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.

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How AI Agents Are Reshaping DataOps for the Always-On Enterprise

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

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For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

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Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

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For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

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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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