AI is being democratised at full throttle – much in sync with the government’s target of a $1 Tn digital economy by 2030 – and what fuels this growth is the economics of AI that businesses across industries are trying to gain from.

Indian infrastructure major Larsen & Toubro (L&T) made no mistake in reading the future. AI is no longer an experimental capability or a future-facing investment – it is increasingly being embedded into the core mechanics of how the company executes projects at scale.

In an interaction with Inc42, R Ganesan, who heads the Corporate Centre at L&T Construction, framed the company’s AI strategy as deliberately multi-layered. “We see different use cases across our various businesses and multiple applications of AI that can impact our execution either in terms of productivity, time to value, autonomous or near-autonomous decision-making and in some cases requiring us to reimagine processes or tasks,” he said.

As India emerges as the third most competitive nation in AI adoption, led by its 6 Mn-strong army of technology graduates and powered by an ecosystem of 1.8 Lakh startups and more than 1,800 global capability centres, infra companies are increasingly adopting AI to improve efficiency, safety, and project timelines.

“How you deploy the technology and how you execute it make all the difference in this increasingly competitive landscape,” Ganesan said. Instead of treating AI as a single transformation lever, his company is deploying it across the enterprise as both an execution accelerator and a structural capability.

The ambition, according to the executive, is to leapfrog to the next generation of EPC and manufacturing businesses by embedding AI and intelligent automation directly into workflows that define scale, cost, and delivery reliability.

AI In Layers From Tenders To Projects

A defining feature of AI adoption at L&T, Ganesan said, is its breadth across the project lifecycle.

AI systems are already active at the tendering stage, where teams analyse large volumes of contracts, technical documents, and risk variables under tight timelines. The entire process was dependent on........

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