Industry 4.0 requires the coexistence of technologies like internet of things (IoT), multi- and hybrid-cloud computing, and edge connectivity. In this hyper-connected landscape, organisations face the challenge of ensuring that their networks are constantly in sync with the evolving business dynamics without compromising on customer experience. To this end, network automation is emerging as a key solution to tackle the intricacy of modern networks and is becoming the backbone of enterprise networks owing to several benefits, including greater efficiency, reduced errors, energy savings, better scalability, consistency in network configurations and faster troubleshooting. A look at the primary components of network automation, key technologies shaping them and major adoption barriers…

Network automation architecture

The key components of network automation that drive its efficiency include:

  • APIs: A crucial component of network automation, application programming interfaces (APIs) facilitate uninterrupted communication between various networking devices and software systems.
  • Configuration management: The configuration of modern network devices such as routers, switches and firewalls can be automated without any human intervention. The primary tasks involved in network configuration management include device discovery, which ensures all network components are identified and monitored; configuration backup, which creates secure copies of device settings to prevent data loss; change tracking, which records and manages any modifications made to device configurations for accountability and auditing; monitoring compliance, which ensures that devices adhere to established security and performance policies; and troubleshooting network issues, which allow rapid identification and resolution of problems. It also reduces downtime if hardware failures and configuration errors occur.
  • Monitoring and analytics: Automation in network management is fundamentally reliant on robust monitoring and advanced analytics to continuously collect and analyse data related to network performance, traffic patterns and potential security threats. By leveraging sophisticated monitoring tools, organisations can gain real-time visibility into their network infrastructure, enabling them to track key performance indicators such as bandwidth utilisation, latency, packet loss and device health. Analytics further enhance this capability by processing vast amounts of data, identifying trends, detecting anomalies and predicting potential issues before they escalate. Beyond performance monitoring, automation systems can detect suspicious activities, identify potential security breaches and initiate predefined responses, such as blocking malicious traffic or alerting network administrators. Further, automated troubleshooting processes can diagnose network issues by analysing performance data, pinpointing the root cause of problems and even initiating corrective actions without human intervention.
  • Network orchestration: A network automation solution typically involves several tasks. These tasks vary from basic configurations, such as setting IP addresses on network devices to advanced operations, such as implementing multi-layer security policies, optimising traffic flows and managing network virtualisation. Given this complexity, there is a need to efficiently coordinate these tasks, ensuring that they are seamlessly connected and executed in a logical, sequential manner. Orchestration entails the coordination and management of network services and devices.
  • Software-defined networking: Conventionally, network devices link control functions and physical hardware. However, software-defined networking (SDN) decouples the control plane from physical devices. In an SDN architecture, the control plane is abstracted and centralised within a software-based SDN controller, which serves as the network’s intelligence hub. This controller is responsible for making network decisions, while the individual devices (data plane) simply execute these instructions. This separation of control and data planes significantly enhances the flexibility of network management, allowing administrators to manage the entire network as a unified system rather than a collection of isolated devices. Moreover, SDN provides a centralised view of the network, making it easier to monitor performance, detect and resolve issues, and implement security measures consistently across all devices.
  • Virtualisation: Through virtualisation, network functions that once required specialised hardware can now be delivered as software-based services running on general-purpose servers or cloud environments. For example, a virtual router can be quickly instantiated in a remote branch office, a software-based firewall can be deployed to secure cloud applications, and virtual load balancers can be scaled automatically based on user demand. These capabilities empower businesses to extend their network reach, optimise resource utilisation and ensure consistent performance.

Technologies shaping network automation

The key technologies that are significantly impacting network automation include artificial intelligence (AI), cloud computing, IoT and machine learning (ML). These technologies are revolutionising how networks are managed, optimised and secured, transforming them into intelligent, self-sustaining systems.

AI and ML

AI and ML in networks allow predictive maintenance and proactive issue resolution. For instance, AI-powered systems can forecast network congestion and automatically reroute traffic to maintain optimal performance. At Mobile World Congress 2025, Jio Platforms, with the support of tech vendors, announced the Open Telecom AI Platform – a common intelligence layer that uses AI/ML to manage its network.

