A CIO's Guide to AIOps

Andy Thurai
The Field CTO

My recent best practices report on AIOps titled A CIO's Guide to AIOps just got published. What started out as an AIOps use cases document morphed into an AIOps emerging trends document. However, when feedback was sought from CIOs and CTOs in our research panel, they suggested best practices and how to strategize AI and IT Operations in a broader sense to be included in the report.

This 30 page report is now available to all Constellation Research subscribers. The report has five major sections:

1. What is AIOps?

2. Benefits of AIOps for CIO's (or any enterprise)

3. AIOps core use cases

4. Recommendations and best practices for the CIO

5. And, more importantly, what are the gotchas and my final thoughts.

With the COVID-19 pandemic forcing every business to move online, the majority of enterprises have had to accelerate the maturation of their digital operations. Out of business necessity, every surviving enterprise has devised a way out of the crisis by adding people, processes, and technology in an approach that was most cost-effective and yet offered them a quick way to sustain their business through the pandemic. Consequently, IT and digital operations have become an integral part of every enterprise. IT leaders face massive challenges to be efficient because they have either:

■ Added too many tools and have become siloed

■ Increased complexity

■ Collected more data than they can handle

■ Lost knowledgeable IT resources

The time has come for the IT leaders to reimagine their IT and make it more efficient. IT is finally starting to turn the technology it has been proliferating across enterprises on itself. One such solution set is artificial intelligence for IT operations (AIOps). The following report gives leaders an idea of what to look for in an AI solution for properly retooling to mature their digital operations.

Start with the Main Use Cases for AIOps

AIOps is more than just bolting an AI/machine learning (ML) engine on top of some of the existing monitoring, logging, observability, or IT service management tools. Its goal is to provide better collaboration between siloed teams, faster time to identify and resolve incidents (mean time to resolution, or MTTR), and the ability to identify and resolve the root cause of the incident so the issue will not happen again. It is also about more than just operations. AIOps can and should include support, security, development, ITSM, business stakeholders, incident management, and observability.

I have identified about 7 core use cases for AIOps based on my conversation with many practitioners. There are other fringe use cases that sometimes are executed as part of an AIOps project, but for an enterprise to consider a true AIOps solution, it should at least consider the use cases outlined below.

Do AIOps Right

Enterprises can't succeed in a post-pandemic digital world without AIOps (or without mature digital operations), given the volume of IT operations data produced. Start with some of the core use cases and add the rest as you needed. No need to boil the ocean and try to execute all of them from the get-go. To scale on a consistent basis, achieve revenue goals and operational efficiency targets, and meet compliance requirements, enterprises can't succeed without scale in automation and AI.

With the volume of data from IT operations exploding, demand from customers to have five-9s service availability, the technical resource crunch and high prices caused by the Great Resignation wave, the knowledge gap created by tribal knowledge walking out as baby boomers retire, and volume/fatigue/long hours that induce stress and mental health issues for technical teams, enterprises have to make a hard decision: Either continue to run the business as is by throwing more bodies at the problem, or use AI tools to improve the efficiency of the processes.

A properly implemented AIOps solution should find critical incidents as soon as — sometimes even before — they happen, identify the root cause with very minimal manual intervention, and either alert the right personnel at the right time or potentially, via IT automation capabilities, make the application truly self-healing.

If you are a CIO/CTO and struggling with this issue, I would love to talk to you. I would love you to be part of my growing panel of IT executives that I speak to regularly and share notes with. More importantly, let me know if I missed anything in this report so I can do a follow-up report.

Do you have thoughts, suggestions, or opposing views to my assessments?

What are the common pitfalls you see with your customers or your enterprise implementations?

Do you use AIOps for a use case that I haven't covered?

Have you faced an issue while implementing AIOps that is not listed in the report?

Did you derive a benefit that is not listed in the document?

Let me know. Please reach out to me. I look forward to engaging with you.

Andy Thurai is Founder and Principal of The Field CTO

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A CIO's Guide to AIOps

Andy Thurai
The Field CTO

My recent best practices report on AIOps titled A CIO's Guide to AIOps just got published. What started out as an AIOps use cases document morphed into an AIOps emerging trends document. However, when feedback was sought from CIOs and CTOs in our research panel, they suggested best practices and how to strategize AI and IT Operations in a broader sense to be included in the report.

This 30 page report is now available to all Constellation Research subscribers. The report has five major sections:

1. What is AIOps?

2. Benefits of AIOps for CIO's (or any enterprise)

3. AIOps core use cases

4. Recommendations and best practices for the CIO

5. And, more importantly, what are the gotchas and my final thoughts.

With the COVID-19 pandemic forcing every business to move online, the majority of enterprises have had to accelerate the maturation of their digital operations. Out of business necessity, every surviving enterprise has devised a way out of the crisis by adding people, processes, and technology in an approach that was most cost-effective and yet offered them a quick way to sustain their business through the pandemic. Consequently, IT and digital operations have become an integral part of every enterprise. IT leaders face massive challenges to be efficient because they have either:

■ Added too many tools and have become siloed

■ Increased complexity

■ Collected more data than they can handle

■ Lost knowledgeable IT resources

The time has come for the IT leaders to reimagine their IT and make it more efficient. IT is finally starting to turn the technology it has been proliferating across enterprises on itself. One such solution set is artificial intelligence for IT operations (AIOps). The following report gives leaders an idea of what to look for in an AI solution for properly retooling to mature their digital operations.

