What is AIOps? AI for IT Operations Use Cases & Platforms

As digital transformation proliferates and tech stacks become increasingly diverse, businesses are challenged to manage the next iteration of IT operations.

Traditional methods often fail to keep pace with the complex, dynamic nature of modern infrastructures, leading to higher downtime and impaired operational efficiency—this is where AIOps-driven IT solutions come in.

What Is AIOps?

AIOps (Artificial Intelligence for IT Operations) refers to the application of AI capabilities like ML and Natural Language Processing (NLP) to automate and streamline IT workflows.

It integrates multiple disparate IT business operations tools into a unified, automated platform, enabling end-to-end visibility and enhanced response efficiency for your business. AIOps bridges the gap between complex IT landscapes and sky-high end-user expectations. 

Given the vast amounts of data sources that most businesses can access, traditional IT management systems simply cannot handle the data efficiently enough.

With AIOps, IT teams know what insights and numerous alerts to prioritize to ensure business continuity and optimal performance.

The benefits of AIOps include:

• Ability to pinpoint critical issues or potential anomalies in real-time
• Unified view of the IT environment, enabling better collaboration
• Greater visibility into usage and performance data
• Automated application performance monitoring
• More transparency on cost patterns
• Proactive problem resolution
• Efficient use of IT resources
• Quicker time to deliver

6 Use Cases of AIOps for Businesses

1. Anomaly detection

AIOPs can study large datasets and detect atypical data points, which serve as indicators of potentially problematic situations. It can also detect the root causes behind those anomalies.

This information lets you set up automated responses to resolve and proactively avoid these anomalies, enabling a much faster mean time to resolution (MTTR) than with human intervention. 

2. Predictive maintenance

AIOps can study historical data on equipment usage to detect signs of wear and tear and predict failures, enabling you to schedule maintenance before issues arise. This reduces downtime, keeps repair costs down, and improves the lifespan of the equipment through timely repair.

3. Automated incident response

Through its ability to monitor your modern IT environments and detect current or potential anomalies, AI can automate incident resolution and significantly reduce the need for manual intervention.

AIOps can quickly set workflows into motion, such as reallocating resources or restarting business services, reducing overall downtime.

AI also plays a key role in preventing future issues through root-cause analysis. It can study incidents in real time (such as a sudden spike in load on one of your servers), detect the reason behind the anomaly (e.g., a bug in an application), and automatically launch corrective action (e.g., reallocating extra resources to handle the load).

4. Security and threat detection

AIOps can detect and respond to security incidents in real time by studying user behavior, network traffic, and other data sources to identify potential threats.

ML algorithms use the data to detect sophisticated behavioral patterns that traditional security measures might miss—such as a sudden surge in data access requests from a single user account—and immediately execute a set of predefined response protocols, for example, blocking the user account or isolating any affected systems.

This type of automated mitigation helps to contain and neutralize the threat, minimizing the impact on your IT systems.

5. Performance monitoring and optimization

AIOps offers continuous insights into the entire spectrum of IT applications, storage systems, physical hardware, and cloud environments, giving you real-time insights into what performance metrics look like and where bottlenecks lie.

It also employs event correlation to consolidate and aggregate information for more specific insights. This helps your IT operation team know exactly what components in the IT infrastructure need optimizing, enabling proper resource allocation and quicker implementation of improvements. 

6. Capacity planning and resource optimization

AIOps plays a key role in capacity planning through its ability to forecast future resource needs based on historical usage data.

By identifying patterns in storage, computing power, and network bandwidth across functions and periods, AI helps you proactively plan out its IT resources to meet future requirements.

In the process, AI can also identify underutilized resources already within the system—such as servers constantly running below capacity—allowing you to reallocate resources and thus cut down on hardware and energy costs.

And finally, by continuously monitoring usage and performance numbers, AI can dynamically scale up or scale down resources to keep things running optimally while cutting down unnecessary usage.

Examples of AIOps in Different Industries

1. Education

With EdTech platforms more popular than ever, AIOps is proving to be a valuable asset for individualized learning at scale.

AI-driven learning platforms automatically adapt to student learning patterns and speeds, ensuring that each student gets customized content. For example, it can analyze student data to identify those at risk of falling behind, enabling timely interventions.

AI also automates the administrative side of EdTech, which helps streamline the teaching process.

2. Retail and eCommerce

AIOps can help retail and eCommerce brands streamline inventory management by studying customer data to predict inventory needs in different geographies and periods. It can also power automated customer support systems, ensuring that customers always have the assistance they need and enhancing customer experience.

3. Financial and banking services

AIOps strengthens security for banking and financial service companies by analyzing transaction patterns to detect and prevent fraudulent activities. AI-powered chatbots are also valuable assets in the financial sector, handling routine customer inquiries like setting up a bank account and keeping human agents free for more complex issues.

4. Healthcare and pharmaceuticals

AIOps can enhance operational efficiency for your healthcare brand through predictive analytics. One application involves forecasting admission rates at hospitals or demand for medicines from a pharmaceutical brand, helping you with resource allocation.

