Energy intelligence meets automation: Building AI factories
- By Gwenaelle Huet
- 07 Apr 2026
- 5 min read
- 1.5%of the world's total energy is consumed by data centers
- 900+ TWhis the projected data center energy demand in 2030
AI factories are driving exponential growth in electricity consumption and thermal complexity. Already, new demand from digital services and IA workload mean that data centers consume around 1.5% of the world’s total energy. This will only grow: the International Energy Agency projects that global data center demand may exceed 900 TWh by 2030.
For reference, that resembles Japan’s total annual electricity consumption today, meaning a single sector will draw as much energy as modern industrial nations. In the United States, the Electric Reliability Council of Texas reports a 2023 peak of about 85.5 GW in one state alone, and is set to soar well above 145 GW.
This is why factors like speed to power, reliability, and sustainability now decide how hyperscaler sites are selected and designed. In this changing environment, we need a new class of capabilities to uphold sustainable growth.
GPUs are arriving faster than power. In several markets, compute sits idle awaiting interconnects or onsite generation. Vacancy is low, preleasing is high, and grid timelines extend into 2027 and beyond. Currently, power constraints limit growth, which makes automation essential for handling capacity and change. The good news? Moving to digitally integrated energy and automation requires minimal engineering lift.
"Data centers are now regarded as critical infrastructure requiring uninterrupted operation. Consequently, operators are prioritizing system reliability, equipment availability, and continuous monitoring. This shift has resulted in greater utilization of industrial automation tools, including advanced control systems and monitoring software, to ensure smooth operations and minimize downtime as data centers expand and grow more complex."
Industrial Automation in Data Centers, ARC Advisory Group
Industrial automation makes mixed energy architectures work. Microgrids, batteries, and onsite generation boost availability and resilience, but also add complexity. Industrial‑grade PLC and DCS control coordinates these energy sources, protects sensitive GPU loads during transitions, and optimizes costs in real time when paired with digital twins and automated triggers.
With sensors, model-based control, and machine learning, operators can reduce cooling energy as much as 40%, which improves Power Usage Effectiveness and carbon performance. Then, higher Mean Time Between Failures, built‑in redundancy, and predictive maintenance reduce lifecycle cost for assets.
Automation is the engine across this stack. It federates grid to rack data, coordinates energy and cooling plant setpoints with IT scheduling, and unlocks agentic optimization with human guardrails. It also gives leaders a common, open architecture to standardize globally across colocation facilities and owned sites, while adapting to regional constraints such as power availability, water use, or permitting.
The race to compute is also a race to power. Simply adding supply will not resolve bottlenecks in energy, cooling, and interconnection. Industry leaders are doing three things differently:
- Software-defined architectures
- Digital twins from grid to rack
- Energy-aware, autonomous operations
Software-defined architectures
Open, software-defined automation is becoming essential for data centers. The market for industrial automation systems in data centers reached about $739 million USD in 2024 and is projected to grow roughly 11% CAGR through 2029. Traditional hardware-centric systems cannot keep pace with rapid changes in workloads, energy constraints, and sustainability requirements. We must decouple intelligence from proprietary hardware and virtualize electrical and control systems. This shift allows vendor-agnostic integration, modular growth, faster commissioning, and an adaptive architecture that evolves with modern demands.
Digital twins from grid to rack
End-to-end models allow teams to simulate grid contingencies, cooling transients, and workload placement, then maintain a live digital twin for operations. This reduces change orders, avoids thermal and harmonic surprises, and guides predictive maintenance at AI densities.
Policy signals support this push. For example, Germany’s Energy Efficiency Act raises the bar for energy efficiency in data centers, with new sites expected to meet tight PUE requirements by the end of 2026 and existing ones by 2030. This requires operators to manage and optimize their facilities in real time.
Singapore’s rules took effect when the government lifted its datacenter moratorium and launched its pilot approval scheme for new capacity. The framework requires new data centers to meet strict sustainability criteria. Operators must continuously validate performance as cooling and energy thresholds tighten. This effectively makes a live digital twin a prerequisite for sustainable AI growth.
Energy-aware, autonomous operations
AI data centers can be flexible grid resources. Trials show that orchestrators can modulate noncritical or time-shiftable AI jobs to shave peaks and stabilize local grids, shortening interconnect queues and improving community acceptance. The urgency is clear. Data centers are expected to drive nearly half of the projected increase in US electricity demand by 2030, which makes grid-aware controls a strategic necessity. ARC highlights the development cycle mismatch that makes onsite orchestration essential, with data center builds in two to three years versus four to eight years for interconnections.
It's not only data centers and hyperscalers that need energy intelligence. IDC expects global edge spending to reach roughly $380 billion USD by 2028, driven by analytics and AI inference at the edge. That pushes decisions on energy, cooling, and IT into milliseconds and across thousands of nodes, feasible only with automation and real-time industrial intelligence.
Software-defined automation
Standardize on open, software-defined automation across sites and utilities, then connect them with software-defined power for unified orchestration. This creates the adaptive backbone for energy-aware operations.
Industrial intelligence
Instrument for federated data using lifecycle digital twins from grid to line and adopt platforms that enable agentic optimization with human guardrails. This supports reliable autonomy at scale.
Outcome-driven procurement
Shift procurement toward outcomes. Specify time to power, uptime, efficiency, interoperability, and carbon performance, not just equipment lists. Align contracts to recurring services that guarantee results.
Modular automation
Invest in open, modular architectures for automation of the critical systems at the edge. This leads to faster design, build, test and maintenance and allows prefabricated modules to speed up project timelines significantly.
Data centers and industrial operations are converging into intelligent, energy-aware ecosystems. The winners will standardize on modular, instrumented designs, bring automation to the edge, and treat power as a programmable resource – and our solutions are here to help. Market data shows the urgency. Global capacity is on course to nearly double by 2030. Industrial automation is the silent engine that turns constraints into advantage for data centers.
It’s how energy technology delivers resilience and profitability at scale.
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