What is AIOps?

To understand how AIOps impacts business decisions, it is helpful to understand how AIOps is different from other technologies that gather, analyze, and report on data. Tools that work on data, such as business intelligence platforms, will generally not recommend a certain decision. Decision-makers still need to decide what to do with the graphs and charts provided. 

With AIOps, AI and ML will learn to mimic human thinking and decision-making. Therefore, AIOps can equip computers to make decisions regarding IT operations and execute them without human intervention. AIOps is also different from DevOps—although the two can work hand in hand. DevOps streamlines collaboration between software development and IT operations teams, while AIOps is the use of AI technologies to improve IT operations. 

Besides giving computer systems decision-making capabilities, AIOps improves decision-making for IT teams and management because it:

1. Simplifies IT solutions
2.  Exposes the source of technological and security issues
3. Identifies patterns and their significance to operational performance
4. Provides actionable insights to drive policy-making and decisions

The fast pace of digital transformation can create what is sometimes referred to as IT lag. This happens when digital transformation outpaces IT performance, overwhelming IT resources and causing a gap that may result in unnecessary downtime, security breaches, and a drop in overall performance and productivity.

AIOps is a solution to this gap. By combining machine learning and data science into a single solution, IT operations become more efficient and able to evolve at scale. Additionally, AIOps increases visibility by eliminating silos. AI capabilities include the ability to ingest data from a variety of sources and formats.

There is no single AIOps application. Rather, AIOps is a platform that uses various technologies to leverage the benefits of AI in IT operations. Therefore, you can expect to see these features or components in an AIOps solution:

1. Data aggregation: AIOps software must be able to collect data from various sources and in various formats. By eliminating data silos, AIOps increases the visibility of data across the system.
2. Real-time data processing: The massive amount of data most organizations need to process would overwhelm traditional IT operations. AIOps leverages AI technologies to process data in real time, so decision-makers can respond to issues promptly before security incidents result in unnecessary downtime.
3. AI-driven automation: Many programs can automate processes, but AI-driven automation mimics human decision-making, reducing the IT team's workload while providing deeper insights.
4. AI and ML technologies: AI and ML solutions can distinguish patterns, spot abnormalities, automate testing, provide actionable data insights, and even predict possible outcomes through analytics. 

AIOps can benefit enterprises in various ways. Here are some of the most popular AIOps use cases:

1. Anomaly detection: Because AI technologies can scan and recognize patterns faster than human operators, they can recognize and categorize anomalies and their root causes within moments. 
2. Dynamic infrastructure topology: A topology is a map of the resources across a network and how they are connected. Many companies document their topologies, but in most cases, the resulting maps are static. AIOps provides a dynamic topology that evolves with your organization, ensuring visibility. This makes risk assessment and management more dynamic and adaptable.
3. Noise reduction: AIOps can sift through an overwhelming amount of alerts to verify relevance and prioritize response. Through intelligent analysis, low-priority alerts can be suppressed and redundant alerts grouped, increasing efficiency and productivity.
4. Performance monitoring: AIOps streamlines performance monitoring, effectively changing the approach from reactive to proactive. Performance issues can be resolved in real time, saving your IT team valuable time. 
5. Process automation: Because process automation can replace certain time-consuming and labor-intensive operational functions, it frees up IT staff for more critical functions and saves time and money.

Although there is no way to accurately predict the future, trends surrounding AI technologies and the evolving requirements of IT operations can help to indicate where the future of AIOps is headed.

1. Improvement of data quality: The data AI feeds on and learns from must be as high quality as possible. Data quality must continually improve for organizations to continue leveraging AIOps solutions. Data annotation and proper data management are key.
2.  Integration of AIOps into other security solutions: Since AIOps uses ML technologies, in time it can provide deeper insights and make more comprehensive solution recommendations. There will be a need to integrate AIOps with existing security solutions to allow it to grow, evolve, and improve the security posture of organizations that rely on it.
3. The ability of AIOps to predict and prepare for zero-day attacks: As AI and ML solutions grow and evolve, they will be used to create more-complex attacks. Adversarial machine learning is a possible trend that IT teams must prepare for, and AIOps solutions must be up to the task.
4. Changing best practices: As AIOps solutions are implemented and their abilities improve, traditional best practices for IT operations will likely change.
5. Exponentially increasing AI technology adoption: AI is here to stay, and it is expected to surpass human intelligence. Whether or not that happens, the increased use of AI solutions will change the landscape for businesses globally.
6. AI as a necessity for IT operations: It is likely that AI solutions will become intrinsically linked to IT operations, so  IT teams that begin leveraging AIOps solutions now may have the upper hand moving forward.