What Should Be Your AI Strategy? Business Leaders Weigh In Companies are realising that business model reinvention is necessary, and AI must be at its core

AI is no longer just a technology—it's a survival strategy. According to the 28th CEO survey by PwC, four out of ten CEOs globally believe that if they continue business as usual, they will cease to exist in 10 years. Companies are realising that business model reinvention is necessary, and AI must be at its core. But how do you build an AI strategy that drives real business value? Top industry leaders shared their perspectives.

AI for all: small vs. large businesses

Many thinks AI is only for big companies with deep pockets but Krishnan Venkateswaran, Chief Digital & Information Officer at Titan, disagrees. "Frankly, there's no difference between a large company and a small company," he said. Instead of a rigid AI strategy, Titan takes an iterative approach—"creating excitement, inviting discussions, discovering use cases, and hoping the virtuous cycle will click."

One such success story at Titan involves AI-powered eye measurements for spectacle fitting. "We use open-source AI to take a photograph, identify your pupil's frame, and calculate the fitting height. It's perfectly accurate," he shared.

Data vs. AI: what comes first?

Many organisations struggle with whether to first focus on AI or data management. Satya Kalki, Chief Technology Officer at Infra.Market, believes in balancing both. "You have to shorten the cycle. You cannot think that I will have a large phase of building AI and a large phase of building data." He emphasised the importance of "continuously enriching data, even borrowing attributes from different industries."

Managing AI risks: are companies doing enough?

AI risks are real, from biased models to regulatory concerns. Munjay Singh, Chief Operating Officer at Tredence, highlighted a hidden risk: "Companies think their developers are not using ChatGPT, but they are. They take a photo, upload it to ChatGPT, get the code cleaned up, and paste it back into GitHub." He further suggested that organisations need to recognize how AI is being used informally and set up guardrails.

Pilots vs. full-scale AI adoption

Should companies start small with AI pilots or go all in? Shyam Eneti, Chief Delivery Officer at Encora, advocated for decentralisation. "If you wait for a centralised AI model, you'll lose to competitors. The smarter companies should go straight to building MVPs instead of running endless pilots."

However, Venkateswaran warned against getting stuck in "the use case trap—where you keep building use cases without scaling anything." Instead, he suggested quick proof-of-concept tests before full investment.

AI: a fear or an opportunity?

One major concern is whether AI adoption will threaten jobs. But Kalki found that "whatever resistance we thought we had was not there." Employees embraced AI when they saw it made them more productive, helping sales teams plan better and reducing errors in manufacturing.

Singh predicted a major shift: "AI will de-skill expertise. Earlier, you needed 10-20 years of experience—now younger employees can use AI and deliver superior results…The younger generation has already figured out how to use AI tools. Organisations that dictate how AI should be used will lose the edge," he concluded.