If 2020 was a year of turbulence, 2021 was the year of complete digital transformation. Enterprises across the globe focused their efforts on enabling stellar digital experiences — both to customers and internal stakeholders alike. This had a significant impact on the IT landscape.
The number of applications that were ‘mission-critical’ increased overnight. In a recent survey, respondents said that they have an average of 71.4 mission-critical applications. This means that an IT incident can have a direct and immediate impact on the company’s bottom line. Even an outage for a few minutes can shake up the foundation of businesses today. So, a reactive incident resolution is no longer enough.
What enterprises need in 2022 and beyond is proactive and preventive IT operations. This is only possible with machine learning and artificial intelligence-driven ops, or what’s widely being known as AIOps. In this blog post, we discuss what we believe will be the future of AIOps.
#1 Mere monitoring to preventive problem-solving
Enterprises globally have signed up for dozens of monitoring tools to keep track of their IT landscape. But monitoring alone doesn’t solve problems, it only tracks occurrences in real-time. While this is necessary, it’s not enough.
In the next year, enterprises will increasingly look for a robust AIOps tool that collects data from across monitoring tools, processes the information, makes predictions, and autonomously resolves them.
#2 Accelerated time-to-value
With the speed and complexity of digital transformation, enterprises don’t have months to set up AIOps. So they will look for AIOps tools that are easy to set up and use. They will demand time-to-value to be days.
#3 Exceptional data capabilities
The foundation of AI is good data. To leverage that, enterprises will expect their AIOps tools to:
- Ingest data from any source in any format
- Go beyond metrics and event data to also ingest workload data.
- Work independently or in tandem with other tools as necessary with out-of-the-box connectors
- Leverage advanced and accurate machine learning algorithms to better correlate and identify anomalies
- Prevent alert storms and false positives by capturing the right metrics and making accurate correlations to identify anomalies
#4 Flexibility
Enterprises today have complex multi-cloud/hybrid cloud environments. While choosing the right AIOps tool, they will expect it to have multiple deployment options customizable to their needs. They will demand that it have options beyond agent/agentless.
As a result, in the future, AIOps will play a fundamental role in bringing together BizOps, DevOps and ITOps. With auto-remediation scripts, orchestration workflows and intelligent change management, enterprises will expect their AIOps tools to power DevOps to be more efficient, taking products to market at an accelerated pace. Ingesting data from the CI/CD pipeline, they will need their AIOps tool to leverage data from the code push and transaction for deep dives and early warning alerts to map incident impact on user journeys across services spanning silos.
#5 Price sensitivity
The return on investment on tools like IT operations management has become highly critical. Therefore, enterprises will look for cost-effective pricing models. They will frown upon large overheads/licensing costs and seek better immediate value through SAAS-based products.
#6 Autonomous is the name of the game
The more intelligent an AIOps tool is, the more time it can save for the SRE because it can automate redundant tasks with a degree of autonomy. Unlike a regular IT ops and monitoring mechanism, which only builds dashboards and sends alerts, an intelligent AIOps tool can perform proactive remediation.
#7 Capacity forecasting and planning
Operations management has long been looking backwards, waiting for incidents to occur and resolving them post facto. In 2022, that won’t be enough. Enterprises will look for AIOps tools that can forecast dynamically and adapt autonomously. For instance, an e-commerce platform might have seasonal spikes during sales, promotions etc. There might also be sudden spikes at specific times within these days. An AIOps platform should forecast these, factoring the right metrics and autonomously adjust asset provisioning.
If there is one thing we know for sure, in 2022, things are not going to slow down on the digital front. Digital transformation and consumer expectations will only skyrocket. Therefore, artificial intelligence and machine learning are no longer just good-to-have.
In 2022, robust AIOps will be a must-have — a fundamental differentiator and a competitive advantage.