Moreover, AI can study datasets on drug development to identify new compounds that could be effective future treatments.  

5. Manufacturing and supply chain

A classic application of AIOps in manufacturing involves predictive maintenance. Algorithms study machine usage data to detect early signs of wear and tear, thus allowing you to schedule repairs in advance and minimize downtime and repair costs.

In the supply chain context, AIOps can study demand patterns and shipping routes to help you plan your delivery routes, shorten delivery time, and thus boost user experience.

AIOps Solutions for Businesses: What to Choose

There are two main ways to classify AIOps solutions:

• On-premises vs. cloud-based solutions
• Open-source vs. commercial solutions

Making a choice depends on the specific needs and priorities of your business outcomes. Let’s study each one in detail.

1. On-premises vs. cloud-based solutions

On-premise AIOps solutions are deployed through your in-house data centers. You can customize them to suit your exact needs, and you get full control over your data security and compliance—a big plus if your industry has stringent regulatory requirements.

This option, however, does involve a substantial investment upfront. Maintenance costs can add up, too. 

On the other hand, these solutions are hosted on remote servers. Many businesses avoid these due to data privacy concerns. However, they offer significant advantages in terms of scalability and flexibility as they operate on a subscription or pay-as-you-go model.

Cloud-based AIOps can be quickly deployed and updated, providing access to the latest features and continuous improvements without the need for extensive in-house IT support. They are particularly beneficial for businesses with fluctuating workloads and those looking to minimize capital expenditures.

2. Open-source vs. commercial solutions

Open-source AIOps solutions are developed and maintained by a contributor community and are free to use as desired. This makes them a low-cost option that can be tailored to your needs, and you also get the benefit of readily available community support.

At the same time, you do need some technical expertise to make the most of these solutions.

Commercial AIOps solutions are owned and developed privately and available to buy through a licensing or subscription model. Naturally, they are much more expensive than open-source options.

On the flip side, you get the advantage of dedicated support and more advanced features. Commercial solutions also tend to be much easier to deploy and manage, even if you don’t have significant technical expertise.

How Does AIOps Work?

There are three main phases to AIOps, each of which connects with the others. This interconnectedness helps IT teams boost their situational awareness and automatically respond to incidents.

Here’s how it works:

1. Observe

This stage involves real-time data collection from your IT environment. AIOps solutions deploy ML and data analytics to enhance observability among disparate data sources and devices, thereby enabling pattern identification and event correlation.

2. Engage

This stage brings in human IT experts to study the data insights delivered, coordinate workloads on multi-cloud services environments, share information with cross-functional teams for faster diagnosis, and share real-time alerts with the right response teams. 

3. Act

This stage involves the concrete actions that an AIOps solution takes to protect and maintain your IT infrastructure. It involves creating automated responses based on data analysis and deploying intelligent systems to learn from historical data and preempt similar problems. 

How Intuz Can Help You Integrate AI Into IT Operations Management

Imagine your IT operations running smoother, with fewer disruptions and faster resolutions to issues, all while freeing up your team’s time to focus on strategic initiatives. That’s exactly what Intuz can offer.

We understand that your current IT infrastructure and tools are the backbone of your business, and the idea of integrating AI might seem daunting. But with Intuz, it doesn’t have to be.

Our custom AIOps solutions are designed to fit seamlessly into your existing environment, enhancing what you already have without overhauling your systems.

By automating routine operations tasks, predicting and preventing potential issues, and optimizing your resources, we help you achieve greater service reliability and efficiency. You won’t be disappointed.

Best AIOps Platforms & AIOps Tools

• Splunk offers actionable insights into IT operations by leveraging advanced analytics and ML to monitor and visualize data. This enables higher efficiency and smarter decision-making.
• Dynatrace initiates proactive problem-solving with a full suite of data-driven valuable insights, from automatic discovery to performance analysis to real-time monitoring.
• Moogsoft uses multi-source event correlation to cut down the noise and detect anomalies more accurately, equipping DevOps teams to respond before the issue escalates.
• BigPanda consolidates and studies incident critical alerts across multiple monitoring tools to identify the root cause and quickly resolve incidents.
• IBM Watson AIOps leverages ML and NLP to detect anomalies, identify the root causes, and recommend corrective actions.

Final Words

There is little doubt that AIOps is the future of IT operations in pretty much every industry. However, the journey to implementing AIOps depends on your specific needs, priorities, and resources.

Take the time to understand where your IT infrastructure lies regarding interconnectedness, flexibility, feature richness, and security. Then, choose the solutions that offer you the most benefit.

The earlier you make the transition to AIOps, the sooner you can enjoy the tremendous benefits in terms of efficiency, low operational costs, issue resolution, enhanced visibility, and predictive management. Invest today—it’ll be worth your while.

Book a free consultation with Intuz for a detailed discussion to find out more. You will also receive a complimentary roadmap on AIOps integration.