The Day AI Became a Telecom Specialist
The alarms flash red across the network operation center. A base station serving a downtown district is congested again. But before a human can escalate, an AI suggests three corrective actions—each backed by historical performance, configuration data, and real-time traffic patterns. This AI doesn’t just analyze; it proposes, adapts, and learns like a telecom engineer.
Welcome to the age of the Large Telecom Model (LTM) by SoftBank.
In response to the inadequacies of general-purpose LLMs in specialized environments like telecom, SoftBank built LTM to bridge the gap. This domain-specific AI is trained on vast, real-world operational datasets, public telecom standards, and even low-layer signal simulations. The goal? To create an AI that can operate with human-level telecom insight.
A New Breed of AI, Tuned for Telcos
SoftBank’s LTM was developed by further training a general-purpose LLM with proprietary data, including:
- Base station configurations: orientation, frequency, azimuth, location
- Performance data: connected users, signal-to-noise ratios, throughput
- Industry standards: 3GPP, ETSI, ORAN Alliance, ITU
- Simulation outputs: network behavior modeling at physical and virtual layers
This diverse dataset helps LTM evolve from an LLM with general understanding to a domain-native intelligence engine, purpose-built for telecommunications networks.
Telco AI Meets Agentic Intelligence
LTM’s emergence parallels the rise of agentic AI—systems composed of interlocking modules that collaborate to achieve autonomous decision-making across dynamic environments.
The six foundational modules of agentic AI—perception, cognition, action, collaboration, learning, and security—mirror LTM’s architecture and future potential. LTM isn’t simply reacting to commands. It observes, reasons, and proposes just like a distributed cognitive agent. This is especially relevant in telecom, where real-time adaptation is critical.
SoftBank’s leadership in the AI-RAN Alliance aligns with this vision. The alliance focuses on:
- AI-on-RAN: managing AI workload traffic and evolving the balance between uplink and downlink
- AI-for-RAN: improving planning and operational efficiency with dynamic, AI-driven decisioning
- AI-and-RAN: leveraging shared infrastructure between RAN workloads and AI compute needs
Together, LTM and agentic AI point toward a self-optimizing, self-healing network future.
Three Game-Changing Applications of LTM
1. Base Station Configuration Optimization
LTM evaluates local area characteristics and traffic conditions to:
- Suggest dynamic adjustments to cell selection thresholds
- Propose handover parameters
- Assist with automated script generation for base station tuning
What once took hours of engineering analysis is now achieved in near real-time.
2. Network Maintenance and Fault Response
LTM analyzes multi-layer logs and performance data to:
- Detect anomalies
- Diagnose probable causes
- Recommend or automate remedial actions
This shortens MTTR and reduces human dependency for diagnostics, enabling more resilient networks.
3. Customer Engagement and Marketing
By learning from customer feedback, segmentation models, and past promotions, LTM enhances:
- Targeted marketing strategies
- Churn prediction and retention planning
- Offer personalization for both B2B and B2C customers
It becomes not just a technical tool, but a revenue enabler.
Human-AI Collaboration in Practice
Echoing sentiments from Optus CEO Stephen Rue, AI’s true power lies in augmentation—not replacement. SoftBank’s LTM embodies this by operating in a Human-in-the-Loop (HITL) configuration. AI surfaces insights; humans validate and apply strategic judgment.
Examples include:
- Field engineers accessing real-time config proposals
- Customer service agents diagnosing issues faster with AI triage
- Marketers launching micro-campaigns based on AI-informed segmentation
The model complements human capability across functions—from NOC to CMO.
Preparing for the Future of AI-Enhanced Networks
LTM isn’t just an R&D experiment—it’s a foundational move toward AI-native telecom operations. As telcos wrestle with scale, complexity, and customer expectations, SoftBank’s LTM offers a roadmap for:
- Smarter OSS/BSS orchestration
- Hyper-personalized CX
- Network automation with agentic intelligence and auditability
Still, industry-wide transformation requires standards, oversight, and trust. Agentic systems, including LTM, must maintain transparent audit trails and robust governance to ensure decisions can be interpreted and improved upon.
The Strategic Opportunity Ahead
By embedding LTM into their operational stack, SoftBank is:
- Leading AI-for-RAN maturity
- Optimizing CapEx through smarter infrastructure use
- Repositioning AI as a value-generating layer across OSS, BSS, and CX
The model’s future applications could include predictive asset maintenance, dynamic spectrum allocation, or even LEO-satellite-driven coverage optimization—particularly as governments push for universal mobile access via hybrid terrestrial and satellite integration.
