Why Telefónica’s Journey to Autonomous Network Level 4 Depends on Operations

At FutureNet World 2026, Nilmar Seccomandi David, Director of Autonomous Network & Infrastructure at Telefónica, gave an interesting update on the operator's journey towards Level 4 autonomous networks.

The presentation, “Autonomous Operations: Why the Journey to AN L4 Depends on It?”, went beyond the increasingly familiar discussion about using AI in telecom networks. The more important message was that Level 4 is not something an operator achieves simply by deploying enough AI use cases. It requires a much broader transformation of network technology, operational platforms, processes and people, together with a way of measuring autonomy across the whole organisation.

Telefónica launched its Autonomous Network Journey (ANJ) programme in 2021 with its three main operating companies in Spain, Germany and Brazil. David explained that it adopted a holistic approach from the beginning, covering the main network lifecycle processes of planning, testing, deployment and operations, as well as domains including IP, transport, radio, fixed access, core and cloud.

This is important because Telefónica does not view the autonomous network programme as a collection of isolated automation projects.

The programme has four main workstreams. The first is technology transformation, including more open, softwarised and disaggregated networks as well as the removal of legacy technologies. The second is what Telefónica calls the brain, its automation platform, covering data integrity, data lakes, OSS modernisation and the platforms required to implement automation and AI.

The third is the heart, involving process redesign. David's point here was that simply automating an existing process does not necessarily capture the full benefits of automation. Processes themselves may need to be redesigned as new capabilities are introduced.

The fourth is people, including new ways of working, reskilling and creating an AI culture.

This framework is not new. In his FutureNet World presentation a year earlier, David used essentially the same four dimensions, describing them as The Network, The Brain, The Heart and People. The 2025 material already included open and softwarised networks, data and automation platforms, process redesign and organisational change.

That earlier presentation also showed Telefónica moving from conventional automation towards what it called Hyper Automation, with GenAI, agentic AI, cognitive cross-domain AI and digital twins becoming increasingly important between 2025 and 2030.

The difference in 2026 is that the discussion has become much more focused on how the operator measures progress and what autonomous operations actually look like in production.

Telefónica estimates that when the programme started five years ago, its overall autonomy level was around 1.1. It has progressed through the maturity levels year by year and closed 2025 at 3.42.

The company has now made its longer-term objectives public. It is targeting an average autonomy level of 3.75 by 2028 and Level 4 by 2030 across Spain, Brazil and Germany. Those targets formed part of Telefónica's November 2025 Capital Markets Day and its wider Transform & Grow strategy.

David made an interesting observation about the importance Telefónica now attaches to the number: autonomy level has become a KPI followed by the company's board, alongside conventional indicators such as revenue and Net Promoter Score.

That raises the obvious question: what exactly does a figure such as 3.42 mean?

This was perhaps the most useful part of the presentation.

Image: Telefónica breaks its autonomy measurement down from Group and operating-company level through network domains, sub-domains, processes and individual activities. Source: Telefónica / FutureNet World 2026

Telefónica uses the TM Forum Autonomous Networks framework as its basis, but David stressed that operators have to adapt the methodology to their own networks.

At the top level, Telefónica's Group autonomy figure consolidates the results from Spain, Brazil and Germany.

Within each operating company, the network is divided into domains such as IP, transport, radio, fixed access, core and cloud. Those can then be divided into sub-domains. Transport, for example, may include optical and microwave, while the core can include voice and packet-core functions.

Each domain is then assessed across processes such as planning, testing, deployment and operations. Those can be broken down again into individual activities. Operations, for example, includes areas such as fault and performance management.

The result is a large hierarchy of measurements which eventually rolls up into the Telefónica autonomy figure.

David's argument was that this complexity is necessary. Knowing that an operator has one, ten or even twenty impressive autonomous scenarios tells you relatively little about the autonomy of the network as a whole.

The same point was already visible in Telefónica's 2025 reporting. Its autonomy index showed progress occurring at different speeds across six network domains and multiple processes. IP and RAN were among the more advanced areas, while Core and Telco Cloud were further behind.

The 2026 results make that variation particularly clear.

Image: Telefónica's current autonomy assessment shows considerable variation between network domains and operational processes even though the consolidated Group autonomy level is 3.42. Source: Telefónica / FutureNet World 2026

The slide shows the results in two different ways.

On the left are Telefónica's three operating companies. The countries are deliberately anonymised, but the data is real. IP is the most autonomous domain in all three operating companies, while Cloud is the least autonomous.

The right-hand view breaks the assessment down by process. Even inside IP, which is the most mature overall domain, testing is less autonomous than the other main processes.

This illustrates one of the difficulties with discussions about “reaching Level 4”. An operator does not suddenly become Level 4 everywhere. Different countries, technologies and processes progress at different rates, and the overall figure can hide significant differences underneath.

Telefónica closed 2025 with 12 individual Level 4 use cases, spread across Spain, Germany and Brazil. Two were associated with planning, five with deployment and five with operations. Telefónica describes Level 4 cases as those capable of acting autonomously based on an intention provided by a human.

These include autonomous network-capacity creation, transport digital twins, autonomous IP fault resolution, 5G Core self-healing, fibre planning, software changes and multi-domain correlation.

The first Level 4 operations example David discussed was NetOptimizer, a digital twin of the O2 Germany transport network.

Image: O2 Germany's NetOptimizer uses a digital twin of the transport network for network analysis, simulation and proactive bottleneck detection. Source: Telefónica / FutureNet World 2026

NetOptimizer maintains an end-to-end representation of the German transport network and is used for analysis, simulation and bottleneck detection.

One particularly interesting capability is resilience testing. David explained that the system can periodically simulate the failure of individual links across the thousands of links in the transport network and analyse what would happen elsewhere.

Potential bottlenecks can therefore be identified before an actual failure exposes them.

The benefits shown on the presentation slide include 80% less time required for analysis, 40% fewer transport capacity issues, more than 90% fewer sites with very high capacity loading and a 5% latency improvement.

