Apr 19, 2021. By S V Aditya
When something becomes a buzzword, it becomes so overused that it loses a lot of its intended meaning. This is what is happening with AIOps. AIOps has huge potential - to drive productivity, automate actions and manage systems smoothly. However, it is lost in a sea of misinformation and unrealistic expectations from marketing hype built around it. Today we cut through that hype and 5 of the most common myths about AIOps.
1. AIOps is about Application Monitoring(APM) and only Root Cause Analysis (RCA) Many application monitoring tools position themselves as AIOps tools. APM and RCA are the most common use-case of AIOps. This has created a belief that AIOps is all about APM. However, this is only one-third of the Observe, Engage, and Act elements. AIOps practices also involve realignment in ITSM activities as well as changing how incident auto-remediation and service auto-fulfillment are handled. While APM is the low-hanging fruit, AIOps platforms must look to enable Engagement from ITSM teams and Automation in resolving ITOps tasks. AIOps also drives better governance by enabling capacity planning and compliance using AI.
2. AIOps requires a large investment in all IT Operations. Alright, there's some truth to this. AIOps is transformative. Its adoption requires changing processes, and every CIO is concerned about how the interdependence between processes can affect their DevOps cycle. It is also a large unknown, which makes it difficult to decide on large-scale changes. But AIOps transformation doesn't have to be a huge transformation overnight. It can also be improved upon in a modular form. CIOs can choose a small-scale implementation on a particular division - or even a team - to measure its impact. This is particularly easy to test in APM scenarios as MTTR reduction is one of the most verifiable value propositions of AIOps adoption. Moreover, many vendors offer plug-and-play microservices that let you gradually transform your enterprise. This can enable you to secure buy-in from stakeholders and decision-makers by talking real results like RoI of transformation.
3. AIOps transformation is a one-time activity. AIOps is no more a one-time transformation than DevOps is a one-time transformation. Of course, there are one-time changes at the beginning of the cycle - especially as your team is trained to use an AIOps platform or tool. However, AIOps is about process change as much as products. It is also a people change by creating a culture of working with AI in the ITOps space. This requires training teams to become increasingly comfortable with how AI works and designing for it. Developers, for example, must keep AIOps tools in mind during development. Knowing the pain points in their software, they can best decide what log messages, exporters, and KPIs give the most relevant data for AI models. Testers, on the other hand, can develop and write simple workflows for the auto-remediation of incidents. ML Developers can build more complex Deep Reinforcement Learning models that can auto-remediate complex incidents. The AIOps transformation journey is about building these skills and culture into your team and deriving accelerated returns from it.
4. AIOps is for large enterprises. This seems obvious at first glance - only large enterprises have the budget to spend on training employees and building platforms or tools. However, they are also slow to change. Small and medium enterprises, on the other hand, are more agile. They can circumvent the training cycle by using external platforms and tools. Most AIOps vendors design their platforms to be used with the ITOps skillset alone. They do not require Data Scientists to operate. Hence, the adoption is quicker, and the results - like MTTR reduction - are seen faster. Free developer and tester time are also more valuable in smaller enterprises. Finally, AIOps is slowly shifting left. As the industry matures, you can expect to see more advanced use cases that help developers directly. Enterprises that start with the AIOps fundamentals will be better positioned to adapt to such mature deployments.
5. AIOps means job losses. We have saved the most egregious one for the last. One of the chief cultural barriers to adoption is that Automation of ITOps would result in downsizing. However, these tools are not meant to be a replacement for humans. Most of the Automation is on mundane, time-consuming activities. There still will be oversight and review of their actions using audit trails - which have to be done by humans. There will also be higher-level activities that require human action. The role of ITOps teams is simply going to evolve to do much more than before with the help of AI-based tools.
Myths about evolving technology are only natural. As AIOps evolves, its use cases are going to become more nuanced and its application more intelligent. Getting the foot in the door now is essential to developing your enterprise to that level.
To learn more about Algomox AIOps, please visit our AIOps Platform Page.