Start with the Main Use Cases for AIOps

AIOps is more than just bolting an AI/machine learning (ML) engine on top of some of the existing monitoring, logging, observability, or IT service management tools. Its goal is to provide better collaboration between siloed teams, faster time to identify and resolve incidents (mean time to resolution, or MTTR), and the ability to identify and resolve the root cause of the incident so the issue will not happen again. It is also about more than just operations. AIOps can and should include support, security, development, ITSM, business stakeholders, incident management, and observability.

I have identified about 7 core use cases for AIOps based on my conversation with many practitioners. There are other fringe use cases that sometimes are executed as part of an AIOps project, but for an enterprise to consider a true AIOps solution, it should at least consider the use cases outlined below.

Do AIOps Right

Enterprises can't succeed in a post-pandemic digital world without AIOps (or without mature digital operations), given the volume of IT operations data produced. Start with some of the core use cases and add the rest as you needed. No need to boil the ocean and try to execute all of them from the get-go. To scale on a consistent basis, achieve revenue goals and operational efficiency targets, and meet compliance requirements, enterprises can't succeed without scale in automation and AI.

With the volume of data from IT operations exploding, demand from customers to have five-9s service availability, the technical resource crunch and high prices caused by the Great Resignation wave, the knowledge gap created by tribal knowledge walking out as baby boomers retire, and volume/fatigue/long hours that induce stress and mental health issues for technical teams, enterprises have to make a hard decision: Either continue to run the business as is by throwing more bodies at the problem, or use AI tools to improve the efficiency of the processes.

A properly implemented AIOps solution should find critical incidents as soon as — sometimes even before — they happen, identify the root cause with very minimal manual intervention, and either alert the right personnel at the right time or potentially, via IT automation capabilities, make the application truly self-healing.

If you are a CIO/CTO and struggling with this issue, I would love to talk to you. I would love you to be part of my growing panel of IT executives that I speak to regularly and share notes with. More importantly, let me know if I missed anything in this report so I can do a follow-up report.

Do you have thoughts, suggestions, or opposing views to my assessments?

What are the common pitfalls you see with your customers or your enterprise implementations?

Do you use AIOps for a use case that I haven't covered?

Have you faced an issue while implementing AIOps that is not listed in the report?

Did you derive a benefit that is not listed in the document?

Let me know. Please reach out to me. I look forward to engaging with you.

Andy Thurai is Founder and Principal of The Field CTO

Related Links

Hot Topics

The Latest

Observability Is the New Control Plane for Enterprise Transformation

The enterprises that will define the next decade are not the ones that deployed the most technology. They are the ones who understood what their technology was actually doing. That distinction is not a philosophical point. It is the central operational challenge facing every organization that has spent the last five years modernizing at speed ...

When AI Becomes the Corporate OS

AI is becoming the operating system of the enterprise. It acts as an invisible coordination layer that understands intent, connects systems, and executes work across complex SaaS environments. Previously, employees had to click through multiple systems — CRM, ERP, support tools, collaboration platforms — to complete a single task. Now, instead of navigating each application manually, they can simply state what they need to accomplish ...

The $600 Billion Wake Up Call

In 2026, the cost of downtime or an outage is no longer just a technical inconvenience; it's a $600 billion wake up call for global businesses. As our digital ecosystems become  more interconnected, each touchpoint introduces new risks and multiplies the consequences when things go wrong. And the data is clear: aggregate downtime costs  for Global 2,000 companies have surged 50% since 2024, reaching a staggering $600 billion ...

Breaking Down Agentic AI Fragmentation and Complexity with Governance

Deloitte found that 74% of enterprises expect to deploy agentic AI solutions in the next 24 months. However, the rush to deployment is outpacing foundational work, though. Only 21% of enterprises have fully formed agent governance models in place. The result? AI agents deployed without guidance or governance begin to function as fragmented islands of complexity ...

Cloud Spend Is Rising and Cloud Optimization Is Key to Funding AI and Protecting Margins

Cloud spending is no longer viewed as a passthrough IT expense, but as a strategic financial lever that directly impacts innovation capacity, profitability and enterprise resilience, according to the CFO Cloud Cost Optimization Report from Azul ...

Building AI Agents That Build Trust

As AI moves from generating responses to performing actions, the need for trust increases exponentially. And as organizations enlist AI agents for increasingly sophisticated business processes, trust is going to be the single most important theme for spurring adoption. What can organizations do to build trustworthy AI agents? ...

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I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

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 ...

AI Deepfakes: Rethinking Trust in the Workplace

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 ...