Telefónica has separately published the same figures for NetOptimizer, confirming the 80% reduction in time spent on planning, operations and network-optimisation analysis.

This is also worth noting because the automatic transcript of the presentation renders the first figure as 8%; the slide itself and Telefónica's published material make clear that the figure is 80%.

The second Level 4 example addressed IP interface flapping, where an interface repeatedly transitions between active and inactive states because of problems such as fibre attenuation, hardware faults, temperature or other instability.

Telefónica's system detects the condition, attempts to establish its cause and applies corrective action automatically. Where the problem cannot be resolved automatically, the affected port can be blocked and the appropriate field process triggered.

The company says the solution has reduced the impact of flapping on services by 70%, while removing the need for manual intervention in the closed-loop resolution process.

David then presented several other examples which are not yet Level 4 but are interesting because they demonstrate that Telefónica is not relying on a single flavour of AI.

One is Correlax in Brazil.

The starting problem was surprisingly mundane: NOC staff complained about gaps in the network inventory, which made it difficult to determine relationships between apparently separate incidents.

Rather than waiting for a perfect inventory, Telefónica applied graph techniques similar in principle to those used to establish relationships in social networks. It inferred relationships between network elements from available information and then used these relationships to correlate trouble tickets.

David said Correlax is reducing the relevant tickets by around 44%.

Another example is ATEA, an AI Factory deployed in Germany in collaboration with Google and using Gemini. One of the scenarios David described was cell-health analysis.

What had previously been a manual process involving the correlation of KPIs from multiple databases can increasingly be automated, bringing together information from different sources to analyse the condition of the network.

David also discussed rApps as a Service. Telefónica is testing an Ericsson solution using AWS which, according to the presentation, could be deployed in only a few days. The initial application involved anomaly detection, with additional agents being developed.

These examples are useful because the FutureNet World agenda specifically asked about the roles of predictive, generative and agentic AI, but the presentation suggests that this may be the wrong way to think about autonomous networks.

Telefónica is using whatever technique is appropriate to the problem: conventional automation, traditional algorithms, machine learning, graph analytics, digital twins, GenAI, rApps and increasingly AI agents.

The 2025 FutureNet World presentation made the same point in a different way. Telefónica already had more than 400 use cases spanning AI-driven network design, digital twins, capacity forecasting, GenAI document analysis, predictive maintenance, incident correlation and automated optimisation.

Its main case study that year was Vivo's Fractal system, which automated network creation using a mixture of DBSCAN clustering, Coral Reef algorithms, Telefónica's internally developed House of Cats algorithm and Dijkstra's shortest-path algorithm. The accompanying TelcoTitans report said Fractal had helped reduce mobile-site deployment time from around three months to one week.

The progression between the two FutureNet World presentations is therefore quite revealing. In 2025, the centrepiece was an advanced individual planning and network-creation use case. In 2026, the discussion is much more about spreading autonomy through the wider operating model and measuring that transformation systematically.

One of the most interesting parts of the 2026 presentation was David's reminder that AI by itself is not always enough.

Image: Telefónica's RAN energy example illustrates the limits of software optimisation: power-saving features and AI can reduce consumption significantly, but further improvements may ultimately require hardware modernisation. Source: Telefónica / FutureNet World 2026

He used RAN energy consumption as an example.

Traditional network power-saving features can already deliver substantial reductions. David suggested savings of around 20–30% compared with a network that does not use them.

AI can then improve when and how those features are activated. Depending on the implementation, David suggested that another 5–10% might be achievable.

Eventually, however, optimisation runs into the physical characteristics of the installed equipment.

At that point, modernising the hardware can potentially produce another significant improvement. David suggested that combining power-saving features and AI with newer hardware could yield an additional 20–30% and allow the energy-consumption curve to track the traffic curve much more closely.

The precise savings will clearly vary between networks, configurations and equipment generations, but the underlying message is important:

software intelligence cannot indefinitely compensate for inefficient hardware.

This is a useful counterweight to the idea that every network problem will eventually be solved by more sophisticated AI.

The final example in the presentation brought service observability into the autonomous-operations picture.

Traditional network operations tend to concentrate heavily on network KPIs. David argued that improving the network view is not sufficient: operators also need to understand the service view.

The example combines network status with service information and external factors such as weather conditions. Problems can then be analysed not only according to what is happening inside the network, but according to which services and customers are actually affected.

David said this approach is reducing Mean Time To Repair (MTTR) by around 30% and helping Telefónica prioritise which field tickets need attention first.

This may ultimately be one of the most important requirements for Level 4. A genuinely autonomous network should not merely know that a KPI has crossed a threshold or that a network element has failed. It needs enough context to understand what that failure means for the service, decide its relative importance and take the appropriate action.

There has already been further progress since the FutureNet World presentation.

Recently Telefónica provided a newer update saying it now has more than 500 AI use cases in production, with 15 already at Level 4. The targets remain Level 3.75 in 2028 and Level 4 in 2030.

That update reinforces the main message I took from David's FutureNet World presentation.

Level 4 is not something an operator installs.

It is the result of progressively changing how the network is designed, observed, deployed and operated; improving the data and OSS platforms underneath it; redesigning processes around closed-loop operation; and changing how people interact with increasingly autonomous systems.

AI is becoming increasingly important to that journey, particularly as GenAI and agentic approaches mature. But Telefónica's experience also shows that autonomous networking is much broader than AI.

It includes digital twins, conventional algorithms, closed-loop automation, observability, modern network architectures, accurate data, process redesign and, where necessary, replacing the physical infrastructure itself.

Perhaps that is why the title of the presentation was so appropriate.

The journey to Autonomous Network Level 4 ultimately depends on autonomous operations.

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Telecom Argentina's View of Autonomous Networks and the Road to Level 4

At FutureNet World 2026, Eduardo Panciera from Telecom Argentina gave a useful operator view of where autonomous networks are heading and, more importantly, what stands in the way of getting there.

