The role of AI and automation in modern network management

Managing networks today is more challenging than ever. Businesses are increasingly dependent on cloud computing, IoT, and digital collaboration, leading to complex infrastructures that are tough to maintain manually. Traditional network monitoring, configuration, and security enforcement methods are no longer enough. AI and automation are transforming network management, making it more efficient, secure, and proactive.

The growing need for AI and Automation in network management

With the rapid expansion of digital infrastructure, network traffic is growing at an unprecedented rate. Why AI and Automation Are No Longer Optional in Network Management

Global IP traffic is on an explosive rise, with DE-CIX reporting over 68 exabytes of data traffic at Internet Exchanges in 2024, signaling that the 500 exabytes/month projection by 2025 is well within reach.

Driving this surge is the exponential growth in connected devices. According to Cisco’s Annual Internet Report, the number of connected devices globally is expected to exceed 29.3 billion by 2023, forming the backbone of today’s hyperconnected ecosystems [Cisco, 2020].

At the same time, cloud adoption is transforming IT landscapes. Cisco’s Global Cloud Index projected that 94% of workloads would be processed in cloud data centers by 2021 [Cisco, 2018], a shift that continues to accelerate as businesses embrace hybrid and multi-cloud environments.

However, this complexity comes at a cost. A recent Entrepreneur and McKinsey report noted that 68% of enterprises are struggling to manage hybrid models effectively, citing challenges in visibility, control, and performance consistency [Entrepreneur, 2023].

Network performance demands are also intensifying. Real-time applications like video conferencing, online gaming, and AI workloads now account for more than 50% of peak-hour traffic, requiring ultra-low latency and high availability to function effectively.

The stakes are high. According to Gartner, the average cost of IT downtime is $5,600 per minute [Atlassian, quoting Gartner, 2014]. In this context, downtime isn't just a nuisance, it’s a direct hit to business continuity and revenue.

Manual network management is no longer sustainable. Human-led processes often suffer from inefficiencies, delayed responses, and a higher margin for error, all of which are unacceptable in mission-critical environments. Furthermore, the growing frequency and sophistication of cybersecurity threats require instantaneous detection and response capabilities.

This is where AI and automation step in as essential pillars of modern network operations. They enable real-time monitoring, predictive maintenance, automated troubleshooting, and intelligent threat response, delivering the speed, accuracy, and scalability that manual processes simply cannot match.

In a world where digital resilience equals business survival, investing in AI-driven network management is no longer a futuristic ambition; it's a strategic necessity.

AI-driven network optimisation and security enhancement

AI revolutionises network monitoring and optimisation by leveraging real-time analytics and predictive capabilities. It can analyse vast amounts of historical and live data to predict potential network congestion, identify anomalies, and suggest preventive measures. Automated anomaly detection allows AI to spot irregularities and security breaches, minimising risks and downtime. AI-powered self-healing networks can diagnose and resolve issues autonomously, ensuring seamless operations without human intervention. Traffic routing also benefits from AI, as it dynamically adjusts data flow to optimise speed and efficiency, reducing latency and improving user experience.

Security is another area where AI is playing a transformative role. AI-based threat detection systems analyse network behaviour to identify suspicious activities and neutralize potential threats before they cause harm. Automated security compliance ensures network policies and protocols are consistently enforced, reducing human errors and vulnerabilities. AI is also instrumental in mitigating large-scale cyberattacks such as Distributed Denial-of-Service (DDoS) attacks by detecting malicious traffic patterns early and deploying countermeasures in real time.

Streamlining network configuration and management

The manual configuration of networks is time-consuming and prone to errors, making AI-driven automation essential for modern businesses. Zero-touch provisioning (ZTP) simplifies network deployment by enabling devices to configure themselves without manual input. Intent-based networking ensures network configurations align with business objectives, reducing the risk of misconfigurations. AI-driven orchestration streamlines the management of hybrid and multi-cloud environments, optimising workload distribution and providing seamless connectivity across platforms.

Automated software updates and patch management further enhance network security and performance. AI can efficiently handle updates, ensuring the latest security patches are deployed without disrupting operations. This proactive approach prevents potential vulnerabilities and keeps the network infrastructure up to date with minimal downtime.

The benefits and challenges of AI in network management

The integration of AI and automation in network management offers several advantages. Businesses experience reduced operational costs as automation minimises the need for extensive human oversight. Proactive issue resolution and self-healing capabilities improve network reliability and uptime. Faster response times enable networks to address security threats and performance issues in real-time, preventing disruptions. Additionally, AI-driven solutions scale effortlessly with growing network demands, making them ideal for businesses undergoing digital transformation.

However, AI-driven network management is not without challenges. AI models require high-quality data for accurate predictions, and dataset biases can lead to incorrect decision-making. Many enterprises still rely on legacy network infrastructure that may not be fully compatible with AI-powered solutions, requiring significant upgrades. Moreover, while AI reduces manual workloads, skilled professionals are still needed to oversee and manage AI-driven networks, ensuring that automation functions as intended and troubleshooting complex issues when necessary.

Conclusion

AI and automation are redefining the future of network management by enhancing efficiency, security, and scalability. As businesses adopt digital-first strategies, integrating AI-driven network solutions will be crucial for staying competitive. Organisations must prepare for this transition by investing in AI capabilities, upgrading legacy systems, and upskilling their workforce to leverage the full potential of intelligent network management.

 By Sudhir Kunder, Chief Business Officer, DE-CIX India