More organizations are moving more workloads to public clouds. The pandemic has only accelerated this trend. Reports indicate that demand for Amazon AWS, Microsoft Azure and Google Cloud public cloud services has surged since the onset of the pandemic.
Despite this growing movement, the reality is that most enterprises will have hybrid environments that support on-premises, private cloud and multicloud workloads and may be moving workloads among them. This makes managing these environments extremely complex.
This complexity is compounded by factors such as having multiple infrastructure teams, with each managing its own stack, which may include IBM, Oracle or SAP, and the variety of tools enterprises use in these environments. This produces a deluge of data creating a great deal of “noise.” Plus, there are the pressures of adapting and scaling the business, keeping operations running seamlessly, controlling costs, and focusing human resources on high-value tasks.
Automation has been a staple of the IT industry to address inefficiencies by replacing repetitive and well-defined tasks. However, as our IT environments grow in complexity, more than traditional automation is needed. Enterprises need to have a strategic approach to automation across their IT stack to make sure it scales with their business needs and strategy.
AI-led operations (AIOps) can help businesses manage this complexity. It does that by using artificial intelligence (AI) to provide data-driven insights and visibility about hybrid and multicloud environments. End-to-end AIOps includes observability and pattern detection, leading to predictive decision-making and recommendations that trigger intelligent automation.
AI and automation can exist without one other. However, they are better together as AIOps.
Consider The Full Spectrum Of Automation
The process of automation that allows a large reduction of human intervention and the mechanization of repetitive tasks has continued onward since the Industrial Age. In general, automation allows a process or function to be performed with minimal to no human intervention. In IT operations, automation typically involves using scripts, bots and robotic process automation (RPA) to handle repetitive tasks.
But many enterprises have already used these approaches to address the low-hanging fruit — or obvious tasks — that were ripe for automation. Instead of just looking for the quick and easy things to automate, you should now look at your enterprise more holistically.
As Gartner recently noted, infrastructure and operations leaders need to adopt a more strategic stance to automation. It’s time for you to consider the full spectrum of automation.
Identify Business Pain Points That Can Benefit From AI
The best way to do that is to think about your real pain points. Explore the points at which you run into issues that create friction and can lead to cybersecurity incidents or service outages. AIOps is being used today to reduce event and alert noise, discover likely root cause analysis, optimize resources, detect anomalies, perform self-healing and enhance situational awareness.
You may find an opportunity to apply AIOps to these scenarios. Automation can make day-to-day processes run more efficiently. But cognitive automation can take that value to a higher level. It can also help you to address and avoid events that can cost your organization a lot of money because they prevent you from hitting your service level agreements (SLAs) or your experience level agreements (XLAs).
Look at where you’re using a lot of human resources, and decide whether some of those higher cognitive processes could be automated. Automation is an essential ingredient for digital transformation, but AI plus automation drives cognitive transformation. AI/ML itself is an advanced form of automation, mimicking higher-level reasoning or tasks that are impossible for a human to do because of the time it would take to analyze the sheer volume, velocity and variety of data. It addresses the fact that humans can’t keep up with some things.
Ask your experts what they envision when they picture their dream work world. AI and automation are not just about replacing humans. They’re about augmenting what humans do.
Automate The Automation
The most effective AI systems need to learn and get better over time. This is no different for AIOps. As such systems accumulate more information, they improve in their ability to identify patterns, anticipate events, and make helpful adjustments or interventions.
Fortunately, the line is starting to blur for continuous integration and continuous delivery (CI/CD) as a software engineering best practice and the deployment of machine learning models. Namely, early deployment sets the stage for a cycle of regular and continuous improvements. Even if the initial scope is modest, implementing a fully end-to-end automation pipeline creates a foundation that can be built upon incrementally, as well as a framework that can be extended throughout the organization.
After an initial pipeline is in place, aim for continuous learning, and automate as much of your machine learning from data ingestion and transformation to feedback loops and other triggers to update and retrain the models. Monitoring efficacies at each stage of the AIOps pipeline can be a powerful tool in demonstrating the impact on the organization.
Understand That Automation Doesn’t Have To Be A Heavy Lift
It’s useful to understand that there are differences between using automation in public clouds versus private cloud or on-premises environments. The public cloud was built for automation.
There’s a huge opportunity in the public cloud space for you to orchestrate automation without a lot of coding. The major cloud vendors have invested heavily in automation. That means you just need to instrument and orchestrate automation. You don’t need to create the capability.
Often the most challenging components are not at the level of technical instrumentation but of organizational introspection — pinpointing organizational inefficiencies is more than half the battle.
With AIOps, we don’t have to think of the complexities of organizations as a barrier, but rather an opportunity. The most complex organizations are likely to reap the greatest rewards from intelligent automation. When used together, AI and automation are a force multiplier and a catalyst for change across the organization.
