Skip navigation
Long exposure photograph of the night sky

Transforming complexity into opportunity

Learn how AI is maximizing business value in telecom operations

AI in networks

AI is radically transforming how to manage, deploy, and evolve mobile networks. Below, we cover everything you need to know about AI in networks, including how it can help you to unleash mobile networks that are efficient, secure, resilient, and innovative.

AI in networks explained

Advanced AI network technologies harness ever-powerful forms of machine learning and reasoning, generative AI, and more, to deliver on complex and diverse business goals, taking service providers closer than ever to fully autonomous intent-based networks with minimal human touch.

This is achieved by deploying AI agents within the network itself, replacing existing rule-based algorithms, adding new AI-based components with new functionality, or by adding AI-based controls to existing networks components. These agents then leverage the massive real-time data generated by today’s networks to predict outcomes, detect anomalies, dynamically allocate network resources, optimize network quality of service, and more.

The impact is transformative; not only on network performance, but also in optimizing and reducing costs, resources, and energy across the network, as well as unleashing future business possibilities.

Everything is possible with AI in networks.

Key benefits

As networks grow increasingly complex, ensuring high network performance while meeting key business deliverables such as energy reduction, cost optimization and service enablement, is challenging. AI makes this possible by leveraging powerful new capabilities across anomaly detection, dynamic resource allocation, quality-of-service optimization, and more.

Improves performance

AI enables dynamic network, spectrum and computational resource allocation. This optimizes the routing of data to reduce congestion and improve throughput, bandwidth, and latency in high service demand areas.

Intent-based outcomes

Through closed loop automation that includes business intent capture, translation and activation, the network will continuously monitor and intuitively adjust to ensure that network performance aligns with your dedicated business goals.

Prevents faults

AI opens a new paradigm of self-healing networks, enabling pinpoint accuracy of root cause detection and reducing time to resolution. When deployed at telecom sites, AI also reduces the need for hazardous field work, remotely notifying engineers about failures or predicted service degradations.

Saves energy

AI improves the energy efficiency of networks, maximizing network utilization without impacting the performance of energy-saving features. AI can also act autonomously on real- time – or even predicted – traffic, helping CSPs reduce their energy consumption and lower their carbon footprint.

Evolves business models

Unleashing large language models on network behavioral data can produce a deeper understanding of customer intent. This enables opportunities for more targeted, personalized services. It also enhances tasks such as idea generation, concept validation, knowledge extraction from primary research and product documentation, and predictive modeling.

Enhances security

By detecting and preventing security threats, such as cyberattacks and fraud, AI enhances network security and protection of users' data. AI algorithms can also be used for fraud detection and prevention in areas like billing and subscriber management, helping CSPs reduce losses and improve their financial performance. 

Dive deeper into the benefits

Discover the AI business potential for telecom operation

AI implementation isn't just a one-off solution – it's a whole new way of working, which means its value is often hard to measure. To calculate how AI is driving growth, we surveyed telecom professionals, using the insights to develop an industry-unique measurement framework.

Read the report

Explore the benefits of AI in telecom, with real-world examples and insights

Learn where the value of AI is already being demonstrated in telecom networks, what new opportunities are likely to emerge, and how to successfully navigate your AI journey in our benefits of AI in telecom blog series.

Read the blog series here

Essential technologies explained

The AI network space is moving fast, creating a new world of complex terms and concepts to those working in telecom. Take a crash course in some of the essential AI technologies below.

ML vs RL vs MR: Understanding the different Al technologies in networks

As a broad field, AI comprises many various technology concepts that, when combined, allow us to solve all manner of problems. This includes machine learning, reinforcement learning, and machine reasoning.

Machine learning applies advanced statistical techniques capable of finding subtle patterns in very large collections of data. In doing so, it can capture the hidden patterns needed to effectively predict outcomes. Read our machine learning use cases blog post.

Reinforcement learning takes this a step further, enabling AI agents to operate based on goal states rather than pre-defined programmed behaviors as with machine learning. In doing so, it enables networks to learn, optimize and automate almost entirely autonomously. Read our reinforcement learning report.

