AIOps: Making DevOps Even Better with Smart AI Tools

Mohammad Abu Mattar
AIOps , DevOps , AI , Automation , Observability
Published: 14 Jun, 2025
11 Mins read

You know how things in the tech world keep getting more complicated? Well, managing all that IT stuff can be a real headache. DevOps has been a game-changer for making software development and deployment smoother, but with everything getting so big and moving so fast, can it really keep up? That’s where Artificial Intelligence for IT Operations, or AIOps, comes into play. Think of it as giving DevOps a super-smart brain. By using AI, AIOps brings a whole new level of smartness and efficiency to how DevOps works, helping with things like too many alerts, fixing problems faster, and keeping systems running smoothly without you even having to ask.

So, What’s AIOps All About, and Why Should DevOps Teams Care?

AIOps is basically the next step in managing IT. Gartner came up with the term back in 2017, and it’s all about using machine learning to look at tons of data to automate and manage IT operations. That means using smart algorithms to handle all the complicated stuff in IT. Gartner also says that an AIOps platform uses big data and machine learning to automatically handle important IT tasks. These tasks include figuring out how different system events are related, spotting when something isn’t acting normal, and finding out why problems are happening in the first place.

It’s interesting to see how the name itself has changed. Back in 2016, people were calling it “Algorithmic IT Operations,” but the switch to “Artificial Intelligence for IT Operations” just a year later shows that folks realized AI, including machine learning, could do even more to solve the tricky problems in DevOps. This change highlights a growing understanding of how AI can go beyond just simple algorithms and offer more advanced solutions for how we build and release software today.

For DevOps teams, AIOps is like having a powerful set of tools to deal with the growing complexity and huge amounts of data that come with today’s IT environments. The main thing it does is make things more reliable and available, which ultimately saves money and speeds up digital transformation. Plus, AIOps helps DevOps teams by giving them smart, useful insights that lead to more automation and better teamwork between the development and operations sides. You’ll notice that automation keeps popping up in different definitions and that’s because it’s a key part of making DevOps better. AIOps isn’t just about looking at data; it’s about using what you learn to automatically fix things and make workflows smoother, which is exactly what DevOps is all about – faster and more dependable software delivery through automation.

How Does AI Make DevOps Smarter?

AI doesn’t just fit into one part of DevOps; it makes the whole process smarter, from beginning to end.

When you’re planning, AI can look at old data and trends to guess what resources you’ll need in the future and where problems might pop up, helping you make better decisions. While you’re coding, AI-powered tools can give you suggestions as you type, find potential bugs early on, and make sure your code is good overall, which really helps developers work more efficiently. The building phase gets a boost from AI’s ability to make build processes better, predict when builds might fail, and automate the whole build management thing.

Testing is another area where AI makes a big difference. It can automatically create test cases, decide which tests are most important based on risk, and analyze test results to give feedback to developers faster, making the testing process more efficient and thorough. When it’s time for releasing, AI can predict how likely the deployment is to succeed, spot potential issues before they happen, and even automatically roll things back if something goes wrong after deployment. The deployment phase sees AI making deployment schedules better, automating deployment pipelines, and making sure deployments are smooth and consistent across different environments.

The operating phase gets a big upgrade with AI-driven monitoring tools. These tools constantly learn how your system behaves, spot unusual activity in real time, and give you smart alerts, so you can fix problems before they affect users. Finally, in the monitoring phase, AI can look at tons of monitoring data, connect events that might seem unrelated, and figure out the real reasons behind incidents much faster and more accurately than if you were doing it all by hand.

Basically, AI’s involvement in every part of the DevOps process is a big shift from just reacting to problems and manually fixing them to actually predicting issues and automatically taking care of them. This not only makes the software development and delivery process smoother but also makes it more reliable and faster, which ultimately leads to happier customers.

DevOps Phase	AI Application	Snippet ID(s)
Planning	Predict resource needs, identify bottlenecks	12
Coding	Code suggestions, bug identification, quality assurance	12
Building	Optimize build processes, predict failures, automate management	15
Testing	Automate test case generation, prioritize tests, analyze results	12
Releasing	Predict deployment success, identify issues, automate rollback	12
Deploying	Optimize schedules, automate pipelines, ensure consistency	14
Operating	Real-time anomaly detection, intelligent alerts, proactive resolution	4
Monitoring	Analyze data, correlate events, identify root causes	4

Can AIOps Really See What’s Coming? The Power of Predicting Problems.

