Many network teams struggle with the challenge of managing vast amounts of data across multiple dashboards, often dealing with 15 to 30 different systems to track their environments. This chaotic data landscape makes it difficult to find relevant information and can lead to prolonged troubleshooting when incidents arise. The introduction of artificial intelligence, specifically AI agents, holds the potential to alleviate this burden by reducing ticket volumes, speeding up resolutions, decreasing downtime, and allowing network engineers to concentrate on higher-value tasks. However, effective implementation of AI agents requires a focus on data hygiene.
According to John Capobianco from Selector AI, poor data can diminish the effectiveness of AI solutions. Selector’s Data Hypervisor plays a critical role in this process by normalizing diverse operational data—such as logs and metrics—into a unified and structured format. This improvement facilitates better analysis and enhances the context of the data through added metadata, which helps transform raw data into actionable insights. The incorporation of machine learning allows for automatic parsing and enrichment of unstructured data, significantly reducing the risk of errors linked to manual processes.
To set the stage for successful AI deployment, network teams need to address data hygiene by undertaking a multi-faceted initiative. This should include:
- Conducting a comprehensive inventory of devices and systems for better correlation and root cause analysis.
- Standardizing device descriptions to aid AI in interpreting metadata, thereby achieving accurate insights.
- Establishing a centralized metadata repository to provide a single source of truth, enhancing root cause analysis capabilities.
- Ensuring secure data transport that adheres to necessary compliance and integrity standards during the movement of data.
- Promoting collaboration across various IT teams—including network, security, and databases—to enhance the AI’s operational effectiveness.
- Utilizing tools that can normalize heterogeneous data, creating a cohesive view across diverse sources for enhanced analysis.
AI’s role in network operations goes beyond just aggregating data; it helps revolutionize how network teams perceive and use that data to optimize performance and reduce downtime. As Capobianco states, AI can be thought of as a digital co-worker that understands the infrastructure’s health and status. In practical terms, one client was able to decrease its daily ticket volume from over 5,000 to less than 75 by employing AI agents, drastically reducing the time taken for troubleshooting from days to mere minutes while simultaneously freeing up resources for engineers.
By dismantling silos, aggregating clean data, and enabling a proactive management approach, intelligent agents can guide network teams away from a reactive firefighting stance to a more strategic operational framework. This transformation promises to create a resilient and efficient network infrastructure.
For more information on enhancing network operations with AI-driven solutions, visit Selector or join the Webinar featuring insights from Selector’s John Capobianco.
