Where value will concentrate, why it will be hard to navigate, and how IP leaders should respond
In the previous article, “What Is AI-RAN and Why Is Telecom Shifting from Hardware to Intelligence?”, we explained how telecom architecture is moving toward intelligence-driven, learning networks.
The next step is understanding what that shift means for value and control.
As networks become programmable, value will not remain evenly distributed—it will concentrate around the intelligence layer. Identifying and securing these control points is now a strategic priority.
In this article, we examine where that value will concentrate, why it is difficult to navigate, and how IP leaders should respond.
AI-RAN is not a feature upgrade. It is a platform transition: from radio networks configured and optimized manually to radio networks continuously steered by software intelligence. In every platform transition, value concentrates around new control points. In AI-RAN, those control points sit in the intelligence layer: data, control apps, orchestration, trust, and standards-adjacent implementations.
Why AI-RAN matters now
THE SHIFT
The business case is straightforward. Operators are under pressure to improve capacity, automate operations, and control energy costs simultaneously. The RAN remains the largest energy lever in the mobile network, making it the natural place for AI-led optimization. NEC notes that the RAN accounts for over 70% of mobile network energy consumption, while McKinsey has highlighted energy spending of up to 5% of telecom revenue.
At the same time, the industry architecture is changing. Open RAN has created a structured software control layer around the network, and 3GPP Release 18 has already placed AI/ML and network energy savings into the 5G-Advanced roadmap. That means intelligence is no longer peripheral to the RAN; it is becoming part of how network performance is designed, delivered, and bought.
From fixed-function radio to a programmable network platform
AI-RAN is commonly described through three lenses. Together, they show how the RAN is moving from a hardware-centric system to a software-centric platform.
- AI for RAN: Use AI to optimize radio performance, mobility, interference management, and energy consumption.
- AI and RAN: Run AI workloads and RAN workloads on shared infrastructure to improve asset utilization and open new monetization paths at the edge.
- AI on RAN: Use the RAN edge as the execution layer for low-latency AI applications, including industrial automation, video intelligence, and enterprise edge services.
What matters strategically is not the terminology. What matters is that the intelligence layer is becoming the new decision surface for performance, cost, and ecosystem participation. Once that happens, the competitive question shifts from “who owns the radio box?” to “who controls the learning loop?”
Where value will concentrate—and why it will be hard to navigate
EMERGING CONTROL POINTS
Data and telemetry rights
The first control point is access to network telemetry: KPIs, radio conditions, traffic patterns, and operational context. AI performance depends on the quality, continuity, and governability of that data.
This is difficult to navigate because multi-vendor environments rarely produce clean, comparable data, and because ownership is spread across operators, vendors, cloud partners, and service layers. In practice, the party that can collect, normalize, and operationalize telemetry at scale will shape the network’s control logic.
The RIC app ecosystem
O-RAN creates a software insertion point through the Non-RT RIC and Near-RT RIC, where rApps and xApps can influence policy and optimization. This looks open on paper, but in practice, it introduces a new form of dependency: certification, performance accountability, integration, and lifecycle management. The market will not reward “open” alone; it will reward trusted, measurable intelligence that operators can deploy without operational risk.
Shared edge infrastructure
AI-and-RAN assumes that edge infrastructure can support both network workloads and AI workloads. That creates a powerful economic proposition, but also a difficult orchestration problem. RAN workloads require deterministic performance, while AI workloads are bursty and compute-intensive. The winning platforms will be those that can guarantee network SLAs while monetizing spare capacity. That is a much harder problem than simply placing GPUs at the edge.
Standards, gravity, and implementation-essential features
As 3GPP and O-RAN move further into AI-assisted behaviors, some capabilities will become “adoption essential” even if they are not formally standard-essential in the classical sense. Once procurement teams begin to expect them, vendors must implement them to stay relevant. That increases the strategic value of patents around control policies, energy-saving mechanisms, and orchestration methods—and makes early standards intelligence more important.
Trust and assurance
AI transforms the network from a configured system to an adaptive one. That creates new attack surfaces: unsafe automation, model drift, data poisoning, and explainability failures. Security and assurance, therefore, become control points in their own right. Enterprises that can prove safe, verifiable automation will have stronger positioning than those that can only claim raw performance improvement.
Why AI-RAN requires a different portfolio strategy
THE IP PLAYBOOK
- First, benchmark by control point—not by raw filing count. Leadership teams need to know who is building a position in telemetry, RIC applications, orchestration, energy optimization, and assurance. Filing volume alone will not tell you where the future licensing leverage sits.
- Second, separate core assets from trade-secret assets. Not every intelligence-layer advantage should be patented. Some of the most defensible values in AI-RAN will sit in feature engineering, model tuning, training data curation, and operational policies. The right portfolio architecture will blend patents, trade secrets, and standards monitoring.
- Third, align filings with deployment reality. AI-RAN is an ecosystem market. The strongest assets will map to what operators will actually test, certify, and buy—not only to what is technically elegant. That means the IP strategy must be tightly tied to product roadmaps, interoperability milestones, and procurement expectations.
AI-RAN will reward companies that treat intelligence as an asset class. The strategic question is no longer how to protect radio innovation. It is about identifying, securing, and monetizing the control points that sit above the radio layer.
What enterprises need to decide now—and how Evalueserve supports that decision
HOW WE HELP
AI-RAN is a strategic decision with long-term lock-in risk. Enterprises need clarity on where to invest, where to partner, and what to protect. That requires more than patent counts or standards summaries; it requires a control-point view of the market.
- Competitive benchmarking to identify which players are building position across telemetry, RIC apps, orchestration, energy optimization, and assurance.
- Portfolio strategy and governance support to determine what should be patented, what should remain a trade secret, and how filing should align with product roadmaps and standards direction.
- Standards-to-IP monitoring across 3GPP and O-RAN to identify where adoption-critical features may create future licensing leverage or exposure.
- FTO and risk screening for enterprises building xApps, rApps, telemetry pipelines, orchestration layers, or AI-driven assurance capabilities.
- Monetization readiness support, including claim charts, evidence packs, and portfolio positioning for strategic partnerships or licensing discussions.
