Bringing IBM i Into the Modern Observability Stack: The Missing Piece of Enterprise AIOps

Slawomir Michalik
Omnilogy

In the ever-evolving digital landscape, enterprises are increasingly focused on enhancing their observability stacks to gain deeper insights into their IT environments. Observability has become a cornerstone of modern IT operations, enabling organizations to monitor, diagnose, and optimize their systems with unprecedented precision. However, a critical piece of the puzzle often goes unnoticed in this transformation: IBM i.

The Legacy and Importance of IBM i

IBM i, formerly known as AS/400, may be considered a legacy system, but it is far from obsolete. It powers a significant portion of the world's critical business applications, especially in industries such as banking, manufacturing, and retail. IBM i systems are renowned for their robustness, reliability, and security. They manage enormous amounts of business logic that are fundamental to enterprise operations. Despite their importance, these systems remain largely invisible to modern observability platforms.

In many organizations, IBM i operates as the "dark matter" of enterprise infrastructure. It is vital yet often hidden from the spotlight, running seamlessly behind the scenes. This invisibility poses a challenge for CIOs and IT leaders aiming to create a comprehensive observability stack, as the lack of visibility into IBM i systems can lead to blind spots in monitoring and diagnostics.

The Gap in Modern Observability Platforms

Modern observability platforms are designed to provide granular insights into cloud-native architectures. They excel at monitoring microservices, containers, and distributed systems. However, they often fall short when it comes to traditional systems like IBM i. This gap can have significant implications for enterprises that rely on these systems for mission-critical operations.

Without integration into the observability stack, IBM i systems can become points of failure that are difficult to detect and diagnose. When issues arise, the delay in identifying and resolving them can lead to costly downtime and impact business operations. As enterprises transition to AI-driven operations (AIOps), the need for comprehensive visibility across all systems becomes even more pronounced.

Bridging the Gap: Integrating IBM i into the Observability Stack

To address this challenge, organizations must consider integrating IBM i into their observability ecosystems. This integration requires a thoughtful approach and the deployment of specialized tools that can capture telemetry data from IBM i systems and feed it into modern observability platforms.

Integrating IBM i into observability platforms can be achieved through middleware solutions, which act as bridges between IBM i and observability platforms. These solutions convert signals from IBM i systems into logs, metrics, and traces understandable by observability platforms. APIs and connectors offered by many modern observability tools make it possible to incorporate critical metrics from IBM i in the overall observability stack. Additionally, partnering with specialized vendors is an effective strategy. These vendors specialize in IBM i integration and offer tailored solutions to enhance visibility, providing tools and services specifically designed to monitor IBM i systems and integrate them seamlessly into existing observability stacks.

Benefits of a Unified Observability Strategy

By bringing IBM i into the fold of the observability stack, CIOs and IT leaders can unlock several benefits:

  • Holistic Visibility: Integrating IBM i with modern observability platforms provides a unified view of the entire IT environment, reducing blind spots and enabling comprehensive monitoring.
  • Proactive Issue Resolution: Enhanced visibility allows organizations to detect anomalies early, leading to quicker diagnostics and proactive issue resolution, minimizing downtime and its associated costs.
  • Improved AIOps Capabilities: A unified observability strategy enhances the effectiveness of AIOps by providing the necessary data to drive intelligent automation and decision-making.
  • Operational Efficiency: With a complete picture of the IT landscape, organizations can optimize performance, allocate resources more effectively, and improve overall operational efficiency.

As enterprises continue to modernize their IT operations, the integration of IBM i into the observability stack is no longer optional — it is essential. Recognizing IBM i as a vital component of the enterprise infrastructure and making it visible within modern observability platforms will empower organizations to achieve a truly comprehensive and effective AIOps strategy.

For CIOs and IT leaders, the path forward involves bridging the gap between legacy systems and modern observability tools, ensuring that IBM i is no longer the "dark matter" but a visible and manageable part of the IT ecosystem. By doing so, enterprises can harness the full potential of their technology investments, driving innovation and maintaining their competitive edge in an increasingly digital world.

Slawomir Michalik is VP at Omnilogy

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Bringing IBM i Into the Modern Observability Stack: The Missing Piece of Enterprise AIOps

Slawomir Michalik
Omnilogy

In the ever-evolving digital landscape, enterprises are increasingly focused on enhancing their observability stacks to gain deeper insights into their IT environments. Observability has become a cornerstone of modern IT operations, enabling organizations to monitor, diagnose, and optimize their systems with unprecedented precision. However, a critical piece of the puzzle often goes unnoticed in this transformation: IBM i.

The Legacy and Importance of IBM i

IBM i, formerly known as AS/400, may be considered a legacy system, but it is far from obsolete. It powers a significant portion of the world's critical business applications, especially in industries such as banking, manufacturing, and retail. IBM i systems are renowned for their robustness, reliability, and security. They manage enormous amounts of business logic that are fundamental to enterprise operations. Despite their importance, these systems remain largely invisible to modern observability platforms.

In many organizations, IBM i operates as the "dark matter" of enterprise infrastructure. It is vital yet often hidden from the spotlight, running seamlessly behind the scenes. This invisibility poses a challenge for CIOs and IT leaders aiming to create a comprehensive observability stack, as the lack of visibility into IBM i systems can lead to blind spots in monitoring and diagnostics.

