Opinion: Energy Continuity - The Real Constraint on Automation in 2026

Artificial intelligence has dramatically improved how supply chains make decisions. Autonomous fleets now optimize routes in milliseconds. Orchestration systems dynamically rebalance workloads across hundreds of assets. Predictive models anticipate failures before they occur. Decision quality is no longer the dominant constraint.

Yet a structural contradiction remains in modern automation: systems are designed to operate continuously, but their energy architecture is not.

Robots are intelligent, yet periodically unavailable. Algorithms are adaptive, yet bound by fixed charging cycles.

In high density, 24/7 environments, this contradiction becomes economically visible.

In 2026, the defining question will not be how intelligent automated systems are. It will be whether their infrastructure allows that intelligence to translate into uninterrupted execution.

Intelligence layered over discontinuous infrastructure reaches diminishing returns.

In 2026, as optimized processes become the industry benchmark, seamless scale ups will be required, where energy functions as a flexible utility, an enabler rather than a bottleneck.

The next stage of automation maturity is therefore not about making fleets smarter. It is about eliminating the deterministic interruption embedded in how they are powered.

Capital Efficiency, Not Fleet Size

For the past decade, automation ROI was justified by labor substitution and theoretical throughput expansion. The underlying assumption was simple: more robots equal more output. That assumption is now under scrutiny.

In a documented Hyundai Glovis deployment, traditional AGV clusters operated under a 6.75:1 work to charge ratio. In modeled 100 unit scenarios, maintaining full throughput required approximately 15 additional robots to compensate for charging downtime.

Similarly, in a Tier 1 automotive manufacturing environment, charging inefficiencies produced 20% solution downtime and 25% fleet inflation. Fully synchronized production, where all robots were simultaneously productive, occurred during only 30% of production time in certain clusters.

These are not marginal losses. They reshape the economics of capital allocation.

In 2026, leading operators will evaluate automation not by how many robots are deployed, but by what percentage of installed capital is continuously productive.

Energy as a Performance Variable

Historically, energy has been treated as static infrastructure: chargers installed at fixed nodes, with workflow adapting around them.

That model is structurally incompatible with high density, AI driven operations.

Charging stations consume productive floor space. Fleets deviate from operational routes. Capital expands to compensate for deterministic downtime.

When energy delivery is embedded into motion rather than separated from it, the economics shift. In controlled deployments, fleets receiving energy during operation maintained full uptime, while traditionally powered clusters experienced a 33% operational inefficiency window during extended shifts. In manufacturing, eliminating charging interruption reduced automation related production losses by more than 50%.

Energy architecture directly shapes throughput, capital intensity, and facility design.

In 2026, energy will no longer be treated as background utility. It will be managed as a performance variable.

Labor constraints remain a defining pressure across logistics and manufacturing.

AI improves coordination. It does not eliminate structural interruption.

As labor shortages persist, organizations increasingly rely on automation not only for efficiency, but for resilience. Yet resilience cannot be achieved if the physical layer of automation remains intermittently unavailable.

If 15% to 25% of assets are cyclically unavailable due to charging architecture, organizations compensate with additional capital or human buffers. Neither approach scales efficiently.

The next phase of automation design must extend beyond smarter algorithms to smarter infrastructure, systems engineered for continuity.

AI optimizes decisions. Continuous energy enables execution.

Outlook for 2026

2026 will not be defined by additional layers of intelligence, but by the removal of structural interruption from automation systems. Throughput will increasingly be measured not by installed capacity, but by sustained operational density.

Optimization at the software layer has largely been achieved. Yet much of the physical infrastructure beneath it remains architected for interruption. When infrastructure is discontinuous, scale amplifies distortion rather than efficiency.

The competitive shift now lies in whether optimized processes can expand without architectural constraint. Organizations that treat energy as a flexible utility rather than a fixed infrastructure limitation will be better positioned to scale without capital distortion.

Intelligence has matured. Performance will now depend on whether infrastructure is redesigned to support scale, rather than managed around its constraints.

The opinions expressed in this article are those of Prof. Mor Peretz, Co Founder and CEO of CaPow. The Supply Chainer’s Insights are submitted content. The views expressed in this column are those of the author and do not necessarily reflect the views of The Supply Chainer.