2022 to 2025 AIOps Predictions

Where will AIOps be in five years? Here's what the vendors and thought leaders in the AIOps space had to share
Sean McDermott
Windward Consulting Group

From forecasting the next binge worthy show on your Netflix to advancing algorithms for self-driving cars, AI is more integrated into life than we thought possible. When it comes to AIOps predictions, there's no question of AI's value in predictive intelligence and faster problem resolution for IT teams. In fact, Gartner has reported that there is no future for IT Operations without AIOps. The estimated market size for AIOps is $1.5 billion, with a compound annual growth rate (CAGR) of around 15% between 2020 and 2025.

So, where is AIOps headed in five years? Here's what the vendors and thought leaders in the AIOps space had to share on the Find Flow broadcast:

BigPanda– Build confidence in AI with transparency

"… just like you would expect with any kind of application development shop, they can test and preview the results of those changes to the machine running logic before it's deployed in production. […] And what that means for enterprise and users is they can actually start to gain trust in […] machine learning, because they know that they can see what it is doing."
Mohan Kompella, Product Marketing Lead at BigPanda

AIOps can be shrouded in a lot of mystery for many IT professionals, especially if they don't understand how the AI works in the IT environment. When it comes to machine learning, engineers want to know what it's doing and how it works. Mohan said, "AIOps customers are looking for a product that provides full transparency, testability, and controllability."

That's why AIOps features and platforms of the future should be human-explainable. Not only are systems deployed with an out-of-box ML model, they should also start reducing noise in 6-8 weeks. Over the course of those weeks, the noise reduction rate is around 70-75 percent as the AI learns the environment.

That's great, but what is most important is that the AI begins to make suggestions to IT teams for how to improve the system. It doesn't make changes on its own. An AI should not make autonomous decisions for users. It should suggest a new correlation pattern and then the user can examine it, test it, make changes, and deploy it in production as they see fit. That's the power of the AI + Human collaboration that we'll see more of in the next three to five years.

"Everybody understands what AIOps is, what are the benefits. Now they're all interested in 'how do we get there?' So I think the discussion now is that frictionless is the keyword."
Bhaskar Krishnamsetty, Chief Product Officer at CloudFabrix

Over the last two years, we've seen a shift in the dialogue around AIOps. The question used to be "What is AIOps?" Now most IT leaders are aware of what AIOps is and the benefits. The question now is "How do we get there?" More importantly, "How do we achieve frictionless AIOps maturity?"

Bhaskar and Tejo Prayaga, CloudFabrix Sr. Director, Product Management, both highlight that frictionless AIOps adoption is measured by the ease of implementation. There are two categories: technically frictionless and culturally frictionless. You need both for a smooth AIOps adoption.

Technically frictionless adoption refers to the time to value, data integrations, and data plumbing. All of this is part of the AIOps journey. While it may be time consuming for adopters, there are experts out there to help guide the process to complete maturity. But technical application is only part of AIOps adoption.

People management is the other half of frictionless AIOps adoption. Those looking to integrate AI in the IT department should carefully plan and consider how this change will affect the team. People are an important part of the success of an AIOps strategy. So, taking steps to train your team and make them integral to the implementation process will ensure frictionless AIOps adoption at a cultural level.

"… there's gonna be a shakeout as people realize when they're looking for a vendor in this space, the stuff that looks shiny … claims to be AIOps turns out to be a first generation SaaS product using the rules based system … The future looks like a tool that can cover the entirety of the observability data sets, and provide timely insights, using advanced algorithms that are easy to use because they're advanced, and delivering in a package that can scale."
Phil Tee, CEO of Moogsoft

To say that a lot has changed in the last few years is an understatement. If we thought technological advancements were happening rapidly, it's nothing compared to where we are today with AI integrations in IT Operations. In fact, we're already pioneering the next generation of AI algorithms. Hello, Gen 2 AIOps!

The real reason behind our latest advancements in AIOps is due to the 2020 pandemic. When everybody shifted to remote, this also forced IT operations to shift the whole infrastructure from centralized to application-based. Not only were data points growing exponentially, but they were also sporadically dispersed everywhere. Making work accessible and available, but at the same time secure and contained has been nothing short of a big lift for IT teams.

Phil says that AIOps has become an inevitable necessity for many organizations. But more important are the vendors that have evolved the AI algorithms to suit these new IT environments. These are the vendors to watch, because they will re-imagine and renovate their entire platform to suit this new Gen 2 AIOps. Organizations aren't going to get less complex and data usage isn't slowing down.

So, it's crucial that the AIOps of today comfortably consume metrics and process them through an infrastructure that is adept at pinpointing incidents and taking action.

The full version of this blog was first posted on Windward's website. Click here to read the original blog.

