In an interview with Karan Chechi, CEO and Founder of TechSci Research, TimesTech explored how robotics, AI, industrial IoT, and automation are transforming India’s manufacturing ecosystem. Karan highlighted the growing role of smart factories, predictive technologies, workforce upskilling, and sustainable automation in enhancing productivity, efficiency, and global competitiveness across sectors such as automotive, electronics, pharmaceuticals, and engineering.
Read the full interview here:
TimesTech: How do you see robotics and automation shaping the future of manufacturing in India?
Karan: Robotics and automation will play a defining role in the next phase of manufacturing growth in India by helping factories become more productive, precise, scalable, and globally competitive. Indian manufacturing is steadily moving from labour-intensive, variability-prone operations to digitally enabled production environments where automation improves consistency, quality assurance, traceability, safety, and throughput. This shift is especially relevant as India seeks to strengthen its position in sectors such as automotive, electronics, engineering, pharmaceuticals, and capital goods. Government-backed initiatives such as SAMARTH Udyog Bharat 4.0 are also helping accelerate this transition by building awareness, demonstration capacity, and Industry 4.0 ecosystems across the country. The on-ground momentum is already visible: according to the International Federation of Robotics, India recorded 8,510 industrial robot installations in 2023, a 59% increase over 2022, making it the 7th largest market globally by annual installations. From a market perspective, Techsci Research also underscores the scale of this trend: the India Industrial Automation Market was valued at USD 16.20 billion in 2024 and is projected to reach USD 37.42 billion by 2030, reflecting how automation is becoming central to manufacturing competitiveness, resilience, and future readiness in India.
TimesTech: What role are AI, industrial IoT, and data analytics playing in the evolution of smart factories?
Karan: AI, industrial IoT, and data analytics are together forming the core intelligence layer of smart factories. Industrial IoT connects machines, sensors, controllers, and production systems so that factory data is captured continuously rather than remaining isolated across separate equipment or departments. Data analytics converts this flow of information into actionable visibility by identifying inefficiencies, enabling real-time monitoring, detecting bottlenecks, and supporting faster operational decisions. AI builds on this by making factories predictive and adaptive: it can power demand forecasting, predictive maintenance, machine vision-based quality inspection, process optimization, warehouse automation, and intelligent scheduling. NITI Aayog describes this broader transformation as enabling the “Factory of the Future,” where technical systems become flexible and capable of smart responses in dynamic environments. The Telecommunication Engineering Centre has similarly highlighted AI, IIoT, robotics, and real-time data evaluation as the foundational building blocks of Industry 4.0-enabled manufacturing. This evolution is also reflected in Techsci Research’s India-focused outlook: the India IoT in Manufacturing Market was valued at USD 58.91 billion in 2024 and is projected to reach USD 112.69 billion by 2030, showing the growing role of predictive maintenance, asset tracking, real-time monitoring, and smart factory solutions in India’s manufacturing landscape.
TimesTech: What are the biggest challenges manufacturers face while adopting automation technologies at scale?
Karan: The biggest challenge in adopting automation at scale is that automation is not merely a technology purchase; it is an operational transformation. For many Indian manufacturers, especially MSMEs, the first barrier is the high upfront cost of equipment, software, retrofitting, systems integration, training, cybersecurity, and maintenance. Even where the long-term gains are clear, the initial financial commitment can delay decision-making. The second challenge is legacy infrastructure. A large portion of Indian manufacturing still depends on older machines and fragmented control environments that were not designed for digital interoperability, making integration difficult and sometimes expensive. The third major constraint is the shortage of skilled talent capable of working with automation systems, industrial data, controls, AI-enabled tools, and cybersecurity requirements. Technical reports on Industry 4.0 in India also point to interoperability issues arising from proprietary protocols, as well as increased cyber risk as machines, IoT devices, and cloud platforms become interconnected. Beyond technology, there is an organizational challenge too: automation at scale requires process redesign, leadership commitment, change management, and clarity on return on investment. In short, the real hurdle is not adoption alone, but aligning machines, software, people, and processes into one coherent production strategy.
TimesTech: How is automation changing workforce requirements and the demand for new skill sets in manufacturing?
Karan: Automation is reshaping manufacturing jobs from routine task execution toward technology-enabled supervision, diagnostics, and decision support. The shop-floor workforce of the future will increasingly be expected not just to operate machinery, but to work with automated systems, interpret equipment data, manage exceptions, oversee digital workflows, and collaborate with robots and connected production lines. As a result, demand is rising for skills in mechatronics, robotics integration, PLCs and industrial controls, predictive maintenance, machine vision, digital quality systems, data interpretation, and operational cybersecurity. The International Labour Organization has observed that in Indian manufacturing, the share of higher-skilled occupations has increased while the share of middle-skilled occupations has declined over time, indicating a structural shift in workforce demand. This does not mean labour becomes less relevant; it means labour becomes more technical, adaptive, and multidisciplinary. India’s skill development architecture is beginning to reflect this transition. Under IndiaSkills, new-age skill categories now include robot system integration, additive manufacturing, industrial control, and Industry 4.0, which signals the direction in which manufacturing employability is moving. The challenge ahead is therefore not simply job replacement, but large-scale upskilling and reskilling so that workers can transition into more digitally intensive, higher-productivity roles within modern manufacturing systems.
TimesTech: How can companies balance productivity gains from automation with sustainability and energy efficiency goals?
Karan: The best way to balance automation with sustainability is to embed energy, material efficiency, and emissions performance directly into automation strategy rather than treating them as separate ESG objectives. When deployed effectively, automation can support sustainability by reducing rework, stabilizing process conditions, minimizing scrap, improving maintenance cycles, and lowering energy consumed per unit of output. Connected systems also allow companies to monitor energy consumption machine by machine, optimize motors and compressed air systems, reduce idle-time losses, and schedule high-load operations more intelligently. In India, the value of combining industrial performance with energy efficiency is clearly reflected in the Perform, Achieve and Trade (PAT) framework led by the Bureau of Energy Efficiency. Official data shows that PAT Cycle I delivered 8.67 MTOE of energy savings and avoided about 31 million tonnes of CO2, while PAT Cycle II delivered around 14.08 MTOE in energy savings and avoided roughly 68 million tonnes of CO2 emissions. The implication for manufacturers is clear: productivity and sustainability do not have to be competing goals. Companies that integrate automation with real-time monitoring, predictive maintenance, process optimization, and energy-management KPIs will be better positioned to improve profitability while also advancing decarbonization and resource efficiency goals.
