AI Is Driving Network Energy Costs Up. Here’s How to Control Them

As AI becomes deeply embedded across 5G and future 6G networks, communications service providers are facing a new challenge: how to scale intelligence without letting energy costs spiral out of control.

A new whitepaper from TNS and Kaleido Intelligence, "Optimising Network Planning for the AI Era with Common Language," explores how rising AI workloads are reshaping network planning, and why energy efficiency is now a strategic, commercial, and regulatory priority.

The report shows how AI-driven networks are driving rapid growth in data centre capacity (expected to increase 2–6× by 2030) and why CSPs must rethink how they measure, plan, and optimise network infrastructure.

Key insights from the whitepaper include:

1. Why AI and advanced 5G are increasing pressure on network energy consumption and OPEX, even as efficiency gains are promised
2. How granular, standardised energy metrics enable more accurate network planning and avoid over-engineering
3. Why multi-vendor and Open RAN environments make consistent measurement harder, and why a common frame of reference is essential
4. How Common Language frameworks, including CLEI equipment identifiers and CLLI location codes, provide the data needed to optimise power usage, cooling, site selection and asset lifespan
5. How combining equipment-level and location intelligence helps CSPs reduce energy costs while maintaining performance and reliability

For network planners and operations teams, the takeaway is clear:

AI-ready networks require standardised intelligence to balance capacity, cost and sustainability.

Join experts from TNS, Kaleido Intelligence to discover how standardisation and a Common Language framework can bring clarity, cost control and efficiency to network operations.

Register now to read the full whitepaper to learn how Common Language supports smarter, more energy-efficient network planning in the AI era.