The presentation was titled Autonomous Networks and AI: A Perfect Match, From Automation to Level 4 Autonomy. The main message was simple but important. Operators cannot keep managing future networks with the same operating model they use today. Automation helps, but automation alone is no longer enough. As networks become more complex, operators need to move towards real autonomy.

Telecom Argentina is an interesting company to hear this from. It is not just a mobile operator. It provides connectivity, IPTV, OTT services, B2B services and fintech services. It has also been rebranding products under the Personal brand, with the aim of giving each user a more personal and digital experience. Eduardo linked this ambition to TM Forum's Zero-X vision, where the goal is to provide zero wait, zero touch and zero trouble experiences for customers. TM Forum describes Zero-X in similar terms, focusing on zero wait, zero touch and zero trouble as guiding principles for future digital operations.

The problem is that simplicity for the customer usually means more complexity inside the network.

Telecom Argentina's view is that operators need self-X networks, cloud-based programmable infrastructure, AI-driven assurance, automated decision-making and APIs that expose network capabilities. In other words, the customer-facing experience may become simpler, but the operational layer behind it has to become much more intelligent.

This is particularly true as 5G networks expand. 5G enables more personalised services and more programmable capabilities, but it also brings additional operational complexity. Operators have to deal with multi-cloud environments, heterogeneous networks, rising costs and more demanding service expectations. Traditional automation can support some of this, but the network is becoming too complex for humans to remain involved in every design, decision and recovery process.

That is the gap between automation and autonomy.

In a normal network operations cycle, the operator defines targets and KPIs, observes the network, identifies deviations, analyses what is happening, designs possible solutions, decides which one to apply and executes the action. Today, parts of that cycle are already automated. Execution is often automated. Observability and alarm handling are increasingly automated. But solution design and decision-making still often sit with human teams. Eduardo's point was that autonomy requires these cognitive steps to move into the system itself.

This is why Level 4 matters.

TM Forum classifies Autonomous Network levels from Level 0 to Level 5, ranging from manual management to fully autonomous networks. Its Autonomous Networks Mission describes Level 4 as introducing decision-making based on intent-driven, predictive analysis and closed-loop management of service-driven and customer experience-driven networks, supported by AI modelling and continuous learning.

In practical terms, Level 4 is where autonomous networks stop being a future vision and become an execution problem. TM Forum made a similar point in June 2026, saying that Level 4 is where autonomous networks move from ambition into an industry execution challenge.

Level 5 remains the longer-term aspiration. It implies full autonomy across a much wider range of domains and scenarios. Level 4 is the more immediate challenge because it requires operators to trust the system to make decisions in defined areas, under defined policies, using reliable data and closed-loop control.

AI is central to that transition, but Telecom Argentina's message was not simply that AI can be added to existing operations.

Eduardo described a layered autonomous network architecture, aligned with TM Forum thinking, with business, service and resource layers. The resource layer includes familiar network domains such as mobile core, RAN and transport. Intents flow down from higher layers, while reports and feedback flow up. Each domain can have its own closed loop, and there can also be closed loops between layers.

This is an important distinction. Autonomous networks are not just about automating individual tasks. They are about connecting business intent, service requirements and resource behaviour through closed-loop systems.

The AI components in this architecture can be split into copilots and agents.

A copilot is triggered by a human. It can help with suggestions, data analysis, troubleshooting and natural language interaction. It supports the human operator.

An agent goes further. It observes, analyses and decides without direct human intervention. This is where the shift towards autonomy starts to become real.

The agent model described in the presentation is also worth noting. Agents receive intent from humans or from other agents. They observe the network environment using logs, KPIs and alarms. They use knowledge and memory to understand context. Short-term memory provides the current situation, while long-term memory captures past experience, domain knowledge, design documents and technology information.

Agents can work in two ways. They can be reactive, responding to real-time events. They can also be proactive, anticipating problems or recommending network parameter optimisation before an issue becomes visible to the customer. In practice, operators will need both. Reactive autonomy helps with fault handling. Proactive autonomy is where networks start to become self-optimising and, eventually, self-evolving.

However, one agent is not enough.

Telecom Argentina's view is that operators will need a network of agents distributed across business, service and resource layers. These agents will need to collaborate, negotiate and communicate with each other. That brings a new challenge. If an operator has hundreds or thousands of agents working across domains, then it also needs a governance framework for those agents.

Eduardo highlighted several elements of this governance framework: registry, identity, guardrails, orchestration, observability and a shared data model. Agents must be able to discover each other. They must be authenticated. They must have rules and boundaries. Their workflows must be orchestrated. Their actions must be observable. Most importantly, they must all understand the network in the same way.

That final point may be the most important part of the presentation.

Telecom Argentina's argument is that operators cannot just place AI on top of the way networks are operated today and expect Level 4 autonomy to emerge.

Today, many telecom operations still work from the "how". Teams have runbooks and documents that describe how to configure the network, how to configure assurance and how to update inventory. Different teams perform different steps. The result is often fragmented data. The real network configuration may not match the inventory. The topology used by assurance systems may not match the live network. Different systems can hold different versions of reality.

In that environment, AI may improve some tasks, but it will not be reliable enough for true autonomy. Agents making decisions on top of poor or inconsistent data will make poor or inconsistent decisions.

This is why trusted data is central to autonomous networks.

Telecom Argentina's proposed shift is to move from operating from the "how" to operating from the "what". Instead of starting with runbooks and configuration steps, the operator starts with intent. What service should be delivered? What resources does it require? What SLA must it meet? What assurance should be associated with it? This information should be captured through a catalogue and a source of truth, then orchestrated across resources, inventory and assurance.

This is a different operating model.

It means that autonomy is not just about AI tools. It is about trusted data models, service catalogues, intent-based operations, orchestration and closed-loop assurance. The agents only become useful when they operate on a consistent representation of the network and the services running over it.

