Trusted intelligence is crucial for operators in progressing to Level 4+ autonomous networks. This research explores the role of digital twins, agentic AI and operator knowledge in achieving trusted intelligence.

Operators may hit a wall en route to fourth level of automation

Many operators have ambitions to achieve Level 4 autonomous networks by 2030, as outlined in the TM Forum’s Autonomous Network Framework. Level 4 is defined as cross-domain, intent-based, closed-loop automation, enabling networks to autonomously assess real-time conditions and adjust operations based on high-level intent, such as business goals or customers’ service requirements. While some operators claim to have achieved this level of autonomy, this often refers to small-scale deployments that are focused within a specific network domain – and to truly reach Level 4, automation must be cross-domain.

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Most operators are currently at Level 2 on the framework, where automation is partially achieved in certain areas but still requires significant human oversight. This level of automation unlocks key ‘low-hanging fruit’ use cases for operators, such as:

• Restarting of sleeping cell sites based on predetermined load parameters;
• Detection of performance degradation in a single domain (e.g., transport or core network);
• Automating provisioning and configuration of network devices (e.g., routers or switches) within a particular network segment.

This is a good start for operators on their automation journey, focusing investment first in individual network domains and limiting the need for wide-spanning, resource-intensive transformation projects.

However, without a broader transformation of data skills and technology as well as a shift to more cross-domain and customer journey-driven practices, operators will eventually encounter significant limitations on their automation journey. They will reach a ‘brick wall’ in their ability to reach Level 4 and higher in networks autonomy, and meet their overall ambitions for network autonomy.

Trusted intelligence, underpinned by operator knowledge, bridges the gap from Level 2 to Level 4+ autonomous networks

Table of contents

• Executive summary
• Introduction: Operators may hit a wall en route to fourth level of automation
• Network digital twins can accelerate autonomy through knowledge and trusted automation
  • Evolve data strategies to develop and harness knowledge
  • Effectively integrate trusted intelligence through AI, GenAI and agentic AI
  • Digital twins: Harness knowledge to drive trusted intelligence
• Defining the stages of knowledge maturity
  • Stage 1: Network topology
  • Stage 2: Service topology
  • Stage 3: Ontology
• Steps to progressing knowledge maturity
  • 1. Launch targeted campaigns to upgrade legacy infrastructure and modernise inventory management
  • 2. Adopt modern data architectures and GenAI to improve data management
  • 3. Develop key skills and capabilities required to effectively implement AI
  • 4. Break down data and organisational silos
• How knowledge and digital twins impact network automation use cases
  • Example use cases: Zero-touch provisioning and self-healing networks
• Conclusion
• Appendix

Related research

• The journey to a self-healing network: Intelligence, agents and complexity
• Achieving next-level network autonomy
• Finding value from AI, analytics and automation in the telco: Part 2
• Network innovation as an engine for growth: A manifesto

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Jonas Topp-Mugglestone

Jonas Topp-Mugglestone

Consultant

Jonas is a Consultant at STL Partners, specialising in data centres and M&A.