One of the coolest things AIOps can do is predict when problems are going to happen before they actually do. By using machine learning, AIOps can look at patterns and unusual activity in both past and current data to guess when things might go wrong. This lets DevOps teams take action early, like putting in patches or fixing potential errors, before they cause trouble for users, which makes the whole system much more reliable. AI algorithms can go through tons of data, like server logs and past trends, to find patterns that might mean a problem is on its way, allowing teams to take steps to prevent it. Predictive analytics can even see potential failures and let teams know, so they can step in before anything bad happens. The power of AI is in its ability to use system data for real-time analysis, finding small changes, patterns, and connections that can be used to understand what’s normal and what’s not, so it can accurately spot unusual activity and predict problems.

Think about how this works in the real world. Netflix, for example, uses AI to watch its huge streaming system, predicting potential outages or performance issues before they even occur. Similarly, AI can look at old incident data along with current system information to pinpoint which parts are most likely to fail, helping teams focus their maintenance efforts where they’re needed most. AI can even keep an eye on things like how much of a server’s CPU is being used and predict when it might get too high, automatically taking action to add more resources before performance suffers. This ability to see problems coming is a big step up from old monitoring systems that mostly just react after something has already gone wrong. This proactive approach can really cut down on downtime, make users happier, and save organizations a lot of money by preventing service disruptions.

More Than Just Basic Scripts: How AIOps Makes Automation Super Smart for DevOps.

Automation is a key part of DevOps, but AIOps takes it to a whole new level by making it intelligent, thanks to the power of AI and machine learning. While regular DevOps automation often involves writing scripts for routine tasks, AIOps can automate more complex things, like figuring out the root cause of problems, sorting incidents, and even automatically fixing them. AIOps can automatically fix issues by intelligently recognizing and categorizing IT events, and then triggering actions to resolve them without any human help. AI-powered bots can handle repetitive tasks like testing code, deploying it, and monitoring systems, which really cuts down on manual work and speeds up the whole development process. Plus, AI powers systems that can heal themselves, automatically finding and fixing issues as they happen, often without needing anyone to step in.

This advanced automation has some big benefits, including a significant decrease in the time it takes to resolve issues (MTTR), better overall efficiency, and the important ability to free up DevOps teams to work on more strategic and innovative projects. AIOps plays a big role in reducing MTTR by helping IT Operations find, investigate, and fix problems much faster than traditional methods. Automating the process of checking and fixing things makes issue resolution smoother, reduces errors, and further improves MTTR. AIOps can even automate the ticketing process while making sure the Configuration Management Database (CMDB) stays up-to-date. AIOps makes automation in DevOps smarter. Regular DevOps automation usually relies on pre-written scripts and rules. However, AIOps can learn from past incidents and data patterns to make better decisions about what to automate and how to respond to different situations. This leads to a more flexible and efficient automation approach that can handle a wider range of problems with less human intervention.

Splunk and Moogsoft: Big Names in the AIOps and DevOps World.

When it comes to AIOps tools that really boost DevOps practices, Splunk and Moogsoft are two of the big players. Splunk is well-known as a leader in IT Operations Management (ITOM) and offers a comprehensive AIOps solution. This solution effectively connects and applies machine learning to a huge amount of data, giving DevOps teams real-time, predictive performance monitoring and integrated IT management workflows. Splunk AIOps uses machine learning to automatically handle important tasks like spotting unusual activity and connecting related events, analyzing tons of network and system data to find the underlying causes of problems. Specifically, Splunk IT Service Intelligence (ITSI) is designed as an AIOps solution to help DevOps teams get the most out of AIOps, providing real-time, predictive performance insights and smoother IT management workflows. Additionally, Splunk uses machine learning for broader operational intelligence, enabling predictive analytics for deployment data and also for automated security analysis within the DevOps process.

Moogsoft, on the other hand, is recognized as a pioneer and a leading provider in the AIOps space, offering a cloud-based platform specifically designed for DevOps and Site Reliability Engineering (SRE) teams. Moogsoft AIOps is great at reducing unnecessary alerts, effectively connecting related alerts, and providing strong built-in monitoring capabilities, including collecting lots of metrics and using advanced methods to detect unusual activity, all of which helps DevOps teams resolve incidents faster. The Moogsoft platform is powered by its own AI and machine learning algorithms, giving DevOps teams complete visibility into potentially critical incidents and allowing them to address these issues quickly and proactively. Moogsoft APEX AIOps Incident Management further shows this by offering an advanced AI-driven platform that helps software engineers, developers, and operations teams quickly understand their systems, identify problems, and fix them more efficiently.

