Discovering AIOps - Part 10: Expert Tips

Pete Goldin
APMdigest

In Part 10, the final installment of this blog series, the experts offer tips on how to get started and how to succeed using AIOps:

Start with: Discovering AIOps - Part 1

Start with: Discovering AIOps - Part 2: Must-Have Capabilities

Start with: Discovering AIOps - Part 3: The Users

Start with: Discovering AIOps - Part 4: Advantages

Start with: Discovering AIOps - Part 5: More Advantages

Start with: Discovering AIOps - Part 6: Challenges

Start with: Discovering AIOps - Part 7: The Current State of AIOps

Start with: Discovering AIOps - Part 8: The Future of AIOps

Start with: Discovering AIOps - Part 9: Auto-Remediation

Define Your Needs and Goals

"Before investing, you should define your needs and priorities both within and across silos. This ideally requires a team with executive support, as your AIOps investment will have process as well as technology implications. EMA recommends dialog to document requirements and priorities across different stakeholders, including cost and deployment concerns. Once you've assimilated these inputs and reconciled them, you are ready to begin shopping," advises Dennis Drogseth, VP at Enterprise Management Associates (EMA).

Charles Burnham, Director, AIOps Engineering at LogicMonitor says, "My biggest piece of advice for getting started with AIOps is for each company to focus on the individual problems they're trying to solve with these tools — i.e what they are trying to automate. Are they dealing with consistent outages and don't know why? Are they funneling through a lot of noise and it's taking them far too long to track down the root cause? They need to be purposeful about the issues at hand and the tech they're looking at in order to remediate these problems because AIOps capabilities are designed to automate and improve very specific aspects of the IT Operations process."

"Hunt down the platforms that offer the level and type of AI that is best suited to your organization's needs; it's that simple. Don't get caught up in some advanced bells and whistles that won't move the needle for your team today. Map the capabilities that you need directly to the pain points of your teams and existing environments," Asaf Yigal, CTO of Logz.io, recommends.

"Like anything else, it's about ensuring that you have the right platform to address your specific environment, use cases, skills, and budget. Buying something fancy that no one can truly wrap their heads around or benefit from is not going to bring you much value," Yigal adds.

Prepare for a Cultural Shift

"Prepare your teams for the cultural shift that AIOps might bring. Encourage openness to new processes and technologies and address any concerns proactively," Monika Bhave, Product Manager at Digitate.

Investigate the Vendors

"IT leaders should be interrogating their incumbent vendors about their AIOps roadmaps. At least 50% of them will have a good answer. They should read and study this subject. Learn terminology and concepts enough to understand what's real and what's marketing hype," says Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at Enterprise Management Associates (EMA).

"Also, when vendors start talking about their AIOPs goodness, ask for a proof-of-concept deployment. Make them prove themselves in your environment," he adds.

"The user must ask their AIOps provider questions about how their platform operates and differentiates from other providers," advises Thomas LaRock, Principal Developer Evangelist at Selector. "If they can't offer more details than using buzzwords, chances are they aren't as robust a platform as you want or need. If they say, 'We use AI and ML,' then you want to press them and ask about specific models and techniques. You will be surprised as to how many vendors promising AIOps are offering nothing more than rules-based recommendations, the equivalence of an IF THEN ELSE statement."

"Beware of any vendor who promises you nirvana through the use of their platform," LaRock continues "Every company is different and has different needs. An AIOps platform that worked well for a colleague at a different company may not be the right choice for your company at this point in time."

Bhave from Digitate recommends that you should evaluate different solutions based on their features, scalability, extensibility, maintainability, ease of integration, and end-to-end play.

Start with the Data

"We think that taking the approach of using AI to inform the type and volume of data that you are sending to the platform, and subsequently paying for, is a great example of a sensible starting point. This way you are already making life easier by reducing complexity and cost through AI-backed data optimization. This allows you to start from a place that is already better informed and simpler, based on the ability to reduce the data the system is now being asked to analyze," says Yigal from Logz.io.

Bhave from Digitate says to make sure that your data is accurate, consistent, and comprehensive. Cross stack data availability is needed for useful insights.

Encompass the Entire Data Processing Chain

"AIOps should encompass all four stages of data processing — Collect, Aggregate, Analyze and Execute — in a single platform approach. Only an approach that encompasses the entire data processing chain using causal AI and continuous automation can keep pace with the volume, velocity, and complexity of distributed microservices architectures," says Bob Wambach, VP of Product Marketing at Dynatrace.

Establish KPIs

"Define Key Performance Indicators (KPIs): Establish clear KPIs to measure the success of your AI Ops implementation. This could include metrics like MTTR reduction, alert noise reduction, and increased availability," says Monika Bhave from Digitate.

