IT Operations (ITOps) has played an important role in enterprise development. Especially, the pandemic situation and the paradigm shift to innovative technology have put immense pressure on ITOps to perform better and provide smooth experiences. With enough options in the market, the consumer is quick to quit an application or service if they encounter issues. This is where the adoption of Artificial Intelligence for IT Operations or AIOps, reduces the tremendous load on ITOps and helps improve efficiency, and allows quick remediation and automation.
IT operations deal with a huge volume of data every day which puts tremendous pressure on the IT workforce. In addition, this also results in loss of optimization and real-time monitoring and resolution of issues. Artificial Intelligence in IT Operations or AIOps, efficiently handles these basic tasks to reduce the burden on IT operations and automates basic functions like monitoring, service desk, technical support amongst other solutions.
What is AIOps and how it can help your company?
AIOps works on three aspects: monitoring, engaging, and acting on big data. AIOps basically includes the application of machine learning and big data in IT operations. AIOps not just benefits IT and cloud-based companies but also see implementation in healthcare, finance, insurance, and other sectors.
Some use cases of AIOps include:
- Automation tools for service desk
- Realtime user monitoring tools
- Application performance monitoring
- Ingestion of data to recognize events and remediation
AIOps monitors data across IT systems, devices, and processes and helps companies control the ace at the following:
- Root-cause Analysis
- Anomaly Detection
- Realtime Notification
- Automated Event Management
- Dependency Mapping
This results in reduced costs for companies and less reliance on the human workforce. It also helps in scaling down errors and increasing the productivity of the workforce by organizing shifts for a smooth experience. It offers service reliability as the tool can be operated 24/7.
For example, AIOps in healthcare can easily replace the helpdesk and takeover booking appointments, generating triggers for issues, flagging important and emergency requests along with assigning them to the relevant teams. The prediction and remediation of issues can be a game-changer in the healthcare industry.
How to choose the right AIOps tool for your business
While the application of AIOps is very beneficial for a company, the implementation of the right tool is critical. So how do you identify the best AIOps solution for your business? All AIOps tools do not fit every business. Choosing the right tool depends on a match between your company’s IT goals and the features offered by the AIOps tool. The suitability of the two will determine whether an AIOps product works for your company.
Here are some factors to consider while picking an AIOps tool
- Complexity – The first factor is the level of complexity involved in your business. Higher complex environments require expensive AIOps to be deployed with better features. Understand what kind of features and functions are helpful for your business before implementing an AIOPs tool for your business. AIOps do not reduce complexity but give the company a tool to deal with large sets of data and process it in real-time for better decision making.
- Monitoring – The monitoring features of an AIOps tool are critical while selecting the right tool. However, it is not limited to only monitoring. A tool cannot entirely be considered AIOps if it offers only storage and retrieval of data.
- Connectivity – Connectivity to systems varies for every company and finding an AIOPs tool that offers connectivity to systems like Kubernetes, SAP and others is important. It isn’t easy to deploy such connectivity on your own. It is easy to determine what kind of connectivity your business needs. The factors involved include connectivity to a system and the ability to gather data while controlling that system.
- Return on investment – To measure returns on AIOps, you need historical data and monitor the progress. Typically, the ROI can be measured within 6 months of deployment. The result may not yield 100% results, but it definitely offers increased efficiency. One must also take into consideration the time taken to resolve issues using the human workforce to measure the value of your investment.
- Observability – Through observability, companies can monitor internal systems and use predictive analytics models to find anomalies and detect issues. After detection, the companies can then administer resolution and remediation of such issues. It also helps companies in being proactive in finding solutions for issues and predicting and detecting abnormal behaviors.
- Root-cause analysis – To know the origin of a failure or issue is one of the main features that help businesses trace and remedy an incident or event. Root-cause analysis helps businesses understand the primary cause even in complex and interdependent systems. AIOps tools that provide this feature, help companies that have multi-dependent and interwoven systems.
- Automation features – It is not enough for many businesses to ingest, correlate and understand data, events, and anomalies. The deployment of automated remedies not just saves time and effort but also reduces the costs involved. Automation features replace manual labor and save on human resource costs. It also helps in 24/7 monitoring and resolution which is beneficial to both the company and customers.
