Automation alone won’t make Indian manufacturing safer in 2026: Intelligence will

By Praveen Arora, Vice President – IoT Business Unit, Tata Communications

At first glance, it appears counterintuitive. Even as Indian manufacturing embraces higher levels of automation, thousands of workers continue to suffer serious workplace injuries every year. Automation, it turns out, does not automatically translate into safety.

The CRUSHED 2025 report by the Safe in India Foundation offers a sobering reality check for the automotive sector. It highlights that more than 8,500 workers have suffered serious injuries due to workplace accidents, pointing to persistent gaps in safety practices on factory floors. While automation has improved output and efficiency, worker protection has not kept pace. Data from labour inspections and safety audits since 2020 reveals a troubling paradox: automation and output have surged post-pandemic, yet serious shop-floor injuries have barely declined, exposing a disconnect between productivity gains and worker safety.

The concern extends well beyond automotive manufacturing. Steel and heavy engineering plants where workers routinely operate amid extreme heat, heavy machinery, dust, chemicals and high-decibel environments remain among the most hazardous workplaces. Various industry reports from steel and heavy engineering sectors note a higher share of fatal and severe industrial accidents than in light manufacturing. A study focused on iron and steel workers found that about 28% of workers reported workplace accidents or injuries over a 12-month period, underscoring a high incidence of non-fatal injuries.

Recognising this, the Economic Survey 2024–25 calls for urgent reforms in Occupational Safety and Health (OSH), warning that unsafe workplaces undermine India’s labour productivity and industrial competitiveness. The Survey explicitly links workplace safety outcomes with manufacturing efficiency, labour participation and India’s ability to attract global supply chains particularly in sectors such as automotive, metals and electronics where international buyers increasingly evaluate ESG and safety benchmarks alongside cost.

The numbers are stark. The Survey estimates that workplace injuries and accidents result in annual productivity losses of nearly Rs 12.5 lakh crore, equivalent to about 4.2% of India’s GDP. This is not just a human cost; it is a material economic one.

Safety Is Not a Cost. It Is a Productivity Multiplier.
A large proportion of industrial accidents are preventable. Most stem from process lapses, equipment fatigue, or early warning signs that go unnoticed. There are always possibilities of machinery overheating, abnormal vibrations, loose electrical connections or compressed air leaks. Yet, workplace safety is often viewed through the narrow lens of compliance costs: inspections, certifications, training programmes and audits.

What this view misses is a simple truth: safe manufacturing facilities are economical to run. Fewer accidents mean lower downtime, reduced absenteeism, stronger morale, fewer regulatory penalties and more stable operations. In an era where margins are under pressure, safety is not a trade-off against productivity, it is a driver of it.

This is particularly relevant as India accelerates its transition to Industry 4.0. While much of the conversation has focused on automation, robotics and efficiency gains, the next phase of industrial growth must be human-centric. Productivity and safety are not competing priorities; they are deeply interlinked.

From Reactive to Predictive: Rethinking Industrial Safety
Traditional safety models are largely reactive. Proactive approaches aim to identify risks early and go a step further, anticipating failures and exposures based on historical and real-time data. This shift enables organizations to move from incident driven compliance to data driven risk prevention mechanisms, reducing both human harm and unplanned operational downtime.

Predictive safety is where Industry 4.0 technologies can make their most meaningful impact. IoT sensors, AI-powered analytics, thermal imaging and condition monitoring systems are now capable of detecting anomalies long before they escalate into accidents. Most incidents do not happen without warning; they are preceded by patterns that machines can identify faster and more reliably than humans.

AI-enabled video analytics, for instance, can monitor shop floors in real time, flag unsafe behaviour, detect environmental risks and trigger alerts before injuries occur. When integrated with security and operational systems, these tools enable continuous compliance and real-time risk mitigation rather than periodic checks. A 2025 ILO global report on occupational safety and health found that AI, automation and smart monitoring systems can reduce hazardous exposures, help prevent workplace injuries and improve overall working conditions by enabling continuous hazard detection and proactive interventions, rather than solely relying on traditional inspections and reactive reviews.

The outcome is a fundamental shift: from safety as an obligation to safety as an intelligent, always-on capability embedded into daily operations.

Building a Scalable Safety Blueprint for Industry 4.0
To achieve this, manufacturers need more than point solutions. They need a scalable, interoperable framework for safety transformation, one that aligns with broader Industry 4.0 goals without disrupting existing workflows.

Modular, cloud-ready IoT platforms allow manufacturers to digitise operations incrementally while maintaining flexibility. Such architectures enable real-time data collection, system integration and AI-driven insights across plants and processes.

Crucially, safety transformation must be implemented in phases:

Phase 1, Foundation and pilots: This includes risk assessments, network security, basic IoT deployment (such as smart meters and sensors), and workforce training. 

Phase 2, Scale and integration: Systems are integrated, digital twins are introduced, and real-time monitoring enables continuous self-assessment at the plant level. 

Phase 3, Optimisation and intelligence: AI-driven insights power predictive maintenance, proactive hazard prevention and the development of a data-led safety culture. 

Phase 4, Closed loop automation: Enable real-time, self-correcting safety systems incorporating AI-driven automated responses (shutdowns, alerts, isolation, rerouting), closed-loop integration (SCADA, PLCs, DCS, robotics, access control) and self-learning safety models that adapt with operations. 

This phased approach ensures that safety systems evolve alongside operational maturity, rather than being bolted on as afterthoughts.

The Business Case for Safer Factories
Manufacturers lose billions annually to avoidable accidents, unplanned downtime and compliance failures. Beyond the tragic human toll, these incidents erode margins, damage reputations and weaken competitiveness.

Therefore, organisations like automotive and steel and heavy engineering would do well to revisit their investments in preventive maintenance, continuous monitoring and predictive safety to see measurable gains: fewer incidents, lower absenteeism, higher productivity and stronger workforce trust.

As India positions itself as a global manufacturing hub, safety cannot remain peripheral to the Industry 4.0 agenda. Automation without intelligence (and efficiency without foresight) will fall short.

A future-ready manufacturing sector must be both smart and safe. When AI, IoT and predictive analytics are deployed with people at the centre, safety becomes not just a moral imperative, but a strategic advantage for businesses, workers and the economy alike.