What is AIOps?
What is AIOps?
AIOps, or artificial intelligence for IT operations, is an industry term coined by Gartner®. It describes technology platforms and processes that transform the IT user experience, enabling IT teams to streamline processes; make faster, more accurate decisions; and respond to network and systems incidents more quickly.
AIOps contextualizes large volumes of telemetry and log data across an organization’s IT infrastructure in real time or near real time. It then combines its gatherings with relevant historical data to generate actionable insights. AIOps is the embodiment of an assistant that has deep knowledge of the IT and network environment and the ability to use that knowledge to provide real-time analysis and execute or recommend next steps.
Why is AIOps important?
AIOps increases the efficiency and performance of individual applications and services and enables IT teams to simplify complex and manual operations, boosting productivity. Organizations using AIOps are automating manual and time-consuming tasks, streamlining workflows, improving overall network performance, and strengthening their security posture.
AIOps is an investment in performance analysis, anomaly detection, and event correlation that gives IT teams the ability to proactively identify and resolve performance-impacting events, reduce downtime and costly outages, and speed up incident response times.
How AIOps works
AIOps consolidates data from multiple sources then processes that data via machine learning (ML) or deep learning (DL) algorithms to provide real-time insights, such as quality of experience (QoE), root cause analysis, and anomaly detection. Good AIOps will reduce false positives, eliminating alarm fatigue so operators can proactively detect issues and resolve them before they impact end user experiences.
Use cases, benefits, and outcomes
Benefits of AIOps
AIOps improves end user and IT operator experiences while boosting productivity and reducing costs.
Simplifies operator experience
- Streamline and automate workflows while reducing errors
- Speed up resolution time by getting proactive and actionable insights to respond to issues before they result in downtime or poor user experiences
- Increase efficiency and productivity, freeing time for strategic priorities
- Improves end-user experience
- Rapidly resolve issues to improves network and application performance
- Ensures IT systems can scale to handle workloads demanded now and in the future
Reduces costs
- Reduce TCO and increase ROI by reducing downtime, improving employee productivity, and reducing OpEx associated with troubleshooting IT issues
Putting AIOps to work
Getting started with AIOps is not much different from deploying any other infrastructure analytics package. The AIOps platform must be connected to (or integrated into) the infrastructure that it will monitor, after which discovery and learning begin. Preliminary insights become available once enough data has been ingested during the AI learning process.
The AIOps platform analyzes the IT environment then offers root cause analyses of problems as they occur. The final phase of AIOps incorporation into an organization’s workflow is automation. Once the AIOps platform has learned enough, it can begin automatically remediating simple problems.
Ideally, AIOps is invisible to the end user and integrated into the administrator’s everyday management tools.
AIOps for networking
AIOps for networking, or AI for networking, provides automation and AI-Native insights across the network. Industry-leading AIOps platforms provide capabilities across wired, wireless, SD-WAN, WAN edge, data center, and security domains, offering end-to-end service assurance. They ensure network connections are reliable, measurable, and secure; increase efficiency and productivity for network operators; and improve experiences for end users.
AIOps is not merely about being able to do what you do today better. The true value is about managing the complexity of IT infrastructure as it surpasses what humans can manage alone, even with the best non-AI tools at their disposal.
AIOps offers a number of benefits to operators of enterprise and commercial networks.
- Accelerates time to incident resolution
- Consolidates and analyzes data from multiple sources
- Observes and learns the details of each unique operational environment
- Provides assessments based on a calculated quality of experience (QoE)
- Provides a conversational interface using natural language processing (NLP)
Easing concerns over AI adoption
As with any new technology, organizations may still be hesitant to adopt AIOps solutions. Concerns over AIOps include data security, integration with existing environment, and lack of understanding of how the AI works. To ease concerns and find the right solution for your organization, here are some key things to look for:
- Security and ethics: Determine what data the AI engine is using and how that data is secured. Ensure that the vendor follows ethical AI principals and guidelines
- Integration: An AIOps solution should simplify operations, not make them more complicated. Look for a solution that can integrate with existing infrastructure or is built in to the IT solution
- Efficacy: Determine if the AIOps has gotten better over time and find out how long the AI has been established. AIOps should be able to provide accurate and relevant information in real time, alerting operators to priority issues (and not causing alarm fatigue). It should get better over time through continuous, closed-loop feedback and development
- Real-world examples: Look for examples where the AIOps solution has provided real results for customers
- Explainable AI: Determine if the vendor can explain the AI techniques behind the solution. If a vendor claims to have AI but can’t explain how it works or what techniques are leveraged, it may be AI in name only
Juniper AIOps
Juniper’s AI-Native Networking Platform leverages the industry’s most advanced AIOps with Mist AI, an AI engine and common microservices cloud architecture, and the Marvis virtual network assistant to improve operator and end user experiences.
