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AIOps for your business — how it works, implementation tips, and use cases

August 31, 2021 No Comments

Featured article by Emma Jones


As more companies are digitizing their processes, the complexity of enterprise IT deployments is increasing too. This will inevitably lead to Ops inefficiencies — and inefficient back-office operations reportedly cost companies 20–30% of their annual revenue. AIOps could be a silver bullet solution to this problem as it helps teams to detect issues earlier and resolve them efficiently before business operations and customers are impacted.

What is AIOps, and how does it work?

Interest in implementing artificial intelligence solutions in information technology operations has increased dramatically in recent years. As more companies jump on the digital transformation bandwagon, the number of IT systems (and the amount of data they produce) has skyrocketed. As a result, IT operation teams struggle to identify and fix system failures, unauthorized access to corporate data, and other issues that may disrupt business operations. And here’s where artificial intelligence might come into play.

Let’s start with the AIOps definition. AIOps, short for Artificial Intelligence for IT Operations, refers to a multi-layered environment where Ops data and processes are monitored using AI. Typically, the term describes multi-layered technology platforms that automate the collection, analysis, and visualization of large volumes of data gathered from different tools, logs, metrics, and other sources.

AIOps platforms leverage big data, collecting miscellaneous information from different IT operations tools and devices to automatically spot and react to issues in real-time while still providing traditional historical analytics.

In general, an AIOps platform should perform these functions:

1. Automate routine practices
2. Perform real-time and historical analysis of data
3. Recognize and predict issues faster and more accurately than humans
4. Streamline the interactions between data center groups and teams

Check these four steps on how AIOps works:

Step 1 — Getting relevant data

An AIOps platform gathers historical data and events, system logs and metrics, network data, and real-time operations events. AIOps is able to remove noise and duplication and distinguish only the truly relevant data.

Step 2 — Data analysis

Then, AIOps groups and analyzes the information, discovers patterns and root causes of failures/issues in operational data.

Step 3 — Automated responses

Following the data analysis, AIOps automation tools come to action. An AIOps platform can automatically alert IT teams about the problems, define what caused the issues, and suggest solutions. Also, it can implement automatic system responses to solve issues in real-time before users are even aware some problems occurred.

Step 4 — Visualization

AIOps visualization tools provide real-time insights, reports, and graphics that allow IT operations to resolve issues quickly and take the most efficient actions.


AIOps use cases: how can businesses apply artificial intelligence in IT operations?

IT operations management is becoming more challenging and complex due to ever-growing volumes of data generated by different applications and systems. According to the Dynatrace 2020 Global CIO Report, organizations now use an average of 10 and more different monitoring tools in day-to-day IT operations. Artificial intelligence can be a welcome addition to traditional IT infrastructure monitoring solutions, as it automates time-consuming routine tasks and allows Ops specialists to focus on mission-critical issues.

Some of the real-world examples of AIOps usage include:

– Big data management and performance analysis

The IDG Data & Analytics Survey showed that the average enterprise has managed 347.56TB of data, as for 2016. The top three sources of data are sales and financial transactions (56%), leads and sales contacts from customer databases (51%), and email and productivity applications (tied at 39%). Thanks to machine learning algorithms and neural networks, AIOps solutions can gather and structure vast amounts of data, which is extremely difficult to do manually.

– Anomaly detection

AIOps helps detect anomalies in response time, CPU usage, and memory usage faster and more efficiently and alert users in emergency cases. For this, AI algorithms analyze data from application and infrastructure logs and compare insights to historical data. AIOps can also reduce false alarms by up to 90% and minimize the impact of irrelevant notifications.

– Threat detection and analysis

One of the most essential AIOps use cases is applying AI to security management. Advanced machine learning algorithms can analyze data from various internal sources (netflow logs, application event logs, and DNS logs) to identify malicious activity within the infrastructure. In such a way, IT teams can detect a variety of breaches and violations and react accordingly.

– Storage management

AI can handle both basic and complex storage management tasks. Storage resources are constantly tracked for optimal efficiency and usage when handling output workloads. When the performance is reduced due to lower IOPs or when the disk is nearly complete, the administrators receive an alert. AIOps predictive analytics can adjust storage capacity by adding new volumes proactively automatically.

