How to Choose an AIOps Tool

Phil Tee

Out with the old monolithic applications! And in with the new container and microservice-based IT environments!

This shift to containers and microservices is a key component of the digital transformation and shift to an all encompassing digital experience that modern customers have grown to expect. But these seismic shifts have also presented a nearly impossible task for IT teams: achieve ceaseless innovation whilst maintaining an ever more complex infrastructure environment, one that tends to produce vast volumes of data. Oh and can you also ensure that these systems are continuously available?

Once a low-priority task, infrastructure monitoring is now imperative to maintaining system assurance and keeping up with the blinding pace of change.

In the good old days, IT teams could manually monitor infrastructures that changed over months and maybe years. Not so today. Modern application programming interfaces (APIs) that connect computers or programs are highly flexible leading to constant change in application and network topology. The increase in data production and shift to ephemeral machines has consequently rendered manual monitoring impossible for human operators.

So DevOps, SRE and IT operations teams must embrace change while minimizing and mitigating outages. And the secret sauce for making this happen is an effective artificial intelligence for IT operations (AIOps) platform.

AIOps tools use artificial intelligence (AI) and machine learning (ML) to streamline the monitoring of operational data from applications, cloud services, networks and infrastructures. The tool's algorithmic approach to root cause helps DevOps and SRE teams quickly identify and fix issues affecting the performance of an organization's apps and vital services.

Maintaining this uptime and reducing mean time to resolution (MMTR) is critically important in our digital economy where customers, partners and employees rely on seamlessly running systems. And downtime equals big dollars.

So, how do you choose the right AIOps tool to help improve system performance? And how do you identify a real AIOps tool?

Can the Real AIOps Please Stand Up?

Infrastructure monitoring has evolved with our evolving IT environments. While teams historically tried to predict system failures with lists of rules, AIOps is much more flexible and reliable. AIOps replaces rules with AI- and ML-based algorithms that infer the existence of issues and discover incidents that would have evaded rules.

This operational difference is critical. Rules-based legacy solutions can not handle today's complex and unpredictable issues. And they simply can not keep up with the massive amounts of data that modern IT environments pump out every day.

To implement a true AIOps platform and avoid deploying a monitoring tool masquerading as one, make sure you can answer "yes" to the following:

■ Does my AIOps solution automate anomaly detection?

■ Is it operational without definitions or a list of dependencies?

■ Does the vendor do its own data science? How many patents do they have?

■ Does the system operate under changing conditions like shifting data formats, dependencies and applications?

■ Does the solution cover all observability data?

■ Can end-users run the system?

Why is Real AIOps Beneficial?

The advantages of AIOps are likely apparent to those struggling to monitor modern application infrastructures to increase uptime for consumers who expect on-demand digital products and services. Here are specifics around what IT teams should expect, especially from newer providers that offer more innovative cloud and Saas solutions:

■ Decreased downtime: AIOps tools catch incidents as they occur and can even predict service-impact incidents before they affect businesses. With these tools, teams can slash the amount of downtime in applications by at least half.

■ Automated cognitive load: Alert noise and false alarms pull teams away from their tasks and kill productivity. AIOps tools can reduce false alerts by 99%.

■ Reduced cost of ownership: Rules-based systems require constant alterations in monitoring system configurations. AIOps, on the other hand, can handle continuous change.

We live in a digital economy where the digital experience defines the customer experience. And businesses simply cannot afford extended downtime. Modern IT teams need modern AIOps solutions to help avoid outages, improve responsiveness and ensure top performance of apps and services.

Related Links

Hot Topics

Related Vendors

The Latest

The Performance Bottleneck in Enterprise AI Has Been Exposed – Now We Must Fix It

Performance bottlenecks aren't uncommon when it comes to rolling out new technology, regardless of how capable or game-changing that technology might be. Every generation of new tech has encountered roadblocks that had to be overcome before it was truly able to shine. Virtualization forced organizations to rethink resource allocation, cloud transformation had us shift our focus toward scalability and elasticity, and microservices introduced entirely new challenges around observability and distributed systems. There's something different about AI, however ...

What Capital Markets Can Teach Enterprises About Integrated Data Infrastructure

Consider a single order represented across order-management, execution, and settlement systems. Each database, message broker, and application may be online and processing its own records correctly. Yet the workflow has failed if related events arrive on different clocks, rely on inconsistent state, or cannot be reconciled before an operational decision must be made ...

Why AI in IT Is Producing High Productivity but Heavy Workloads

AI now exists in almost every IT workflow. In a recent survey of more than 800 IT service professionals, all respondents indicated the use of AI in some form within their organization. But there's a growing paradox: if dashboards are clearing faster and alerts are resolved at unprecedented speed, why aren't IT service desks reporting lighter workloads? The research found that 71% of IT teams said their actual workload has remained flat or increased since adopting AI. This reality appears to contradict what we’ve been told about AI ...

Implementing AI Isn't the Hard Part Anymore - Adoption Is

Two years ago, almost every customer conversation about AI started with the same questions: Which model should we use? What can it do? Is it ready for the enterprise? Today, those discussions have moved on. CIOs are far more interested in how to govern AI, integrate it with existing systems, prepare their workforce and make it part of everyday operations. The challenge is no longer to prove that AI can deliver value. It's instead about how to embed AI into the business in a way that's secure, scalable and delivers measurable outcomes ...