That also explains why Level 4 is so difficult. The technology is only one part of the journey.

Telecom Argentina described three avenues for reaching Level 4. The first is technology, moving from traditional automation to programmable networks and then to agentic AI. The second is process, because existing operating processes need to change if agents are to be used safely and effectively. The third, and perhaps most difficult, is culture. Operators have to move from thinking primarily in terms of networks to thinking in terms of data models and digital operations.

This is a useful message for the wider industry.

Many operators are now talking about autonomous networks, AI-native operations and agentic AI. The risk is that these terms become marketing labels attached to existing automation platforms. Telecom Argentina's presentation was more grounded. It made clear that autonomy requires a much deeper change in how the operator understands, models and governs its network.

For Operator Watch readers, this is also a reminder that the next phase of operator transformation will not only be about 5G coverage, fibre expansion, cloud migration or customer apps. It will also be about the operating model underneath all of that.

An operator that wants to deliver personalised digital services at scale cannot keep relying on fragmented inventories, disconnected assurance systems and manual decision-making. It needs trusted data, programmable infrastructure, intent-based orchestration and closed loops that can act safely within defined boundaries.

AI can help, but AI is not the starting point. The starting point is a trusted model of the network and the services running over it.

That may be the most important lesson from Telecom Argentina's FutureNet World presentation. Level 4 autonomy is not just a technology milestone. It is a test of whether operators can redesign their operations around trusted data, closed-loop control and governed AI agents.

Level 5 may still be aspirational, but Level 4 is already becoming the practical battleground.

The FutureNet World 2026 presentation by Eduardo Panciera is embedded below:

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Why Operators Must Simplify Before They Automate

The pursuit of end to end automation remains a cornerstone of modern telecommunications strategy as operators transition from traditional manual processes to fully autonomous networks. A recent panel by Mobile Europe, moderated by Inderpreet Kaur from Omdia, featuring Afnan Ahmed of Telenor, José Palma of MEO, and Beatriz Ortega of Red Hat explored the current state of this journey and the significant hurdles that remain before the industry can claim true network autonomy.

The discussion cantered on the TM Forum framework for autonomous networks which categorises progress into six levels from zero to five. While many operators are actively automating tasks, the majority currently sit between level one and level two. These early stages involve static or rule based automation where human intervention is required for most decisions. The transition to level three and level four represents a significant leap. At level four, the system manages observability, analysis, and execution with humans only defining the initial intent. This move from deterministic rule based logic to probabilistic reasoning powered by artificial intelligence is where the industry sees both the greatest potential and the most significant cultural resistance.

A recurring theme throughout the session was the challenge of data management. Although telecommunications networks generate vast quantities of information, this data often remains siloed within specific domains like radio access networks or core networks. Creating a unified data mesh or knowledge graph is essential for achieving cross domain automation. The panellists noted that the solution is not simply building a larger data lake. Instead, the focus must be on data correlation. When an issue occurs in one part of the network, the system must understand how that event impacts other domains in real time. Without this level of visibility, end to end automation remains impossible.

Another major obstacle is the sheer complexity of existing operations support systems. Some large operators manage over one thousand different tools, many of which are homegrown or vendor specific. This fragmentation makes it incredibly difficult to implement a cohesive automation strategy. Panellists suggested that operators must radically reimagine their tool suites. The goal should be a vendor agnostic architecture that follows open standards like the Open Digital Architecture from TM Forum. By simplifying the network environment before attempting to automate it, operators can avoid the trap of merely automating existing inefficiencies.

Moving to higher levels of autonomy requires a fundamental shift in how engineers interact with the network. There is a natural fear of losing control when a system begins making its own decisions. To combat this, experts recommend a phased approach where artificial intelligence is first used in an open loop system. In this model, the system provides recommendations that a human operator must validate. Only after the system has proven its reliability over time is the loop closed, allowing the software to execute changes automatically. This process of building trust is vital for ensuring network resilience as systems move toward self healing capabilities.

While operational efficiency and cost reduction are clear drivers, the panel emphasised that autonomy must be viewed as a business transformation rather than just a technical one. The ultimate goal is to enhance customer experience and enable new revenue streams through services like automated network slicing. By achieving level four autonomy, operators can respond to market demands with a speed that manual processes cannot match. This agility is necessary to compete in a digital ecosystem where customers expect near instantaneous service provisioning and seamless performance across diverse network environments.

The shift toward autonomous operations introduces new risks, particularly in cyber security. An automated network could potentially propagate an attack or a misconfiguration much faster than a manual one. There is also the concern of data poisoning, where malicious actors could inject false information to manipulate the decision making process of the network. To mitigate these risks, operators must maintain rigorous data governance and ensure that artificial intelligence decisions remain explainable. Providing a clear audit trail of why a system took a specific action is essential for security and regulatory compliance. Despite these challenges, the consensus remains that the journey toward autonomous networks is an inevitable and necessary evolution for the telecommunications industry.

The video of the discussion as follows:

Automation and Data Driven Network Optimization in Swisscom’s Mobile Strategy

At Ericsson’s rApp DevCon 2025, Swisscom provided a clear view of how automation and data driven network optimisation are becoming core elements of mobile strategy rather than isolated technical initiatives. In a keynote delivered by Francesco Pellegrini, Product Owner for Radio Network Optimisation at Swisscom, the emphasis was on how long term investment in automation, analytics and innovation supports not only network performance, but also the sustained delivery of a high quality mobile customer experience.

For Swisscom, automation is closely tied to its ambition to offer the best possible mobile experience across Switzerland. This ambition has guided network decisions for more than a decade and is reflected in the operator’s consistent top rankings in independent benchmarks. Rather than treating these results as an endpoint, Swisscom views them as a baseline that must be continuously defended as network complexity increases. Data driven insights and automated decision making now play a central role in translating customer experience expectations into concrete network actions.