– Automation

AIOps allows IT teams to set automatic alerts for incidents and remediate issues by running automatic system responses. The approach helps resolve application performance issues in real-time, thus improving productivity and overall user experience.

AIOPs tools can be beneficial for the healthcare, retail, logistics, banking sectors. Here are just some of the most common AIOps uses cases across industries:

AIOps use cases in healthcare

Healthcare IT departments can apply AIOPs to secure patients’ data and prevent breaches, keeping electronic personal healthcare information (ePHI) safe in compliance with the Health Insurance Portability and Accountability Act (HIPAA). Also, there are cases such as applying AIOPs tools to reduce interruptions during emergency calls or analyze significant data volumes.

AIOps use cases in retail

Big retailers and smaller stores also benefit from applying AI and machine learning to their day-to-day IT operations. Thus, AIOps tools can help syncing data across all retail channels and platforms, including POS, self-checkout systems, mobile-based customer loyalty/referral programs, etc. With the help of AOIps, retailers can better secure customer data and create a personalized customer experience or implement new intelligent devices and checkout-free tools.

AIOps use cases in logistics

Using AIOPs tools, logistics companies can reduce networking errors and fix application issues. Therefore, companies can improve delivery speed and accuracy, which will lead to better customer experience and brand loyalty.

How your business can benefit from AIOps solutions

The pandemic has changed the way most companies work today. For Ops teams working remotely, it is critical to optimize the problem-solving workflow to be completed quicker, more accurately, and with fewer manual procedures. IT departments have faced many challenges, such as ensuring a stable connection between the end-users and the network, data, and/or apps. Companies had to adopt new tools to set up efficient communication within the organization and their customers and partners. Moreover, enterprises’ IT environments are getting more complex to manage, as operational data spread across on-premises and multiple cloud systems. As a result, there’s very little time and limited resources to hunt out, fix issues, respond to every user, and bounce between management and operations tools.

AIOps gives Ops teams the flexibility and time they need to track, recognize, and fix problems efficiently before they might impact business-critical digital systems and customers.

IT departments should use AIOps to:

– Control infrastructure output in a multi-cloud environment
– Achieve more accuracy in capacity planning
– Optimize storage resources by automatically adjusting capacity
– Improve resource usage by analyzing historical data and making predictions
– Detect, predict, and avoid IT operation issues
– Organize and monitor linked devices through a network

AIOps may offer significant business advantages to enterprises by optimizing IT and reducing operational costs. For instance, you can increase the number of satisfied customers by avoiding delays and bring your brand reputation to a higher level.

Moreover, AIOps optimizes revenue generation because when apps malfunction, sales are lost. In the survey conducted by Enterprise Management Associates, companies ranked AIOps as the most successful IT analytics investment, with 81% indicating that the value they get from AIOps exceeds its cost.

Other benefits of implementing AI to IT Ops include:

– Root-cause diagnosis and remediation can be completed faster, saving time, money, and energy for the company
– Service delivery is enhanced by increasing response time and accuracy
– IT executives have more time to collaborate with business colleagues, which improves the quality of the strategic planning and organization of the IT department

Things to consider when implementing AIOps to IT operations

AIOps solutions are getting more popular among businesses, and some may think that if you merge traditional IT operations tools with AI algorithms, you will get an AIOps platform. It’s not true, as a real AIOps platform is not just a set of tools. This is important to understand when you start implementing AI to IT operations as it will determine the result.

When implementing artificial intelligence for IT operations, make sure to follow these steps:

– Identify your main AIOps goals

It is best to implement AIOps step-by-step. Users typically begin to apply machine learning to monitoring, operations, and data to automate IT and helpdesk services. You should specify the key issues your company is trying to solve before you start applying AI tools. AIOps requires strategic planning and thoughtful implementation.

– Provide access to the required data

An AIOps platform is data-driven, requiring access to all relevant operations data, including unstructured machine data such as logs, events, metrics, streaming data, API outputs, and device data. Such various data types allow you to build a comprehensive view of all silos and take the best decisions that are relevant to the situation and data type.

– Gather and analyze as much data as you can

Start with accessing and analyzing past states of your systems to identify trends and patterns. Provide access to a vast range of historical and streaming data types. The data types that you select depend on the problems you’re solving. Then you can begin to analyze streaming data to see how it fits those patterns.