The Incident Load Is Rising, but Recovery Times Aren't Moving

Two things happened to production incidents between 2023 and now, and they did not happen at the same speed. The first is that a class of dependency that barely existed three years ago now accounts for one incident in ten. Incidents disclosed by AI model and AI application providers rose from 1.7% of all disclosed unplanned incidents in 2023 to 10.7% in 2026 year to date, roughly a sixfold rise; that counts only incidents at AI companies themselves, so the true share is higher. The second is that the time to close an incident has not come down ...

Your AI Pipeline May Have a Document Observability Problem

When an AI assistant gives an incomplete or incorrect answer, teams often blame the model. They adjust prompts, switch models, increase context windows or test a new retrieval strategy. However the model may not be a problem. In many enterprise AI workflows, the problem begins inside the document-ingestion pipeline ...

The SIEM Isn't the Problem. Your Telemetry Architecture Is

If you talk to any security or observability teams right now, they're all fighting the same fire: their tooling was built to ingest X, but their sources are pumping Y and soon to be doing Z. The knee-jerk reaction is always the same: we need more platform. However, this reaction is wrong. Let me explain why, because the solution to this problem is foundational, not financial. Instead of hurling yet more money at the problem, make sure you've done what's needed upstream ...

As AI Scales Across Enterprises, Breaking Points Emerge

Rapid AI adoption and the unique ways AI workloads operate is redefining the scope and structure of what these teams must deliver. This shift is forcing organizations to rethink how they manage scale, automation, and control, according to The State of SRE and Platform Engineering 2026, a new report from Dynatrace ...

Preparing Network Infrastructure for AI Starts with the Basics

AI is usually talked about as a software tool, but it also depends heavily on the network behind it. Whether a company is using AI for chatbots, automation, monitoring, analytics, or employee support, all of that information has to move across the network in a reliable and secure way. That means AI is not just an application decision. It is also an infrastructure decision. Before organizations rush into AI, they should ask a simple question: Is our network ready to support it? ...

Agentic Operations Begin with Governed Middleware Access

Enterprise AI often lacks governed access to where business processes actually execute. Without that access, AI agents may be able to reason, but they cannot operate reliably across enterprise workflows. For AI agents to effectively carry out workflows, they will require integration-layer context and controls. Organizations can implement these prerequisites by providing AI with managed access to the middleware layer ...

How to Choose an AIOps Tool

Phil Tee

Out with the old monolithic applications! And in with the new container and microservice-based IT environments!

This shift to containers and microservices is a key component of the digital transformation and shift to an all encompassing digital experience that modern customers have grown to expect. But these seismic shifts have also presented a nearly impossible task for IT teams: achieve ceaseless innovation whilst maintaining an ever more complex infrastructure environment, one that tends to produce vast volumes of data. Oh and can you also ensure that these systems are continuously available?

Once a low-priority task, infrastructure monitoring is now imperative to maintaining system assurance and keeping up with the blinding pace of change.

In the good old days, IT teams could manually monitor infrastructures that changed over months and maybe years. Not so today. Modern application programming interfaces (APIs) that connect computers or programs are highly flexible leading to constant change in application and network topology. The increase in data production and shift to ephemeral machines has consequently rendered manual monitoring impossible for human operators.

So DevOps, SRE and IT operations teams must embrace change while minimizing and mitigating outages. And the secret sauce for making this happen is an effective artificial intelligence for IT operations (AIOps) platform.

AIOps tools use artificial intelligence (AI) and machine learning (ML) to streamline the monitoring of operational data from applications, cloud services, networks and infrastructures. The tool's algorithmic approach to root cause helps DevOps and SRE teams quickly identify and fix issues affecting the performance of an organization's apps and vital services.

Maintaining this uptime and reducing mean time to resolution (MMTR) is critically important in our digital economy where customers, partners and employees rely on seamlessly running systems. And downtime equals big dollars.

So, how do you choose the right AIOps tool to help improve system performance? And how do you identify a real AIOps tool?

Can the Real AIOps Please Stand Up?

Infrastructure monitoring has evolved with our evolving IT environments. While teams historically tried to predict system failures with lists of rules, AIOps is much more flexible and reliable. AIOps replaces rules with AI- and ML-based algorithms that infer the existence of issues and discover incidents that would have evaded rules.

This operational difference is critical. Rules-based legacy solutions can not handle today's complex and unpredictable issues. And they simply can not keep up with the massive amounts of data that modern IT environments pump out every day.

To implement a true AIOps platform and avoid deploying a monitoring tool masquerading as one, make sure you can answer "yes" to the following:

■ Does my AIOps solution automate anomaly detection?

■ Is it operational without definitions or a list of dependencies?

■ Does the vendor do its own data science? How many patents do they have?

■ Does the system operate under changing conditions like shifting data formats, dependencies and applications?

■ Does the solution cover all observability data?

■ Can end-users run the system?

