Modern telecom networks are becoming increasingly complex as they connect various devices, locations and environments, making manual management a cumbersome task. The proliferation of next-generation technologies such as 5G, internet of things and edge computing has further intensified the complexity of network management. Traditional manual methods are proving inadequate for handling the scale and dynamic nature of these technologies.
To this end, network automation has emerged as a promising solution to improve operational efficiency, reduce human errors, enable faster response times and enhance overall network reliability. Network automation refers to the use of software and technology to automate the management, provisioning, configuration and operation of network devices and services. Rather than manually configuring and managing each network device, automation allows these tasks to be performed automatically, reducing the need for human intervention in processes such as device provisioning, configuration changes, software updates, network monitoring and troubleshooting.
Traditionally, network automation has been enabled by software-defined networking (SDN) and network function virtualisation (NFV). SDN separates the control plane from the data plane, enabling centralised network management and configuration. This simplifies administration and reduces reliance on manual device configuration. Additionally, SDN facilitates quick provisioning and deployment of network services through automated configuration, while separating network functionality from hardware. This makes it easier to manage networks without being tied to specific hardware vendors. On the other hand, NFV replaces traditional hardware-based network functions with virtualised software modules that can run on commodity servers. Together, SDN and NFV bring agility and scalability, allowing operators to dynamically allocate resources based on demand and roll out new services without significant hardware investments.
With the advent of artificial intelligence (AI), conventional methods of managing and maintaining networks are rapidly evolving. Telecom providers are increasingly exploring AI-based solutions to automate their networks. This shift is driven by AI’s ability to process large volumes of data, adapt in real time, predict issues and optimise network operations. The integration of AI gives operators greater flexibility, improved control over network management and enhanced customer response times. Another critical development is the convergence of automation and edge computing. Automated edge networks enable real-time analytics, dynamic resource allocation and hyperlocal service delivery, unlocking new business models for telecom operators.
Key drivers of network automation
Several factors are accelerating the shift towards network automation. For one, with the rapidly increasing scale and diversity of traffic patterns, networks must dynamically allocate resources, manage congestion and ensure seamless connectivity.
Traditional manual processes are unable to keep pace with this scale. Automation enables real-time analytics and predictive maintenance, allowing operators to proactively address performance bottlenecks.
The rapid adoption of 5G is also fuelling the need for greater automation. Unlike 4G, where most functions are centralised, 5G operates on a distributed, virtualised and cloud-native framework. This makes automation crucial for orchestrating and managing network slices, edge computing resources and low-latency services. Further, 5G networks are designed to support ultra-low latency, massive machine-type communications and enhanced mobile broadband. These requirements demand a high level of automation for network slicing, real-time service orchestration and dynamic policy enforcement.
Another area undergoing rapid automation is the radio access network (RAN), especially with the adoption of open RAN (O-RAN) architecture. O-RAN promotes interoperability and vendor diversity by disaggregating RAN components and defining open interfaces. It enables operators to mix and match hardware and software components from different vendors. Automation plays a crucial role in ensuring
interoperability, enabling centralised control and managing complex multivendor environments.
The push towards automation is also driven by the need for cost efficiency, a key concern in the face of shrinking profit margins due to rising regulatory costs and the absence of significant tariff hikes. Automation helps telecom operators reduce operational expenditure by streamlining network operations, minimising human intervention and accelerating time to market for new services. For instance, zero-touch provisioning allows operators to onboard new network elements without manual configuration, significantly reducing deployment times.
Moreover, automation is enhancing customer experience through tools such as chatbots, virtual assistants and automated ticketing systems. Telecom operators are increasingly leveraging natural language processing (NLP) and machine learning (ML) algorithms to resolve customer issues in real time, identify churn risks and provide personalised recommendations.
