How a solid generative AI strategy can improve telecom network operations

Generative AI (gen AI) has transformed industries with applications such as document-based Q&A with reasoning, customer service chatbots and summarization tasks. These use cases have demonstrated the impressive capabilities of large language models (LLMs) in understanding and generating human-like responses, particularly in fields requiring nuanced language understanding and inferencing.

However, in the realm of telecom network operations, the data is different. The observability data comes from proprietary sources and encompasses a wide variety of formats, including alarms, performance metrics, probes and ticketing systems capturing incidents, defects and changes. This data, whether structured or unstructured, is deeply embedded in a domain-specific language. This includes terms and concepts from technologies like 5G, IP-MPLS and other network protocols.

A notable challenge arises from the fact that standard foundational LLMs are not typically trained on this highly specialized and technical data. This needs a careful strategy for integrating gen AI into the telecom operations domain, where operational efficiencies and accuracy are paramount.

Successfully using gen AI for network operations requires tailoring the models to this niche context while addressing unique challenges around data specificity and system integration.

The complexity and diversity of network data, along with rapidly changing technologies, presents several challenges for network operations. Gen AI offers efficient solutions where traditional methods are costly or impractical.

• Time-consuming processes: Switching between multiple systems (such as alarms, performance or traces) delays problem resolution. Generative AI centralizes data into one interface providing natural language experience, speeding up issue resolution by reducing system toggling.
• Data fragmentation: Scattered data across platforms prevents a cohesive view of issues. Generative AI consolidates data from various sources based on the training. It can correlate and present data in a unified view, enhancing issue comprehension.
• Complex interfaces: Engineers spend extra time adapting to various system interfaces (such as UIs, scripts and reports). Generative AI provides a natural language interface, simplifying navigation across complex systems.
• Human error: Manual data consolidation leads to misdiagnoses due to data fragmentation challenges. AI-driven data analysis reduces errors, helping ensure accurate diagnosis and resolution.
• Inconsistent data formats: Varying data formats make analysis difficult. Gen AI model training can provide standardized data output, improving correlation and troubleshooting.

While gen AI offers transformative potential in network operations, several challenges must be addressed to help ensure effective implementation:

• Relevance and contextual precision: General-purpose language models perform well in nontechnical contexts, but in network-specific use cases, models need to be fine-tuned with domain-specific terminology to deliver relevant and precise results.
• AI guardrails and hallucinations: In network operations, outputs must be grounded in technical accuracy, not just linguistic sense. Strong AI guardrails are essential to prevent incorrect or misleading results.
• Chain-of-thought (CoT) loops: Network use cases often involve multistep reasoning across multiple data sources. Without proper control, AI agents can enter endless loops, leading to inefficiencies due to incomplete or misunderstood data.
• Explainability and transparency: In critical network operations, engineers must understand how AI-derived decisions are made. AI systems must provide clear and transparent reasoning to build trust and help ensure effective troubleshooting, avoiding “black box” situations.
• Continuous model enhancements: Constant feedback from technical experts is crucial for model improvement. This feedback loop should be integrated into model training to keep pace with the evolving network environment.

Key design principles can help ensure the successful implementation of gen AI in network operations. These include:

• Multilayer agent architecture: A supervisor/worker model offers modularity, making it easier to integrate legacy network interfaces while supporting scalability.
• Intelligent data retrieval: Using Reflective Retrieval-Augmented Generation (RAG) with hallucination safeguards helps ensure reliable, relevant data processing.
• Directed chain of thought: This pattern helps guide AI reasoning to deliver predictable outcomes and avoid deadlocks in decision-making.
• Transactional-level traceability: Every AI decision should be auditable, ensuring accountability and transparency at a granular level.
• Standardized tooling: Seamless integration with various enterprise data sources is crucial for broad network compatibility.
• Exit prompt tuning: Continuous model improvement is enabled through prompt tuning, ensuring that it adapts and evolves based on operational feedback.

Implementing a gen AI strategy in network operations can lead to significant performance improvements, including:

• Faster mean time to repair (MTTR): Achieve a 30-40% reduction in MTTR, resulting in enhanced network uptime.
• Reduced average handle time (AHT): Decrease the time network operations center (NOC) technicians expenditure addressing field technician queries by 30-40%.
• Lower escalation rates: Reduce the percentage of tickets escalated to L3/L4 by 20-30%.

Beyond these KPIs, gen AI can enhance the overall quality and efficiency of network operations, benefiting both staff and processes.

IBM Consulting®, as part of its telecommunications solution offerings, provides reference implementation of the above strategy, helping our clients in applying gen AI-based solutions successfully in their network operations.