Choosing the right AIOps tools varies from company to company as their IT operating systems and requirements are different. However, understanding what your IT infrastructure needs, charting your AIOps transformation journey, and aligning it with your business goals can help you pick the right tool for your business.
Due to the increase in organizational data, IT processes are getting more complex. ITOps teams are finding methods that can increase their productivity. The recent COVID-19 pandemic has also added the pressure of managing remote work infrastructure. The rise of new-age technologies like AI and ML has helped ITOps teams to boost their productivity. AIOps (Artificial Intelligence for IT Operations) strategy is being implemented by many organizations to reap benefits. Read on to know about five use cases that break down AIOps capabilities.
Monitoring of software systems
There are various software systems in an organization used for providing essential system services. Ill-performing software systems will adversely affect the service availability and ROI (Return on Investment). Let’s find out how AI for application monitoring can help an organization:
- Due to a lack of performance monitoring tools, firms cannot predict the exhaustive capacity of their software systems. Application performance management solutions by AIOps can perform continuous monitoring of software systems without any manual efforts.
- AIOps monitors the entire IT infrastructure including virtual desktop infrastructure solutions, cloud resources, and other distributed IT architectures. The performance data collected is then analyzed via relationship mapping to derive insights.
- As new resources are added to the IT infrastructure, you must upgrade your application performance monitoring tools. Contrary to traditional monitoring tools, AIOps is scalable and can automatically adjust its capabilities according to the IT infrastructure.
Many organizations fix an anomaly in their service applications only when a user reports about it. However, this will have a serious impact on service reliability as you will have to fix anomalies in real-time. Predictive analytics models used by AIOps can help you in predicting future anomalies. AIOps based analytics platforms compare the real-time performance metrics with the historical data to predict anomalies within the IT infrastructure.
AIOps sets predefined performance levels and when the performance of a software system goes below that level, it immediately informs the concerned IT teams. Predictive analytics using AI applications can help you in setting automated alarms on the detection of an anomaly. It will help ITOps teams in fixing anomalies before they leave their impact on the service availability.
Root cause analysis with AIOps
AIOps is an AI automated root cause analysis solution that finds the underlying cause of an IT issue/anomaly. How AIOps helps with root cause analysis are as follows:
- Complex software systems make it hard to find the root cause of the issue/anomaly. CXOs have to analyze large data pools from different software systems to find the source of an IT issue. It is time-consuming and tedious for system administrators. AI data analytics monitoring tools can significantly decrease the MTTD (Mean Time to Detect).
- AIOps based platforms help in event correlation that identifies similar IT issues. If an event has occurred before, AIOps platforms will quickly inform that the same event has occurred again. One can follow the same procedure that was used to resolve the IT anomaly earlier and decrease MTTR (Mean Time to Resolve).
Reducing alert noise
The IT infrastructure of organizations is increasing due to a higher demand for their services. However, with more software systems, the number of alerts generated for anomalies can be significantly higher. IT teams often find themselves in the middle of numerous alerts for anomalies and can’t decide which one should be solved first.
With AIOps, you can define the range of normalcy and can proactively take steps when an event comes near that range. Real-time user monitoring tools offered by AIOps uses an inference model to select the most impactful alerts from a group of overloaded alerts. With AIOps, you can determine the critical alerts and can solve them first. Critical alerts are those which have the most adverse impact on essential software systems.
Security use cases
With the increasing complexity of IT infrastructure, ensuring security and compliance is a headache for ITOps teams. The top security use cases of AIOps platforms for organizations are as follows:
- IT operations analytics platform service powered by AI can help you with intelligent threat analysis. Besides finding behavioral anomalies, AIOps can help you with identifying the attacker sources.
- When a security issue is detected, AIOps-based platforms provide actionable insights to reduce the MTTR.
- Besides identifying external attacks on your IT infrastructure, you can also identify internal frauds with the help of real-time user monitoring tools. If a system software shows anomalous behavior, AIOps platforms can quickly identify it.