Evolving technological landscape
The technologies driving telecom automation are also evolving rapidly. While SDN and NFV remain foundational to automation, their integration with cloud and edge technologies is ushering in a new era of agile, distributed network architectures. By enabling data processing closer to the source, this shift reduces latency and enhances performance for applications such as autonomous vehicles, remote surgery and augmented reality. Additionally, deploying SDN at the network edge supports efficient traffic management and resource allocation, both crucial for latency-sensitive applications. It enables the creation of localised networks that can operate independently, ensuring continuity and reliability.
A major leap in recent years is the use of AI in network automation. AI enables predictive maintenance and real-time optimisation, identifying issues before they escalate and minimising downtime. Meanwhile, ML algorithms are being used to analyse traffic patterns to dynamically adjust routing, bandwidth and load balancing, improving performance and resource utilisation. AI also simplifies network management through NLP, allowing administrators to issue commands in everyday language. As AI systems learn over time, their decision-making capabilities improve continuously. Another critical advancement is AI’s role in enabling zero-touch provisioning, particularly useful in remote deployments. By automating device identification, configuration and policy enforcement, AI helps streamline network set-up while reducing the need for manual intervention and lowering operational costs.
Market uptake
The increasing demand for network automation across industries is driving market growth, with a growing trend towards integrating AI into network automation. According to industry estimates, the global network automation market was valued at $3.62 billion in 2022 and is projected to grow by $46.61 billion between 2024 and 2028, registering a CAGR of 21.48 per cent.
Indian telecom operators and technology vendors have also started warming up to the idea of establishing networks that are both intelligent and automated. For instance, Reliance Jio Infocomm Limited has partnered with several vendors to build the Open Telecom AI Platform, a multidomain AI system to improve network efficiency and security and enable new revenue streams. Meanwhile, Bharti Airtel has extended its partnership with Ericsson to deploy AI- and ML-driven automation for improved performance and customer experience, and has also adopted a predictive maintenance solution. Vodafone Idea Limited is employing augmented network automation platform to enhance performance, reduce energy use and manage its multivendor network more efficiently. Further, Bharat Sanchar Nigam Limited has collaborated with a tech vendor for industrial automation solutions.
Major roadblocks to full-scale adoption and the way forward
Although the benefits of network automation are substantial, several challenges must be overcome to ensure smooth integration and effective functioning. One key hurdle is the presence of legacy infrastructure. Telecom networks globally have evolved over decades, with layers of proprietary hardware, siloed systems and inconsistent data formats. Integrating automation into such heterogeneous environments requires significant investment, network re-architecting and vendor coordination. Moreover, the lack of interoperability standards across older systems complicates integration efforts.
Cybersecurity is another pressing issue as networks become more software-driven and interconnected. Automation tools often rely on centralised orchestration systems and application programming interfaces, which, if compromised, can have widespread impacts. Ensuring secure automation requires robust authentication mechanisms, real-time threat detection and continuous monitoring.
Vendor lock-in is also a major challenge. As operators invest in proprietary automation platforms, they risk becoming dependent on a single vendor’s road map, limiting flexibility and innovation. This is especially relevant in the Indian telecom market, where cost sensitivity demands competitive procurement and multivendor strategies.
Given that network automation will be key to improving operational efficiency, enhancing customer experiences and enabling agility for future innovation, there is a need for sustained investment, ecosystem development and policy support to address these challenges. Regulatory clarity is crucial to guide developments in automation, AI, cloud infrastructure and data governance. To tackle vendor lock-in, the industry must push for open standards, interoperability and modular architectures that enable plug-and-play automation. Addressing cyberthreats will require secure authentication, real-time detection and continuous monitoring.
Over the next decade, network automation is expected to evolve from basic script-driven tasks and policy-based configurations to full autonomy powered by AI, ML and edge computing. This transformation will require industry-wide collaboration to develop scalable automation frameworks, establish best practices and ensure that automation evolves as a strategic enabler rather than a fragmented collection of tools. A comprehensive approach to building intelligent and automated networks can deliver long-term benefits for all stakeholders.
