In the high-stakes world of manufacturing, where every minute of downtime can cost millions and supply chain snarls can derail entire operations, Artificial Intelligence (AI) is no longer a futuristic buzzword—it’s a proven profit driver. At the recent Technology Senate North event, Parna Ghosh, President, Group CIO, and Data Protection Officer at Uno Minda, cut through the hype to showcase tangible AI success stories from global and Indian factories.
His session, titled “Real Use Cases and Business Benefits Realized by Manufacturing Companies After Experimenting with AI,” painted a compelling picture: AI isn’t just experimenting anymore; it’s transforming bottom lines with rapid returns.
As Ghosh, an industry veteran who has steered several digital transformations at industry giants, shared, AI’s real magic lies in its ability to turn data into dollars. Drawing from implementations at Siemens, Tata Steel, and beyond, he highlighted how manufacturing firms are harvesting up to 45% efficiency gains, potentially injecting a staggering $50 trillion into the global sector by 2030.
For India’s “Make in India” ambitions, this isn’t abstract—it’s actionable, with local players like Tata Steel recouping AI investments in just 18 months.
From Breakdowns to Breakthroughs: Predictive Maintenance Takes Center Stage
Imagine a factory floor where machines whisper warnings before they fail, slashing unplanned outages by half. That’s the promise of AI-powered predictive maintenance, and Ghosh highlighted Siemens’ global rollout as a benchmark. By feeding sensor data into machine learning algorithms, Siemens has clawed back over ₹2,500 crore annually in savings—equivalent to averting thousands of hours of costly downtime.
Legacy equipment in labor-intensive plants often leads to reactive fixes, but AI flips the script. “We’re moving from firefighting to foresight,” Ghosh implied in his data-driven narrative, emphasizing how real-time analytics on vibration, temperature, and wear patterns enable proactive interventions. The result? Not just cost cuts, but a ripple effect: smoother production lines, happier workers, and greener operations with less waste from emergency overhauls.
For SMEs in the sector—many of whom eye electric vehicle (EV) transitions amid 2025’s PLI scheme incentives—Ghosh’s message is clear: Start small, scale smart. Pilot programs on critical assets like assembly robots can yield 20-30% uptime boosts within quarters, paving the way for enterprise-wide adoption.
Eyes on Quality: AI’s Visual Inspection
Quality control has long been the Achilles’ heel of manufacturing, plagued by human fatigue and inconsistent checks. Enter AI-driven visual inspection, where computer vision systems scan components at speeds and accuracies humans can’t match. Ghosh cited metrics that pack a punch: up to 89% fewer defects and a 96% first-pass yield, meaning far less rework and scrap.
Imagine a manufacturer’s bustling plants in Manesar or Pune, churning out mirrors and horns for millions of vehicles yearly. AI cameras, integrated with edge computing, now can detect micro-flaws in real-time—cracks invisible to the naked eye or misalignments in wiring harnesses. This isn’t sci-fi; it’s scaling in India, where precision is paramount for safety-critical auto parts. By reducing error rates, firms aren’t just saving on materials (up to 15% in some cases) but also fortifying compliance with stringent standards like AIS-140 for connected vehicles.
Supply Chains Unchained
If predictive maintenance is the guardian and visual inspection the inspector, AI’s supply chain optimization is the strategist. Ghosh zeroed in on how IoT sensors, ML forecasting, and analytics are rewriting logistics playbooks. The numbers speak volumes: ₹340 crore in direct savings and a 47% inventory trim for some adopters.
Tata Steel’s 18-month ROI story exemplifies this for India, where volatile raw material prices and Red Sea disruptions have hiked logistics costs by 20% in 2025. AI dynamically routes shipments, predicts demand spikes from weather data or market signals, and even negotiates with suppliers via smart contracts.
For Indian manufacturers, which typically source parts globally, this means leaner warehouses and faster time-to-market—crucial as India’s auto exports surge toward $30 billion this fiscal.Ghosh wove in a nod to resilience: Post-pandemic, AI helped firms like hers weather chip shortages by simulating “what-if” scenarios. Today, it’s evolving into generative AI for scenario planning, promising even sharper edges against 2026’s anticipated trade volatilities.
Global vs. Indian Maturity
Comparing global and Indian deployments, Ghosh noted that companies like Siemens and BMW are already at the maturity stage, having scaled AI across multiple plants. In India, companies such as Tata Steel, Maruti, and Uno Minda are fast catching up, experimenting across functions like quality, safety, document management, and predictive analytics.
“At Uno Minda, we’ve already rolled out several GenAI-based use cases internally,” he shared. “We believe the next wave of industrial AI will be led from India.”
Key Success Factors for AI in Manufacturing
Clear Business Objectives: “AI projects must be guided by clear business goals—not technology fascination.”
High-Quality Data: “Garbage in, garbage out. Data accuracy and structure are the foundation of all successful AI outcomes.”
Change Management: Beyond technology, human alignment is critical. “You can have 700 dashboards, but if no one looks at them, you’re back to Excel,” he cautioned.
Phased Implementation: “Start small—pilot, learn fast, fail early, and scale quickly.”
Robust Architecture & Cybersecurity: Ghosh underscored the need for a solid data architecture and strong OT–IoT cybersecurity framework. “AI runs on trust—of data, systems, and security.”
Investment Structure and Payback
AI project investments typically allocate the highest share—around 25% or more—to software and algorithms, followed by hardware, integration, and training.
However, Ghosh noted that most AI projects deliver full payback within 12–18 months, especially when applied to energy, maintenance, and supply chain optimization.
Building the Right Ecosystem
Ghosh concluded with practical recommendations for manufacturing leaders:
Start with pain areas and build the data foundation first.
Choose the right partners, including startups and system integrators.
Upskill and reskill teams to build internal capability.
Educate users and leaders on AI literacy—“AI for All,” as Uno Minda calls its internal program.
Above all, trust the data—AI is only as good as the belief and discipline with which it is used.
“AI is not a buzzword anymore—it’s a proven business accelerator,” Ghosh concluded. “When implemented with clarity, data discipline, and conviction, AI can unlock unprecedented efficiency, productivity, and sustainability for manufacturing. The time to scale is now.”