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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.

The Latest

Artificial intelligence (AI) is core to observability practices, with some 41% of respondents reporting AI adoption as a core driver of observability, according to the State of Observability for Financial Services and Insurance report from New Relic ...

Application performance monitoring (APM) is a game of catching up — building dashboards, setting thresholds, tuning alerts, and manually correlating metrics to root causes. In the early days, this straightforward model worked as applications were simpler, stacks more predictable, and telemetry was manageable. Today, the landscape has shifted, and more assertive tools are needed ...

Cloud adoption has accelerated, but backup strategies haven't always kept pace. Many organizations continue to rely on backup strategies that were either lifted directly from on-prem environments or use cloud-native tools in limited, DR-focused ways ... Eon uncovered a handful of critical gaps regarding how organizations approach cloud backup. To capture these prevailing winds, we gathered insights from 150+ IT and cloud leaders at the recent Google Cloud Next conference, which we've compiled into the 2025 State of Cloud Data Backup ...

Private clouds are no longer playing catch-up, and public clouds are no longer the default as organizations recalibrate their cloud strategies, according to the Private Cloud Outlook 2025 report from Broadcom. More than half (53%) of survey respondents say private cloud is their top priority for deploying new workloads over the next three years, while 69% are considering workload repatriation from public to private cloud, with one-third having already done so ...

As organizations chase productivity gains from generative AI, teams are overwhelmingly focused on improving delivery speed (45%) over enhancing software quality (13%), according to the Quality Transformation Report from Tricentis ...

Back in March of this year ... MongoDB's stock price took a serious tumble ... In my opinion, it reflects a deeper structural issue in enterprise software economics altogether — vendor lock-in ...

In MEAN TIME TO INSIGHT Episode 15, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses Do-It-Yourself Network Automation ... 

Zero-day vulnerabilities — security flaws that are exploited before developers even know they exist — pose one of the greatest risks to modern organizations. Recently, such vulnerabilities have been discovered in well-known VPN systems like Ivanti and Fortinet, highlighting just how outdated these legacy technologies have become in defending against fast-evolving cyber threats ... To protect digital assets and remote workers in today's environment, companies need more than patchwork solutions. They need architecture that is secure by design ...

Traditional observability requires users to leap across different platforms or tools for metrics, logs, or traces and related issues manually, which is very time-consuming, so as to reasonably ascertain the root cause. Observability 2.0 fixes this by unifying all telemetry data, logs, metrics, and traces into a single, context-rich pipeline that flows into one smart platform. But this is far from just having a bunch of additional data; this data is actionable, predictive, and tied to revenue realization ...

64% of enterprise networking teams use internally developed software or scripts for network automation, but 61% of those teams spend six or more hours per week debugging and maintaining them, according to From Scripts to Platforms: Why Homegrown Tools Dominate Network Automation and How Vendors Can Help, my latest EMA report ...

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.

The Latest

Artificial intelligence (AI) is core to observability practices, with some 41% of respondents reporting AI adoption as a core driver of observability, according to the State of Observability for Financial Services and Insurance report from New Relic ...

Application performance monitoring (APM) is a game of catching up — building dashboards, setting thresholds, tuning alerts, and manually correlating metrics to root causes. In the early days, this straightforward model worked as applications were simpler, stacks more predictable, and telemetry was manageable. Today, the landscape has shifted, and more assertive tools are needed ...

Cloud adoption has accelerated, but backup strategies haven't always kept pace. Many organizations continue to rely on backup strategies that were either lifted directly from on-prem environments or use cloud-native tools in limited, DR-focused ways ... Eon uncovered a handful of critical gaps regarding how organizations approach cloud backup. To capture these prevailing winds, we gathered insights from 150+ IT and cloud leaders at the recent Google Cloud Next conference, which we've compiled into the 2025 State of Cloud Data Backup ...

Private clouds are no longer playing catch-up, and public clouds are no longer the default as organizations recalibrate their cloud strategies, according to the Private Cloud Outlook 2025 report from Broadcom. More than half (53%) of survey respondents say private cloud is their top priority for deploying new workloads over the next three years, while 69% are considering workload repatriation from public to private cloud, with one-third having already done so ...

As organizations chase productivity gains from generative AI, teams are overwhelmingly focused on improving delivery speed (45%) over enhancing software quality (13%), according to the Quality Transformation Report from Tricentis ...

Back in March of this year ... MongoDB's stock price took a serious tumble ... In my opinion, it reflects a deeper structural issue in enterprise software economics altogether — vendor lock-in ...

In MEAN TIME TO INSIGHT Episode 15, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses Do-It-Yourself Network Automation ... 

Zero-day vulnerabilities — security flaws that are exploited before developers even know they exist — pose one of the greatest risks to modern organizations. Recently, such vulnerabilities have been discovered in well-known VPN systems like Ivanti and Fortinet, highlighting just how outdated these legacy technologies have become in defending against fast-evolving cyber threats ... To protect digital assets and remote workers in today's environment, companies need more than patchwork solutions. They need architecture that is secure by design ...

Traditional observability requires users to leap across different platforms or tools for metrics, logs, or traces and related issues manually, which is very time-consuming, so as to reasonably ascertain the root cause. Observability 2.0 fixes this by unifying all telemetry data, logs, metrics, and traces into a single, context-rich pipeline that flows into one smart platform. But this is far from just having a bunch of additional data; this data is actionable, predictive, and tied to revenue realization ...

64% of enterprise networking teams use internally developed software or scripts for network automation, but 61% of those teams spend six or more hours per week debugging and maintaining them, according to From Scripts to Platforms: Why Homegrown Tools Dominate Network Automation and How Vendors Can Help, my latest EMA report ...