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Pillar: No-Code OSS

Generative AI in Telecommunications: Co-Pilots, Agents, and the New BSS Paradigm

GenAI and Agentic AI are redefining BSS in telecom. Understand the difference between a co-pilot and an agent, and what it means in practice.

By Cynthia Naveira
generative AI in telecommunications

TL;DR

GenAI and Agentic AI are redefining BSS in telecom. Understand the difference between a co-pilot and an agent, and what it means in practice.

Key takeaways

  • Generative AI in telecommunications isn’t just an analyst trend.
  • It’s already transforming how telcos run their operations.
  • And to understand its real impact on BSS, you need to start with a distinction that matters: not all AI is the same.

Generative AI in telecommunications isn’t just an analyst trend. It’s already transforming how telcos run their operations. And to understand its real impact on BSS, you need to start with a distinction that matters: not all AI is the same.

Since 2022, Large Language Models (LLMs) have accelerated a transition that was already underway. The TM Forum report describes it in two moves: first, the leap from traditional discriminative AI to generative AI; then, the leap from GenAI to Agentic AI. Each represents a higher level of autonomy and capability.

Traditional AI excels at specific, predefined tasks: classifying a ticket, detecting a payment anomaly, predicting churn. It’s precise within its domain, but it doesn’t go beyond it.

Generative AI adds the ability to create content, process natural language, and act as a co-pilot: it assists a human, suggests options, generates responses. It doesn’t replace human decision-making; it amplifies it. In the BSS context, this translates into call center agent assistants, campaign design tools, or offer configuration co-pilots.

Agentic AI is the most disruptive leap. An agent doesn’t wait for instructions at each step: it interprets a goal, determines what actions are needed, and executes them autonomously. It can handle end-to-end workflows, coordinate with other systems, and adapt in real time to context changes.

In BSS, this means an agent can handle a billing dispute without human intervention, configure a personalized offer based on customer behavior, or coordinate the resolution of a network issue affecting multiple contracts, all at any hour and at scale.

Integrating these systems also requires new integration patterns: MCP (Model Context Protocol), so agents can securely access external data and APIs, and A2A (Agent-to-Agent), so multiple agents can collaborate with each other. The future BSS isn’t just a system; it’s a coordinated ecosystem of agents.

In the next blog, we get into concrete use cases: how AI is transforming customer management and sales in real time.

This blog is part of a series. Start with the first post if you haven’t already.

👉 Download the full TM Forum report: AI for BSS — GB1082

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