Companies are using AI to personalise discovery, improve marketing efficiency, reduce manual work in logistics, handle customer support, reduce failed deliveries, improve store productivity and speed up internal technology deployment. The use cases differ across companies, but the common thread is that AI is now being applied extensively to operating functions where scale, speed and accuracy matter.
The shift comes as listed internet companies are under pressure to show that growth can come without costs increasing at the same pace. AI is being positioned as one of the tools to add users, orders and transactions more efficiently by reducing manual work, improving conversion and making existing teams and networks more productive.
In logistics, Delhivery said it has deployed large language models and multimodal AI across voice, vision, location intelligence and real-time transaction processing. The company said AI is being used across order manifestation, mid-mile, last-mile and post-delivery processes, while also reducing the time and cost needed to deploy new technology.
The company’s management said on an earnings call that AI is helping reduce documentation, improve customer communication and make claims handling more efficient. It also said that the company had been able to trim teams earlier handling claims and parts of customer service, without a material increase in technology team size or inference costs.
For consumer-facing companies, AI is showing up more in discovery and conversion. Nykaa said it has built an online experience that includes an AI-powered skin scan, allowing customers to identify skin concerns and get product recommendations. AI is also helping personalise the app experience for newer users where the company does not have enough purchase or browsing history, and improve marketing efficiency on platforms such as Meta and Google, its management said on an earnings call.
At Lenskart, AI is being tied to offline productivity. The eyewear retailer said AI-driven scheduling and shorter eye-test cycle times helped stores handle higher footfall without similar headcount additions, contributing to a 270 basis point improvement in employee costs as a percentage of revenue in the March quarter. A basis point is a hundredth of a percentage point.
The company said its priority for 2026-27 is to move from being a consumer-tech company to a “consumer-AI company”.
“Our learning has been the bigger the surface, the bigger the impact of AI,” Lenskart cofounder and CEO Peyush Bansal said on an earnings call.
The company said its vertically integrated model allows it to connect intelligence across eye-test data, product design, manufacturing and store operations.
Meesho has also tied AI to operating efficiency. The ecommerce company said more than 70% of its code is now generated using AI, while its personalised feed engine PRISM drives more than 75% of orders. Its voice-shopping agent improved conversion by 22% among users who adopted it.
The company said its address intelligence model improved geocoding accuracy by 20 percentage points and reduced misroute-related costs by 5%, while another model reduced failed deliveries by more than 10%. Its voice and chat agent handled 19 million customer calls without human intervention in 2025-26 and helped reduce customer support costs by 23%.
“Every paisa of cost we engineer out of the system is a paisa that makes a previously unviable transaction possible online,” Meesho said in a shareholder letter.