Baseline Key Metrics

"Baseline key business metrics early in the process. Correlation is not causation, but in a year of focused work, if your team has reduced downtime, increased availability, improved break/fix work for faster delivery of features and reduction of tech debt and CSAT/NPS has improved, that's a great place to point to how you are delivering value above and beyond MTTR," says Heath Newburn, Distinguished Field Engineer at PagerDuty.

Use Out of the box models

"The reality is that it has never been easier for an organization to get started with AIOps. The key is to have a focused approach to implementing an AIOps solution that leverages out-of-the-box machine learning models and begin where the impact is the greatest. With pre-trained machine learning models, there is no downtime while you wait for your solution to absorb data and learn. AIOps can begin making recommendations and resolving issues right off the jump, accelerating time to value for organizations. And finally, taking that intelligence and automating resolutions without having to build yet more customization and integrations," Brian Emerson, VP & GM, IT Operations Management at ServiceNow

Focus on Productivity

"Let machines do what machines are good at and people do what people are good at. Design your system not for the smartest/most experienced people on your team, but for your newest members. How can AIOps become the context engine to make them more effective quickly? Focus on removing noise, creating context and removing toil," says Newburn from PagerDuty.

Start Small

"As for getting started, it's just a matter of making the decision to move forward and taking the first step. Focus on a small subset of the environment that might be easily isolated in terms of discovery and management. This lets everyone get a good grasp of what's possible before going across the enterprise and exposing all the information to everyone in operations," Carlos Casanova, Principal Analyst at Forrester Research, advises.

Begin with a pilot project or a specific use case to test and validate AI Ops capabilities, Bhave from Digitate advises. This allows you to gain insights, gather feedback, and demonstrate value before scaling up.

"Pick something in your environment, perhaps network monitoring, and run a POC with some AIOps tools to see how they compare and contrast. As you expand your AIOps footprint, you'll have the experience necessary to ask more informed questions of an AIOps provider, essentially building towards an AIOps platform that is right for your enterprise," says Thomas LaRock, Principal Developer Evangelist at Selector.

Bhave from Digitate also recommends gradual implementation. Roll out AIOps gradually, considering the impact on existing processes and workflows. Aim for a smooth transition to minimize disruption.

Integrate with Every Layer of Your Stack

"AIOps is increasingly effective with additional data — plan to integrate your AIOps product with every layer of your stack, make sure it supports future platform investments, and consolidate events from across your enterprise's various tools so that insights are not limited to only part of your organization," Michael Gerstenhaber, VP of Product Management at Datadog.

Link ITSM and ITOM

Another best practice is to ensure your organization is using a solution that natively integrates AIOps within IT Operations, as well as ensuring that both IT Service Management and IT Operations Management are unified on a single platform, according to Emerson from ServiceNow.

The convergence of ITSM and ITOM is quickly becoming a necessity for modern organizations to ensure they are meeting the delivery and agility demands of our new world of work. By linking ITSM and ITOM on a single platform, both solutions can leverage shared data, allowing teams to resolve incidents faster, predict them before they occur, and ensure great IT experiences for all their users.

Emerson says this also sets up a key foundation for AIOps, which is only successful if it has access to historical incidents and change data. Both ITSM and ITOM help gather and process this data, so by linking the two and having everything on one unified platform, it ensures that AIOps will have consistent and easy access to the data it needs to succeed.

Foster Cross-Functional Collaboration

"Foster collaboration between IT and business stakeholders. AIOps implementation should be a cross-functional effort to ensure alignment with business goals," says Bhave from Digitate.

Invest in Training

Bhave from Digitate also recommends training and skill development. Invest in training your IT teams on the AIOps platform. Developing in-house expertise is crucial for successful implementation and ongoing management.

Get Feedback

"AIOps is an ongoing journey. Regularly assess its performance, gather feedback, and iterate on your implementation to optimize results. The AIOps platform gets better with SME feedback," says Bhave from Digitate.

Keep Stakeholders Informed

"Keep stakeholders informed about progress, milestones, and results. Transparency builds trust and support for your AI Ops initiative," Bhave concludes.

Pete Goldin is Editor and Publisher of APMdigest

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Discovering AIOps - Part 10: Expert Tips

Pete Goldin
APMdigest

In Part 10, the final installment of this blog series, the experts offer tips on how to get started and how to succeed using AIOps:

Start with: Discovering AIOps - Part 1

Start with: Discovering AIOps - Part 2: Must-Have Capabilities

Start with: Discovering AIOps - Part 3: The Users

Start with: Discovering AIOps - Part 4: Advantages

Start with: Discovering AIOps - Part 5: More Advantages

Start with: Discovering AIOps - Part 6: Challenges

Start with: Discovering AIOps - Part 7: The Current State of AIOps

Start with: Discovering AIOps - Part 8: The Future of AIOps

Start with: Discovering AIOps - Part 9: Auto-Remediation

Define Your Needs and Goals

"Before investing, you should define your needs and priorities both within and across silos. This ideally requires a team with executive support, as your AIOps investment will have process as well as technology implications. EMA recommends dialog to document requirements and priorities across different stakeholders, including cost and deployment concerns. Once you've assimilated these inputs and reconciled them, you are ready to begin shopping," advises Dennis Drogseth, VP at Enterprise Management Associates (EMA).

