AIOps is a powerful blend of artificial intelligence (AI) and machine learning (ML) technology, built to ensure reliability.

As the love child of DevOps engineers, site reliability engineers (SRE), and network operations center teams (NOC), this tool helps provide internal support for data ingestion and incident response.  

This is only the tip of the iceberg when it comes to the workflow capability of AIOps. Keep reading as we break down the real intelligence of this technological beast and discover what benefits it may hold for you!

AIOps Workflow 

The key theme to understand when learning about AIOps is its focus on reliability. Every aspect of this tool is to support teams in achieving structural reliability for themselves and their clients. 

AIOps leverage immense loads of big data and provide real-time reactions with significant analytics. What’s more, is AI and machine learning thrive off large amounts of data. The more data it processes, the more it learns and can apply that knowledge to future analytics. This tool becomes more efficient and tailored the more you use it. 

So, now that we know what AIOps do, it’s time to ask how. There are two big ways AIOps achieve this level of analytics, through data ingestion and incident resolution. 

Data Ingestion

Data ingestion is the act of incorporating data from multiple sources and assimilating it into storage for data analysis. Common storage spaces are cloud databases and document stores, any place where you can access stored data.

Data extraction comes from sources like SaaS data, in-house apps, spreadsheets, or website extraction.

It’s not as simple as finding data and putting it away, though. Companies often have multiple areas of storage. For example, sales data goes to a sales database, like Salesforce, while product data goes to a relational database. AIOps have to distinguish the data it’s ingesting to store it in the right place. 

On top of that, it must also format the data so you can use it together, even if it’s stored in different databases. This tool ensures the data has a composition that works for both the database and integrated work. 

Types of Ingestion

There are three ways to ingest data; real-time, batch, or lambda. 

Real-time, or streaming data, is when the data is ingested in real-time. While generating data, the tool extracts, processes, and stores it. It also gives real-time decision-making support. This is useful for businesses that need constant updates on their work. Think of power plants or stock markets where data has to be monitored continuously to guarantee accuracy. 

Batch data ingesting is when the data is ingested at scheduled intervals. Instead of processing constantly, the tool moves data a few times a day, or even less. Great for the repeated processes. Think of daily fiscal reports or handling end-of-year reporting. 

Lambda architecture is when the two methods above are both utilized. Real-time handles time-sensitive data, while batch data handles broader collections. 

Incident Resolution

AI and ML technology secure the ability to leverage data while understanding human activity.

These techniques allow AIOps to offer a hands-on and proactive approach to incident resolution. It detects errors as well as repairs errors. It can even spot anomalies before issues begin. Offering preventative measures for your system and helping create a stable environment for workers and customers. 

AIOps maintain a standard of reliability because it focuses on issue identification. It builds quicker awareness, enabling accelerated response times, and it automates responses to known events. 

The Big Picture 

Data ingestion and incident resolution are the most notable actions of AIOps workflow. However, there are a few general actions it does that you should know. Simply put, these are the big picture advancements.

Selective Data

Along with ingesting data, AIOps select data. Standard IT environments generate mass amounts of noisy and redundant data. Data that takes focus away from the vital information and causes distraction. Encouraging reliability, it filters out nearly 99% of data, focusing on the bits showing errors. 

Alert fatigue is a huge issue in incident response, especially in the healthcare and cybersecurity industries. But, with smart alerting, it’s able to suppress low priority alerts and related group alerts. This allows for a top-to-bottom work structure. You can focus on critical issues and then move on to low priority.

Pattern Recognition 

AI and machine learning at its core are built for finding patterns. Finding meaningful data, understanding why it’s significant, and further finding patterns within the meaningful data. 

Filtering out unnecessary noise and building prolific analysis from purposeful information.  

The most powerful thing about this platform is that it consistently learns from the data, recycles knowledge, and continuously improves responses. Delivered content will become more tailored and precise, revamping the operations.


AIOps promote and facilitate collaboration between departments and teams. Its remote accessibility allows teams wherever they are to work together on issues—streamlining a frictionless and secure workspace for different specializations to perform in. 

It can preserve data from these operations to analyze and put towards developing future diagnosis of similar problems. 


If there’s one thing AI and ML bring to the table, it’s automation. Automations do the tedious work, so DevOps have more time to solve complex issues. It automates responses and repairs, constructing more efficient and correct solutions.

AIOps Workflow: The Future of Tech

AIOps is crucial to operation because it takes action to the next level. With applications like data ingestion and incident resolution, your company will be operating at a higher level than ever before.

AIOps catches the minutiae humans cannot, but simultaneously allows humans to do the work machines can’t. It’s an ebb and flow of machine and human integration that can only be achieved through AI and machine learning technology. 

We hope you found this article helpful and have gained a more fruitful understanding of AIOps. Here at Apica, we aim to educate as much as we strive to help. If you have any further questions, do not hesitate to contact us or view our blog for more insight!