Synopsis
A survey found meeting customer expectations (49%) and boosting operational efficiency (47%) were the primary forces driving AI adoption among manufacturers. However, cost reduction, often touted as a top reason for AI adoption in enterprises, ranked last at just 23%.Listen to this article in summarized format
The survey found meeting customer expectations (49%) and boosting operational efficiency (47%) were the primary forces driving AI adoption among manufacturers. However, cost reduction, often touted as a top reason for AI adoption in enterprises, ranked last at just 23%.
Instead of slashing the workforce, manufacturers are chasing real-time data insights (46%) and improving product quality/defect detections (36%) to speed up factory-floor decision-making and overall efficiency. Sustainability and safety were also major winners, with 39% of manufacturers leveraging AI to optimise energy use and 36% saying protection of workers were top AI impact areas.
"Rather than isolated automation investments, manufacturers are building intelligent systems where data flows seamlessly between machines, operators, and enterprise applications—enabling real-time decisions, improving worker safety, and driving more sustainable operations at scale," Himani Agrawal, chief operating officer, Microsoft India and South Asia, told ET.
She said manufacturers were moving decisively from experimentation to scaled deployment of AI, with the fastest adoption seen across automotive, industrials, and energy-intensive sectors.
"Global industrial leaders like ABB are using AI to unify operational data and enable predictive insights at scale, while Indian enterprises such as the Mahindra Group are applying AI across both customer engagement and core operations," Agrawal added. "What is also emerging strongly is agentic AI, where systems can reason and act across workflows, enabling manufacturers to move from insights to execution at scale."
The survey found that connecting machines for real-time data (36%) was rated the foremost prerequisite for AI in manufacturing, followed by strengthening data security and privacy (30%), hiring AI and data talent (30%), and embedding AI into processes (29%).
"The most meaningful impact is coming from connecting data across the value chain and turning it into real-time, actionable intelligence—whether it is improving quality, optimising supply chains, or enhancing customer engagement," Agrawal explained.
German luxury carmaker Mercedes-Benz said it was using AI and future technologies in every aspect from product development, to production, to internal processes. The company has been using industrial robotics technology for decades to perform particularly monotonous and physically demanding tasks. However, Santosh Iyer, managing director, Mercedes-Benz India highlighted the transformative impact that AI has had on these functions.
"AI is directly getting deployed into production making it intuitive, accessible, and usable for everyone," Iyer said. "AI-supported virtual assistants analyse complex data in real time and instead of laborious, manual root-cause analysis, the engineers now rely on AI agents from a virtual data-science team. These AI agents quickly and reliably analyse available data, identify patterns, and offer comprehensive analysis and solutions resulting in higher efficiency in production."
For Danish multinational Danfoss, AI has been most effective in prototyping, simulations, software testing and coding, where AI significantly accelerates new product development cycles. What used to take two years is now being done in about six to seven months, Ravichandran Purushothaman, president of Danfoss India said. Reducing wastage, both material and time, as well as enhanced worker productivity and safety have been other impact areas.
"Industry 4.0/AI enabled production lines are delivering 15-20% higher productivity," he said. "In the supply chain environment, an operator on a machine without digitalisation was a bit blindfolded. Today, they have got a lot of meaningful data that can help them take actions. So, AI is improving the human interface to machines with quality, which brings a lot of safety improvements as well in the factory leading to higher productivity for blue collar workers too."
While the use cases across sectors are varied, the impact is across the board. Industries-to-services conglomerate RPG Group too said AI and generative AI have delivered a measurable impact across the group with around 28% energy savings in manufacturing to 25% faster supply chains and accelerated product engineering and innovation.
"Generative AI and AI is driving end-to-end value across RPG Group, with the strongest impact in manufacturing efficiency, supply chain agility, engineering and R&D delivering faster product innovation," Amol Deshpande, vice president and head, innovation and digital at RPG Group, said. "Business strategy, HR, finance and customer facing functions have reported productivity and efficiency gains."
Data driven maintenance operations have been on for a while in RPG group companies. With over 1,000 IoT sensors and AI-led analytics, Deshpande said the RPG Group has shifted to proactive maintenance in many cases — reducing downtime and improving asset reliability at scale.
The AI Readiness & Adoption survey also found that the human–machine relationship can create significant value in manufacturing with faster onboarding and skill transfer through AI (57%) being the top collaboration opportunity. Other important avenues included improving shop floor safety (46%), AI-assisted decision-making for operators (50%), and automating repetitive tasks (47%). AI was being viewed as a talent multiplier and safety enabler — not just an efficiency tool.
While the potential was immense, the survey found that most manufacturers are at a transitional security stage — where it is visible but not yet automated. Only 3% have security fully embedded into their operational architecture - automated, continuous and AI-augmented. Some (16%) said they were relying primarily on network separation by keeping OT (operational technology) and IT (information technology) environments isolated while 59% said they were moving beyond isolation towards actively monitoring their OT environment while only 13% said they have deployed AI-driven threat detection across OT and IT.
(This article is part of the AI Vantage series, developed in partnership with Cisco)
