AI at Both Ends of the Data Center Equation

Fueling Demand Growth While Unlocking New Efficiencies Across Infrastructure and Operations

On one side, artificial intelligence (“AI”) workloads are driving unprecedented demand for compute, storage and power, driving a new wave of capacity expansion and infrastructure upgrades unlike anything seen since the rise of cloud computing.

On the other side, AI is reshaping how data center facilities are operated, maintained and protected. AI can dynamically balance cooling loops and power routing to match live information technology loads — moving facility management toward increasingly autonomous operations. Reinforcement learning models, similar to those deployed by Google at scale, are now being used to reduce cooling energy consumption.¹

AI is also being used to serve as an early-warning system for critical infrastructure — continuously monitoring uninterruptible power supply systems, switchgear, chillers and thermal patterns to identify anomalies weeks before they escalate into major outages. This enhanced visibility enables a shift away from static schedule-based maintenance toward predictive maintenance dispatching: servicing equipment exactly when needed, not earlier and not later.

But, the opportunity extends even further. When mapped against a data center’s actual cost structure, the operational AI lever becomes even clearer:

Energy: AI-driven dynamic cooling and load shifting can improve power usage effectiveness — creating a structural reduction in the largest operating cost category for most data centers.
Labor: Automated network operating center tools can handle Level 1 alerts and routine monitoring, enabling human operators to focus on more complex events. Predictive maintenance can also help reduce emergency callouts and reactive interventions.
Capital expenditures & asset efficiency: AI-enabled capacity planning can help prevent over-provisioning — historically a major source of stranded capital.
Downtime risk: Continuous anomaly detection and automated disaster recovery simulations can replace reactive annual testing processes, potentially reducing both the frequency and cost of unplanned outages.
Data & storage: Intelligent data tiering automatically migrates cold data to lower-cost environments, while AI-enabled deduplication technologies can reduce raw storage footprints across many enterprise deployments.

Each of these levers matters because the data center industry’s greatest constraint is no longer simply capacity — it is power availability and operational efficiency.

Operators that view AI purely as a demand story are missing a substantial portion of the opportunity it offers. This perspective aligns with findings from the FTI Consulting 2026 Private Equity AI Radar, in which cost optimization and asset utilization ranked among the highest-priority AI initiatives for the funds and operating leaders surveyed, reinforcing the point that AI’s value can be as much about improving cost structure and capital efficiency as it is about driving growth.

The next generation of leading data center operators will not simply use AI to support rising demand. They will embed AI directly into the operating model itself to improve efficiency, utilization, resilience and long-term profitability.

AI is not only the force fueling data center growth. It is increasingly becoming the operational foundation that will determine which platforms scale most efficiently and profitably over time.

The views expressed herein are those of the author and not necessarily the views of FTI Consulting, Inc., its management, its subsidiaries, its affiliates, or its other professionals. FTI Consulting, Inc., including its subsidiaries and affiliates, is a consulting firm and is not a certified public accounting firm or a law firm.

Footnote:

1: Shead S., Google harnesses the power of AI to cut energy use, World Economic Forum, July 2016.