Mist AI is trained on the right data, ingesting telemetry from all network devices and processing that data to provide accurate responses in real time. Juniper’s customer support and data science teams work together to uncover common customer challenges and have developed AI algorithms so Mist AI can find and detect problems before users experience performance issues. With over nine years of reinforced learning and development, Mist AI provides accurate responses with the fewest false positives.
Further, Mist AI was purpose built with a closed-loop modern and elastic cloud that provides the compute power needed for AI/ML and scalable networks.
Juniper AIOps solution deployment stages
Juniper’s AIOps solution streamlines operations across all deployment stages. Automated templating and zero touch provisioning speed up and simplify Day 0/1 configuration and onboarding and AI-Native user experience insights. Marvis VNA and self-driving actions speed up issue resolution and overall network performance for simplified Day 2 management.
Deployment stages for AIOps are broken down into four parts: Day 0, planning and configuration; Day 1, onboarding and implementation; Day 2, day-to-day management and maintenance; and Day 2+, proactive management with AIOps.
Juniper’s AI-driven customer support model
The Marvis virtual network assistant increases efficacy by leveraging years of training and development.
At Juniper, our AI engine is constantly improving through a closed loop system. Our customer support and data science team collaborate to uncover common customer challenges and improve the AI algorithm through reinforced learning. With over nine years of reinforced learning and feedback from customers and customer support, Mist AI and Marvis have improved efficacy over time.
Stemming the tide of trouble tickets
Juniper AIOps impact: As the number of customers and network complexity grow, trouble tickets fall or remain at the same level.
AI and cloud computing are changing the support model between customer and vendor. In this figure, you see the total aggregated tickets coming in, illustrated by the dotted line. Customer growth is shown in terms of devices, sites, and organizations added, while trouble tickets remain relatively unchanged. The figure illustrates how AIOps impacts customer help desk tickets, resulting in fewer escalations, fewer incoming tickets, and faster troubleshooting and resolution.
AIOps FAQs
What problems does AIOps solve?
AIOps analyzes and consolidates data from multiple sources. It observes and learns details from the environment and provides assessments based on overall quality of experience (QoE). In this way, AIOps is able to correlate network activities to determine and resolve problems before they’re noticed by end users or IT operations staff.
AIOps provides root cause analyses of problems as or before they occur based on ML algorithms and contextualized data. Above all, AIOps democratizes the ability to troubleshoot among IT personnel with different levels of expertise, increasing overall operations efficiency across the team.
What are the components of AIOps?
An AIOps platform uses ML algorithms and contextualized data to provide root cause analyses and automatically remediate simple problems in the network. AIOps requires an AI engine able to correlate events and ML algorithms that extract knowledge or patterns from a set of observations. A virtual network assistant using natural language processing (NLP) enhanced by natural language understanding (NLU) and language generation (LG) offers a powerful conversational interface that can contextualize requests, accelerate troubleshooting, and make intelligent decisions or recommendations to streamline operations.
What are the key capabilities of AIOps?
- Problem isolation/root cause analysis: With the large volumes of data in today’s networks, it’s difficult to pinpoint problems raised in trouble tickets, much less those that haven’t been brought to the attention of IT. AIOps correlates events in real time by processing contextualized data, allowing operations teams to identify and rectify issues in a timely manner
- Data-driven decision-making: ML algorithms drive data-based analysis, which offers operational recommendations or remediations rather than predetermined responses to networking faults or anomalies. This data-centric approach improves operations staffs’ troubleshooting efficiency
- Predictive reporting: AIOps predicts network behavior and offers recommendations or remediations for fixing degraded performance and other anomalies within the network. This fundamental shift benefits operations teams by allowing them to be proactive in managing network operations, rather than chasing down issues that have already had an impact on users and the business. As a result, IT frees up time once spent in firefighting mode to tackle future business objectives,
What AIOps solutions does Juniper offer?
Juniper’s AI-Native Networking Platform is foundational to how we deliver Juniper AIOps. Our wired access, wireless access, SD-WAN, Enterprise WAN, data center, and security solutions, for example, are all unified by a common cloud and AIOps engine, Mist AI. These AIOps solutions simplify end-to-end troubleshooting, Self-Driving Network™ operations, and client-to-cloud insight into customer experiences. In addition, Marvis, the industry’s first AI-Native virtual network assistant, has an interactive conversational interface that provides simple recommendations to complex problems. Marvis Minis, the industry’s first AI-Native digital experience twins, also work in the background to uncover issues without users having to be present. All of these tools, driven by Mist AI, can save you time and money while maximizing the value of your network infrastructure.
Juniper is a recognized leader in AIOps. See why Gartner positioned Juniper Mist farthest in “Completeness of Vision” and highest in “Ability to Execute” in its 2024 Magic Quadrant™ for Enterprise Wired and Wireless LAN Infrastructure for the third time in a row.
