Sagar Nangare
July 14, 2026
5 Minutes read
The Real Roadmap to Autonomous Telecom Networks Starts with Inference
There is plenty of optimism about AI in telecom, and for good reason. In telecom networks, AI promises something truly transformative, specifically TM Forum’s Level 4 autonomous network. At this stage, network systems can reason, decide, and act on their own across different areas, without needing humans to step in.
Reaching this level does not happen all at once. It depends on something more specific, AI inference working inside the network. Across thousands, and eventually millions, of network components, trained models are making real-time decisions to keep the network running, efficient, and responsive. This is what drives the story of autonomy. So where is this inference happening today, and where is it going next? Let’s take a straightforward look at both.
Where AI Inference is Happening in Telecom Networks Today
Artificial Intelligence (AI) inference has moved from the lab into live network operations, but unevenly. It is mature and production-grade in some layers, and still experimental in others.
- RAN baseband — beamforming, channel estimation, and link adaptation now run on learned models, replacing fixed algorithms
- Near-RT RIC (xApps) — real-time traffic steering, interference management, and mobility control
- Non-RT RIC (rApps) — longer-horizon policy generation and model retraining inside the SMO domain
- NWDAF (5G Core) — mobility prediction, Quality of Service (QoS) optimization, and anomaly detection
- AIOps / fault management — predictive maintenance and root-cause analysis, already cutting major faults by up to 80% and saving over a billion kilowatt hours of electricity
- SMO orchestration — intent-based, zero-touch network configuration
- Cell-site GPU compute (“AI Grid”) — general-purpose inference co-located at towers for vision, voice, and robotics workloads
- MSO/Central Office compute — regional inference hosting at network data centers
- Device/CPE edge — on-device vision-language models for real-time, connectivity-free inference
The core pattern: AI running the network through RIC, Core Functions, and AIOps, is mature and delivers measurable results. AI running on the network as general-purpose compute, what the industry is calling the “AI Grid”, is still a bet, not yet a proven business.
Where AI Inference Will Happen Next
The next wave moves inference from sitting beside the network to being woven into the waveform itself, and from reactive optimization to genuine, agentic decision-making.
- Air interface itself — two-sided AI models split inference across device and base station simultaneously for CSI compression (3GPP standard, 2026–27)
- AI-driven mobility — learned models replace rule-based handover logic entirely
- Integrated Sensing & Communication (ISAC) — the RAN becomes a sensor, inferring object position, motion, and material composition — a new revenue category beyond connectivity
- Ambient IoT — inference pushed to battery-free, energy-harvesting silicon at the extreme edge
- NTN/satellite — inference moves off terrestrial infrastructure entirely for GNSS-resilient positioning
- Uplink-heavy multimodal inference — new compression layers required as XR and agentic traffic invert today’s downlink-dominant pattern
- Network digital twins — continuous AI simulation of the live network before any configuration change is deployed
- Agentic AI layer — LLMs that decide what to optimize, not just execute one model (already in production at SoftBank and Deutsche Telekom)
- Physical AI/robotics control loop — RAN, edge, and device inference fused into one closed loop for AVs, drones, and smart manufacturing
The shift: inference moves from sitting beside the network to being woven into the waveform, and from reactive optimization to autonomous, agentic decision-making.
What This Means for Integrators, Operators, and Solution Vendors
The technology roadmap is one thing. What you do with it is another matter. Here is a practical read for the three groups shaping this space.
If you are a System Integrator
Stop chasing the model. Chase the mess in between.
Every operator will eventually have a tangled architecture of RAN components, core network functions, and emerging agentic AI tools from different vendors that do not communicate cleanly. Those challenges, not the AI itself, are where real, durable work lives. The opportunity is in building the connective tissue: the knowledge layers, data pipelines, and orchestration frameworks that turn a collection of point solutions into an autonomous, functioning network.
Digital twins are no longer a future concept, but they are now moving into the network. Integrators and solution vendors who build reusable IP and frameworks around these layers will be ahead of the curve, not catching up to it.
If you are a Telecom Operator
Be deliberate about which bets are safe and which ones are speculative and invest in each accordingly.
Running the network with AI, fault detection, energy savings, and self-healing is proven. It is already saving real money. Putting GPUs at every cell tower for general-purpose AI hosting is not proven yet. It is a bet on a business model that does not fully exist. Do not fund both with the same confidence.
The journey toward TM Forum Level 4 autonomous networks is the right destination, but it requires careful, staged steps. AI infrastructure will demand increasing attention and capital, but the operators who plan that transition deliberately, rather than reactively, will be better positioned when the inflection points arrive.
If you are a Network Solution Vendor
The architecture debate is still open, and that is both a risk and an opportunity.
GPU-everywhere (Nokia/NVIDIA) versus custom-silicon (Ericsson) is not settled. Operators are visibly hedging, which means vendors who make procurement easy to revisit through interoperable, portable solutions will earn more trust than those who push for lock-in. The vendors who move first toward full-stack, fully trusted solutions, rather than impressive point capabilities, will define the reference architectures that everyone else builds around.
The Bigger Picture
The telecom network is becoming something that senses, reasons, and decides, not just something AI rides on top of. That shift is already underway in the operations layer, and it is moving steadily toward the waveform itself.
The winners across all three groups will not be those with the most impressive models. They will be the ones who build the trust, orchestration, and integration layer that holds the whole system together, and who understand that the path to Level 4 autonomy runs through every component, from the baseband unit to the agentic AI sitting above the core.
Frequently Asked Questions (FAQs)
1. What is a TM Forum Level 4 autonomous network?
It is the industry benchmark for full network autonomy, where systems can independently reason, decide, and act across multiple domains without human intervention, representing the highest tier of self-operating telecom infrastructure.
2. Where is AI inference already running in telecom networks today?
AI inference is mature in layers such as RAN baseband, Near-RT and Non-RT RIC, 5G Core via NWDAF, AIOps, and SMO orchestration, where it is already improving fault detection, energy efficiency, and network optimization.
3. What is the difference between AI running the network and AI running on the network?
AI running the network refers to proven use cases, such as fault management and traffic steering, that are already delivering results. AI running on the network refers to hosting general-purpose compute at cell sites and edge locations, a business model that remains unproven.
4. What role will agentic AI play in future telecom networks?
Agentic AI introduces LLMs that can decide what to optimize rather than simply executing a single model, thereby moving networks toward autonomous decision-making. This is already in production with operators like SoftBank and Deutsche Telekom.
5. Should telecom operators invest in AI Grid or GPU-based cell site compute right now?
It depends on risk appetite. Proven applications like predictive maintenance and self-healing networks are safe investments with demonstrated returns, while GPU-everywhere compute at every tower is still speculative and should be funded with a different level of confidence.
Related Insights
Zero-Trust AI: Securing MCP-Based LLM Systems in Production
Siddharth Dange
Building Intelligent Agents on the Databricks Stack
Divyesh Patel