Machine reasoning is applied to integrate intent within those processes – bringing human-like common sense to analyze and translate vast knowledge and learned network data into clear explainable insights. Learn more about machine reasoning.

Generative AI can deliver significant value for CSPs, enhancing AI applications based on human-readable content, machine-readable content, semantic communication, and digital twins – harnessing new ways to improve RAN performance, network management, and more.

Read the blog post

Symbolic AI processes sequences of characters that represent real-world knowledge, such as objects (mobile devices, radio units, etc.) or concepts (KPIs, routing rules etc.). Applying symbolic techniques such as reasoning and planning can generate new knowledge and insights.

Read the blog post

Hybrid AI combines symbolic approaches with data-driven AI, such as ML-trained models. It has the potential to enhance the efficiency of data-driven, ML-based approaches by addressing their weaknesses, for example in network automation.

Read the blog post

Explainable AI methods and techniques can produce accurate, explainable models of why and how an AI algorithm arrives at a specific decision. This makes AI outputs (such as predictions, decisions, actions, and recommendations) interpretable, transparent, and understandable.

Read the white paper

Knowledge-sharing techniques enable the reuse of AI systems both within the telecom industry and across adjacent sectors. These techniques comprise ontologies that describe concepts, entities, and the relationships between them, as well as semantic interoperability and large language models.

Graph machine learning is an effective method to analyze and interpret critical information such as logical dependencies, connections, relationships among data entities. Such 3D models have shown promising results, for example, in predicting anomalous behavior within networks.

Read the blog post

How and where to apply AI in networks

The emergence of cloud-native networks opens the entire network architecture to the possibilities of AI. Today, AI can be deployed across all network domains – from the network core to the RAN, and the network edge – and at every stage of the network lifecycle.

Top AI use cases in networks

Customer- and service experience

Cognitive tuning and optimization technologies contribute to a superior user experience, reducing poor quality areas through right-on-time expansions, timely launch and proactive network optimization. AI can also be deployed to improve customer service experience, with use cases that cover customer relationship management, channels, sales, and marketing.

Network planning and design

To deliver cost efficiency, AI-based network planning and design leverages deeper analytics that can accurately predict needs and optimize TCO. This includes high-accuracy traffic forecasts, KPI predictions, bottleneck identification and load balancing opportunities across the network lifecycle. Through live radio measurement utilization, such as subscriber traffic patterns, service providers can also ensure optimal 5G network expansion.

Network optimization

AI-based network diagnostics continually optimizes and improves network performance and efficiency, scanning all network cells in minutes – identifying issues quickly and with high precision. This makes it possible to proactively identify 50 percent more issues with up to 98 percent field-validated accuracy, increasing operational efficiency by up to 30 percent.

AI and cyber security

Through new innovative means and techniques, AI can enhance and automate the current security of 5G networks to detect zero-day attacks, as well as predict upcoming attacks, detect ongoing attacks, and test and deploy new defense mechanisms at run time. Read more: Ericsson drives research into AI-based cyber security.

Network operations

AI is revolutionizing the concept of network operations, taking service providers closer towards zero-touch end-to-end automation of the network. Step by step this is reducing manual operations, enabling improved business agility based on augmented decision making and data-driven, predictive and proactive operations.

IT operations: related to improving IT operations, performance, and service availability for systems such as order fulfillment, charging, and billing.

Application development and management: related to the development and management of different applications and capabilities, such as smart code completion and optimized testing.

Cloud and infrastructure planning and design: related to improving the cloud and infrastructure planning and design, such as capacity prediction.

Cloud and infrastructure operations: related cloud and infrastructure operations to improve effectiveness, availability, and stability.

Telco enterprise management: related to improving and automating general enterprise applications.

Meet the change agents behind AI network solutions

Explore how cognitive network planning, design, tuning, and optimization is transforming network operations, blending predictive intelligence with unique network domain expertise.

Explore our solutions

What the experts are saying

Want to get ahead of the hype cycle? In our AI blog series, we cut through the noise around AI to bring you real, industry- leading insights to get your AI journey off the ground. Explore the latest insights below.

