According to Mahindra & Mahindra Group CEO and Managing Director Anish Shah, artificial intelligence is now delivering measurable business outcomes beyond productivity improvements. As reported during the company's post-earnings media briefing, AI is helping the company acquire customers it would likely not have got otherwise, generating incremental revenue through technology-led initiatives. Shah emphasized that "Just using AI, these are customers that we would likely not have got otherwise, and that's incremental revenue that comes in leveraging this technology." This strategic shift demonstrates how M&M's AI implementation has evolved from cost optimization to revenue generation across multiple business segments.
According to reports from CNBC TV18, Mahindra Group is executing 15 large-scale AI transformation projects across its businesses, but emphasizes that the technology will be judged by its ability to drive revenue growth, reduce costs and improve customer experience rather than by AI adoption alone. Speaking during the company's earnings press conference, management stated that AI must ultimately translate into higher revenue, lower costs and a better customer experience, with the company tracking outcomes across quality, customer experience, reach and operational efficiency. Shah reinforced this approach, stating that "Being a tech leader in every industry is not just a logo or a statement we have up there. It's something that each of our businesses are living and striving to build that expertise."
The company's latest financial results demonstrate the tangible benefits of its AI strategy, with M&M reporting a 34% year-on-year rise in consolidated profit after tax to ₹5,455 crore for the June quarter, while revenue climbed 28% to ₹58,188 crore. As Shah noted, AI is helping the company compress engineering timelines, improve customer service and speed up lending decisions, while also lowering customer acquisition costs. The company's 2,600 workshop staff now use AI-powered assistance to diagnose vehicle issues and resolve customer complaints faster, while 65% of loan files are currently processed through its AI platform, "Samurai", significantly reducing processing time. AI-led cross-selling initiatives have lowered customer acquisition costs by 30%, demonstrating the practical impact of the technology across multiple customer touchpoints.
As reported by CNBC TV18, Mahindra's AI strategy is built on three pillars—deploying AI, transforming business processes and operations, and inventing AI-native models. The company aims to make Mahindra a technology leader across the industries in which it operates, with the strategy focused on delivering measurable business outcomes by embedding the technology across multiple functions within the group rather than pursuing AI for its own sake. Shah emphasized that "The broader technology-led strategy, combined with stronger collaboration across group companies, was helping improve business performance." The company has built 19 proprietary AI models, employs more than 50 AI engineers and specialists, has trained 1,900 employees through its AI academy and is currently executing 15 enterprise-wide AI transformation projects across businesses.
Beyond customer-facing applications, M&M has achieved significant operational improvements through AI implementation. According to Shah, one of the most significant gains has come in vehicle development, where tasks such as predicting drag coefficients through simulations, which earlier took 8-12 hours, can now be completed in around two minutes. This technological advancement allows engineers to focus more on product development and building better cars faster. The AI push has extended beyond corporate offices to manufacturing facilities, with Shah recalling a recent visit to one of the group's manufacturing facilities, where shop-floor employees explained how AI was helping improve quality and productivity. The company has also used AI to fulfil over five lakh customer service requests, demonstrating the comprehensive nature of its AI deployment strategy across all operational levels.