Charles Burnham, Director, AIOps Engineering at LogicMonitor says, "My biggest piece of advice for getting started with AIOps is for each company to focus on the individual problems they're trying to solve with these tools — i.e what they are trying to automate. Are they dealing with consistent outages and don't know why? Are they funneling through a lot of noise and it's taking them far too long to track down the root cause? They need to be purposeful about the issues at hand and the tech they're looking at in order to remediate these problems because AIOps capabilities are designed to automate and improve very specific aspects of the IT Operations process."

"Hunt down the platforms that offer the level and type of AI that is best suited to your organization's needs; it's that simple. Don't get caught up in some advanced bells and whistles that won't move the needle for your team today. Map the capabilities that you need directly to the pain points of your teams and existing environments," Asaf Yigal, CTO of Logz.io, recommends.

"Like anything else, it's about ensuring that you have the right platform to address your specific environment, use cases, skills, and budget. Buying something fancy that no one can truly wrap their heads around or benefit from is not going to bring you much value," Yigal adds.

Prepare for a Cultural Shift

"Prepare your teams for the cultural shift that AIOps might bring. Encourage openness to new processes and technologies and address any concerns proactively," Monika Bhave, Product Manager at Digitate.

Investigate the Vendors

"IT leaders should be interrogating their incumbent vendors about their AIOps roadmaps. At least 50% of them will have a good answer. They should read and study this subject. Learn terminology and concepts enough to understand what's real and what's marketing hype," says Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at Enterprise Management Associates (EMA).

"Also, when vendors start talking about their AIOPs goodness, ask for a proof-of-concept deployment. Make them prove themselves in your environment," he adds.

"The user must ask their AIOps provider questions about how their platform operates and differentiates from other providers," advises Thomas LaRock, Principal Developer Evangelist at Selector. "If they can't offer more details than using buzzwords, chances are they aren't as robust a platform as you want or need. If they say, 'We use AI and ML,' then you want to press them and ask about specific models and techniques. You will be surprised as to how many vendors promising AIOps are offering nothing more than rules-based recommendations, the equivalence of an IF THEN ELSE statement."

"Beware of any vendor who promises you nirvana through the use of their platform," LaRock continues "Every company is different and has different needs. An AIOps platform that worked well for a colleague at a different company may not be the right choice for your company at this point in time."

Bhave from Digitate recommends that you should evaluate different solutions based on their features, scalability, extensibility, maintainability, ease of integration, and end-to-end play.

Start with the Data

"We think that taking the approach of using AI to inform the type and volume of data that you are sending to the platform, and subsequently paying for, is a great example of a sensible starting point. This way you are already making life easier by reducing complexity and cost through AI-backed data optimization. This allows you to start from a place that is already better informed and simpler, based on the ability to reduce the data the system is now being asked to analyze," says Yigal from Logz.io.

Bhave from Digitate says to make sure that your data is accurate, consistent, and comprehensive. Cross stack data availability is needed for useful insights.

Encompass the Entire Data Processing Chain

"AIOps should encompass all four stages of data processing — Collect, Aggregate, Analyze and Execute — in a single platform approach. Only an approach that encompasses the entire data processing chain using causal AI and continuous automation can keep pace with the volume, velocity, and complexity of distributed microservices architectures," says Bob Wambach, VP of Product Marketing at Dynatrace.

Establish KPIs

"Define Key Performance Indicators (KPIs): Establish clear KPIs to measure the success of your AI Ops implementation. This could include metrics like MTTR reduction, alert noise reduction, and increased availability," says Monika Bhave from Digitate.

Baseline Key Metrics

"Baseline key business metrics early in the process. Correlation is not causation, but in a year of focused work, if your team has reduced downtime, increased availability, improved break/fix work for faster delivery of features and reduction of tech debt and CSAT/NPS has improved, that's a great place to point to how you are delivering value above and beyond MTTR," says Heath Newburn, Distinguished Field Engineer at PagerDuty.

