As businesses continue to revolutionize their operations with digital tools, the demands on IT just keep growing. All of these systems generate various signals to keep IT apprised of their current status. For enterprises, IT can receive hundreds of thousands of different signals every day from different systems. And as companies introduce more and more digital solutions, the number of signals is constantly growing.
It’s easy to assume that more data is better. But at a certain point, the amount of data can make sorting through it for meaningful insights an insurmountable task — for humans. This is where artificial intelligence operations (AIOps) come into play.
The ultimate goal of AIOps is to create business efficiencies. Although AIOps has the potential to impact business operations across the board, currently the most common use case is IT administration. The boom of unified communications (UC) and collaboration platform usage, along with a highly distributed workforce, has shown that AIOps should become a core function of IT.
In fact, Gartner estimates that by 2023, 30% of large enterprises will be using AIOps platforms and digital experience monitoring (DEM) technology exclusively to monitor the nonlegacy segments of their IT estates, up from 2% in 2018.
One area where AIOps is already proving its value for IT is UC administration. In our experience helping enterprises manage their UC platforms, we’ve seen up to an 85% reduction in average monthly service requests for companies that utilize AIOps.
Let’s look at four AIOps use cases within UC administration: reducing monitoring tool sprawl, enforcing collaboration security and governance, accelerating response times, and increasing cost savings.
AIOps Use Case #1: Reducing Monitoring Tool Sprawl
It’s not at all unusual for a single enterprise to use two or more unified communications platforms. For instance, a company may choose to use Microsoft Teams for their internal UC needs and Zoom for their external communications. Some departments may even deploy Slack as a Shadow IT initiative.
All of these options provide for greater end-user choice and flexibility. Unfortunately, they also increase the workload for IT — including monitoring crucial performance signals which requires separate admin console dashboards for each platform.
AIOps helps IT manage this workload sprawl in three key ways:
- It aggregates this data into a single pane of glass. IT benefits from a more monocular focus for UC platform monitoring rather than using the individual platform admin centers.
- Combining the information from different sources into one data lake allows AIOps to turn seemingly unrelated data into actionable insights. Trends which might otherwise go unnoticed are easily surfaced.
- In addition to aggregating data, AIOps does the same for issues across all UC platforms and then works to prioritize these UC issues for IT. Then IT can acknowledge which ones they are going to focus on, and which will be temporarily put on the backburner.
When selecting an AIOps solution for UC management, make sure to choose a solution that includes a dashboard with detailed information on each identified issue, like the issue severity, number of occurrences, and users impacted.
AIOps Use Case #2: Enforcing Collaboration Security and Governance
With the proliferation of unified communications as a service (UCaaS) platforms comes an increased surface area for risk. And with employees more distributed than ever before, the task of keeping tabs on the health of digital security for an enterprise is becoming more difficult.
AIOps makes collaboration security and governance far more manageable for IT administrators by reducing the amount of manual sleuthing required. By automatically creating alerts for security issues, AIOps helps IT admins focus on corrective action rather than tracking down potential issues in the first place.
IT can use AIOps to monitor security and governance policies such as guest access to UC platforms, proper file sharing and access permissions, and minimum number of owners per team or workspace. Without the use of AIOps, it’s much more likely that issues like these will sneak through the cracks.
AIOps Use Case #3: Accelerating Response Times
Having a human dig through the thousands of UC data points created from IT systems every day isn’t just a daunting task — it can be downright impossible for even a mid-size business, much less a global enterprise. Unsurprisingly, this can cause slower than ideal response times to issues that arise.
AIOps significantly reduces mean time to detect (MTTD) by having a computer analyze these data points instead of a human. Generally, a framework that gives artificial intelligence (AI) and machine learning (ML) context on the data must be put in place for this to be effective.
Once that initial process is complete, human intervention is largely only necessary when an issue or anomaly is identified. By using AIOps–generated predictive alerts, root cause determination, and automated case openings, IT admin teams can redirect their time to addressing active issues.
In addition to saving time during the data analysis process, AIOps contributes to faster mean time to repair (MTTR) by also taking the next step — suggesting solutions for the identified issues. Based on historical data of what solutions have worked in the past, AIOps provides IT admins with targeted and prescriptive remediation advice.
Currently, if an AI-suggested solution does not resolve the issue a human must perform further troubleshooting. But as AIOps functionality continues to evolve, more of the process will be managed by computers rather than humans. We are nearing the point where once an issue has been identified by AIOps, the computer can take the next step to implement a solution such as rebooting the system, increasing storage, or resetting a password for a user.
AIOps also helps IT teams take a proactive approach to administration rather than reactive. Historically, much of the monitoring by IT has been focused on UC system uptime. By constantly monitoring system health, IT can now put the focus on quality rather than availability.
AIOps Use Case #4: Increasing Cost Savings
We’ve touched on how many of the simple, redundant tasks that have historically been performed by a human member of the IT team are an opportunity to create efficiencies through AIOps. These changes can lead to more than just time efficiencies — they also create cost savings.
For instance, a common IT admin task is adjusting when an employee has a name or department change. In the past, a member of the IT team would need to manually implement this change in the backend of the UC platform. But with AIOps, this process can be automated.
Another example is phone number provisioning. Say an employee transfers to an office in a different country — they will need a new local phone number. Again, this is a task that is currently performed by a human but can be automated through AIOps.
Both examples may seem like they would only create minor efficiencies. But take the amount of human resources needed for each of these tasks and multiply it by the number of times this occurs across an enterprise in a single year — now we start to see how the potential cost savings add up.
AIOps also creates cost savings by reducing noise for IT departments. By quickly analyzing big data, AIOps can identify false alerts and redundant events. This saves the IT team from having to manually dig into the data to determine what is a real issue and what’s not.
Perhaps most importantly, AIOps helps IT leaders understand system usage. By analyzing data from multiple sources, AIOps surfaces insights on which systems are overworked and where there is opportunity to remove redundancies. This allows IT leaders to make proactive decisions about which systems to keep (and maybe even upgrade) and which are no longer necessary.
Despite the obvious benefits, AIOps is still an emerging area in IT and many companies are not yet ready to take the plunge. This is largely due to a lack of trust when it comes to AI, ML, and automation. As AIOps continues to prove its value in smaller, less risky areas — such as UC platform administration — its degree of adoption and use case targeting will increase.
That said, the benefit of AIOps may be more apparent for large businesses with multiple office locations. The larger the company, the longer it can take for minor issues to make their way to IT. AIOps takes some of the burden off end users to report issues, and instead surfaces them directly within the IT organization.
When thinking about implementing AIOps, it’s important to consider all your uses case scenarios and whether to use a general AIOps vendor or those with experience in specific areas. Without knowledge specific to UC and collaboration, a general AIOps vendor may be able to surface trends and anomalies — but will it be data that is actually meaningful?
An AIOps partner with UC expertise can help you surface meaningful, actionable insights with proposed solutions. Our PowerSuite software integrates AI and ML at its core — and also folds in Unify Square’s UC and collaboration expertise — to isolate and then prioritize issues on which IT teams should focus their attention. PowerSuite’s Insights Center clearly displays these issues along with proposed solutions based on past performance so IT can quickly and easily take action.