Published: February 2026
Category: Business & AI / Retail Technology
Lenskart is doubling down on artificial intelligence, with CEO Peyush Bansal calling AI the “backbone” of the company’s operations. What began as early investments in analytics and data infrastructure has now evolved into a full-scale AI-driven growth engine and the numbers suggest it’s working.
Early AI bets paying off
Years ago, Lenskart invested in AI capabilities through platforms such as Tango Eye and GeoIQ, initially focused on improving store productivity and site selection. According to Bansal, those systems have become significantly more powerful with the rise of generative AI and agentic AI.
“What that investment could do was ‘X’ till last year now it is able to do 10X,” he said, crediting recent operational acceleration to advanced AI deployment across the company.
Profit surge in Q3 FY26
The impact is visible in Lenskart’s latest earnings. In Q3 FY26, consolidated net profit jumped more than 70 times year-on-year to ₹131 crore, while revenue from operations rose 38.3% YoY to ₹2,307.7 crore.
The company attributes much of this performance to AI-led optimisation across logistics, supply chains, and store networks. AI has also enabled next-day delivery coverage to expand to over 60 cities, up from roughly 30 cities last year.
Driving efficiency and expansion
AI is also streamlining warranty servicing and diagnostics, making processes faster and more cost-effective. Diagnostics, in particular, are central to Lenskart’s strategy of expanding India’s eyewear market by attracting first-time users.
In Q3 FY26 alone, the company opened 169 new stores, marking a 160% increase over the same period last year. Notably, 41% of these stores were launched in Tier-2 and beyond markets signalling a deeper push into underserved regions.
AI-powered market intelligence
Beyond operational gains, Bansal says AI is transforming how Lenskart understands consumer behaviour. Instead of relying solely on transaction data, the company now analyses social media trends, local content preferences, and influencer engagement to detect emerging style demands.
This granular insight allows Lenskart to tailor inventory and expansion strategies market by market something traditional retail analytics could not deliver at scale.
As AI becomes more deeply embedded in retail infrastructure, Lenskart’s performance suggests that early investment in data and machine learning may offer a significant competitive edge especially in fast-growing, digitally aware markets.
Author. Adedoye Adigun