The Gap in Modern Observability Platforms

Modern observability platforms are designed to provide granular insights into cloud-native architectures. They excel at monitoring microservices, containers, and distributed systems. However, they often fall short when it comes to traditional systems like IBM i. This gap can have significant implications for enterprises that rely on these systems for mission-critical operations.

Without integration into the observability stack, IBM i systems can become points of failure that are difficult to detect and diagnose. When issues arise, the delay in identifying and resolving them can lead to costly downtime and impact business operations. As enterprises transition to AI-driven operations (AIOps), the need for comprehensive visibility across all systems becomes even more pronounced.

Bridging the Gap: Integrating IBM i into the Observability Stack

To address this challenge, organizations must consider integrating IBM i into their observability ecosystems. This integration requires a thoughtful approach and the deployment of specialized tools that can capture telemetry data from IBM i systems and feed it into modern observability platforms.

Integrating IBM i into observability platforms can be achieved through middleware solutions, which act as bridges between IBM i and observability platforms. These solutions convert signals from IBM i systems into logs, metrics, and traces understandable by observability platforms. APIs and connectors offered by many modern observability tools make it possible to incorporate critical metrics from IBM i in the overall observability stack. Additionally, partnering with specialized vendors is an effective strategy. These vendors specialize in IBM i integration and offer tailored solutions to enhance visibility, providing tools and services specifically designed to monitor IBM i systems and integrate them seamlessly into existing observability stacks.

Benefits of a Unified Observability Strategy

By bringing IBM i into the fold of the observability stack, CIOs and IT leaders can unlock several benefits:

  • Holistic Visibility: Integrating IBM i with modern observability platforms provides a unified view of the entire IT environment, reducing blind spots and enabling comprehensive monitoring.
  • Proactive Issue Resolution: Enhanced visibility allows organizations to detect anomalies early, leading to quicker diagnostics and proactive issue resolution, minimizing downtime and its associated costs.
  • Improved AIOps Capabilities: A unified observability strategy enhances the effectiveness of AIOps by providing the necessary data to drive intelligent automation and decision-making.
  • Operational Efficiency: With a complete picture of the IT landscape, organizations can optimize performance, allocate resources more effectively, and improve overall operational efficiency.

As enterprises continue to modernize their IT operations, the integration of IBM i into the observability stack is no longer optional — it is essential. Recognizing IBM i as a vital component of the enterprise infrastructure and making it visible within modern observability platforms will empower organizations to achieve a truly comprehensive and effective AIOps strategy.

For CIOs and IT leaders, the path forward involves bridging the gap between legacy systems and modern observability tools, ensuring that IBM i is no longer the "dark matter" but a visible and manageable part of the IT ecosystem. By doing so, enterprises can harness the full potential of their technology investments, driving innovation and maintaining their competitive edge in an increasingly digital world.

Slawomir Michalik is VP at Omnilogy

Related Links

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The Latest

Observability Is Becoming the Grid That Powers AI - and It Isn't Ready

For fifteen years, observability lived downstream of everything else. Code shipped, something broke, an engineer went to the dashboards. The job was forensic. The pillars we built, such as logs, metrics, and traces, were designed for that role: tell a human what just happened, fast enough that they can make it stop. That role has quietly ended ...

Hybrid IT Dominates Enterprise Architecture, with Colocation Anchoring Critical Workloads

Hybrid IT has become the standard operating model for enterprises — but that companies are still looking for the right hybrid IT mix, according to the 2026 State of the Data Center Report from CoreSite. After years of cloud migration and hybrid adoption, organizations are shifting their focus from deciding whether to use cloud, colocation or on-premises infrastructure to determining which workloads belong in each environment ...

AI is Moving Fast, and Enterprise File Data Is Not Ready

Pilots are everywhere, stakeholders are seeking results, businesses are pushing for new tools, and IT teams are being asked to make AI secure, reliable, and useful at scale. But as organizations move from testing AI to operationalizing it, many are discovering that the biggest barrier is not the model, the use case, or even the budget. It is the file data foundation within ...

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Fast or cheap? For most of my career in engineering, speed and quality sat on opposite ends of a seesaw. The "OR" in "fast or cheap" was non-negotiable. It was expected that pushing for faster releases meant that something in quality would give way. Tightening quality controls meant the schedule slipped. Every engineering leader I know has lived some version of that tradeoff ... The seesaw is starting to level out ...

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I have been building enterprise software for more than 20 years ... One thing stays true across all of it: You do not find out your foundation is wrong during the crisis. You find out when the debt comes due. For a lot of organizations, that bill is arriving now. New research ... puts hard numbers on something practitioners have been sensing for a while. The telemetry problem isn't coming. It's already here ...

AI is Breaking Enterprise Log Management

The rapid growth of AI workloads is pushing traditional log management approaches to their limits, according to The State of Log Management 2026 report from Dynatrace. Modern logs have become critical to understanding, validating, and securing AI-driven decisions, helping organizations ensure reliability, compliance, and performance at scale. However, the volume and complexity of AI telemetry are overwhelming legacy tools ...

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For years, secure connectivity has relied on a familiar pattern: route traffic back to centralized gateways, inspect it, and then allow access. This model worked when applications lived in a handful of data centers and users were largely confined to offices. That model is now under strain. Applications are distributed across clouds, users connect from everywhere, and real-time workloads demand performance that centralized inspection points struggle to deliver. As traffic volumes grow and latency expectations shrink, routing everything through a small number of control points has become both a performance bottleneck and a resilience risk. The future of secure connectivity requires a different approach ...

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