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CEAT Limited’s Rahul Gama makes a case that manufacturing safety programs are failing not because of inadequate technology but because of a behavioral mismatch that most safety frameworks ignore. In a detailed interview on generational workplace risk, Gama identifies two distinct failure modes running simultaneously on the shop floor: younger workers underestimating risk, and experienced workers becoming complacent through familiarity. His argument is that AI-enabled monitoring, IoT sensors, and predictive dashboards expand what organizations can see, but the action that follows still depends entirely on human behavior that a single safety message cannot shape.
What this means for your business
Manufacturing CHROs who’ve spent the last three years justifying safety technology budgets now face a harder question sitting inside their own argument. The technology story was always about earlier detection. Gama’s observation is that detection without differentiated behavioral intervention is incomplete, and organizations treating a safety rollout as finished once the dashboards go live are measuring the wrong thing. Whether this applies to you depends on whether your safety training is designed around the risk profile of the actual workforce receiving it, or around the average employee who doesn’t exist.
The generational split Gama describes, what I’d call divergent-risk symmetry, is genuinely underappreciated in how safety programs get structured. Two employees can be equally dangerous through opposite psychological mechanisms, one through inexperience and one through over-familiarity, and a single compliance module addresses neither. The practical implication isn’t just a training design problem. It’s a measurement problem. If your leading indicators (near-miss reporting, hazard identification, unsafe-act reporting) are low, that can mean the workplace is safe or it can mean the culture doesn’t support speaking up. Most organizations can’t tell the difference from the data alone.
Gama’s point about production pressure is where this becomes a structural management question rather than a safety culture question. When supervisors are evaluated primarily on output, employees read the real priority signal accurately, regardless of what the posted policy says. The CHRO who can tie supervisor performance evaluations to behavioral safety metrics, not just lagging incident counts, is the one actually moving the underlying incentive. I’d revise this framing if an organization could show that incident rates declined durably without also changing how frontline managers are measured.
Concept deep-dive: Leading vs. lagging safety indicators
A lagging indicator, like a lost-time injury rate, tells you what already went wrong. A leading indicator catches signals before an incident happens, things like near-miss reports, hazard identification submissions, or safety committee participation rates. The distinction matters the same way a credit card statement differs from a spending alert. One describes the past, the other gives you a chance to act. Organizations anchored to incident counts as their primary safety metric are, by definition, waiting for harm before they measure anything.
Based on reporting from One safety message does not work for every generation: CEAT CHRO on managing workplace risk, originally published 2026-07-13 01:00:00.