The integration of AI and ML in network operations also bolsters cybersecurity by enabling the detection and mitigation of threats in real-time through the incorporation of zero-trust security solutions. A case in point is Airtel’s AI-powered spam detection (rolled out in September 2024) filters 1.5 billion messages and 2.5 billion calls daily and aims to promote a “spam-free network”.

ML can be effectively utilised to discover IoT endpoints within a network by analysing data collected through network probes or by employing application layer discovery techniques. Network probes gather traffic data, packet metadata and communication patterns, which ML algorithms can process to identify unique device signatures and distinguish IoT devices from other network elements. Meanwhile, application layer discovery techniques involve examining higher-level protocols and application data, allowing ML models to detect IoT endpoints based on specific behaviours and communication protocols used by these devices.

IoT

IoT has significantly increased the scope of network automation by linking a vast array of devices across the network, ranging from sensors and smart appliances to industrial machines. For instance, Airtel Business now connects more than 48 million IoT devices nationwide, while Vodafone Idea Limited is offering consulting-led IoT solutions for sectors like automotive and logistics.

These devices generate massive amounts of data, which can be monitored, managed and used efficiently in real time through automated processes. Network automation tools can sense new IoT devices, apply predetermined security policies and facilitate the seamless transmission of data by harnessing a variety of wireless networks, including WiFi, Bluetooth and cellular networks. Common conventions governing data transfer include the Internet Engineering Task Force’s constrained application protocol (CoAP), message queuing telemetry transport (MQTT) and ZeroMQ. They ensure that information is shared swiftly and securely, minimising latency and augmenting the overall performance of the IoT system. The collated data is then assessed using AI and ML. IoT-driven networks tend to be self-healing and adaptable as they can detect and resolve issues, such as adjusting configurations without any human intervention.

Cloud automation

At its core, cloud network automation allows enterprises to automate the configuration, provisioning and management of network devices and services across public, private and hybrid cloud environments. This includes automating tasks such as setting up virtual private networks, configuring security policies, managing traffic flows and dynamically scaling network resources based on demand. Through automation, organisations can ensure consistent network performance, minimise manual intervention and reduce the risk of configuration errors.

Before integrating their networks with a cloud provider, enterprises should carefully assess how the provider employs network automation within its data centre. This evaluation is crucial because the provider’s automation capabilities will directly impact the efficiency, security and flexibility of the enterprise’s cloud-based network. Cloud network automation also enables multi-cloud and hybrid cloud strategies, where enterprises can automate network connectivity between on-premises data centres and multiple cloud providers.

Cloud-native network automation takes this concept a step further by leveraging cloud-native principles like microservices and containers to build and deploy network functions. It offers benefits such as increased agility, faster time to market, improved resource utilisation and enhanced reliability. For instance, Jio has enabled edge computing on its cloud-native 5G network across more than 50 facilities.

Adoption barriers

While network automation allows enterprises to enhance network security more effectively and efficiently, it also comes with its own set of challenges. For instance, network administrators may not have the necessary expertise or experience needed to understand network protocols. In FY2025, India will need 3.9 million cloud specialists against the current 1.5 million professionals. Adding to this issue, organisations typically rely on multiple vendors, leading to a heterogeneous network infrastructure. This makes the incorporation of diverse network automation tools into the existing network infrastructure complex due to challenges pertaining to vendor equipment interoperability and security. An industry report highlights that 61 per cent of Indian respondents cite security as their biggest challenge, while another study found 21.5 per cent of respondents mention lack of support from vendors as a key bottleneck. Further, legacy network devices may not be compatible with network automation tools. For instance, 24.3 per cent of organisations lack open APIs. Another challenge is that adopting network automation entails high upfront costs, making adoption difficult for small enterprises. A study found that 25 per cent of respondents faced budget constraints as a key pain point.

Regarding technology-specific challenges, AI is associated with problems such as its use for malicious activities and biases that seep into algorithms. Meanwhile, IoT devices generate large volumes of data, which can overwhelm network management systems and analytics platforms, hampering the ability to drive meaningful insights. Other challenges pertain to scalability and connectivity. Further, cloud-native network automation faces issues such as increased resource consumption due to multiple layers of virtualisation, the need for adaptable infrastructure, the unique requirements of microservices and difficulties in service discoverability.

Conclusion

Although network automation offers numerous benefits, it is still an evolving technology with limitations. As its adoption expands across diverse use cases, addressing these challenges is essential to ensure effective implementation.