Use Out of the box models

"The reality is that it has never been easier for an organization to get started with AIOps. The key is to have a focused approach to implementing an AIOps solution that leverages out-of-the-box machine learning models and begin where the impact is the greatest. With pre-trained machine learning models, there is no downtime while you wait for your solution to absorb data and learn. AIOps can begin making recommendations and resolving issues right off the jump, accelerating time to value for organizations. And finally, taking that intelligence and automating resolutions without having to build yet more customization and integrations," Brian Emerson, VP & GM, IT Operations Management at ServiceNow

Focus on Productivity

"Let machines do what machines are good at and people do what people are good at. Design your system not for the smartest/most experienced people on your team, but for your newest members. How can AIOps become the context engine to make them more effective quickly? Focus on removing noise, creating context and removing toil," says Newburn from PagerDuty.

Start Small

"As for getting started, it's just a matter of making the decision to move forward and taking the first step. Focus on a small subset of the environment that might be easily isolated in terms of discovery and management. This lets everyone get a good grasp of what's possible before going across the enterprise and exposing all the information to everyone in operations," Carlos Casanova, Principal Analyst at Forrester Research, advises.

Begin with a pilot project or a specific use case to test and validate AI Ops capabilities, Bhave from Digitate advises. This allows you to gain insights, gather feedback, and demonstrate value before scaling up.

"Pick something in your environment, perhaps network monitoring, and run a POC with some AIOps tools to see how they compare and contrast. As you expand your AIOps footprint, you'll have the experience necessary to ask more informed questions of an AIOps provider, essentially building towards an AIOps platform that is right for your enterprise," says Thomas LaRock, Principal Developer Evangelist at Selector.

Bhave from Digitate also recommends gradual implementation. Roll out AIOps gradually, considering the impact on existing processes and workflows. Aim for a smooth transition to minimize disruption.

Integrate with Every Layer of Your Stack

"AIOps is increasingly effective with additional data — plan to integrate your AIOps product with every layer of your stack, make sure it supports future platform investments, and consolidate events from across your enterprise's various tools so that insights are not limited to only part of your organization," Michael Gerstenhaber, VP of Product Management at Datadog.

Link ITSM and ITOM

Another best practice is to ensure your organization is using a solution that natively integrates AIOps within IT Operations, as well as ensuring that both IT Service Management and IT Operations Management are unified on a single platform, according to Emerson from ServiceNow.

The convergence of ITSM and ITOM is quickly becoming a necessity for modern organizations to ensure they are meeting the delivery and agility demands of our new world of work. By linking ITSM and ITOM on a single platform, both solutions can leverage shared data, allowing teams to resolve incidents faster, predict them before they occur, and ensure great IT experiences for all their users.

Emerson says this also sets up a key foundation for AIOps, which is only successful if it has access to historical incidents and change data. Both ITSM and ITOM help gather and process this data, so by linking the two and having everything on one unified platform, it ensures that AIOps will have consistent and easy access to the data it needs to succeed.

Foster Cross-Functional Collaboration

"Foster collaboration between IT and business stakeholders. AIOps implementation should be a cross-functional effort to ensure alignment with business goals," says Bhave from Digitate.

Invest in Training

Bhave from Digitate also recommends training and skill development. Invest in training your IT teams on the AIOps platform. Developing in-house expertise is crucial for successful implementation and ongoing management.

Get Feedback

"AIOps is an ongoing journey. Regularly assess its performance, gather feedback, and iterate on your implementation to optimize results. The AIOps platform gets better with SME feedback," says Bhave from Digitate.

Keep Stakeholders Informed

"Keep stakeholders informed about progress, milestones, and results. Transparency builds trust and support for your AI Ops initiative," Bhave concludes.

Pete Goldin is Editor and Publisher of APMdigest

Related Links

Hot Topics

The Latest

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 ...

The Network Data Problem Slowing Down Enterprise AIOps

Enterprise networks rarely behave the same way for very long. A routing adjustment in one region may unexpectedly alter application performance in another. A cloud migration may introduce hidden dependencies that go unnoticed until an outage occurs. All the while, the network is managed by several different teams, each of whom use different tool sets — and as a result, have different views of the network ... There’s usually an engineer who remembers why traffic fails over a certain way between sites, or which transparent firewall was added where. The problem is that human memory cannot scale alongside enterprise-scale networks ...

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Ask an infrastructure team how confident they are in their ability to govern AI, and most will tell you they've got it handled. A recent survey of 406 IT decision-makers and platform engineering leaders found 86% expressing exactly that confidence. Ask the same group whether they have a formal written AI governance policy, and the number drops to 30%, according to Spacelift's Infrastructure Automation Report ...

MEAN TIME TO INSIGHT Podcast - Episode 27: Observability Unification

In MEAN TIME TO INSIGHT Episode 27, Shamus McGillicuddy, EMA VP of Research, Network Infrastructure and Operations, and Parker Hathcock, EMA Research Director covering IT Service/Operations (ServiceOps), discuss observability unification in modern IT operations ...