AI workloads are accelerating global electricity demand at a pace grid operators cannot match. In the US, connection wait times for new power loads, especially hyperscale data centers, have risen to an average of five years.
During the “Powering the AI Revolution: The Energy Capacity Imperative,” session at the IFS Industrial X Unleashed event, Dr. Sabine Erlinghagen argued the AI economy will stall without a new model for how utilities design, operate, and maintain grid infrastructure.
That urgency underpins Siemens’ strategic partnership with IFS, announced this month, aimed at merging Siemens’ grid simulation and equipment health monitoring with IFS’ asset, service, and capital planning solutions.
The companies demonstrated how shared data models and AI-driven scenario planning help utilities identify optimal investment pathways such as balancing reliability, financial efficiency, and speed to deployment. Their showcase example involved a Canadian utility with $5.2 billion in annual CAPEX, which discovered that neither organization’s standalone scenario produced the optimal plan. A joint simulation revealed a “scenario 3” that reduced financial waste and improved resiliency simultaneously.
For CIOs and asset leaders in energy, utilities, and heavy industries, this represents a shift toward truly data-driven capital allocation. The grid modernization market is expanding at double-digit rates as electrification, EV charging, and AI data centers reshape demand patterns.
Utilities face rising outage impacts, as well. For example, US grid-related outage costs have increased 175% year over year, reaching $150 billion. Executives are now prioritizing platforms that unify planning, operational telemetry, and field execution.
From Predictive Maintenance to Autonomous Grid Orchestration
Siemens and IFS showcased an end-to-end workflow combining Siemens’ real-time asset health data, IFS anomaly detection, autonomous work order creation, automated scheduling, and human-validated field bundling. Their reference customers reported 35% reductions in unplanned downtime and 40% improvements in asset intervention efficiency, largely through bundling site visits and eliminating unnecessary truck rolls.
For technology leaders, the key takeaway is operational integration. ERP teams historically treated grid simulation, asset health monitoring, and service scheduling as separate towers with different data taxonomies.
The Siemens–IFS model points to a new best practice: Treating grid operations as a closed-loop system where every sensor reading can cascade into automated workflows across planning, maintenance, and field service.
Companies exploring similar architectures should evaluate solutions based on:
- Ability to ingest and normalize real-time OT and sensor data
- Simulation engines capable of running hundreds of planning scenarios
- Autonomous work execution, including agent-driven scheduling
- Cross-vendor interoperability, given most utilities run mixed operational estates.
What This Means for ERP Insiders
There is a strong push toward faster, more defensible capital planning. Technology executives can now anchor investment decisions in joint technical-financial models rather than siloed engineering assumptions. That means fewer overruns and clearer justification for board-level CAPEX requests, which can reduce delays and other potential issues.
Integration between OT and ERP teams is deeper than ever. OT teams will need to unify asset health data with service and work management processes, improving uptime forecasting and resource allocation. This raises expectations for ERP architects to prepare their estates for high-velocity operational telemetry.
There is a strong shift toward autonomous operations. Field service, scheduling, and maintenance will shift toward agent-based workflows that reduce manual coordination. Day-to-day roles will rely more on exception management and less on routine administrative planning.
