Making AIOps a Practical Reality

Daniel Lakier
Anexinet

The goal for every infrastructure team, indeed every IT team, is being able to move from set up automation towards a successful operational automation deployment. Until recently this was almost unattainable. The tools associated with the AIOps tool set provide the catalyst to make this goal a reality.

The consolidated toolchains that AIOps provides give us means to create continuous improvement/continuous delivery cycles that include infrastructure management and application performance management.

AIOps tools bring big data concepts to technology operations. They enable the aggregating, processing, and patterning of data from multiple sources, culminating in data-driven decisions made across millions of data points. AIOps tools are bringing a massive accelerator to the market — a single platform with a hub and spoke design can aggregate and automate development and business outcomes across hybrid IT environments based on live streams of data.

Because IT environments have become increasingly complex with software as a service, platforms as a service, traditional infrastructures, multiple public clouds, micro-services, and IoT all playing a role, technology teams cannot keep up with the pace of managing, documenting, and ensuring compliance in a manual or semi-automated work effort. In a digital world, digital platforms are king and the lifeblood of the business. With AIOps, traditional IT operations can become DevOps-ready focusing on service and site reliability and not constant system-level changes or remediations to maintain green check marks on a dashboard.

The Short List of AIOps benefits are:

■ Reduce event volumes and false positives.

■ Detect anomalous events.

■ Perform root cause analysis using distributed tracing data along with graph analysis for application performance management.

■ Provide real-time data analyses and automation.

■ Increase IT and developer productivity with consolidated DevOps toolchain.

■ Increase operational efficiency by tying allocation triggers to infrastructure outcomes ( operational automation).

There can be no shortcuts on the journey to AIOps. While a company can make their own AIOps toolchain creating data streams, data lakes, big data analytics, and low-code robotic process automation, it might be favorable to find vendors that have developed AIOps domain-centric frameworks and out-of-the-box integrations to alleviate the burden.

Once a vendor is selected, I recommend reviewing the existing toolset and developing a strategy to integrate or consolidate where possible. Focus on business outcomes, faster delivery through automation and how the end state needs to take shape. Underneath the core objectives, focus on areas of pain in the environment today, an inability to scale an application or frequent outages in an application stack.

By focusing on specific issues in defined sprints, your teams will focus on training the AIOps tools with the relevant datasets. It is tempting to pipe every piece of accessible data into a system and expect magical time to value. Success is still a factor of work, knowledge, and time.

The transition to AIOps is extremely difficult. To successfully implement a DevOps mentality and technical capability requires massive culture shifts, change champions, and significant amounts of custom integration and automation between different toolchains. It is not an easy task and frequently fails or is fractionally implemented.

AIOps is an extension or level of increased maturity of DevOps. Companies that have successfully embraced DevOps as an operational paradigm will benefit and have an easier transition to AIOps maturity. Those that have not, will undoubtedly struggle with the traditional DevOps adoption challenges.

In order to have success with an initiative that changes how the entire IT department operates, one needs senior leadership buy in. DevOps is not just a mind set and does require a serious commitment, but the agility it offers is well worth the effort. Building consensus and tying in success milestones to each department can be key. AIOps is a tool for helping a DevOps mind set succeed and should help transform IT departments but only if security and operational stability continue to be a guiding factor.

Companies can sometimes benefit from outside help. Leveraging structured assessments and consulting programs can help mature IT operations models if an organization wants to build them internally. Or they can embrace DevOps and AIOps maturity using a managed services/security managed services provider with mature toolsets and processes.

Daniel Lakier is Network and Security Solution Lead at Anexinet

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? ...

Keys to Building a Partner Ecosystem That Scales

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 ...

Nine in Ten Enterprises Plan Cloud Data Repatriation amid Rising Cloud Costs and Data Sovereignty Mandates

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 ...

Making AIOps a Practical Reality

Daniel Lakier
Anexinet

The goal for every infrastructure team, indeed every IT team, is being able to move from set up automation towards a successful operational automation deployment. Until recently this was almost unattainable. The tools associated with the AIOps tool set provide the catalyst to make this goal a reality.

The consolidated toolchains that AIOps provides give us means to create continuous improvement/continuous delivery cycles that include infrastructure management and application performance management.

AIOps tools bring big data concepts to technology operations. They enable the aggregating, processing, and patterning of data from multiple sources, culminating in data-driven decisions made across millions of data points. AIOps tools are bringing a massive accelerator to the market — a single platform with a hub and spoke design can aggregate and automate development and business outcomes across hybrid IT environments based on live streams of data.

Because IT environments have become increasingly complex with software as a service, platforms as a service, traditional infrastructures, multiple public clouds, micro-services, and IoT all playing a role, technology teams cannot keep up with the pace of managing, documenting, and ensuring compliance in a manual or semi-automated work effort. In a digital world, digital platforms are king and the lifeblood of the business. With AIOps, traditional IT operations can become DevOps-ready focusing on service and site reliability and not constant system-level changes or remediations to maintain green check marks on a dashboard.

The Short List of AIOps benefits are:

■ Reduce event volumes and false positives.

■ Detect anomalous events.

■ Perform root cause analysis using distributed tracing data along with graph analysis for application performance management.

■ Provide real-time data analyses and automation.

■ Increase IT and developer productivity with consolidated DevOps toolchain.

■ Increase operational efficiency by tying allocation triggers to infrastructure outcomes ( operational automation).

There can be no shortcuts on the journey to AIOps. While a company can make their own AIOps toolchain creating data streams, data lakes, big data analytics, and low-code robotic process automation, it might be favorable to find vendors that have developed AIOps domain-centric frameworks and out-of-the-box integrations to alleviate the burden.

Once a vendor is selected, I recommend reviewing the existing toolset and developing a strategy to integrate or consolidate where possible. Focus on business outcomes, faster delivery through automation and how the end state needs to take shape. Underneath the core objectives, focus on areas of pain in the environment today, an inability to scale an application or frequent outages in an application stack.

By focusing on specific issues in defined sprints, your teams will focus on training the AIOps tools with the relevant datasets. It is tempting to pipe every piece of accessible data into a system and expect magical time to value. Success is still a factor of work, knowledge, and time.

The transition to AIOps is extremely difficult. To successfully implement a DevOps mentality and technical capability requires massive culture shifts, change champions, and significant amounts of custom integration and automation between different toolchains. It is not an easy task and frequently fails or is fractionally implemented.

AIOps is an extension or level of increased maturity of DevOps. Companies that have successfully embraced DevOps as an operational paradigm will benefit and have an easier transition to AIOps maturity. Those that have not, will undoubtedly struggle with the traditional DevOps adoption challenges.

In order to have success with an initiative that changes how the entire IT department operates, one needs senior leadership buy in. DevOps is not just a mind set and does require a serious commitment, but the agility it offers is well worth the effort. Building consensus and tying in success milestones to each department can be key. AIOps is a tool for helping a DevOps mind set succeed and should help transform IT departments but only if security and operational stability continue to be a guiding factor.

Companies can sometimes benefit from outside help. Leveraging structured assessments and consulting programs can help mature IT operations models if an organization wants to build them internally. Or they can embrace DevOps and AIOps maturity using a managed services/security managed services provider with mature toolsets and processes.

Daniel Lakier is Network and Security Solution Lead at Anexinet

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? ...

Keys to Building a Partner Ecosystem That Scales

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 ...

Nine in Ten Enterprises Plan Cloud Data Repatriation amid Rising Cloud Costs and Data Sovereignty Mandates

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 ...