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.