Both Splunk and Moogsoft work smoothly with existing DevOps workflows to significantly improve monitoring, incident management, and automation capabilities. These platforms can take in data from a wide variety of DevOps tools and platforms, providing a central and complete view of overall system health and performance. Their built-in AI capabilities enable smart alerting, which is really important for reducing the often overwhelming number of alerts and allowing DevOps teams to focus on the most critical issues that need their attention. Moreover, both platforms help with faster and more accurate root cause analysis and automatically respond to common and recurring incidents, thereby improving the overall stability and resilience of the entire system. The presence and advanced features of platforms like Splunk and Moogsoft clearly show how AIOps principles are being put into practice within the DevOps environment. These tools offer real solutions to the complex challenges of managing modern IT environments by effectively using AI for intelligent monitoring, prediction, and automation, making them invaluable for DevOps teams striving for better efficiency and reliability.

Got Questions? Common Queries About AIOps in DevOps.

As organizations think about bringing AIOps into their DevOps practices, some common questions often come up. Understanding these key points is important for making it work well.

DevOps is all about bringing together and automating the processes between software development and IT operations to speed up software delivery. AIOps, on the other hand, focuses on using AI and machine learning to automate and improve IT operations, especially in areas like monitoring, data analysis, and incident management. It often works alongside DevOps practices to make the operational parts of the software lifecycle better.

AIOps uses smart filters and advanced techniques to connect related events, which really helps reduce the noise and overwhelming number of alerts that DevOps teams often face. By looking at how different events are related and intelligently figuring out which alerts are false alarms, AIOps makes sure that teams are only alerted to the really important issues, allowing them to focus their attention and resources where they’re most needed.

Getting AIOps to work isn’t always easy. One big thing is making sure you have good data from your entire IT infrastructure. People might also worry about IT teams losing their jobs. It’s also important to find the right balance between automation and human oversight, deal with any cultural changes within teams, and have realistic expectations about what AIOps can do.

Organizations can start by really looking at their current IT setup, figuring out where their biggest problems are, and clearly defining what they want to achieve with AIOps. Choosing the right AIOps platform that works well with their existing systems and processes is crucial. It’s usually a good idea to create a solid plan, start with a small test project, and then gradually expand the solution across the organization.

No, AIOps isn’t meant to replace DevOps engineers. Instead, it’s designed to help them do their jobs better. By automating routine and less interesting tasks and providing smart insights from data analysis, AIOps allows engineers to focus on more strategic, innovative, and complex work that requires their special skills.

For AIOps to really work well in a DevOps environment, it needs to have access to a lot of data from the entire IT landscape. This includes logs, performance metrics, various system events, and traces collected from infrastructure components, applications, and existing monitoring systems.

Addressing these common questions gives a better understanding of how AIOps and DevOps relate, the real benefits and potential challenges of using AIOps, and practical advice on how organizations can start using intelligent IT operations.

The Smart Future: What’s Next for AIOps and DevOps?

Looking ahead, the connection between AIOps and DevOps is likely to become even stronger and more advanced. We’ll probably see more and more automation, handling even more complex tasks, and IT systems might even start to heal themselves. Improvements in AI and machine learning will make predicting problems and figuring out their root causes even more accurate and effective, leading to even more proactive issue resolution. New trends like Generative AI also have the potential to really impact AIOps and DevOps, possibly automating tasks like writing code, creating documentation, and even helping with incident response. A key part of this future will be a greater focus on making AIOps customer-centric. The goal will be not just to make systems more efficient and reliable but also to actively improve the experience for end-users and ultimately drive better business results through smart insights and automated actions.

In Conclusion: Get Ready for the Intelligence Revolution in Your DevOps Practices.

Bringing AIOps together with DevOps is a powerful step forward in how organizations manage their software development and delivery. By using the intelligence of AI, DevOps teams can see big improvements in how efficiently, reliably, and quickly they work. AIOps helps with major issues like alert overload and slow problem-solving, while also enabling proactive prediction of incidents and advanced automation. As shown by leading platforms like Splunk and Moogsoft, AIOps isn’t just a concept; it’s a real thing that’s changing IT operations. Organizations should definitely look into the potential of AIOps tools to enhance their DevOps practices and move towards a smarter and more efficient future for IT operations.