Scaling AI in Telecom with Small Language Models

Artificial intelligence is rapidly reshaping telecom by automating operations, improving customer experience, and enabling new digital revenue streams. However, as telecom operators move from isolated AI pilots to enterprise-wide deployment, they often run into practical barriers: volatile token-based costs, latency constraints, data sovereignty requirements, and governance risks tied to non-deterministic model behavior.

This white paper argues that successful AI adoption in telecom requires a paradigm shift toward a hybrid, SLM-first architecture. Small Language Models, when fine-tuned on telecom-specific data and workflows, deliver domain-embedded intelligence with significantly lower cost, faster inference, and greater operational control.

Topics covered in this white paper

• Why LLM-only deployments struggle at telecom scale: cost, latency, predictability, sovereignty, and governance
• What Small Language Models are, where they fit, and how they compare to LLMs across key operational dimensions
• Fine-tuning approaches for SLMs, including full fine-tuning, LoRA, adapters, and prompt tuning
• RAG vs fine-tuning: choosing between knowledge freshness, traceability, and behavioral specialization
• A decision framework to determine when to fine-tune, when to retrieve, and when to escalate to a larger model
• A real-world scaling pattern inspired by AT&T, showing how hybrid model orchestration can dramatically reduce operating costs while maintaining accuracy