Breaking the Myth: Achieving Data Center Reliability and Sustainability With AI

Across the data center industry, a persistent belief continues to influence decision-making: improving sustainability will inevitably jeopardize reliability. For years, operators have been forced to choose between uptime and environmental targets. But that trade-off no longer holds true. With recent advancements in AI and machine learning, data centers can now strengthen reliability, reduce risk, improve sustainability, and boost team productivity at the same time.

This article explores the myths behind the perceived trade-off and highlights how AI-driven intelligence enables operators to optimize their facilities without compromising uptime.

The Myth of the Trade-Off: Reliability vs. Sustainability

An industry article recently ranked uptime as the top priority for liquid cooling, followed closely by infrastructure costs. Sustainability barely made the top ten. This mirrors a long-standing assumption that efficiency and environmental performance come at the cost of reliability. In reality, both are achievable together.

Data centers face growing demands and increasing constraints. Limited energy availability, workforce shortages, denser IT loads, and stricter regulations are reshaping operational priorities. To keep pace, digital infrastructure must undergo a transformation that enables operators to extract maximum performance from the resources they already have.

AI is a powerful enabler of this shift. When used correctly, it provides operators with clearer insight into how their environment behaves, why inefficiencies arise, and what to do next. With better information, teams can improve reliability and sustainability without increasing risk.

Lucend has helped optimize more than 28 data centers across three continents. These include both new and legacy sites. The results show a consistent pattern: facilities that optimize intelligently become more reliable, more sustainable, and easier to operate.

Hybrid Cooling Optimization

Hybrid cooling systems are increasingly used to help meet aggressive PUE (Power Usage Effectiveness) targets and to maximize free cooling. However, these systems often do not operate at their intended efficiency. Gradient, Lucend’s intelligence platform, identifies which hybrid cooling units are underperforming and quantifies the energy impact of current settings compared to an optimized configuration.

Gradient then provides operators with clear recommendations for improving system performance. By implementing these recommendations, teams reduce unnecessary energy and water use and minimize chiller runtimes. This not only lowers operational costs but also reduces strain on critical assets, extending their lifespan and lowering operational risk.

So far, AI-driven hybrid cooling optimization has delivered more than 10 GWh in energy savings for customers.

Facilities Harmonization

Chillers are only one component of a broader facilities ecosystem. Pumps, valves, fans, heat exchangers, and airflow conditions all interact continuously. When one part of the system operates inefficiently, others compensate, often consuming more energy than necessary.

AI insights from Gradient ensure the entire facility operates in harmony. For example, if chillers are not running efficiently, pumps often work harder to maintain conditions. These upstream and downstream interactions introduce hidden inefficiencies and unnecessary wear. Pump overspeeding is a common case where runtimes are higher than required, increasing energy use and reducing reliability.

Gradient highlights these interdependencies and shows operators what adjustments will have the most meaningful impact. Optimizing at the system level reduces waste, increases asset reliability, and improves facility-wide performance.

Floor Pressure and Fan Speeds

Air handling systems (CRAC and CRAH units) frequently consume more energy than needed due to imbalances or inefficiencies elsewhere in the facility. Gradient identifies where airflow or pressure is misaligned and recommends changes that lower fan speeds or stabilize pressure without compromising cooling performance.

These optimizations have delivered 11 GWh of savings for customers. They also reduce equipment runtimes and support more stable environmental conditions across the data hall.

Prescriptive Maintenance

Before adjusting a facility’s settings, it is critical to address underlying operational issues. Gradient continuously monitors data patterns to detect issues operators may not see manually. These often appear as unexplained spikes in energy or water usage.

Clogged filters are one of the most common examples. They frequently occur outside maintenance windows and even in new facilities. Gradient detects these issues by identifying subtle patterns in temperature, pressure, or chiller behavior. The platform then provides actionable recommendations for addressing root causes and preventing further inefficiencies.

This is one of many ways prescriptive insights help operators maintain optimal system performance and reduce troubleshooting time.

Productivity Gains

Another advantage of AI is the impact it has on team productivity. Data centers generate enormous volumes of information. Interpreting this data manually is time-consuming and inconsistent.

Gradient’s intelligence engine, trained on billions of data points, identifies optimization opportunities automatically and quantifies their expected impact. Operators are then able to prioritize actions more efficiently. Teams spend less time investigating symptoms and more time implementing solutions that matter.

AI does not replace operational expertise. It helps teams focus on the highest-value work while strengthening confidence in their decisions.

Improve Reliability and Increase Sustainability While Saving Time

Across customer deployments, Lucend has delivered more than 21 GWh in energy savings, largely by reducing unnecessary equipment runtimes. When systems operate as designed, risk decreases and reliability improves. With transparent AI supporting operators, facilities can achieve their uptime goals while also improving sustainability.

Your team works more confidently. Equipment runs more efficiently. And the path to sustainable, reliable operations becomes clearer.

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