Sean McDermott is the Founder of Windward Consulting Group and RedMonocle

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2022 to 2025 AIOps Predictions

Where will AIOps be in five years? Here's what the vendors and thought leaders in the AIOps space had to share
Sean McDermott
Windward Consulting Group

From forecasting the next binge worthy show on your Netflix to advancing algorithms for self-driving cars, AI is more integrated into life than we thought possible. When it comes to AIOps predictions, there's no question of AI's value in predictive intelligence and faster problem resolution for IT teams. In fact, Gartner has reported that there is no future for IT Operations without AIOps. The estimated market size for AIOps is $1.5 billion, with a compound annual growth rate (CAGR) of around 15% between 2020 and 2025.

So, where is AIOps headed in five years? Here's what the vendors and thought leaders in the AIOps space had to share on the Find Flow broadcast:

BigPanda– Build confidence in AI with transparency

"… just like you would expect with any kind of application development shop, they can test and preview the results of those changes to the machine running logic before it's deployed in production. […] And what that means for enterprise and users is they can actually start to gain trust in […] machine learning, because they know that they can see what it is doing."
Mohan Kompella, Product Marketing Lead at BigPanda

AIOps can be shrouded in a lot of mystery for many IT professionals, especially if they don't understand how the AI works in the IT environment. When it comes to machine learning, engineers want to know what it's doing and how it works. Mohan said, "AIOps customers are looking for a product that provides full transparency, testability, and controllability."

That's why AIOps features and platforms of the future should be human-explainable. Not only are systems deployed with an out-of-box ML model, they should also start reducing noise in 6-8 weeks. Over the course of those weeks, the noise reduction rate is around 70-75 percent as the AI learns the environment.

That's great, but what is most important is that the AI begins to make suggestions to IT teams for how to improve the system. It doesn't make changes on its own. An AI should not make autonomous decisions for users. It should suggest a new correlation pattern and then the user can examine it, test it, make changes, and deploy it in production as they see fit. That's the power of the AI + Human collaboration that we'll see more of in the next three to five years.

"Everybody understands what AIOps is, what are the benefits. Now they're all interested in 'how do we get there?' So I think the discussion now is that frictionless is the keyword."
Bhaskar Krishnamsetty, Chief Product Officer at CloudFabrix

Over the last two years, we've seen a shift in the dialogue around AIOps. The question used to be "What is AIOps?" Now most IT leaders are aware of what AIOps is and the benefits. The question now is "How do we get there?" More importantly, "How do we achieve frictionless AIOps maturity?"

Bhaskar and Tejo Prayaga, CloudFabrix Sr. Director, Product Management, both highlight that frictionless AIOps adoption is measured by the ease of implementation. There are two categories: technically frictionless and culturally frictionless. You need both for a smooth AIOps adoption.

Technically frictionless adoption refers to the time to value, data integrations, and data plumbing. All of this is part of the AIOps journey. While it may be time consuming for adopters, there are experts out there to help guide the process to complete maturity. But technical application is only part of AIOps adoption.

People management is the other half of frictionless AIOps adoption. Those looking to integrate AI in the IT department should carefully plan and consider how this change will affect the team. People are an important part of the success of an AIOps strategy. So, taking steps to train your team and make them integral to the implementation process will ensure frictionless AIOps adoption at a cultural level.

"… there's gonna be a shakeout as people realize when they're looking for a vendor in this space, the stuff that looks shiny … claims to be AIOps turns out to be a first generation SaaS product using the rules based system … The future looks like a tool that can cover the entirety of the observability data sets, and provide timely insights, using advanced algorithms that are easy to use because they're advanced, and delivering in a package that can scale."
Phil Tee, CEO of Moogsoft

To say that a lot has changed in the last few years is an understatement. If we thought technological advancements were happening rapidly, it's nothing compared to where we are today with AI integrations in IT Operations. In fact, we're already pioneering the next generation of AI algorithms. Hello, Gen 2 AIOps!

The real reason behind our latest advancements in AIOps is due to the 2020 pandemic. When everybody shifted to remote, this also forced IT operations to shift the whole infrastructure from centralized to application-based. Not only were data points growing exponentially, but they were also sporadically dispersed everywhere. Making work accessible and available, but at the same time secure and contained has been nothing short of a big lift for IT teams.

Phil says that AIOps has become an inevitable necessity for many organizations. But more important are the vendors that have evolved the AI algorithms to suit these new IT environments. These are the vendors to watch, because they will re-imagine and renovate their entire platform to suit this new Gen 2 AIOps. Organizations aren't going to get less complex and data usage isn't slowing down.

So, it's crucial that the AIOps of today comfortably consume metrics and process them through an infrastructure that is adept at pinpointing incidents and taking action.

The full version of this blog was first posted on Windward's website. Click here to read the original blog.

Sean McDermott is the Founder of Windward Consulting Group and RedMonocle

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Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

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