by Pulkit Ahuja, Founder & CEO, ProxgyMay 17, 2026

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For years, we measured industrial progress in terms of size and scale — larger facilities, faster equipment, and centralised control systems. That equation is now changing. From what I see across the industries we work with at Proxgy, the real transformation is not about doing more in one place but about bringing intelligence closer to where work actually happens.

We are moving into a phase where decisions are no longer confined to distant control rooms or cloud infrastructure. Instead, they are being made directly on the shop floor, out in the field, and increasingly through the perspective of frontline workers. In every conversation I have with manufacturing leaders, this is the change that comes up first. This is what defines edge intelligence.

Why edge computing is becoming central to industrial strategy

Traditional cloud-first systems were built to collect and consolidate data, not to act on it instantly. In industrial environments, even a small delay is not just a technical hiccup; it can quickly turn into a business problem. A delay of just a few seconds in spotting a defect, identifying a safety lapse, or flagging unusual machine behaviour can trigger compliance issues, hold up production, or, in the worst case, put workers at risk.

Edge computing is beginning to take on a far more important role than before. Industry estimates now suggest nearly 75 per cent of enterprise data is being processed outside traditional centralised data centers, changing how industrial systems are built and managed.

Computer vision turning observation into action

Industrial environments have always relied on visual monitoring through cameras, surveillance systems, and manual checks. The real limitation has been the inability to analyse this information at scale and in real time.

AI-driven computer vision is helping bridge that gap. When cameras are combined with edge-based AI, they can detect defects instantly, flag process deviations as they occur, and ensure safety protocols are being followed without delay.

This goes beyond a simple upgrade in automation. It reflects a meaningful shift toward systems that are capable of producing their own insights.

Also read: Building green data centres for the AI era

Across the deployments I have seen, real-time edge analytics is already delivering a 15 to 25 percent improvement in operational efficiency, mainly by reducing the time between detecting a problem and taking action.

It also changes how accountability is managed. When systems can both observe and interpret simultaneously, the likelihood of oversight reduces considerably.

Wearables bringing intelligence to the workforce

While much of the Industry 4.0 conversation tends to centre on machines and digital technologies, my own conviction, built over years of working with frontline teams, is that it is people who form the backbone of every industrial setup. The real challenge lies in enabling them with the right tools and support so they can stay informed, remain connected, and effectively adapt to evolving demands.

This is where IoT-powered wearable technology is making a difference. Tools like smart glasses, connected helmets, and body-worn devices — the category we work in at Proxgy — have moved far beyond basic recording. They have evolved into smart touchpoints that gather real-time, context-driven data and instantly convert it into meaningful insights. This allows teams to tap into live remote support, understand situations more clearly from the worker’s point of view, and detect as well as fix issues far more quickly.

In practice, enabling workers with this kind of intelligence creates a compounding effect. It reduces dependence on layered supervision structures and supports faster, more distributed decision-making.

Research also suggests that these methods can cut downtime by as much as 22 percent, while enhancing overall operational efficiency by enabling real-time visibility and faster decision-making.

The role of infrastructure: low latency as the foundation

None of these advancements can function effectively without a strong underlying infrastructure.

Edge intelligence relies on networks that can offer low latency along with dependable performance. The emergence of 5G is central to this shift, allowing devices, systems, and decision points to share information almost instantly.

At the same time, smooth coordination between cloud and edge environments continues to be important. The way ahead is not about picking one over the other but about using the strengths of both in a complementary way.

Decisions that require immediate action will increasingly be managed at the edge, while the cloud will remain important for handling large data sets, building AI models, and enabling long-term improvements. This balanced approach helps industrial systems expand efficiently without compromising on speed or the quality of decisions.

Digital twins: From monitoring to simulation

Digital twins are emerging as one of the clearest outcomes of this shift in technology. Using real-time data captured from edge devices to drive virtual replicas, manufacturers can simulate and evaluate multiple scenarios before implementing any changes on the ground. This reflects a shift beyond basic monitoring toward a more forward-looking approach that focuses on predicting outcomes and steadily enhancing overall performance.

Digital twins now allow companies to plan maintenance in advance, test different situations without putting actual operations at risk, and steadily enhance overall performance. In sectors where expenses are significant and downtime has serious consequences, this capability can drive better efficiency and more effective use of assets.

From supervision to autonomy

A key impact of edge intelligence lies in what it eliminates. It reduces the reliance on constant human oversight, lowers the chances of error in repetitive monitoring, and replaces delayed updates with immediate visibility.

Industry data already points to this shift. More than 68 percent of manufacturers have experimented with edge computing solutions, and nearly 90 percent are expected to move toward edge-led systems in the near future.

What is unfolding is the beginning of autonomous industrial setups, where systems are able to sense, interpret, and respond with very little human involvement.

What this means for the future of manufacturing

The next phase of industrial advancement will not hinge on isolated breakthroughs. It will be defined by how effectively multiple technologies work in sync, where edge computing, artificial intelligence, IoT, wearables, and digital twins operate as part of a unified system.

For organisations, the direction ahead is straightforward. The focus is on building operations powered by real-time data, enabling frontline teams with actionable insights they can use immediately and shifting from reactive responses to anticipating and avoiding issues before they occur.

This transition also introduces new challenges. Businesses will have to reassess their existing infrastructure, invest in solutions that integrate seamlessly, and build teams that are comfortable working with advanced technologies in their everyday roles.

Edge intelligence is not a distant idea anymore. It is already shaping how industries function today. The real benefit lies not in adopting one piece of technology but in combining them into a well-aligned approach.

In a landscape where timing is critical, the ability to act instantly at the point of origin will separate the leaders from the rest, and that, in my view, is where the real competitive line is being drawn. The edge is no longer just a network extension. It is becoming the foundation for the next stage of industrial advancement.