According to one survey, 94% agree that AIOps is “important or very important” to manage network and cloud applications performance.
AIOps intends to help customers contextualize humongous data volumes and streamline IT operations with automation. As IT infrastructure grows in complexity, alerts flood IT Ops centers and Ops teams drown in managing the deluge.
While we’ve extensively covered AIOps use cases and the challenges it solves for enterprises, little has been said about the hurdles that line up on the path to AIOps implementation.
Let’s see what some of these struggles are and how organizations can move past them and ensure better IT resilience, security and compliance through AIOps.
Hurdles to AIOps Companies Face
- Lack of trust in AI
Many companies face distrust in AI in their employees. According to a survey of companies that reported the highest success with AIOps, 22% of respondents said that “fear or distrust of AI” was a major challenge in implementing AIOps.
Employees distrust the black-box approach of AI, where you can’t ask or know how AI came to a certain conclusion and are expected to trust the decision anyway. With explainable AI, organizations can address this challenge. Your preferred AIOps platform should provide you with the option to validate the algorithmic decisions it makes.
Modern AIOps platforms come with open box ML models that are fully customizable and has full traceability. - Lack of a strategy
Organizations often function under time and resource constraints, especially when recession looms. In that case, it becomes necessary to roll out a new technology after carefully considering its cost and benefits implications.
Companies must decide the problem(s) they want to solve with AIOps, how urgent these problems are, and what the solution can do for them. This understanding will propel an organization toward AIOps.
A strategy can help decision makers see the bigger picture and know what to aim for, eliminating distractions on the way and helping prioritize objectives. - Poor quality data
Data issues are another significant problem in implementing AIOps. Any AI/ML application is as good as the underlying data. An organization’s legacy and rules-based AI/ML systems may be inconsistent at collecting data and optimizing it for utilization.
AIOps defines what data actually needs to be collected and in what format to make it for a qualified training set. However, some AIOps platforms like CloudFabrix AIOps can ingest data in multiple formats and process it just the same. - Organizational silos and blindspots
In order to extract all potential benefits from AIOps, companies need to bring as many systems as possible under its purview. Since all systems are interconnected, a network issue may have originated from a cybersecurity incident, for instance.
Organizational fragmentation leads to solution silos. AIOps solutions need data from across the board to be effective. An AIOps solution such as CloudFabrix AIOps that collects real-time monitoring data in various formats can become a shared business service, reducing blindspots and improving observability. - Change management
Change at any scale invites resistance from people. Implementing AIOps can be a huge shift for an organization where people are already attuned to working with fragmented tools and comfortable with the status quo.
It might take deliberate communication from executives and leaders to convince the last person in the hierarchy why AIOps is a much-needed change and how it would impact not just the business but the daily lives of Ops teams.
Communicating the benefits of this heavy change may ease some resistance and help organizations get their workforce on board. - Cost of AIOps
Another hurdle on the path to AIOps is the existence of a preferred toolset for IT Ops that teams are not willing to abandon. It costs companies to get rid of the existing infrastructure. Then, it costs to implement AIOps.
An AIOps solution that seamlessly integrates with legacy systems can save costs for an organization. To reduce cost and benefit from AIOps, some organizations go for application performance monitoring tools, for instance, that use AI/ML to spot anomalies.
However, this convenience comes at the cost of leveraging a multi-domain, multi-cloud view of operations and using it to build a truly autonomous IT infrastructure. - Lack of interoperability with existing infrastructure
AIOps caters to a wide range of chores, including alerts management, anomaly detection, event correlation, noise reduction, diagnostics, root cause analysis and security management.
Typically, organizations already have a set of solutions and tools to perform a subset or all of these tasks. Most AIOps platforms don’t integrate with these tools. Even if they do, the process is complex, time-consuming and expensive.
The Solution
We advise organizations to onboard an AIOps solution like CloudFabrix AIOps that integrates seamlessly with the most prominent legacy solutions. This ensures wide AIOps coverage, maximum observability, low cost of ownership and, consequently, a high return on investment.
- Non-availability of on-premise AIOps as an option
Most AIOps tools offer a SaaS-only or on-premise-only solution, but not both. Organizations can look for AIOps that supports on-premise, cloud and dully managed SaaS options, like CloudFabrix AIOps.
Such AIOps systems offer horizontal as well as vertical scalability and support modern applications with cloud-native architecture, microservices and container architecture, distributed platforms and edge applications. - Lack of support for multiple stakeholders
Most AIOps tools are KPI and metrics-driven and intended for a narrow set of stakeholders. Thus, they don’t drive buy-in and sponsorship. Typically, AIOPs use cases are aimed at ITOM and DevOps.
An outcomes-driven approach such that CloudFabrix AIOps adopts can bridge the gap between business and operational outcomes. Use cases of CloudFabrix AIOps attract IT stakeholders across DevOps, ITOps, ITSM and IT planning.
Datacentric AIOps by CloudFabrix addresses hurdles to implementing AIOps and combines observability, security and automation to cause ripples of its benefits across the organization. Find out more here.