Many AIOps platforms are used to focus solely on a single data source, which limits your understanding of application behavior. It is better to use AIOPs platforms for IT that can consume and analyze data from a variety of sources.

– Focus on priority problems

Concentrate on finding the root cause of your key problem and track the data. Then, start with implementing the AIOps platform, which gives you both an effective foundation for organizing large amounts of data and monitoring capabilities to reveal similarities. Use machine learning root-cause analysis to move into a predictive state. You will be able to identify an incident and evaluate its impact before it even affects key business services and customer experience.

– Test & deploy

Run the system in testing mode for at least a couple of weeks to determine that the outputs are accurate and that users are happy with the recommendations. Once you are comfortable with your testing results, it is time to turn the system on in production.

Custom AIOPs vs. vendor solutions

AIOps is a rapidly growing area, which offers many ready-made tools such as Splunk’s IT Service Intelligence (ITSI) tool, BMC’s TrueSight platform, Cisco’s Crosswork Situation Manager, Moogsoft AIOps, and DRYiCE AIOps from HCL Technologies Ltd.

Alternatively, you can choose to build your own custom AIOps platform using open-source frameworks. Among the benefits of in-house built AIOPs platforms for IT departments are data security and multifunctional performance.

Vendor AIOps tools require access to your data, which can be sensitive and impact business in case of breaches. When deciding to buy an AIOPs tool, you must rely on a trusted vendor. Compliance issues can also arise, especially if the tool moves user data into the vendor’s own infrastructure for processing or storage.

Also, you should think about the range of real-time and potential needs you want to resolve with an AIOPs platform. Not every vendor provides products in each category. So it’s best to look at the vendor’s full range of AIOps options and consider future needs as you start deploying your own AIOps platform.

However, this way can be risky, as it takes considerable expertise to not only build an AIOps platform but also to integrate and maintain it.

Here are the pitfalls you may face when developing your own AIOps platform:

– Data management

IT teams that build their own AIOps platforms need to make sure they collect all relevant logs, metrics, and traces along with data collected from IT service and incident management platforms. A poorly constructed AIOps platform will show insights incorrectly and will not accurately reflect what’s actually happening in the IT environment.

– Deployment, service, and support

The deployment is complex. After developing several algorithms for AIOps to produce meaningful results, the next challenge is figuring out how to deploy it in architecture.

The internal IT teams will need to create a product that requires ongoing service and support. The total cost of a custom platform will continue to rise as the IT team may end up spending most of their time managing the AIOps platform instead of improving it. Even if the IT team has the experience required to build an AIOps platform, there is no guarantee they will always be available to maintain and update it.

– Keeping up with the trends

Finally, AIOps as a field is still in its infancy, and most internal teams are not keeping up with the rapid development of artificial intelligence in IT operations alone.

Take-home message

Today, as the competition level is high and new reality requires a search for optimization of IT operations, AIOps platforms are becoming not just a trend but a must-have solution. Being able to automate processes, analyze big data, recognize problems, and aid in smoothing communications between teams, artificial intelligence helps many large companies with complex IT environments. Forward-thinking enterprises will use AIOps to draw valuable insights from their IT data that will help drive strategic business decisions.

There are many opinions and theories on how AI will transform end to end DevOps services and solutions and IT operations. But today, we already have an opportunity to explore successful AIOps use cases across the different industries, included but not limited to retail, logistics, healthcare, banking, and many others. According to the OpsRamp survey, 87% of AIOps implementation cases ended successfully. Here are three leading insights from The State of AIOps report, which included reviews from 200 IT leaders that had applied AIOps in their organizations:

– AIOps adds value

87% of technology pros agree that AIOps tools are delivering value through proactive IT operations and improved hybrid infrastructure resilience.

– AIOps improves operations

The three biggest benefits of AIOps tools include the productivity gains from the elimination of low-value, repetitive tasks across the incident lifecycle (85%), rapid issue remediation with faster root cause analysis (80%), and better infrastructure performance through noise reduction (77%).

– AIOps requires a solid expertise

IT leaders identified the main concerns and pitfalls while implementing AIOps tools, which are: that data accuracy/trust in the reliability of AIOps tool recommendations (67%), lack of skilled employees with data science and machine learning skills to support AIOps deployments (64%), and loss of control (52%).

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