Advanced analytics allow Swisscom to better understand how customers experience the network in real conditions and to prioritise optimisation accordingly. Automation then becomes the mechanism that allows these insights to be acted upon at scale and with consistency. As mobile networks evolve, with new spectrum layers, denser deployments and growing 5G usage, traditional manual optimisation approaches are no longer sufficient to maintain efficiency or performance.

Swisscom’s journey towards automated radio network optimisation started several years ago with early self organising network capabilities such as antenna tilt optimisation in LTE. Over time, this expanded into a broader portfolio of automation use cases, including open loop optimisation driven by customer experience data and AI supported solutions for performance analysis. Centralised optimisation algorithms for 5G mobility and the introduction of closed loop automation further strengthened this approach. Today, much of the 4G network is optimised through automation, while 5G tuning is already at an advanced stage.

Pellegrini highlighted that achieving this level of automation required more than deploying new tools. One of the main challenges was introducing innovation while continuing to operate one of the highest performing networks in the market. This demanded changes in processes and mindset, particularly within radio optimisation teams. Engineers increasingly moved away from manual, vendor specific tools towards programmable, data centric workflows that support repeatability and scale.

The next phase of Swisscom’s mobile strategy builds on this foundation through its expanded partnership with Ericsson. A key component is the integration of the Ericsson Intelligent Automation Platform into Swisscom’s existing automation framework. This enables coordination between existing use cases while providing access to a standardised rApp environment and to the wider ecosystem. Just as importantly, it allows Swisscom to leverage data already available within its internal data lake to support more advanced optimisation and automation scenarios.

In radio network optimisation, Swisscom is already working with several AI enabled rApps, including anomaly detection, root cause analysis and antenna optimisation capabilities. At the same time, the operator is exploring the development of its own rApps, with radio optimisation as the starting point. The ambition, however, extends beyond optimisation alone. Network deployment and network healing are also seen as key areas where automation can deliver measurable benefits, particularly through zero touch approaches that accelerate cell acceptance and improve network health monitoring.

A central enabler of this strategy is the evolution of skills within Swisscom’s engineering teams. Radio engineers are increasingly expected to combine deep domain expertise with capabilities in coding, data handling and AI. While radio knowledge remains the foundation, closer collaboration with internal data science teams is becoming essential. This balance allows Swisscom to develop more sophisticated automation use cases without diluting its core engineering strengths.

The keynote also underlined the importance of open ecosystems in sustaining differentiation. Swisscom sees value in combining vendor developed rApps with innovations from a broader community, enabled by a standardised automation platform. This approach supports experimentation, accelerates innovation and reduces dependency on bespoke integrations, all while maintaining control over network performance and quality.

Swisscom’s experience illustrates that automation and data driven network optimisation are not short term initiatives, but long term strategic capabilities. As network complexity continues to grow, the ability to combine customer experience insights with intelligent, coordinated automation will be critical to maintaining leadership. Swisscom’s mobile strategy shows how these elements can be embedded into daily operations, positioning the operator to continue delivering a high quality mobile experience in an increasingly demanding environment.

The embedded keynote video provides additional depth and context, offering valuable insight into how Swisscom is translating automation concepts into real world operational practice.

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Telefonica’s Journey Towards End-to-End Autonomous Networks

At Mobile Europe’s The Briefing event in October, Jose María Ramón Pardo, Autonomous Networks and AI Senior Manager at Telefonica, shared how the company is building the foundations for truly autonomous networks. His presentation offered a clear picture of why automation is no longer optional for operators and how Telefonica is reshaping its operations to meet rising expectations across efficiency, agility and customer experience.

He began by setting out the reality for major operators today. Networks are growing in complexity and customers expect faster services, better reliability and more sustainable operations. At the same time, operators face pressure to support new business models and new digital services. Fortunately, the industry is benefiting from cloud native architectures and software driven networks, which make advanced automation and AI techniques far easier to apply than in the past.

Telefonica describes its evolution in three broad phases. The first phase involved building a basic automation foundation, mainly using rule based systems, device level scripting and early machine learning. Progress was held back by monolithic architectures and vendor dependency, which limited the scale of automation that was possible. The second phase marked the beginning of the company’s Autonomous Network Journey programme, which introduced data driven processes, orchestration, the first closed loop systems and centres of excellence for AI. Machine learning became part of day to day operations, although intelligence was still limited.

Telefonica is now in the phase it calls hyper automation. The company is accelerating its autonomous network ambitions by embedding AI directly into network platforms and operational processes. It is deploying generative AI, digital twins and agent based systems, while investing in the knowledge bases required to support more context aware intelligence. The goal is to enable networks that can plan, adjust, repair and optimise with minimal human intervention.

The Autonomous Network Journey programme brings these efforts together across four dimensions. The first covers the physical network and the shift to open architecture, virtualisation, cloudification and data centre consolidation, along with the retirement of legacy technologies such as 3G and copper. The second dimension is known as the brain, which focuses on the automation platform that manages data, orchestration, knowledge and decision making. The third involves adapting processes along the full service lifecycle, from planning through to operations, to take advantage of autonomous capabilities. The fourth dimension is people, covering skills, culture, organisational structures and new ways of working.

Telefonica tracks its progress using KPIs that include the TM Forum autonomy levels to benchmark maturity across domains. The company has already deployed hundreds of autonomous use cases across its markets, supported by a range of AI techniques. In planning, an AI driven design solution has cut fibre planning time from 60 days to less than a week. In Germany, a large scale digital twin enables mobile site configuration changes to be simulated and optimised before any live implementation, reducing planning and analysis time significantly and helping prevent capacity issues.

Operational use cases are also demonstrating clear value. In Brazil, AI driven self healing in the 5G core detects and resolves anomalies without manual intervention and has reduced average repair times. Agent based systems allow technicians to interact with IP networks using natural language. Large language models support internal documentation queries, and generative AI is used to improve contract management and workforce efficiency.

