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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

Hot Topics

The Latest

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...

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

Hot Topics

The Latest

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...