Why is Real AIOps Beneficial?

The advantages of AIOps are likely apparent to those struggling to monitor modern application infrastructures to increase uptime for consumers who expect on-demand digital products and services. Here are specifics around what IT teams should expect, especially from newer providers that offer more innovative cloud and Saas solutions:

■ Decreased downtime: AIOps tools catch incidents as they occur and can even predict service-impact incidents before they affect businesses. With these tools, teams can slash the amount of downtime in applications by at least half.

■ Automated cognitive load: Alert noise and false alarms pull teams away from their tasks and kill productivity. AIOps tools can reduce false alerts by 99%.

■ Reduced cost of ownership: Rules-based systems require constant alterations in monitoring system configurations. AIOps, on the other hand, can handle continuous change.

We live in a digital economy where the digital experience defines the customer experience. And businesses simply cannot afford extended downtime. Modern IT teams need modern AIOps solutions to help avoid outages, improve responsiveness and ensure top performance of apps and services.

Related Links

Hot Topics

Related Vendors

The Latest

The Performance Bottleneck in Enterprise AI Has Been Exposed – Now We Must Fix It

Performance bottlenecks aren't uncommon when it comes to rolling out new technology, regardless of how capable or game-changing that technology might be. Every generation of new tech has encountered roadblocks that had to be overcome before it was truly able to shine. Virtualization forced organizations to rethink resource allocation, cloud transformation had us shift our focus toward scalability and elasticity, and microservices introduced entirely new challenges around observability and distributed systems. There's something different about AI, however ...

What Capital Markets Can Teach Enterprises About Integrated Data Infrastructure

Consider a single order represented across order-management, execution, and settlement systems. Each database, message broker, and application may be online and processing its own records correctly. Yet the workflow has failed if related events arrive on different clocks, rely on inconsistent state, or cannot be reconciled before an operational decision must be made ...

Why AI in IT Is Producing High Productivity but Heavy Workloads

AI now exists in almost every IT workflow. In a recent survey of more than 800 IT service professionals, all respondents indicated the use of AI in some form within their organization. But there's a growing paradox: if dashboards are clearing faster and alerts are resolved at unprecedented speed, why aren't IT service desks reporting lighter workloads? The research found that 71% of IT teams said their actual workload has remained flat or increased since adopting AI. This reality appears to contradict what we’ve been told about AI ...

Implementing AI Isn't the Hard Part Anymore - Adoption Is

Two years ago, almost every customer conversation about AI started with the same questions: Which model should we use? What can it do? Is it ready for the enterprise? Today, those discussions have moved on. CIOs are far more interested in how to govern AI, integrate it with existing systems, prepare their workforce and make it part of everyday operations. The challenge is no longer to prove that AI can deliver value. It's instead about how to embed AI into the business in a way that's secure, scalable and delivers measurable outcomes ...

The Incident Load Is Rising, but Recovery Times Aren't Moving

Two things happened to production incidents between 2023 and now, and they did not happen at the same speed. The first is that a class of dependency that barely existed three years ago now accounts for one incident in ten. Incidents disclosed by AI model and AI application providers rose from 1.7% of all disclosed unplanned incidents in 2023 to 10.7% in 2026 year to date, roughly a sixfold rise; that counts only incidents at AI companies themselves, so the true share is higher. The second is that the time to close an incident has not come down ...

Your AI Pipeline May Have a Document Observability Problem

When an AI assistant gives an incomplete or incorrect answer, teams often blame the model. They adjust prompts, switch models, increase context windows or test a new retrieval strategy. However the model may not be a problem. In many enterprise AI workflows, the problem begins inside the document-ingestion pipeline ...

The SIEM Isn't the Problem. Your Telemetry Architecture Is

If you talk to any security or observability teams right now, they're all fighting the same fire: their tooling was built to ingest X, but their sources are pumping Y and soon to be doing Z. The knee-jerk reaction is always the same: we need more platform. However, this reaction is wrong. Let me explain why, because the solution to this problem is foundational, not financial. Instead of hurling yet more money at the problem, make sure you've done what's needed upstream ...

As AI Scales Across Enterprises, Breaking Points Emerge

Rapid AI adoption and the unique ways AI workloads operate is redefining the scope and structure of what these teams must deliver. This shift is forcing organizations to rethink how they manage scale, automation, and control, according to The State of SRE and Platform Engineering 2026, a new report from Dynatrace ...

Preparing Network Infrastructure for AI Starts with the Basics

AI is usually talked about as a software tool, but it also depends heavily on the network behind it. Whether a company is using AI for chatbots, automation, monitoring, analytics, or employee support, all of that information has to move across the network in a reliable and secure way. That means AI is not just an application decision. It is also an infrastructure decision. Before organizations rush into AI, they should ask a simple question: Is our network ready to support it? ...

Agentic Operations Begin with Governed Middleware Access

Enterprise AI often lacks governed access to where business processes actually execute. Without that access, AI agents may be able to reason, but they cannot operate reliably across enterprise workflows. For AI agents to effectively carry out workflows, they will require integration-layer context and controls. Organizations can implement these prerequisites by providing AI with managed access to the middleware layer ...