Physical AI: The Next Frontier for Industry – Insights from HCLTech’s Sukant Acharya

When you hear the term “physical AI,” most people picture robots on an assembly line or autonomous trucks zipping through warehouses. But Sukant Acharya, Vice President at HCLTech, argues that the reality is far richer – it’s about weaving intelligence into the very fabric of machines, products and even the environments they inhabit.

“We’re moving beyond the classic software‑only AI models,” he says, a hint of excitement in his voice. “Think of a smart sensor that not only measures temperature but also predicts when a motor will overheat, or a conveyor belt that learns the optimal speed based on real‑time demand fluctuations.” In Acharya’s view, the convergence of edge computing, IoT, and advanced algorithms is creating a new breed of “thinking hardware.”

This shift, he explains, isn’t just a tech upgrade – it’s a strategic lever. Companies that embed AI directly into their physical assets can slash downtime, boost yield and, perhaps most importantly, respond to market swings in near‑real time. “It’s the difference between reacting to a problem after it happens and preventing it before it even surfaces,” he notes.

Acharya points to several sectors where physical AI is already making waves. In automotive manufacturing, for example, HCLTech has helped a tier‑one supplier retrofit its stamping machines with vision‑based defect detection. The result? A 12 % reduction in scrap and a smoother production flow. In logistics, a partner of HCLTech deployed AI‑enabled forklifts that adjust their paths based on floor‑traffic analytics, cutting travel time by roughly 15 %.

But the journey isn’t without hurdles. Data quality, cybersecurity, and the need for new skill sets top the list of challenges. “You can’t feed a machine garbage and expect it to make sense,” Acharya warns. “Organizations must invest in clean, labeled data pipelines and robust security frameworks before they even think about scaling.” He also emphasizes that upskilling the workforce is non‑negotiable. “Your people need to understand the ‘why’ behind the algorithms, not just how to push a button.”

To help firms navigate this terrain, HCLTech offers a three‑stage roadmap: first, identify high‑impact use cases where physical AI can unlock immediate value; second, pilot those solutions in a controlled environment, gathering feedback and fine‑tuning models; and third, roll out at scale, leveraging the company’s cloud‑edge platform to ensure seamless connectivity across sites.

Looking ahead, Acharya is bullish about the role of generative AI in shaping physical systems. “Imagine a design tool that not only drafts a new turbine blade but instantly simulates stress, wear and even suggests manufacturing tolerances,” he muses. “That’s the kind of closed‑loop innovation we’ll see become commonplace.”

For business leaders listening today, the takeaway is clear: physical AI isn’t a distant futuristic concept—it’s already here, quietly reshaping the back‑office of industry. The question is whether you’ll be a passive observer or an active architect of that new reality.