Digital transformation powered by artificial intelligence has led to more complex IT infrastructure, which also means more complicated operations management. However, the organizations that will overcome the inherent challenges of this new reality will have a clear competitive advantage given by the in-depth insights from analyzing Big Data.
A possible solution is AIOps, an umbrella term short for "AI in IT operations" (source: Siscale.com). This can replace multiple human-lead IT operations with an automated response by a platform. Artificial intelligence for IT operations can help with alert noise reduction, cut down on the number of alerts, and improve distributed systems' operations.
Current IT challenges
The technology landscape is changing, and several obstacles could benefit from the implementation of AIOps platforms. The most prominent are:
- The 3 Vs. of Big Data: volume, variety, and velocity. There is an exponential growth of data types and data volumes produced by the IT infrastructure. IT experts alone don't have the time to analyze all the data streams and extract business value. There is a clear demand for artificial intelligence tools.
- There is a surge in cloud migration, higher demand for distributed systems. The new environments are hybrid, and there are multiple data centers with their specific architecture.
- The number of warnings and alarms can become counterproductive, and most IT teams are facing alarm fatigue. Therefore, automated monitoring tools can speed up the process from days to seconds and improve customer service.
Using AIOps for Cost-Effective Business Success
As organizations are moving towards a digital enterprise status, the problem of optimal performance with minimal costs is still present. Deploying AIOps can help with multiple targets, including spending fewer resources and money, improving customer experiences, better data visualization, performing root cause analysis, avoiding outages, and using ai and machine learning for automation.
Saving money with AIOps
Cost management is on the top of the list of every CIO. Using AI techniques to analyze and manage costs will help companies get a competitive edge over peers. Without using data science, it is necessary to either increase the IT team's headcount or deliberately ignore some of the system warnings, resulting in downtime, frustrated clients, and lost business.
Root cause analysis to save time
AIOps is an excellent tool for performing root cause analysis, saving days or weeks of investigation time. It is excellent at identifying what caused the problem as well as proposing solutions. Since it uses machine learning algorithms, it can also be applied to unstructured data such as customer reviews or call center logs to identify what caused the incident. Otherwise, a classic analysis would take weeks instead of minutes.
Prevent threats and frauds with anomaly detection
AI algorithms take massive amounts of data from every business process and perform pattern discovery through deep learning. AI learns what can go wrong for each data stream, including seasonal cyclicity and allowing some variation. It compares actual values with expected patterns and sounds an alert when the discrepancies are too high. The actual threshold is also determined automatically. It also closes a feedback loop and notes future similar events to create a better environment.
This is especially important for banking and financial applications, which are primary targets of cyber-attacks.
Predict and prevent downtime and outages
AIOPs platforms can also use their predictive capacities to improve operational efficiency by continually analyzing the system's state. When necessary, it can supplement resource allocation to prevent hyper-solicitation, especially in a distributed cloud environment. Since any minute of an outage can cost thousands of dollars, putting in place, such a platform will have a positive influence on the bottom line.
Future technology trends
AIOps relies on Big Data and machine learning, which helps process automation and takes decision-making in DevOps to a whole new level by replacing the expert's decision at some incidents with automated responses.
This will also change the scope of work for some individuals. Augmented decision-making, a process through which humans accept and execute, modify, or abandon an algorithm's decision, will become more common for Ops teams.
Self-healing systems a step closer to reality
Self-healing IT systems are still a distant projection and just a potential reality. Also called NoOps, it implies that IT operations will be entirely handled by AI through complete automation.
This will be possible through a well-calibrated feedback system, including constant system monitorization, problem discovery, in-depth learning analysis, problem identification, future predictions, and starting over again.
Ways Big Companies Are Leveraging AI for Competitive Advantage
A recent article from Inc.com describes the strategies successful companies use to leverage the advantage of using AI in general, ideas that could be narrowed down to the AIOps case.
The first strategy is bundling services together. An AIOps platform can help organizations to have an overview of their KPIs in the same place. It can bundle different services, like monitorization, security, and optimization.
Next, it is all about being an early adopter of technology, as with any new technology, the ones who have it first encounter some problems and pick the low-hanging fruit.
Third, it is good to focus on using data analytics to enhance both the company's business outcomes by making data-based decisions and customer experience by offering uninterrupted services and personalized features.
Lastly, don't live on an island. AI works best when it has lots of data to use for analysis, and it can make context-dependent decisions. Create partnerships with companies in your business field or those in related businesses upstream or downstream of your value chain and create integrated AI systems.
Keeping Pace with a Maturing AIOps Market and Its Adoption
Using a NoOps approach doesn't mean that experts become jobless. It just changes their focus from firefighting problems to taking a strategic, added value position to their jobs. Their role focuses on growth and optimization instead of troubleshooting.
It is hard to predict the future, but it will most likely belong to those companies that will make continuous intelligence part of their corporate culture.
Since this is a disruptive technology, it can result in some downfalls, especially if the organization is not ready for a change or has not done the audit correctly. These obstacles are inherent to any new technology and should be appropriately mitigated during the feasibility analysis.