What is AIOps?

A major trend across the IT industry is IT teams are constantly being asked to “do more with less.” Even if a company is growing, the IT team probably isn't, or at least, not at the same rate! So how can modern IT teams increase the efficiency and productivity of their operations to be able to support larger and larger user-bases? The answer is AIOps!

AIOps is the practice of bringing AI into day-to-day tasks and workflows usually embedded into software tools and workflow surfaces such as chat. These new tools and techniques allow relatively smaller IT teams to be more efficient and productive and accomplish fewer tasks in shorter amounts of time.

AIOps Recent Changes and Trends

In the past, bringing AIops into the IT toolkit could be a challenge. It might require complex data pipelines or tech stacks built to ingest big data flows and create and deploy new ML models from scratch. Today, with the advent of foundational LLM models, AIOps is easier and more powerful to deploy than ever.

For example, using an LLM to assist in day-to-day IT operations is fast becoming a table-stakes for high-performing IT teams. LLMs are well-versed in understanding questions, commands, and quickly searching and sorting through large amounts of information to make recommendations and expedite impactful outcomes.

Benefits of AIOps

There are many benefits to using AIOps, which is a big driving reason more and more IT teams are using AIOps more frequently. The list of benefits is long, but it can also vary from project to project and company to company. At a high level, however, the benefits of AIOps largely fall into four major categories: Speed, Quality, Security, and Cost-savings.

Speed

To anyone who has learned how to use AI in their workflows, they can attest that it’s so much faster than doing things manually. AI can not only understand what you are trying to do, but it can also use information from multiple sources and help you synthesize that data, make decisions, and even take actions.

For example, by leveraging GPT’s function-calling ability, an AIOps tool can make decisions such as which API might need to be called or how it can gather more information with which to make a decision. This rapid ingestion, processing, and output of information is recently possible with these cutting-edge foundational models. While a person without an AIOps tool may eventually achieve similar results, the massive boosts in speed that can be achieved when that same person uses AIOps cannot be understated.

Quality

AIOps also have a tremendous impact on the output and quality of work. Machine learning models can learn through feedback to detect common mistakes or omissions. Depending on the tool, potential mistakes can be flagged to a person or even fixed automatically.

For example, during a software access review, an AIOps tool could attempt to predict which individuals may no longer need access based on their usage patterns, departments, and current projects. This set of “extra eyes and ears” which are constantly ingesting and assessing new data points, can be invaluable to ensuring high-quality output.

Security

Any good security team will tell you that security works best in layers. No single layer is meant to protect against all possible vectors or mistakes, but combined multiple layers work best to prevent leaks, attacks, and data exfiltration. When used correctly, an AIOps toolkit can become an extra layer in the security stack. It can not only help prevent bad things from happening, but it can also help find and fix problems if they do happen.

For example, by leveraging an AIOps tool like Risotto, it’s possible to embrace more time-based software access and conduct more effective security reviews. All of this creates a new effective security layer, which helps uphold the guiding principle of “least privileged access.”

Cost Savings

Saving costs is a natural outcome of improvements in speed, quality, and security. However, even beyond that, AIOps can help save costs in three additional ways:

• Reduce software license costs: With AIOps, being able to quickly manage complex working environments unlocks new opportunities for license cost savings. For example, using an AIOps tool like Risotto, companies can use more time-based access. This reduces the need for birthright access and accounts that are perpetually active, even if they are no longer needed. This means fewer licenses can be stretched further, which can significantly save money when all added up!

• Displace old, more expensive tools: Just as cloud software replaced on-prem tools because it was a cheaper and better way to run software, AIOps powered tools will replace non-AI tools because they can do more things but cost less and/or do multiple things at once. This will remove the need for complex stacks of specialized tools and drive down costs overall.

• Help IT teams stay lean: By being able to handle more diverse tasks and accomplish routine things more quickly, IT teams can stay lean and effective and do more with less.

How to get started with AIOps

The best advice to get started with AIOps is to not be intimidated and just dive in! There will be a learning curve like with all new tools, but you'll be surprised how quickly AIOps tools can be used well because LLMs make it easy to use.

One super easy and low-friction way to get started is to sign up for an account for a ChatGPT business. While it won’t be automatically integrated with your tools or be able to take automated actions, it can do on-demand tasks to make you more effective and speed up your decision-making. Here are some ways even this simple approach can help in day-to-day IT work.

• Analyze screenshots and make recommendations: GPT 4.o in particular can pluck out error codes and bad UX states from screenshots to determine what might be the root cause and to make suggestions.

• Quickly analyze documents and files: If you have a user manual or complete configuration file, GPT

• Get troubleshooting tips for common problems: GPT can analyze strange and confusing application errors. It also often finds the cause of a problem. For example, if there is a permissions error, GPT might be able to help suggest the permission setting that needs to be changed and how to change it.

Getting started with an LLM is a great way to dip your toe in the water of AIOps. If you want to improve your team's AIOps toolkit even further, try our Risotto. It can help you avoid common tickets and run common IT actions to significantly bolster productivity and security. 

Popular AIOps Tools

IT has many, many responsibilities, and so naturally the landscape of possible AIOps tools is quite large. For example, here are some powerful tools in the AIOps landscape that are worth checking out:

Splunk IT Service Intelligence (ITSI): Offers insights into the health and performance of IT services through machine learning, enabling proactive management.

Risotto: Risotto is an AIOps IT co-pilot on Slack. It helps improve productivity and employee experience with fast response times and 24/7 support. With Risotto, you can eliminate repetitive tasks with smart automation so you can focus on more important things.

BigPanda: Focuses on event correlation and automation, helping IT operations teams manage incidents more efficiently.

Datadog: Provides monitoring and analytics for cloud infrastructure, applications, and logs, with AI-driven alerts and anomaly detection.

Difference and Similarities between AIOps and ChatOps

While similar in name, and both containing the suffix “ops”, AIOps and Chatops have different meanings and it’s good to know the differences. While not a complete list, the following gives you some sense of how to understand and interpret the two different terms.

Similarities between AIOps and Chatops

• Both are a set of tools and ways to make internal operations more efficient, especially those related to the IT department.

• Chatops tools that use AI are a subset of the broader AIOps umbrella. Which AI is not required in Chatops. It’s getting more and more common. AI-powered Chatops tools are part of the AIOps toolkit, but not necessarily the other way around.

Differences between AIOps and Chatops

• AIOps uses AI, but ChatOps doesn’t necessarily need AI. For example, some ChatOps tools leverage slash commands and additional forms and UX flows to kick off workflows and tasks

• AIOps can leverage broad User Experiences while ChatOps focuses on chat. Other AIOps tools might focus more on data streams or logging, or asset management or other IT problem spaces

Where AIOps is headed next

As AI continues to advance, new tools and techniques will continue to be unlocked. In the future, LLMs will be a lot more versatile, handling not just text but images and even audio or video, all at once.

They will also get better at making responses personal, recognizing and responding to each company's unique qualities and preferences. This will make interactions feel more natural. With ongoing work in AI ethics, we can also expect these models to become safer. Just like we see in advancements with companies like Anthropic, they are doing a better job at minimizing biases and avoiding harmful content. Finally, they will become smarter at thinking. This will make them better at solving problems and helping with making decisions in different situations.

Apple bringing LLM featuresRisotto