AI in managed network services shifts enterprise infrastructure toward proactive observability and automated SASE

Gartner research indicates that nearly all global wide-area network (WAN) and managed network service (MNS) providers evaluated in 2024 have deployed artificial intelligence to oversee enterprise infrastructure. This shift incorporates several key technological implementations to support complex networking environments:

• AI for IT operations (AIOps) for foundational network management.
• Generative AI (GenAI) acting as interactive network assistants.
• Enhanced service delivery through automated customer support bots.
• AI-driven security within secure access service edge (SASE) frameworks.

Leading providers including HCLTech, Microland, and NTT Data are now utilizing AIOps to automate service onboarding and improve the reliability of customer network environments. These organizations are deploying machine learning models within network operations centers (NOCs) to monitor health metrics and identify anomalies before they impact system availability.

The objective is a fundamental shift from reactive troubleshooting to proactive assurance by identifying precursors to hardware or link failures. If latency on a WAN link spikes intermittently, machine learning models can recognize the pattern as a precursor to failure and trigger failover before an outage occurs.

Tata Communications has reported achieving 85% accuracy in AI-based fault diagnosis, using telemetry to predict and address issues for proactive network monitoring. Service providers are also integrating GenAI to create interactive assistants that allow operations teams to manage infrastructure through natural language interfaces.

According to the ComputerWeekly report, these network AI assistants provide a specific operational interface:

Network AI assistants are interactive digital tools backed by AI/ML that human users can communicate with via conversational, natural language chat interfaces. Rather than using command line interface (CLI) or dashboard-style user interfaces (UIs), users enter a chat text request to perform tasks such as configuration, troubleshooting or accessing an automation framework.

HCLTech is specifically focusing on GenAI integrations with software-defined wide-area networking (SD-WAN) to automate lifecycle operations through a supplier-focused large language model (LLM). These assistants facilitate configuration management and troubleshooting by generating technical responses and specific task hotlinks based on conversational queries.

Within the security layer, AI and machine learning are becoming essential components for managing local area networks (LAN) and cloud-based security perimeters. Platforms from providers like Xtium and Microland use advanced analytics to strengthen SASE deployments and simplify the management of distributed security policies.

AI-driven systems can now automatically quarantine suspicious devices or enforce multifactor authentication when a user exhibits abnormal behavioral patterns. Policy optimization benefits from AI recommendations that adjust zero-trust rules based on real-time context, such as user location, time, and department.

Vendors like Versa Networks and Cato Networks are emphasizing cloud-native SASE services that use machine learning to adapt to changing network conditions and emerging threats. Some advanced providers are exploring the use of LLMs to assist security analysts by summarizing complex multistep attacks or drafting firewall rules from high-level descriptions.

As managed services evolve, the reliance on manual configuration and traditional command-line interfaces is expected to diminish in favor of intent-based orchestration. Enterprise architects must evaluate how these AI-enhanced services integrate with existing governance frameworks and data privacy requirements.

The shift toward automated network management introduces new considerations for vendor lock-in, as proprietary AI models become deeply embedded in service delivery. Future iterations of these services are expected to move toward agentic AI, where autonomous systems handle more complex decision-making processes without human intervention.

Gartner anticipates that by 2028, the widespread adoption of NOC assistants and automated service delivery will fundamentally change the cost structure of managed networking. Technical leaders should monitor the maturity of these AI tools to ensure they provide verifiable improvements in uptime rather than just adding complexity to the stack.

Decisions regarding managed service contracts should now prioritize providers that demonstrate a clear roadmap for AI-driven proactive monitoring and security automation. This strategic alignment ensures that enterprise infrastructure remains resilient against both operational failures and sophisticated cyber threats.