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.

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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

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Escaping Pilot Purgatory: How AI Becomes an Operational Advantage

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Closing the Gap in Modern Tech and the Tools Meant to Monitor Them

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

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Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Clearing the Path to AI: Why Vendor Consolidation Matters Now

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

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The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

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In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI Scale Is Outpacing Infrastructure - and IT Leaders Are Running Out of Time

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.