts.

He also highlighted a gap between the rapid advancement of AI capabilities and their actual adoption by enterprises. Many organisations still face challenges around data readiness, infrastructure upgrades and application modernisation. This lag, he said, provides a buffer period and an opportunity for IT service providers to reposition themselves.

Looking ahead, Lambu expressed confidence in delivering net positive growth. He clarified that this outlook already factors in the headwinds from productivity-linked pricing adjustments. Moreover, it accounts for the lower capacity requirements resulting from AI-driven efficiencies.

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