Looking ahead, Telefonica aims to move beyond isolated use cases to an environment where automation can be delivered at scale. This requires focusing on high value use cases, ensuring the cost to deploy is justified by the expected benefit, and enhancing the automation platform so that new use cases can be rolled out consistently across all network layers. AI needs to be integrated across the full lifecycle of the network and the company continues to explore new techniques such as intelligent agents, large language models and synthetic data generation.

Telefonica is also strengthening its governance approach to ensure responsible and effective use of AI. Collaboration remains important, with partnerships across the vendor ecosystem and other operators helping to accelerate innovation.

Although AI plays a central role, the company emphasises that real transformation depends on more than AI alone. Open architectures, high quality data, knowledge representation, redesigned processes and new organisational models are all essential to make autonomous networks a reality. Automation is considered mandatory for achieving efficiency, enabling new revenue opportunities and meeting the demands of customers and society.

Telefonica’s message is clear. AI and automation are reshaping telecom operations, but success depends on a balanced strategy that combines intelligent technology with architectural readiness, robust data foundations and a workforce prepared for new ways of working. The journey is well underway, and the early results show the promise of a more autonomous network future.

His talk is embedded below:

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The Power of AI in NTT Docomo’s 5G Journey

At FutureNet Asia 2025 in Singapore, Takehiro Nakamura, Chief Standardisation Officer at NTT Docomo, delivered the closing keynote on day two of the summit. His session focused on how AI has become central to Docomo’s 5G strategy and how those developments are shaping the operator’s path towards 6G. It was a forward looking talk that connected practical achievements with the longer term vision for future networks.

Nakamura-san explained that while 5G and 6G fall within the familiar ten year generational cycle, there is also a broader twenty year technological rhythm that influences how mobile systems evolve. After voice in the first wave and mobile multimedia in the second, the third wave is centred on unlocking new business value. In Docomo’s view, the success of 5G is essential for the success of 6G, especially in enterprise services where operators hope to build new revenue streams.

AI now sits at the heart of Docomo’s capability. The operator has built its data analytics on a very large foundation, spanning data from around one hundred million customers and hundreds of thousands of base stations. By combining this scale with a wide range of AI techniques, Docomo has created applications for enhanced customer service, network optimisation, personalised services and digital transformation across both internal and external domains.

Nakamura-san described a broad AI technology stack that includes natural language processing, customer behaviour modelling, location analysis, advanced analytics and video recognition. These core capabilities feed into applications across marketing, CX, healthcare, finance, network operations and local government. One of the examples he highlighted was Docomo’s LLM value added platform, designed to address security, reliability and safety concerns while offering a user friendly interface for internal teams and partners.

Another focus area is customer understanding through a platform known as Docomo Sense. By analysing subscriber information alongside online and offline behavioural data, the operator can segment customers with much higher precision. This supports personalised services, targeted marketing and new business creation. Nakamura-san shared a successful use case with Audi Japan, where Docomo’s segmentation helped the automaker reach customers with a strong interest in electric vehicles. The result was a significant increase in dealership visit rates and a notable rise in new customer engagement.

Docomo has also embedded AI deeply within its network operations. Silent hardware failures, which previously were often discovered only after customer complaints, can now be detected proactively. AI also enables early identification of device related issues that arise from complex interactions between specific hardware and spectrum conditions. This allows the operator to act before performance degradation becomes visible to subscribers.

Looking ahead to 6G, Nakamura-san emphasised that AI must be native to the design of future networks. AI will optimise the network while the network itself will be designed to serve AI driven applications. This mutual reinforcement is central to Docomo’s AI centric network concept. The ambition is to reduce human error, minimise outages, improve resilience in disaster prone environments and maximise customer experience.

Docomo is collaborating globally on 6G research, including work with Nokia and SK Telecom on an AI native interface. One promising line of research is pilotless transmission. Today’s radio systems use pilot signals to estimate channel conditions, but these signals create overhead. By applying AI on both the transmitter and receiver sides, Docomo tested the feasibility of reducing or eliminating pilots. In indoor trials, static measurements showed immediate gains due to the removal of pilot overhead, while dynamic measurements also delivered positive results despite channel fluctuations. Nakamura-san stressed that more trials are needed across different environments, but the early findings indicate strong potential for efficiency improvements in 6G.

As he concluded, Nakamura-san reinforced that progress in both 5G and 6G will depend on collaboration across the industry, particularly with partners that possess deep expertise in AI. Docomo sees AI as an essential tool for building resilient, efficient and high performing networks and is preparing for a future where AI permeates every layer of the system.

His talk is embedded below:

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CelcomDigi’s Journey Toward AI-Powered Autonomous Operations for Enhanced Customer Experience

CelcomDigi is taking bold steps towards transforming its network operations with the goal of creating a fully AI-powered, autonomous environment. At FutureNet Asia 2025, the company outlined its ambition to evolve beyond traditional network management and place customer experience at the centre of its operational strategy.

As one of Malaysia’s largest operators, serving over 20 million customers and running the country’s most extensive mobile and fibre network, CelcomDigi sees network intelligence and resilience as national priorities. Mobile connectivity has become the backbone of Malaysia’s digital economy, underpinning everything from education and healthcare to e-commerce and industry. Customer expectations are now focused less on basic coverage and more on the quality of experience, whether that means smooth video streaming, consistent gaming latency or reliable digital payments.

To meet these expectations, CelcomDigi has identified three major challenges that must be addressed. The first is the growing complexity of networks as new services such as IoT, network slicing and smart industry solutions are introduced. The second is the demand for real-time responsiveness, with customers expecting zero downtime and instant performance. The third is the ongoing paradox of rising data traffic without equivalent revenue growth, which puts pressure on costs and efficiency.

CelcomDigi believes that the way forward lies in AI-driven autonomous operations. By 2028, the company aims to achieve level four autonomous operations, supported by closed-loop systems, predictive intelligence and generative AI. The transition involves moving from reactive processes towards predictive, self-diagnosing and self-healing networks that can assure customer experiences at scale.

