As digital infrastructure has become more complex, observability tools are playing a very important role in giving companies insight into the state of their IT systems. IT operations teams are increasingly flooded with monitoring data from across the IT stack. Observability solutions help them organize all that data so they can design and deploy infrastructure components more efficiently, as well as predict and remediate issues faster.
While IT monitoring is nothing new, in today’s highly complex hybrid multicloud environments, observability solutions offer a more sophisticated approach that gives context to the data being collected and delivers more meaningful insights. Gartner® estimates that “by 2026, 50% of enterprises implementing distributed data architectures will have adopted data observability tools to improve visibility over the state of the data landscape, up from less than 20% in 2024.”[1]
As AI becomes more integrated into observability solutions, there’s a lot of excitement about its potential. While AI isn’t a magic wand that solves everything, it has great promise for accelerating processes and delivering more precise outcomes in the increasingly complex realm of distributed IT infrastructure networking. And now, generative AI is bringing a further level of intelligence to network observability, allowing users to monitor their networks, manage alerts, proactively predict issues and mitigate problems faster—all using natural language.
Three ways AI is enhancing IT network management
It’s important for organizations to be pragmatic about how they use AI. As enterprises explore AI use cases, we’re starting to see a growing adoption of AI for automating network operations. For large enterprises, everything from finance companies to retailers to airlines, ensuring smooth network operations is crucial for the business and helps deliver the best customer experiences. By automating more aspects of the network, AI can significantly enhance network service quality, boosting the overall performance of enterprises’ customer services.
Let’s look at three ways AI is already enhancing the management of IT networks:
Design
AI can be used to facilitate the discovery of network capabilities and optimize networking solution design. For instance, AI can help you select the building blocks of a network solution, choose the best connectivity options, recommend resource placement and encapsulate everything into clear network change objectives.
Deploy
AI can streamline the deployment of new functionalities in a network. For example, AI can help you:
- Translate your intent into deployment plans
- Forecast the impact of new rollouts
- Assist with infrastructure-as-code (IaC) deployments
Operate
AI can enhance the security and reliability of networks, as well as compliance with regulatory requirements. This area—network operation—is where AI and observability come together.
Observability goes beyond monitoring; it involves understanding network performance and behavior to reduce configuration errors, minimize downtime, ensure consistent security policies, profile network consumption and enable proactive performance management. AI helps with this by modelling the network, establishing relevant operational metrics and events, and conducting in-depth data analysis.
With an AI-powered observability solution, enterprises can:
- Intelligently track the lifecycle of physical and virtual network resources, eliminating the need for semi-manual configuration management database (CMDB) updates
- Detect network failures immediately with real-time root-cause analysis
- Assess network service performance holistically, beyond simple threshold checks
- Identify resource scaling needs in a proactive manner rather than a reactive one
How does generative AI unlock more network insights?
Since the release of ChatGPT to the public in November 2022, generative AI has been advancing quickly, with great potential for improving productivity, efficiency and data-driven decision-making across industries and business domains. Fundamentally, generative AI’s ability to employ natural language makes AI tools accessible to far more people than ever before.
We’ve already been in the process of moving beyond traditional IT monitoring to observability for several years. Observability solutions leverage AI and machine learning to do more sophisticated analysis and learning from IT data than IT monitoring alone offers. Now, thanks to generative AI, observability tools can offer even better insights to help organizations ensure the stability and efficiency of their IT infrastructures.
Here are some examples of how generative AI facilitates easier network observability:
- Teams can use natural language to prompt an AI tool to collect telemetry data from various sources in the company network and then translate it into insights. They don’t have to set up individual alerts and dashboards like they did in the past.
- They have new ways of interacting with the AI-powered observability solution: They can access it through a Slackbot or Microsoft Teams channel. They can use a chatbot within a network management portal. Or they can use an AI-powered command line interface (CLI) tool. These are just some examples of how generative AI is democratizing interfaces for users.
We’ve only begun to scratch the surface of generative AI’s potential in the observability space. New AI capabilities are being added to major third-party observability tools all the time. And now, industry-leading multicloud network providers have begun offering generative AI-based interfaces as networking assistants. We’re quickly moving toward a future where networking will no longer be an area that only people with specialized knowledge can understand. AI makes it faster and easier for anyone to get network insights. As this trend continues, it’s becoming an expectation that every company and service provider will operate this way.
Improving hybrid multicloud networking with AI-driven observability
For enterprises that use multiple clouds and private infrastructure distributed across many locations, multicloud networking has gotten complicated. Organizations need visibility and control across their entire heterogeneous hybrid IT landscape in order to ensure optimal performance and reliability. But every environment offers different monitoring tools and interfaces. For enterprise users, cloud networks can feel like black boxes: It’s extremely hard to troubleshoot when something goes wrong.
Observability tools that employ generative AI along with open interfaces offer a better way for organizations to draw insights from complex environments. This leads to faster detection of issues and faster response times, saving teams both time and costs, while simplifying compliance.
With generative AI-driven observability, you can:
- Create your observability environment by simply expressing your intent: Prompt what you want to observe and where to stream data.
- Set up intelligent alerts: Simplify network alert configuration by declaring your objectives, creating alerts for entire resource groups with a single prompt rather than configuring thresholds for each individual metric.
- Find patterns and make predictions: Move beyond basic thresholds to proactively anticipate problems by learning network behavior patterns and flagging deviations in real time.
- Drill down and investigate: Easily retrieve resource status and correlations across events to reduce mean time to detect issues and resolve problems more quickly.
The new world of observability, enhanced with AI
The evolution of observability solutions leveraging generative AI doesn’t just help simplify life for network teams. It also offers numerous benefits to the business. It saves time and effort, allowing teams to focus on high-value tasks with less manual work. It helps organizations reduce costs by avoiding incidents and addressing issues quickly. It boosts network resiliency and compliance with operational resiliency requirements. And importantly, it improves customer experiences and customer relationships by delivering more stable, secure network services.
In complex modern IT environments, AI-driven observability is a game changer that not only improves network operations but also enhances enterprise applications and services to fuel competitive advantage.
Learn more about how AI is impacting enterprise networking in the Futuriom Multicloud Networking and NaaS Report.
[1] Gartner, Market Guide for Data Observability Tools, by Melody Chien, Jason Medd, Lydia Ferguson, Michael Simone, June 25, 2024.
Disclaimer:
GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved.
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