The promise of AI-Radio Access Networks (AI-RAN) is real. But unless the data problem is solved first, the industry’s most ambitious AI initiatives risk becoming expensive demonstrations rather than operational systems.
The telecom industry is racing toward AI-native networks. Operators are trialing autonomous, multi-agent optimization systems. Vendors are co-innovating on new radio processors and embedding AI into scheduling, energy management, and network operations. And across the industry, one assumption is widely accepted: AI will become a fundamental component of future radio access networks.
The excitement is justified.
The challenge, however, is that most discussions about AI-RAN focus on the intelligence layer while largely ignoring the foundation underneath it. The truth is that what appears to be an AI problem in AI-RAN, is actually a data problem. Without the right data foundation, even the most advanced AI systems are limited in the value they can deliver.
The Gap Between Ambition and Operational Reality
In NVIDIA’s latest State of AI in Telecom report, 54% of telecom respondents identified data-related challenges as the largest obstacle to achieving their AI objectives – up 34 percentage points from the year before. As AI moves from pilots to production, the industry is discovering that the challenge was never really about the algorithms. The data is the missing link, and it is not a lack of data that is the problem.
This is not surprising. Telecom networks already generate some of the largest and most complex operational datasets of any industry. Every day, operators collect performance counters, alarms, traces, configuration snapshots, subscriber events, inventory records, topology information, and user-traffic data across thousands of network elements.
A large problem is that the data rarely exists in the form that humans or machines can consume effectively. RAN telemetry sits in one silo. Core network data in another. Alarms in a third. Different vendors use different schemas, identifiers, and interfaces. Furthermore, user-plane traffic, the real measure of what customers are actually experiencing, is often limited, sampled, or absent altogether. Even when the data exists, correlating it across domains demands significant manual effort. Engineers have, for decades, compensated for this fragmentation through expertise, experience, and intuition. AI systems, however, cannot.
Why Agentic AI Changes the Equation
The challenge becomes more significant as the industry shifts from analytics to autonomous operations. Traditional automation follows predefined rules. Agentic AI systems reason, investigate, and decide. They gather evidence from multiple sources, establish confidence, evaluate alternatives, and select actions.
This capability is powerful. It is also dangerous when it operates on incomplete information, because an AI agent can only reason about the world it can see. If an agent sees control-plane metrics but lacks visibility into the user plane, it may conclude that a network is healthy while customers experience degraded service. If it observes a coverage issue but lacks accurate topology data, it may recommend a change that solves one problem and creates another. If it operates on fragmented or stale data, its confidence becomes disconnected from reality.
The result is not simply a missed optimization opportunity. In a live network, a confident but incorrect action can degrade service for thousands of subscribers at once. The reputational damage to the operator follows.
Telecom’s Missing Layer
The telecom industry often treats AI accuracy as a model problem. One of the most common assumptions in AI discussions is that larger models automatically produce better outcomes. Telecom networks challenge that assumption. The networks themselves are deterministic systems, governed by physics, topology, configuration, and traffic behaviour. The primary challenge is not generating answers but rather creating an accurate representation of reality.
Here’s what many overlook – before any AI system can optimize a network, somebody has to answer a set of fundamental questions: What is happening? Where is it happening? Who is affected? What caused it? And what actions are safe to take?
Many operators still struggle to answer these questions consistently across domains. The missing layer is not another model. It is network intelligence, a trusted, continuously updated representation of network reality that both humans and machines can consume. Without that foundation, AI systems are simply automating uncertainty.
Why User-Plane Visibility Is Non-Negotiable
This matters even more as AI workloads move closer to the network edge. Historically, operators have relied on control-plane measurements and aggregated KPIs to assess network health. Those indicators remain valuable, but they tell only part of the story.
Customers do not experience signalling procedures. They experience applications: video quality, gaming latency, voice quality, throughput, and responsiveness. Understanding that experience requires granular, real-time insight into the RAN, correlated with actual user-plane traffic and the flows in the core. As AI-RAN accelerates the convergence of AI and RAN workloads on shared infrastructure, and as AI inference itself moves to the edge, this visibility gap becomes impossible to ignore. Without deep user-plane visibility, operators are building AI-native networks while flying partially blind.
The Operators That Succeed Will Treat Data as Infrastructure
AI-RAN offers the possibility of networks that optimize themselves, heal themselves, and adapt in real time to traffic patterns no human team could track manually. As Dr. Alex Jinsung Choi, Chair of the AI-RAN Alliance, put it: “AI-native RAN is no longer experimental, it is foundational to the future of wireless networks.” That future is arriving faster than many anticipated.
The data challenge is a hurdle, but it is solvable. It requires investment in end-to-end observability, user-plane visibility, cross-domain correlation, trusted data pipelines, and governance frameworks that allow both humans and machines to act with confidence. Above all, it requires moving beyond the assumption that control-plane health equals network health.
The long-term winners in AI-RAN will not be the operators deploying the largest models. They will be the operators who build the most accurate representation of their networks, those who treat data as infrastructure rather than an afterthought.
Building the Foundation: The RADCOM Approach
This is the challenge RADCOM is built to solve.
RADCOM gives operators clear, real-time visibility into what customers are actually experiencing, with complete RAN-to-core correlation, including 100% visibility into the user plane. It then pinpoints, at the geo-bin level, exactly where RAN issues originate, moving teams from symptom detection to precise root-cause localization.
By combining geospatial analytics, user-experience measurement data, and multi-vendor network intelligence, RADCOM automatically detects and prioritizes complex RAN issues: overshooting and undershooting cells, low-coverage regions, interference hotspots, PCI conflicts, carrier-aggregation imbalance, and more. Environment-aware and band-aware algorithms improve coverage and spectral efficiency, and validate remediation actions with greater confidence.
RADCOM Neura, RADCOM’s AI agent suite, acts as a telecom-hardened funnel architecture that pairs deterministic signal algorithms with guided LLM reasoning. It gives an agent, whether RADCOM’s own or a third party’s, more than the fact that packet loss is occurring. The agent also sees which application flows are affected, on which network path, and for which subscriber segment. With that context, it can take the right action instead of a plausible-sounding one.
Rather than asking AI systems to reason directly over fragmented telecom datasets, RADCOM transforms raw network data into verified operational intelligence, feeding networks and agents only the context that matters. The result is a foundation that lets agentic AI operate with greater confidence, precision, and accountability in the realities of real-world telecom environments: massive data volumes, high operational risk, and strict cost constraints.
In closing, the future of AI-RAN will not be determined by how intelligent the agents become. It will be determined by how accurately they understand the network they are trying to optimize. For more information on RADCOM Neura, see https://radcom.com/agentic-ai/