The operator is already making progress on this roadmap. Working closely with Ericsson, CelcomDigi has deployed AI platforms that enable predictive maintenance, automated fault detection and real-time root-cause analysis. Processes that previously took hours can now be resolved in minutes, and continuous optimisation ensures 24/7 network performance. Examples already live on the network include nationwide AI-powered root-cause analysis, closed-loop traffic balancing with significant efficiency improvements, and intelligent change management that boosts success rates. Even everyday applications such as WhatsApp calls are being enhanced through AI-driven quality optimisation, reflecting how closely network performance is tied to customer experience.

These initiatives are delivering measurable results. The operator reports higher availability, fewer call drops, smoother video streaming and faster recovery times. Improvements in customer experience are also reflected in stronger satisfaction scores.

Looking ahead, CelcomDigi’s vision is a zero-touch, customer-centric network that runs seamlessly in the background. The company sees autonomous operations not only as a means of simplifying complexity and reducing costs, but also as a way to differentiate connectivity, support innovation and sustain market leadership in Malaysia’s fast-growing digital economy.

You can watch the full session in the video below. For more insights like this, make sure to follow FutureNet World, where industry leaders regularly share their strategies for the networks of tomorrow.

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Telstra’s Bold Journey to RAN Autonomy and Beyond

At the recent rApp DevCon 2025 hosted by Ericsson, Gavin Spain, Group Owner of Wireless Network Strategy at Telstra, delivered a keynote that set out an ambitious and compelling vision for the future of RAN autonomy. As operators around the globe seek to simplify operations and reduce cost in increasingly complex networks, Telstra’s strategy places autonomy at the heart of network transformation.

Telstra has set clear targets: level 3 RAN autonomy within three years, and level 4 by 2030, as defined by TM Forum standards. Achieving this will demand deep changes across technology, process, systems, and tools. But more than just a transformation project, Gavin Spain framed autonomy as a strategic necessity to meet rising expectations and scale networks efficiently. For Telstra, autonomy is not about buzzwords but about delivering adaptability, optimisation and self-healing capabilities across its vast and varied network landscape.

With tens of thousands of 4G and 5G sites, spanning dense cities to remote regions, Telstra faces a highly diverse RAN environment. Rather than seeing this as a burden, Telstra views variability as an opportunity. Automation at the cell level enables a granularity of control not possible through manual approaches. Autonomy can unlock previously inaccessible value from the existing infrastructure and allow dynamic, performance-driven decisions at scale.

Central to this transformation is Telstra’s four-year agreement with Ericsson, a cornerstone investment of 800 million Australian dollars. This will upgrade the RAN with Open RAN-ready hardware, integrate 5G Advanced software, and crucially, adopt Ericsson’s Intelligent Automation Platform (EIAP) to power rApps and enable intelligent, programmable networks.

Telstra’s early focus is on two operational journeys: streamlining the planning, design and deployment of network infrastructure, and improving performance management and optimisation. This includes energy efficiency use cases, where Telstra is applying machine learning to find the right balance between performance and consumption. The long-term vision extends well beyond 5G. As future generations like 5G Advanced and 6G arrive, the complexity and costs will only grow. Programmability and intelligence must evolve with them.

Gavin Spain also highlighted the key architectural elements required to make this vision a reality. These include open and standardised interfaces to encourage portability and innovation, conflict resolution frameworks to manage competing app intents, integration of AI/ML pipelines to support closed-loop optimisation, seamless support for both traditional and virtualised RAN, and certification frameworks to ensure rApp quality and reusability.

Beyond the technology, the keynote emphasised economics. Operating mobile networks is increasingly expensive, while revenues per user remain flat or in decline. This puts pressure on operators to lower TCO and improve efficiency. For developers, this presents an opportunity. Even small improvements, such as reducing energy consumption by 1% or minimising truck rolls, can translate into significant cost savings. rApps that can deliver this type of value are well-positioned to scale across the global ecosystem.

Gavin encouraged developers to understand operator challenges, build with purpose, and iterate rapidly. He highlighted that the value of AI is not just in rApps themselves, but also in how developers can use AI tools to speed up development and testing. With the network domain evolving rapidly, speed and bold ambition will be essential.

The call to action was clear: no single player can deliver autonomy alone. Operators, vendors, standards bodies, and developers must collaborate closely. Operators like Telstra will contribute use cases and domain expertise. Vendors like Ericsson will provide platforms. Standards bodies ensure interoperability. And developers will bring the innovation and execution speed required to translate vision into reality.

Telstra has already begun migrating legacy Self Organising Network functions to EAIP. Initial use cases include configuration automation, anomaly detection, and intent-based optimisation. These are the first steps on a much longer journey, one that aims to reshape how networks are designed, deployed, and operated for the next decade and beyond.

Telstra’s message to the ecosystem is simple: collaborate, move fast, and focus on real-world value. With the foundations being laid today, the path to RAN autonomy is no longer just a concept but a concrete roadmap for intelligent, adaptive and customer-centric networks of the future.

The video of Gavin Spain’s keynote at rApp DevCon 2025 is embedded below. It is well worth watching for a deeper understanding of Telstra’s strategy and the broader opportunities for developers and partners across the ecosystem.

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How O2 Telefónica is Redefining the Network with NaaS and Open APIs

At O2 Telefónica Germany, the “network of the future” is no longer a distant vision, it’s becoming an operational reality. Under the leadership of Matthias Sauder, Director Networks, the operator has made substantial strides in transforming its end-to-end infrastructure to meet the demands of a highly agile, programmable, and customer-focused future.

At the FutureNet World conference in London (7–8 May 2025), where leading telco and tech stakeholders gathered to explore network automation and AI in telecoms, Sauder shared how O2 Telefónica’s evolution journey spans radio, transport, core, data centres, and cloud landing zones, all underpinned by a strategy that prioritises automation, flexibility, and openness.

