Synopsis: Across consumer lending, auto manufacturing, and consumer goods, five listed Indian companies are deploying AI not as a headline feature but as a measurable operating lever and the financial impact is beginning to surface in collections recovery rates, revenue projections, and distribution efficiency.
Indian boardrooms spent much of the last two years announcing AI pilots. FY26 has started to reveal which of those pilots converted into measurable operating outcomes. Across sectors as varied as vehicle manufacturing, specialty paints, and retail credit, a set of listed companies are now citing AI directly in the context of revenue generation, cost reduction, and risk mitigation. Five of them are worth examining on the evidence available.
1. Piramal Finance
Piramal Finance is the retail lending arm of Piramal Enterprises, with a portfolio spanning home loans, MSME credit, and consumer finance. Its AI rollout has been among the more operationally specific disclosures in Indian financial services, cutting across the credit lifecycle from origination through collections recovery.
GenAI adoption across the business grew more than 3x in FY26, with the technology deployed in underwriting, fraud detection, customer service, and hiring workflows. AI-driven collections increased nearly 10x during the year. FY26 consolidated PAT rose to Rs. 1,506 crore, nearly three times the prior year, while AUM crossed Rs. 1 lakh crore. How much of that collections recovery is AI-attributable versus broader credit cycle improvement is a fair question, but the 10x collections metric is not a figure Piramal management has qualified with caveats.
With a market capitalisation of Rs. 48,067.01 crore, the shares of Piramal Finance Limited closed on Thursday at Rs. 2,120.50 per share, down 0.67 percent from its previous closing price of Rs. 2,134.80 apiece. It is trading at a P/E of 202.
2. Bajaj Finance
Bajaj Finance is India’s largest consumer lending NBFC by AUM, with a franchise built on cross-sell depth, customer acquisition scale, and disciplined underwriting. Management has identified more than 100 AI use cases and is building what it describes as an AI-first operating model across lending origination, collections, customer engagement, and risk management.
AUM crossed Rs. 5 lakh crore during FY26, while PAT grew 24 percent to Rs. 20,689 crore. At the scale Bajaj Finance now operates, the AI case is primarily about unit economics and headcount efficiency rather than individual productivity wins. Even incremental gains in collections accuracy or credit scoring translate into large absolute rupee outcomes across a book this size. The productivity improvements are difficult to isolate in the numbers, but the addressable gain from any successful AI deployment at Bajaj’s portfolio scale is correspondingly significant.
With a market capitalization of Rs. 6,11,015.16 crore, the shares of Bajaj Finance Limited closed on Thursday at Rs. 981.40 per share, down 0.96 percent from its previous closing price of Rs. 990.95 apiece. It is trading at a P/E of 31.94.
3. Mahindra & Mahindra
Mahindra & Mahindra operates across automotive, farm equipment, financial services, and technology. On the automotive side, it has made the most specific AI revenue commitment of any large-cap in this cohort: management has guided for AI to generate an additional Rs. 4,100 crore in automotive revenue in FY27, through product development, customer acquisition, vehicle sales optimisation, and manufacturing efficiency. For a large industrial, quantified AI revenue guidance is unusual.
FY26 results provide the backdrop. Revenue grew 25 percent to Rs. 1.48 lakh crore, while net profit increased 32 percent to Rs. 15,639 crore, driven by sustained SUV demand and execution. The Rs. 4,100 crore AI revenue projection amounts to roughly three percent of FY26 revenue, material without being the entire growth story. It is specific enough to be held against actual FY27 outcomes, which makes the guidance both credible and accountable.
With a market capitalization of Rs. 3,96,101.24 crore, the shares of Mahindra & Mahindra Limited closed on Thursday at Rs. 3,185.30 per share, up 3.94 percent from its previous closing price of Rs.3,064.50 apiece. It is trading at a P/E of 22.88.
4. Poonawalla Fincorp
Poonawalla Fincorp is a retail-focused NBFC building a technology-led lending platform across personal, business, and consumer durable loans. AI is integral to the company’s model: underwriting, customer acquisition, risk analytics, and loan processing are all designed around automated decision systems rather than traditional credit officer workflows.
Q3 FY26 PAT more than doubled quarter-on-quarter to Rs. 150 crore, while AUM grew nearly 78 percent year-on-year to Rs. 55,017 crore. Poonawalla is still in the growth phase of its AI buildout, and the unit economics of automated underwriting versus traditional models will require several more credit cycles to assess under stress conditions. The pace of AUM growth at stable margins suggests the model is scaling without immediate asset quality deterioration, but a fuller picture will emerge only as the portfolio ages.
With a market capitalization of Rs. 38,697.10 crore, the shares of Poonawalla Fincorp Limited closed on Thursday at Rs. 439.50 per share, up 2.65 percent from its previous closing price of Rs. 428.15 apiece. It is trading at a P/E of 69.58.
5. Asian Paints
Asian Paints enters this conversation from a different position. Competition has intensified, volumes have come under pressure in recent quarters, and the stock has had a difficult year. AI deployment here is less about growth acceleration and more about operational continuity: using advanced analytics to maintain efficiency across a distribution network that spans tens of thousands of dealers, hundreds of SKUs, and significant raw material price exposure.
AI is applied across demand forecasting, inventory optimisation, dealer servicing, pricing decisions, and supply-chain management. For a business running one of India’s largest distribution networks, these are the levers that determine whether margins hold when revenues soften. Asian Paints’ return ratios have remained among the highest in Indian manufacturing for years, and the analytical layer is part of what sustains them. In a difficult competitive environment, that is a different but equally serious use case for AI than anything the lending companies are pursuing.
With a market capitalization of Rs. 2,53,228.22 crore, the shares of Asian Paints Limited closed on Thursday at Rs. 2,640 per share, down 1.03 percent from its previous closing price of Rs. 2,667.50 apiece. It is trading at a P/E of 60.50.
The Common Thread
The emerging pattern across these five companies is instructive. AI adoption among Indian corporates is neither uniform in intent nor at the same stage of maturity. In retail lending, Piramal and Poonawalla are testing AI as a collections and underwriting tool in live portfolios; Bajaj Finance is applying it as a scalability lever across a franchise already at significant AUM scale. M&M has assigned a specific rupee figure to expected FY27 AI contribution, a form of accountability few large-caps are willing to accept on a technology bet. Asian Paints is using analytics to defend margins under competitive pressure rather than project growth. The common thread is that each of these companies is now being assessed on AI outcomes in its results, not just on AI ambition in its strategy presentation.
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