Today’s digital landscapes are evolving rapidly because the complexity and scale of network infrastructure continues to grow exponentially. This surge is making it tougher than ever to administer networks efficiently. While there are a selection of tools designed to assist NetOps teams, Gartner claims that two-thirds of network tasks are still manual. Because of this, there’s a continued demand to streamline network operations and management.
Moreover, the adoption of cloud computing and virtualization technologies combined with recent technologies and services means organizations need more flexible and scalable network management technologies that can assist with the increasing volume of network traffic and devices. While scripting has long been a strategy to automate individual engineering tasks, it isn’t scalable across a whole operations team.
Enter AI and more specifically, the promise of generative AI, which over the past two years has been a catalyst for the market. But with so many AI-enabled technologies now hitting the networking space, it will probably be hard to grasp what functionality is real and what’s AI whitewashing. Let’s take a look at 5 networking tasks AI can assist NetOps teams with today, and 5 areas it will probably’t (but might in the longer term?):
Helps NetOps Teams:
1. Infrastructure Discovery and Configuration Evaluation – It’s standard operating procedure to discover and catalog all of the physical and virtual components that make up a company’s IT infrastructure, and to look at the settings, configurations, and states of the components inside that infrastructure. That is an ongoing process that may take hours per week when performed manually. But AI, utilizing a full Digital Twin of a network, dramatically accelerates this process (for instance BGP tunnel down may be reduced from 2 hours to 10 minutes) pulling up any vital information a NetOps team might need on device hardware or software, configurations, resources, performance, and security risk assessments.
2. Dynamic Mapping – NetOps teams use dynamic mapping for network visualizations, network monitoring, troubleshooting and far more. It routinely discovers, documents, and updates the relationships, paths, and connections between various network devices and components. AI (again with a full Digital Twin of the network) can dynamically draw and map network topology relevant to a question or network issue in minutes, every time they’re needed. Without AI, network engineers must spend a couple of hours per site drawing the maps in Visio (which might add as much as lots of of hours to totally map an enterprise network) and the maps will go outdated in weeks and even days.
3. Root Cause Evaluation and Anomaly Detection – Every networking skilled knows how necessary root cause evaluation and anomaly detection are. They ensure the steadiness, security, and efficiency of systems and processes. Typically, this requires the intuitive expertise of IT professionals with years of experience (using CLI tools, Ansible, Python, etc.). Until AI, there have been no shortcuts to gaining this troubleshooting knowledge. AI, trained by subject-matter experts, can suggest diagnosis or assessment logic to make use of in network automation much like how AI already helps programmers generate code. AI might soon also give you the option to assist reliably replicate, adapt, and scale automation for each device on the network.
4. Really useful Actions – Very similar to troubleshooting, remediating a problem (restoring service degradations to the specified baseline) often requires expert skill. This involves researching vendor documentation and gaining knowledge of best practices and private experience. AI can catalog many years of experience and higher distribute tribal knowledge on novel issues to engineers of each level. Once a diagnosis is made and accepted, or unwanted trends are identified, AI can recommend corrective actions, next steps, follow-up procedures or change proposals.
5. Dashboards and Reporting – Real-time observability, actionable insights, and the power to make informed decisions quickly are all a part of the NetOps job description. Automation can greatly streamline these processes, but how are the automation results presented to human decision-makers? Visualizing useful analytics has develop into its own industry with dozens of graphing and dashboard platforms. But these still require careful consideration and hours or days of labor to construct. AI can significantly ease the visualization of observability and automation results by assisting within the creation of custom dashboards and reports tailored to specific use cases for tracking, monitoring and collaboration. Imagine having to peruse through 1000’s of network insights gathered from telemetry and automatic evaluation after which imagine an AI assistant transforming that data right into a glanceable visual dashboard that highlights urgent issues and priority tasks.
Doesn’t Help NetOps Teams:
1. Approve Network Changes – NetOps wants to attenuate the chance of downtime, ensure compliance, help maintain security, and overall align with business objectives, which is why approving network changes is such an important function. While AI can suggest advisable actions, it cannot make a judgment call to approve or finalize network changes. These changes are complex, every enterprise network is different, and a mistake can cost tens of 1000’s of dollars in downtime. AI hasn’t demonstrated enough advanced networking knowledge for executives to trust it with such a vital task.
2. Design Complex Networks – Every network and its requirements are unique. AI could potentially someday design easy networks for rudimentary use cases, but enterprise networks are too complex and tailor-made to their specific use cases. A micro trading company might require an ultra-low latency network. A video content delivery company might require high bandwidth. A healthcare company might require high availability. Not to say the varied protocols which may best suit each enterprise, from traditional IP, to multicast, MPLS and SD-WAN. AI cannot calculate every possible iteration of a network and select the most effective design. Only a human could make those considerations and decisions.
3. Make Selections – NetOps pros always should make every day critical decisions around traffic management, performance optimization, reply to alerts and incidents, approve network changes and more. AI can actually provide information to those decision-makers, nevertheless it cannot understand the context enough to weigh tradeoffs, make tough decisions, or select compromises. Would you wish AI making a choice which may affect network service delivery of a hospital or government agency?
4. Take Accountability – NetOps teams are judged based on uptime, availability, network performance, problem management, compliance adherence and more. With AI thrown into the combo how are teams measured? Do we predict “it was the AI’s fault” will likely be an appropriate response? AI won’t ever placate key stakeholders or customers.
5. Innovate – Improved efficiency, higher performance, increased scalability, higher user experience…all of these items require innovation. Humans have the power to grasp the complexity of today’s networks, mix that with the business objectives of a company and functions of their role to give you unique ideas and solutions. AI doesn’t have the capability to mutate ideas and create something recent. It cannot think outside the box and supply progressive network solutions for enterprise challenges.
There’s little question that AI is a strong tool that’s being heavily integrated across the technology stack. It might offer beneficial support to NetOps teams by enhancing visibility, automating tasks, and more. But there’s also lots it will probably’t do, and possibly never will give you the option to do. We’re just initially of this symbiotic relationship. What’s the killer AI feature you’d wish to see in NetOps?
