India’s quarterly GDP growth were revealed recently, and one thing is very clear: India needs manufacturing of greater scale. However, India’s manufacturing debate is asking the wrong question.
That means the Indian industrial worker of tomorrow will need a radically different skill set: mechanical or electrical expertise combined with familiarity with sensors, digital twins, robotics, data, AI-assisted diagnostics and increasingly software-controlled manufacturing systems. India should not attempt to reproduce it by simply copying yesterday’s industrial model. It is increasingly framed as a choice between two futures: one in which factories are powered by artificial intelligence, automation and autonomous machines, and another in which India builds a large pool of highly skilled, specialised technicians and engineers. But if India seriously wants to become a manufacturing power – and compete with China in global supply chains – the choice is not between AI and people. India needs both, and more importantly, it needs to understand how the two fit together. At a recent national conference where I led a discussion on advanced military technologies, an observation by an Indian Air Force Vice Chief brought this dilemma into sharp focus. His point was simple but profound: imagine having to address a structural issue on a fifth-generation fighter aircraft. Even something as apparently mundane as fixing a nut or bolt on a stealth airframe can require a highly specialised technician who understands the material, structure, tolerances and consequences of getting the repair wrong. No AI model can substitute for that expertise. But the converse is equally true. The modern factory cannot remain dependent on human intervention for every inspection, calculation, prediction or adjustment. The sheer complexity and speed of modern manufacturing make that impossible. So, is AI the core of the factory or merely a helper? The answer lies somewhere more interesting: AI should become the multiplier of human industrial capability. As Manish Kumar, a global technology leader and CEO of SOLIDWORKS at Dassault Systèmes, puts it: “AI is the multiplier. You are the value. The distinction matters. A multiplier can dramatically increase an existing capability, but anything multiplied by zero is still zero. Without people who understand engineering, materials, machines, production and quality, there is little meaningful value for AI to multiply. Kumar has made the same argument publicly at 3DEXPERIENCE World, stressing that AI is intended to amplify engineering expertise rather than make it obsolete. That should be the starting point for India’s manufacturing strategy. India does not have an AI problem alone. It has an industrial capability problem. Some of the world’s largest technology companies are conducting advanced AI work through their India-based centres of excellence. Indian engineers are building models, algorithms, digital platforms and increasingly sophisticated applications. Yet there is a significant gap between being a major consumer and developer of digital technology and becoming a country capable of using that technology to design, engineer, manufacture and export complex physical products at scale. That distinction is crucial. China’s manufacturing advantage was not created by AI alone. It was built over decades through industrial ecosystems, specialised suppliers, machine tools, materials expertise, engineering capabilities, logistics networks and enormous production scale. AI is now being layered onto that foundation. India cannot afford to jump directly to the AI layer while neglecting the industrial foundation underneath it. India needs AI-native manufacturing built on deep engineering competence. Consider what AI is already good at inside a factory. It can analyse enormous quantities of production data and identify patterns that would be almost impossible for a human to detect. Companies such as Mahindra are already applying AI and computer vision to manufacturing quality and predictive maintenance, while newer manufacturers such as Ather Energy have designed factories around extensive networks of machines, sensors and data systems. AI is exceptionally good at watching everything simultaneously. But watching is not the same as understanding. An AI system may tell a technician that a machine is drifting outside its normal operating parameters. The technician still has to determine why. Is it a tool-wear problem? A material variation? A calibration issue? A software fault? A mechanical failure? And then comes the most important question: should the line be stopped? That decision can involve production schedules, safety, quality, contractual commitments and engineering judgement. This is where specialised human expertise becomes indispensable. The autonomous factory changes the equation This becomes even more important as India begins designing autonomous manufacturing lines. An autonomous factory cannot simply mean installing more robots. Robots executing badly designed processes merely automate inefficiency. The real opportunity is to design the factory digitally before building it physically. Engineers can create a virtual representation of an entire production facility, simulate production flows, identify bottlenecks, test different