How much does AI actually save? A study of the RU in 5G RAN – United States

Name: How much does AI actually save? A study of the RU in 5G RAN – United States
SKU: IRC-AIRU5G-US-2026
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Sizes and forecasts the power savings delivered by Artificial Intelligence (AI) in the Radio Units (RUs) of Fifth Generation (5G) base stations in the United States over 2026–2030.

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SKU: IRC-AIRU5G-US-2026 Category: Mobility Tags: 5G, AI, Energy Management, O-RAN, Power Savings, Radio Unit, RAN, United States

Description

The Radio Unit (RU) is the most power-intensive component of the Radio Access Network (RAN), and the one in which Artificial Intelligence (AI) delivers the most mature power savings. This report puts a dollar value on those savings in the United States.

The report explains how AI is applied to the RU, from the role of Open RAN (O-RAN) in opening the RAN to AI, to a taxonomy of 22 AI-driven RU power-saving scenarios, each illustrated with vendor and operator initiatives. It then analyzes the market in the United States: the absolute power savings, the share of the RU in AI-driven RAN power savings and the savings per 5G subscriber, each with a table, a chart and an Insight Research analysis of the dynamics behind the numbers.

Scope

Introduction – why the RAN and the RU, what the RU is and how it works, and the scope of the report
AI and RU – O-RAN and AI, energy management as an AI use-case and a taxonomy of 22 RU power-saving scenarios
United States Market Analysis – absolute power savings, share of the RU in RAN power savings and savings per 5G subscriber
List of Acronyms

Introduction
1.1 Why RAN, and Why RU?
1.2 What is the RU?
1.3 How does the RU Work?
1.4 Scope of the Report
AI and RU
2.1 O-RAN and AI
2.2 AI Use-Case – Energy Management
2.3 Taxonomy of AI-Driven RU Power Savings
2.4 Traffic-Driven Capacity Reduction
2.5 RF Resource Scaling
2.6 Time-Domain Energy Saving
2.7 Adaptive Power Control
2.8 Hierarchical Network Optimization
2.9 Site Energy Orchestration
2.10 Assurance and Guardrails
United States Market Analysis
3.1 Absolute Power Savings
3.2 Share of the RU in RAN Power Savings
3.3 Power Savings per 5G Subscriber
List of Acronyms

Figures & Tables

Figure 2-1: Reinforcement learning model training and actor locations per O-RAN WG2
Figure 2-2: AI/ML Workflow in the O-RAN RIC as proposed O-RAN WG2
Figure 2-3: AI/ML deployment scenarios
Figure 2-4: Taxonomy of AI-driven RAN power savings with RU/O-RU focus
Table 3-1: United States – projected power savings by AI in 5G RUs, 2026–2030 (US$ million)
Figure 3-1: United States – projected power savings by AI in 5G RUs, 2026–2030 (US$ million)
Table 3-2: United States – projected proportion of power savings by AI in 5G RUs to power savings by AI in the RAN, 2026–2030 (%)
Figure 3-2: United States – projected share of the RU in AI-driven RAN power savings, 2026–2030 (%)
Table 3-3: United States – projected power savings by AI in 5G RUs per 5G subscriber, 2026 (US$)
Figure 3-3: United States compared with other country markets – power savings by AI in 5G RUs per 5G subscriber, 2026 (US$)
Table 4-1: Master list of acronyms

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