The transformation began with a foundational goal: enhancing radio network quality. Once considered the underdog in a three-operator market, O2 Germany set out to radically improve performance by embracing agile practices and reshaping internal structures. Inspired by the Spotify model, the company introduced agility not just in project management but also in technical delivery. This included frequent software release cycles for radio, positioning itself as a global leader in rapid deployment and continuous integration.

A key initiative known as Tech Strategy 25 laid the foundation for modernising radio, transport, and core networks. Today, over 80 percent of the strategy has already been executed. With a largely cloud-native core in place, O2 Telefónica is among the pioneers of this architectural shift. Its collaboration with Ericsson produced one of the world’s first cloud-native digital cores, while a parallel effort with Nokia deployed core services for one million users in a public cloud environment.

The rationale behind both cloud-native and public cloud approaches is clear. Legacy architectures no longer support the operational agility or cost efficiency needed in today’s competitive telecom landscape. Cloud-native systems enable advanced capabilities such as continuous integration, continuous delivery, and seamless in-service software upgrades (ISSU). O2 Telefónica has shown these upgrades can be executed without disrupting live customer services, challenging the long-standing perception that such practices are too risky for telco-grade reliability.

Beyond infrastructure, the company’s future network model hinges on the integration of open APIs and Network-as-a-Service (NaaS) capabilities. These aren’t abstract concepts, they’re practical tools enabling agility, programmability, and new revenue streams. Open APIs expose network functions to external developers and partners, unlocking opportunities for co-creation and monetisation that were previously out of reach.

This openness also extends to industry partnerships. A standout example is the company’s collaboration with Siemens, which now leverages O2's slicing capabilities to deliver tailored network services to its own customers. These kinds of arrangements demonstrate how NaaS, built on secure and standardised APIs, can unlock vertical-specific innovation.

But transformation isn’t just about technology, it’s also about mindset and culture. Simplifying and standardising network configurations (for example, reducing radio setups from over a hundred to just two) and promoting a service-centric approach are part of a broader shift. The focus is firmly on use cases and customer value, avoiding the trap of deploying technology for its own sake. Every new system or tool must demonstrate end-to-end value.

O2 Telefónica also recognises that data, rather than AI alone, is the foundation of intelligent automation. Without a robust data strategy, ambitions around AI, closed-loop automation, or service orchestration are unlikely to succeed. The company’s investment in OSS transformation and data-driven operations is laying the groundwork for intelligent networks that can scale, adapt, and optimise in real time.

As the line between network and IT continues to blur, O2 Telefónica is aligning its BSS, OSS, and IT systems with its network strategy. This integrated approach supports holistic innovation and positions the company to deliver services with faster time to market and greater cost efficiency.

The transformation journey shared by Matthias Sauder is more than a technical roadmap, it’s a call for industry-wide disruption. With revenues flat and operational costs rising, embracing NaaS, open APIs, and cloud-native infrastructure is no longer optional. It’s the only viable path for telcos to stay competitive, innovative, and relevant in a software-defined, platform-centric future.

Sauder’s full presentation at FutureNet World provides deeper insight into this journey. You can watch it below:

Case Study: AIS Thailand’s Transformation to 5G-Driven Autonomous Operations

At FutureNet Asia 2024, held on 17-18 September at Marina Bay Sands, Singapore, AIS's EVP, Mr. Wasit Wattanasap, discussed the company’s 5G growth, AI integration, and contribution to Thailand’s digital economy.

He highlighted AIS’s investment in autonomous networks and intelligent IT systems to enhance operational efficiency, manage costs, and improve customer experiences. AIS aims to future-proof its infrastructure for the next generation of connectivity.

AI will play a key role in AIS’s network operations, aiming for real-time, personalised interactions and fully autonomous network processes by 2025. Already nearing "level 3.5" AI integration, AIS plans to advance towards "level 4" predictive networks that proactively address issues before they impact customers, improving both service quality and customer satisfaction.

His talk is embedded below:

Telecom Review has an interview with him here.

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Swisscom Outlines Challenges and Plan to Transition to a Software Company

In a recent keynote at Mobile Europe's Telco to Techco, Christoph Aeschlimann, CTO & CIO, Swisscom gave a presentation on 'Redefining telco for the digital age'. During the presentation he highlighted the challenges of being a 170 year old operator, from infrastructure to processes and mindset.

To overcome these challenges, Christoph believes that a three pronged approach will be needed as highlighted in the image below:

1. Disaggregation of Hardware and Software
2. Telcos need to be become software companies
3. AI and automation to create new opportunities

Swisscom following footsteps of other telcos like VF & AT&T in asserting importance of inhouse software expertise

I hugely agree with this pic.twitter.com/luai5yCfaC

— Dean Bubley (@disruptivedean) March 29, 2022

The current CEO of Swisscom, Urs Schaeppi, is stepping down from his role as a CEO and the Board of Directors has elected Christoph Aeschlimann as the new CEO of Swisscom. This will allow him to implement his vision of transitioning to a software company where many of the tools will be developed in house.

The presentation below is definitely worth listening to, along with the interesting Q&A at the end. Kudos to him for tackling all difficult questions on how they plan to transition to a software company going forward and how they will operate.

Interested in knowing your thoughts.

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BT UK's Journey to Automation

ETSI's Centre for Testing and Interoperability and the OSM community organized a OSM Hackfest on 1-4 June 2020. The event was run remotely, allowing participants to join the hands-on sessions from home. All videos and slides from the event are available here.

Peter Willis, Senior Manager Software Based Networks Applied Research, BT spoke about BT's Journey to Automation.

BT has deployed an NFV Infrastructure in the UK, using Canonical OpenStack and Juniper Contrail, which will run BT's 5G services intially but grow to support a multitude of BT's network services. This platform will be the foundation for BT's strategic automation initiatives meanwhile BT has many tactical network automation initiatives, many using open source components, plus several Orchestration initiatives, that need to be brought together in concert.

Video is embedded below and slides are available here.




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