layouts, examine safety conditions and model what happens when a machine fails. Problems can be discovered on a computer before concrete is poured. That is what Manish Kumar again emphasised. This is where virtual twins, AI and industrial engineering converge. Industry World Models can take this further by representing the relationships among the product, materials, machines, processes and operating conditions. AI can simulate thousands of possible outcomes and expose relationships that human engineers might otherwise discover only through expensive trial and error. But there remains a fundamental division of labour: AI simulates, the engineer decides. That is not a weakness of AI. It is the architecture of a genuinely intelligent industrial system. AI for Aerospace & Defence Nowhere will this human-machine partnership be more consequential than aerospace and defence. A modern fighter aircraft is not simply an assembly of parts. It is a tightly integrated system of materials, aerodynamics, propulsion, avionics, software, sensors, thermal management, structural engineering and manufacturing tolerances. A stealth aircraft makes that challenge even more unforgiving. AI can optimise designs, detect anomalies, predict component failures, analyse sensor streams and assist engineers in designing complex structures. Digital twins can allow manufacturers to test configurations before physical production. But when the aircraft is sitting on the ground and something is wrong with its structure, actuator, wiring or airframe, somebody still has to understand the physical machine. That person cannot be replaced by a chatbot. Indeed, the more sophisticated the machine becomes, the more sophisticated the human expertise required to maintain it becomes. This is an important lesson for India’s ambitions in aerospace. Building an indigenous fifth- or sixth-generation aircraft is not merely a question of having designers, software engineers or AI researchers. It requires thousands of specialised people who understand composite structures, precision machining, avionics integration, metallurgy, propulsion, testing, maintenance and certification. AI will make these people more productive. It will not make them unnecessary. Warfare is already demonstrating what comes next The same transformation is occurring in military technology. AI is changing defence through autonomous systems, real-time battlefield analysis, predictive logistics, sensor fusion, cybersecurity and increasingly autonomous decision-support. The emergence of unmanned systems and drone swarms means that one operator may increasingly supervise multiple platforms rather than manually control a single machine. China’s military modernisation demonstrates the direction of travel: Beijing is investing heavily in the integration of AI, autonomous systems and civilian technological capabilities into military applications. Instead of controlling every individual platform, the operator will increasingly define objectives, constraints and rules while machines handle enormous volumes of data and coordinate routine actions. That requires a different kind of military professional: someone who understands the machine, the data and the operational environment simultaneously. The same principle applies inside a factory. India’s real competitive advantage could be the combination This is where India has an opportunity. China has built an enormous manufacturing machine. India can potentially leapfrog into a different model – one in which factories are designed from the outset around AI, automation, digital twins, advanced robotics and highly skilled human operators. But that requires a fundamental change in education and industrial policy. India needs to stop treating vocational training and advanced technology as separate policy silos. The country needs AI-enabled technicians, digitally fluent engineers and manufacturing-oriented AI scientists. An industrial training institute should teach more than conventional machining. A modern technician should understand CNC systems, robotics, sensors, digital diagnostics and AI-assisted maintenance. Engineering universities should expose students to actual factories, production constraints and industrial software – not merely theoretical design. And India’s premier universities need to become much more aggressive in frontier AI research. There is already a remarkable concentration of AI talent in India’s technology ecosystem. But, as Kumar argues, India needs the right policy environment, focus and university ecosystem to convert that talent into a global force. The conspicuous absence of Indian-origin frontier models competing at the highest global level should itself be treated as a strategic warning. India cannot aspire to be the world’s factory while remaining dependent on others for the most important digital intelligence embedded inside that factory. Design-to-manufacture must become India’s national strategy. The real prize is not assembling products designed elsewhere. It is moving up the value chain. (Dr Manish Kumar Jha is a national security expert, defence editor, and the founder of the military think tank, Strategic Insights) Disclaimer: These are the personal opinions of the author
The objective must therefore be much more ambitious than “AI-enabled manufacturing”.
