Move over AIOps, AI Automation is Taking Over

According to Gartner, AI Automation “𝘼𝙄 𝙣𝙚𝙩𝙬𝙤𝙧𝙠𝙞𝙣𝙜 𝙤𝙛𝙛𝙚𝙧𝙨 𝙜𝙧𝙚𝙖𝙩 𝙥𝙤𝙩𝙚𝙣𝙩𝙞𝙖𝙡 𝙩𝙤 𝙙𝙞𝙨𝙧𝙪𝙥𝙩 𝙡𝙤𝙣𝙜-𝙨𝙩𝙖𝙣𝙙𝙞𝙣𝙜 𝙩𝙧𝙖𝙙𝙞𝙩𝙞𝙤𝙣𝙖𝙡 𝙣𝙚𝙩𝙬𝙤𝙧𝙠𝙞𝙣𝙜 𝙤𝙥𝙚𝙧𝙖𝙩𝙞𝙤𝙣𝙨 𝙩𝙤 𝙘𝙧𝙚𝙖𝙩𝙚 𝙖 𝙢𝙖𝙨𝙨𝙞𝙫𝙚 𝙥𝙧𝙤𝙙𝙪𝙘𝙩𝙞𝙫𝙞𝙩𝙮 𝙞𝙣𝙘𝙧𝙚𝙖𝙨𝙚.” In the evolving landscape of AI-driven IT operations, two approaches to delivering AI networking stand out: AIOps, a more traditional approach, and AI Automation, pioneered by companies offering Campus Networking as a Service (NaaS).

The comparison between AIOps (Artificial Intelligence for IT Operations) and AI Automation highlights significant differences in approach, capability, and outcome. While AIOps has long been heralded for its ability to process data and provide insights, the burden of resolving the issue is still with the IT administrators.

AI Automation addresses this challenge. Nile defines ‘AI Automation’ as leveraging AI to proactively eliminate root causes of issues and automatically resolve problems without human intervention. This definition pushes the boundaries of what is possible with AI in networking even further. The proactive approach enables AI Automation-based approaches to offer service guarantees. Let’s explore the differences between AIOps and AI Automation in detail.

Purpose-Built for AI

To start, the fundamental architecture of each solution reflects its design philosophy and capabilities.

• AIOps falls short of its full potential when integrated into legacy network architectures. Although these tools leverage machine learning and data processing to generate insights, the diversity of architectures, hardware models, and software versions hinders the ability to perform closed-loop automation confidently. As a result, AIOps often lacks the advanced automation required for proactive, autonomous operations, limiting their ability to deliver robust service guarantees.
• AI Automation is built on a network infrastructure with vertically integrated hardware and software that is purpose-built for AI, seamlessly incorporating advanced AI algorithms and automation capabilities from the ground up. This architecture enables AI to deliver on its promise of proactive and automated issue resolution, zero network alerts, and service guarantees, setting it apart as a next-generation solution.

Service Guarantees

One of the starkest contrasts between AIOps and AI Automation lies in their approach to service guarantees.

• AIOps: Typically, these solutions provide no service guarantees. They are tools focused on helping IT teams identify and troubleshoot issues but don’t offer reliability or performance guarantees tied to end-user experiences.
• AI Automation: AI Automation, on the other hand, is designed to provide guarantees for availability, capacity, and coverage. This enables organizations to trust AI Automation to deliver a consistent user experience, reducing downtime and disruptions with strong service assurances.

Scope of Use Cases

The scope of use cases that each solution can handle is another critical differentiator.

• AIOps is primarily limited to “Day N” troubleshooting, meaning it is more reactive in nature. It becomes valuable once the system has matured and issues arise, providing insights to assist IT teams in addressing problems after they occur.
• AI Automation extends beyond Day N, covering use cases from “Day -1” to Day N. It’s designed to proactively anticipate and address issues from the deployment rather than waiting for them to emerge later in Day N. This proactive approach allows organizations to preemptively address potential problems preemptively, ensuring a smoother, more reliable operation once put into production.

Network Alerts and Triaging

AIOps and AI Automation differ significantly in how they handle network alerts and the associated triage burden.

• AIOps platforms often generate a high volume of network alerts or insights, burdening IT teams to sift through alerts and triage them to identify and resolve issues. This can lead to fatigue and delayed response times, as teams must wade through numerous alerts to pinpoint real issues.
• AI Automation, in contrast, operates with “zero network alerts.” By automating issue resolution through advanced algorithms and softbots, AI Automation eliminates the need for constant alert monitoring and troubleshooting by automating issue resolution through advanced algorithms and softbots. This not only reduces the burden on IT teams but also enables quicker resolutions, enhancing the user experience.

Approach to User Experience Issues

The approach to troubleshooting user experience issues reflects the core philosophy of each solution.

• AIOps typically takes a reactive approach, responding to user experience issues after they have occurred. While it can help identify patterns and potential root causes, AIOps lacks a proactive mechanism to prevent issues before they impact the end user.
• AI Automation takes a proactive approach, aiming to solve user experience issues before they become apparent. Leveraging softbots and advanced AI-based closed-loop automation, AI Automation continuously monitors and optimizes performance, ensuring that user experience issues are minimized or prevented altogether.

Resolution vs. Insight

The final distinction is the level of incident resolution capability each platform offers.

• AIOps primarily provides insights into system performance and potential issues. While these insights can be valuable for troubleshooting, they don’t offer a direct path to resolution, leaving the task of fixing issues to human operators.
• AI Automation goes beyond insights by offering resolution capabilities through AI-based closed-loop automation. This means that issues can be detected and resolved without human intervention, allowing faster, more efficient operations and reduced downtime.

Conclusion

The comparison between AIOps and AI Automation underscores a shift from reactive to proactive IT operations management. While AIOps offers valuable insights and troubleshooting assistance, it falls short of providing comprehensive, automated resolutions. AI Automation offers a proactive approach with service guarantees and closed-loop automation. This is the future of IT operations, empowering organizations to deliver consistent, reliable experiences for end users. As IT environments become more complex, solutions like AI Automation that prioritize proactive management and automation will continue to gain traction, offering organizations a competitive edge in a digitally driven world.

Discover how Nile’s AI Automation can benefit your organization—schedule a custom demo today.