Embedding autonomy
into day-to-day telecom operations
Overcoming the structured data challenge in network operations.
Embedding autonomy into day-to-day telecom operations
Across industries, autonomy is no longer a distant goal. It is already reshaping how businesses operate.
Yet in telecom, network operations have not kept pace. Despite years of investment in AI, machine learning, and big data platforms, many Network Operations Centers (NOCs) remain constrained by manual processes and fragmented automation.
The core issue is not ambition, but data. The structured information that operational teams rely on is often siloed and difficult for AI systems to interpret.
At the heart of network operations is structured data: inventory, service configurations, trouble tickets, and alarms. This data, which is essential to network operations, is fragmented across systems, locked in domain-specific schemas, and difficult to use beyond its original purpose.
Each system—be it inventory, fault management, or service assurance—operates separately and is designed to support human workflows. They can describe an event that happened, but not why it happened or how it impacts the wider network. As a result, data lacks the context and connectivity needed for AI models to understand relationships, not just events.
The challenge isn’t data volume, but data meaning. Rigid schemas, limited interoperability, and disconnected datasets prevent AI from reasoning across domains or adapting to dynamic network conditions. This is why many AI initiatives in operations struggle to scale. Without connected, contextualized data, intelligence remains siloed and impact remains limited.
If siloed data is the chasm, semantic modeling is the bridge across it. It offers a way to reshape structured data, not by moving it, but by describing its meaning and relationships in a way that Gen AI can understand and reason over.
At the core of autonomous networks are ontologies and knowledge graphs. Think of an ontology as a shared vocabulary and map. It defines the important things in a domain (like services, customers, devices, and faults) and how they relate to each other. A knowledge graph then uses that map to connect real data points, creating a web of relationships that reflect how things actually work in the real world.
This connected model becomes a semantic digital twin, a continuously updating, AI-ready representation of how the network behaves, degrades, and recovers. Instead of manually stitching together inventory tables and alarm records, an AI assistant can traverse the graph, tracing a device fault to supporting services and impacted customers.
With semantically modeled data in place, Gen AI can shift from being a disconnected assistant to an operational partner, interpreting, correlating, and acting with awareness. This unlocks a new class of use cases that were previously out of reach for telecom operators:
Autonomous fault triage and root cause analysis: Instead of surfacing alarms and leaving the analysis to humans, Gen AI can now trace relationships across devices and customers to pinpoint the likely root cause. It does not just identify what failed, but why, and who or what is affected.
Predictive maintenance and intelligent orchestration: By reasoning over historical data and network behavior patterns, Gen AI can forecast likely faults or degradation. Combined with an orchestration system, it can proactively take remediation steps before services are impacted.
Service impact correlation across domains: When a service degrades, it’s rarely isolated to one domain. Gen AI, powered by a semantic model, can trace the impact from core to edge, from transport to customer experience, surfacing correlations that span traditionally siloed systems.
LLM-powered NOC assistants (Agentic AI): Meet Incidenta, Resolvix, and other members of the Capgemini AI NOC team—LLM-based agents trained not just on language, but on the operational semantics of the network. They assist with diagnostics, explain network behaviors, highlight hidden insights, and even act, triggering workflows or escalating cases autonomously.
Each of these use cases represents a shift from reactive to proactive, from rule-driven to reasoning-driven. But none of them are possible without structured data that has been semantically enriched and made interoperable across the estate.
Making autonomous networks real
CHALLENGE:
Vodafone wanted to accelerate network automation, improve operational efficiency, and align automation with business intent.
SOLUTION:
The company partnered with Capgemini to assess their current network maturity and define a scalable and actionable roadmap.
OUTCOMES:
Clear view of automation maturity and priority gaps
Structured journey toward Level 4 autonomy
Improved operational resilience and customer experience
Blueprint that scales across markets
Better investment alignment for faster outcomes.
Recognizing the critical importance of autonomous networks, Vodafone set out to improve network performance and efficiency by creating a clear path toward higher levels of automation. This meant understanding where automation could have the greatest impact, how it could support business goals, and what steps were needed to reach higher levels of autonomy.
Vodafone partnered with Capgemini to create the foundation for autonomous networks. Together, they used recognized industry methods, including the TM Forum methodology, to assess automation maturity and explore future opportunities.
Through joint workshops and a shared design process, the team developed a roadmap to build cross-domain maturity and advance toward a more intelligent, resilient network. The roadmap combined quick wins with long-term transformation goals, defining a target architecture, key enablers, and a scalable blueprint for rollout across markets. It also provided clarity on where to focus governance and investment to drive early progress while supporting future growth.
The impact of this engagement is already visible. In Germany, Vodafone achieved a cross-domain maturity score of 3.4 in Fault and Incident Management—an impressive and significant step toward end-to-end autonomy. As a result of our engagement, the company has also been able to improve network monitoring and management, strengthening observability and operational consistency to support future automation and higher service quality.
With a scalable blueprint now in place, Vodafone is positioned to expand these capabilities across markets, moving closer to the company’s vision of Zero-Touch operations, shorter waiting times,
and fewer service issues. Shared governance, stronger use of data, and the planned introduction of AI-driven capabilities will further strengthen this momentum.
Perhaps more importantly, this program has given Vodafone clarity on where to focus future investment to improve resilience and unlock performance gains. These insights help link technology choices to customer and business outcomes in a clear and practical way, helping the company connect network capabilities to business value.
Network transformation starts with clarity: understanding the current state and leveraging expert insight and industry-proven frameworks to shape the journey ahead. Vodafone has invested substantial effort and resources in automating Fault & Incident Management and I am truly pleased with the achievement of autonomy level 3.4 across complex cross‑domain scenarios. Thanks to Capgemini’s expertise and the TM Forum methodology, Vodafone gained a clear view of its network maturity and defined a credible roadmap toward higher levels of autonomy.”
Making autonomous networks real: Vodafone’s journey to self‑healing networks
Learn more >
For years, enterprises have chased automation through scripts, orchestration, and AI models. Some progress has been made, but to unlock true autonomy, we must make structured data semantically interoperable.
Semantic modeling is a game-changer for telecom operators. It transforms siloed, structured data into an interconnected knowledge layer, ready for Gen AI to reason over, act on, and evolve with.
It is the foundation that turns raw information into intelligent decisions and it is what makes the vision of the autonomous networks possible.
Autonomy is part of every network’s future—and Capgemini can help make it real by assessing your organization’s current network maturity, addressing challenges, and identifying quick-win use cases.
