Over the past decade, telecom companies have been focused on streamlining their operations, cutting down on inefficiencies, and keeping up with the increasing demands of digital-savvy customers. Automation has quickly become the key to making this happen.
From Legacy Systems to Modern Networks
The growth of carrier automation has been transformative for the telecom industry. Living and operating within a fast-paced world, running things the old-fashioned way is not working anymore.
In response, carriers needed to find ways to respond faster, manage their networks more effectively, and provide better customer service.
Leading operators are initiating efforts towards unified IT and network operations as today’s demand requires automation that is seamless, modular, and comprehensive throughout the entire network lifecycle.
Overall, telecom operators are focused on cost optimization, reducing manual intervention, improving service quality, enhancing scalability, and bolstering security. To meet these objectives, it is essential to implement capabilities such as end-to-end network automation, robotic operations, and zero-touch provisioning alongside a modernized network.
Modernization, coupled with precise inventory management systems, serves as the foundation for enabling end-to-end network automation. This modernization is often driven by technological advancements such as NFV/SDN, disaggregated networks, segment routing, and 5G deployment.
How Automation Is Driving Innovation and Efficiency in the Telecom Sector
Automation has made tasks easier and faster for businesses, including telcos – from network orchestration and provisioning to AI-powered customer service bots that can handle queries and issues in real-time.
Artificial intelligence (AI) and machine learning (ML) are among the most impactful factors in this space, allowing operators to automate the more repetitive tasks, like setting up network configurations or troubleshooting technical problems.
In this scenario, Argentinean service provider Telecentro is deploying Netcracker’s Digital OSS to advance its operations automation, including problem detection, impact analysis, root cause analysis and network optimization. This enables the operator to significantly improve its service quality and customer experience.
Additionally, with AI-driven analytics, telecom companies can predict and fix network problems before they even happen, which means fewer outages and smoother service for customers.
In the telecommunications sector, B2B contracts for network connectivity often come with intricate, layered SLAs and provisioning targets. By leveraging document intelligence, companies can swiftly analyze millions of these contracts, extracting valuable data that can be compared with actual service provisions and usage.
Generative AI revolutionizes intelligent document querying and information retrieval by utilizing the retrieval-augmented generation (RAG) pattern. This allows for semantic-based querying of text indexes, giving Generative AI the capability to produce summaries based on selected document sections that align with predefined templates.
This technology can swiftly retrieve relevant incidents for investigations and generate formatted summaries of past events, significantly reducing the time required for these tasks.
CSPs are also exploring the use of Generative AI in managing contracts like interconnect and roaming agreements, as well as cell tower leases. With Gen AI-enabled tools, users can swiftly locate specific clauses or ask direct questions, eliminating the need for lengthy, complex searches through extensive contract databases.
This technology minimizes the risk of errors, particularly when dealing with large, intricate document repositories that cover multiple years and regions.
Moreover, with the rise of software-defined networking (SDN) and network functions virtualization (NFV), networks can now be managed more like software, making it easier and faster to deploy new services while keeping costs down and scalability up.
Verizon has introduced the Network Alpha Factory platform to seamlessly migrate millions of customers to advanced networks like 5G, cloud connectivity, and fixed wireless access (FWA) with minimal disruption.
This tool supports legacy Verizon networks and future edge networks, integrating a workflow engine, robotic process automation, and a data engineering framework across over nine legacy systems. It offers data intelligence, device decommissioning capabilities, and network element testing bots, reducing physical site visits.
Verizon has integrated ML for automating tasks like network discovery metadata, aiming for further AI integration. By 2027, Verizon targets 30% operational cost savings and an expansion to 200 million 5G points on its Intelligent Edge Network (iEN).
Governments have also played a role in speeding things up. With policies aimed at expanding broadband access and boosting 5G deployment, there’s been a lot of pressure on carriers to step up their automation efforts. These initiatives aren’t just about staying competitive; they’re about driving economic growth and ensuring that everyone, everywhere, has access to fast, reliable internet.
According to EY Americas Telecommunications Growth Leader, a comprehensive, holistic business transformation leverages intelligent automation technologies to include both customer-facing and back-office applications. The result is a digital enterprise that can stay ahead of customer expectations for an increasingly digital and automated experience.
Looking ahead, carrier automation in North America is only going to become more important. With the continued rollout of 5G and the growth of IoT, automation will be at the heart of building networks that can quickly adapt and scale. From smart cities to connected factories, the future of telecom is all about automation and efficiency, setting the stage for a new era of digital connectivity.
What’s Next for RAN?
The radio access network (RAN) is the most expensive, technically complex and power-intensive part of cellular infrastructure. This is the reason why RAN automation is a key aspect of mobile operators' digital transformation strategies aimed at reducing their Total Cost of Ownership (TCO), improving network quality and achieving revenue generation targets.
When combined with AI and ML, RAN automation can profoundly impact mobile network economics by lowering the OpEx-to-revenue ratio, reducing energy consumption, cutting CO2 emissions, optimizing performance, enhancing user experience, and enabling new services.
Additionally, network slicing, application-aware optimization, and anomaly detection are among the key use cases that have attracted significant interest from operators.
Dell’Oro Group reports that, despite current challenges, the long-term outlook for RAN remains positive and largely stable. Most operators are expected to gradually integrate greater openness, virtualization, intelligence, and automation into their RAN strategies. However, the adoption rate may vary between radios and basebands, and the multi-vendor RAN business case appears to be less persuasive.
North America was the largest region in 2023 and is expected to lead the broader Open RAN movement until 2028.
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