Digital twins: meet the latest disruptors in the network space

Digital twins are one of the most exciting technology concepts to emerge from the field of network AI. As dynamic virtual replicas of physical objects, processes and systems, digital twins make it possible to test and experiment with simulations without risk or disruption to its real-world counterpart.

Digital twins at the network edge and in enterprise applications have attracted most of the hype, with their ability to bring new levels of efficiency for logistical and industrial operations. However, they are also proving to be significant disruptors across network domains, enabling new, game-changing ways to deploy, manage, and optimize networks.

Network digital twins

Network digital twins optimize the ’invisible’ elements of a network: the signals, loverage, interference, and traffic behavior, including user mobility across frequency layers. By replicating an external virtual entity that mimics the behavior of the live network, the network digital twin creates a safe playground for experimentation.

Site digital twin

Site digital twins optimize the ’visible’ lements of a network: the towers, equipment and all other assets included in a physical site. This enables fully digitalized design and lifecycle management of on-site equipment through accurate 3D modeling of each site leveraging laser scanners (LiDAR), cameras and drones.

Subscriber digital twin

Subscriber digital twins enable real-time modeling of subscribers through 3D modeling of complex scenes that make it possible to experiment with network design in an interactive environment while mapping subscriber impact in real time.

Latest insights on digital twins

Future autonomous networks - a tomorrow made possible by the AI journeys starting today

Learn more about what the future holds for intelligent, autonomous networks.

Find out more

Getting the basics right: how to begin your AI journey

Before you embark on your Al journey with Ericsson, remember that there is no one-size-fits-all strategy. The investment you make will be defined by your choice to build, buy, partner or subscribe to managed services powered by Al. These decisions should be shaped and scaled to meet your unique business goals and network topology.

From our experience partnering with leading Al frontrunners across the industry, we have identified six enabling cornerstones that are crucial to get your Al network journey started:

01: Ensure access to quality data through good data architecture and governance

02: Create a roadmap that maps desired strategic outcomes with business relevance

03: Build your technology infrastructure, including cloud, storage and MLOps platforms

04: Develop your expertise and get buy-in between business and technical teams

05: Establish metrics to measure and improve all AI steps across every business area

06: Plan your full lifecycle of developing and deploying AI models, including ethical due diligence

Dive deeper into the topics around network automation and Al

Visit our network automation and Al page to discover more about our offerings, technologies and solutions relating to automation and Al.

Learn more

Explore enabling technologies

Ericsson Al-driven solutions in action

Ericsson delivers an industry-leading suite of cognitive software solutions with multi-vendor support across radio access technologies and wide reach across the entire network lifecycle. Through unique integration of network design and optimization domain knowledge with advanced AI technologies, the Ericsson suite is designed to truly unleash the full potential of next-generation networks.

Performance Optimizers

Prevent and proactively solve issues in mobility, interference, energy management and more using digital twins and AI.

Download the brochure

Cognitive Planning

High-accuracy analysis, predictions and issue identification for proactive network planning, lowering CAPEX and optimizing performance.

Watch the video to learn more.

Cognitive Tuning

Delivering scalable, reliable and replicable performance analytics using data from the live traffic across the network.

Watch the video to learn more.

Performance Diagnostics

Manage the complexity of network performance observability with AI-powered cell issue detection, analysis and classification.

Download the brochure
60 %
faster site-level report generation
20 %
YoY increase on deployment project
18
tons of CO2 saved yearly
80 %
drive test reduction

Customer success stories

Why choose Ericsson for your AI journey?

  • Industry leaders in telco AI technology and innovation
  • Focus on practical business outcomes
  • Unique insights for multi-vendor and multi-technology radio networks
  • Best in class software usability with explainable AI for more actionable insights
  • Connecting research with value-driven use cases
  • ML model reuse and global model training
  • Flexibility to integrate insights and processes with other domains and solutions
  • Trusted support every step of the way and access to global AI partnerships
Woman interacting with immersive experience
11
global development and AI Lab hubs
98 %
Accuracy in issue detection & classification
40 %
reduction in bad quality cells

Explore further

Papers and reports

Blog posts

News