- AI data analytics monitoring tools can identify malware or ransomware in software systems without any manual efforts.
In a nutshell
The global AIOps industry worth will be more than USD 23 billion by the end of 2027. Application performance management solutions powered by AI/ML algorithms can reduce the manual load on your employees. You can handle security issues within your IT infrastructure in real-time via AIOps platforms.
AIOps (Artificial Intelligence for IT Operations) has helped businesses to induce automation in their essential business processes. From monitoring software systems to providing actionable insights for incident resolutions, AIOps has proved to be a boon for organizations. However, you cannot just add an AIOps based analytics platform to your IT infrastructure. For AIOps transformation, you need to have a predefined strategy along with addressing the challenges with AIOps adoption. If you do not have a predefined strategy, your AIOps transformation could fail and leave an impact on the service availability. Read on to know five reasons your AIOps transformation could fail and how to avoid them.
Non-compatibility with existing tools
Are your software systems able to exchange information and data seamlessly? An AIOps based analytics platform will require information from other software systems to generate meaningful insights. Interoperability with existing software systems can lead to the failure of your AIOps transformation. If your software systems do not allow you to work with other products or systems, it is time to consider an IT transformation first.
What’s the point of using a digital service desk AI software when the tickets generated by the service desk are ignored by your legacy tools? Make sure that your legacy tools are forwarding the tickets that are generated by the services desk for further analysis. If your legacy tools are compatible with an AIOps based analytics platform, it will automatically consume IT incidents from the service desk and generate actionable insights.
Not knowing the problematic areas
You are not undergoing AIOps transformation just for the sake of adopting a new-age technology. The main purpose of using AI in operations management services is to increase the productivity of your IT operations. Besides focusing on the latest trends in the AI industry, you should focus on the problematic areas for which an AIOps transformation is needed. Some of your IT operations might already be efficient and not require the support of an AIOps based analytics platform.
AIOps adoption can be costly and it is better to find the main problem areas that are decreasing the ROI (Return on Investment). Even the best AIOps tools and products have fixed use cases and can’t help you with something out of the box. AIOps transformation can be costly but will be profitable in the long run if used appropriately.
Lack of training data
An AIOps based analytics platform will require training data to be more efficient with time. Data is like fuel to AI/ML algorithms which helps them to learn about the IT processes. Organizations lack at providing training data to AIOps based analytics platforms which eventually leads to the failure of AIOps transformation. Even the big organizations fail to provide training data to AI/ML algorithms to make them better.
If your training data is messy and contains many outliers, your AIOps based analytics platform will not produce meaningful insights. The organizational data is always scattered across various software systems and is unstructured. Without getting a complete view of the organizational data, AIOps based analytics platforms cannot perform to their fullest.
Not knowing about performance metrics
How would you know that your AIOps transformation is going wrong? Well, one way is to wait and let the failed AIOps transformation impact your ROI. Another way is to use performance metrics to know the benefit of AIOps adoption. If you get to know about inefficient AI DevOps platform management services in time, perhaps you could switch to another transformation strategy. Some of the major performance metrics that can help in determining the impact of AIOps transformation are as follows:
- MTTD: MTTD (Mean Time to Detect) is the time invested in finding out an IT incident. If you have adopted AI for application monitoring, the MTTD should decrease.
- MTTR: MTTR (Mean Time to Detect) is the time taken to fix an IT incident. AI in operations management service should always reduce MTTR significantly.
- Service availability: AIOps platforms will always boost your service availability and reliability. If your service availability is not improving, you need a change in your AIOps strategy.
Failing to embrace the change in IT culture
Your IT culture will go through a major change due to AIOps adoption. At first, it would be hard for your employees to trust the decisions of AI data analytics monitoring tools. However, you can create awareness among your employees regarding the pros of AIOps adoption. You can use open box AI/ML tools that can be customized according to the current IT culture in your organization.
In a nutshell
Just like AIOps platforms offer enhanced observability into software systems, you should have observability into AIOps platforms. You can use various performance metrics for measuring the impact of AIOps on your organization. The global AIOps industry has a CAGR of more than 20% indicating the rise of AI in operations management services.