APM and Observability: Cutting Through the Confusion — Part 12

Pete Goldin
APMdigest

In Part 12, the final installment in the series, the experts present some final predictions about AI's future impact on APM and Observability.

Start with: APM and Observability - Cutting Through the Confusion - Part 11

AI-powered capabilities such as AI Assistants, zero-config ML-based multi-signal correlation, pattern analysis, failure detection, latency analysis, and more are enriching the APM experience and tightly integrating it with other observability signals, according to Bahubali Shetti, Senior Director, Product Marketing, Elastic. Users can solve problems holistically using all available signals and data, rather than relying on metrics, logs, or traces in isolation.

The integration of AI and machine learning will deepen, enabling faster, more accurate diagnostics and increasingly automated remediation, says Arun Balachandran, Senior Product Marketing Manager, ManageEngine APM Solutions.

"AI will become central, automating anomaly detection, root cause analysis, and performance optimization," adds Varma Kunaparaju, SVP and GM for Cloud Platform and OpsRamp Software, HPE, "making both APM and observability more proactive and predictive. This transformation will enable more agile and resilient IT operations, driving innovation and competitive advantage."

The following are more predictions from the experts:

ASSISTIVE OBSERVABILITY

Observability will move from being reactive to being assistive. As systems grow more complex, organizations will need observability platforms that don't just show what happened, but help explain why. That requires open, high-fidelity data, which is why the CNCF ecosystem is so critical. Projects like Thanos https://thanos.io/ (for scalable metrics), Fluent Bit (for log routing), and OpenTelemetry (for structured, correlated telemetry) are laying the foundation for AI-enhanced, team-centric observability that adapts as fast as the systems it observes.
Brian Douglas
Head of Ecosystem, Cloud Native Computing Foundation (CNCF)

AGENTIC WORKFLOWS AND CONVERSATIONAL EXPERIENCES

Agentic workflows and conversational experiences will completely change IT operations workflows, making it much more practical to find and resolve issues all through conversational experiences including code generation, patching, and deployment.
Bill Lobig
VP of Observability, IBM Automation

SMART DATA PIPELINES

Data pipelines will become smarter — filtering at the edge, routing to multiple destinations, and using AI to recommend what matters. 
Gurjeet Arora
CEO and Co-Founder, Observo AI

The implementation of AI engines is going to dramatically impact the scope and capabilities of APM solutions in a positive way. We will see the rise of tightly correlated data elements that are automatically traced, identified, and presented to IT Operations staff in real time, with targeted guidance on what they should be doing next to support the health of the application. Notably, this will be to a degree that will seem almost magical compared to current solutions. We're just seeing the very beginnings of it already, but I believe that vastly more data will be ingested and understood in real time, leading to what would be interpreted today as a near-perfect understanding of application state.
Bryan Cole
Director of Customer Engineering, Tricentis

OPENTELEMETRY

AI will clearly play a significant role in the future of observability, although it's unclear exactly which use cases will dominate. Some vendors today seem excited about the prospect of AI helping developers manage the overwhelming volume of data that comes from disparate logging, metrics, and APM tools. However, this data volume challenge isn't inherent to building systems — it's a consequence of emitting data in formats designed for previous generations of tooling. I hope that as tool makers bring AI into the observability landscape, they focus more on how AI can help us swiftly move into the OpenTelemetry future, e.g. by speeding the authoring and adoption of custom instrumentation, instead of providing an "intelligent" layer on top of a hodgepodge of existing logging, monitoring, and APM tooling. The higher up in the observability "funnel" we can deploy AI, the more powerful the results will be for our development teams.
Emily Nakashima
VP of Engineering, Honeycomb

SELF-LEARNING AI

In today's increasingly complex environments, visibility alone isn't enough. The next wave of AIOps solutions is being driven by self-learning AI platforms that unify and interpret data across operational domains, transforming it into predictive, prioritized, and actionable insights — without relying on static topologies or predefined rules. AIOps platforms built on a fully AI-native architecture are shifting the focus from simply monitoring systems to enabling intelligent, autonomous operations.

By applying predictive, causal, and generative AI, these platforms not only enhance the value of existing tools but increasingly have the potential to replace standalone observability solutions. They offer a single, intelligent layer that surfaces emerging issues, pinpoints root causes, and drives automated resolution — enabling a shift from fragmented monitoring to proactive, autonomous operations. Self-learning AI will ultimately replace traditional observability platforms by becoming the integrated, real-time source of operational truth. Rather than relying on topology and rules-based AIOps and Observability platforms, the next generation of platforms will continuously learn from live telemetry, historical incidents, human actions, and system behavior to proactively detect, diagnose, and even remediate issues. This real-time learning loop will reduce noise, surface meaningful patterns, and guide teams toward faster, more confident decisions — paving the way for predictive, autonomous, and eventually self-healing IT environments.
Josh Kindiger
President, Grokstream

SELF-HEALING SYSTEMS

It's likely we'll see more widely adopted "self-healing" application capabilities through the use of AI and the observability data that feeds it.
Justin Collier
Senior Director of Product Management, SmartBear

The next few years will be less about manually building SLAs, dashboards, and alerts and more centered on self-healing and adaptive systems. With the rise of AI and ML embedded into observability platforms, we'll see a shift toward systems that can detect anomalies, determine probable root causes, and even take corrective actions without (some) human intervention. The result is not just greater efficiency but a fundamentally more resilient and intelligent digital infrastructure.
Mimi Shalash
Observability Advisor at Splunk, a Cisco Company

We're heading toward Autonomous Service Reliability: systems that not only observe themselves, but also understand, diagnose, and even self-heal with minimal human intervention.
Severin Neumann
Head of Community & Developer Relations, Causely

AI WILL NOT REPLACE HUMANS

AI's role in APM and observability will be to assist and guide, not replace, human expertise. AI will be used to narrow down search spaces, prioritize issues, and support human operators in diagnosis rather than providing fully automated solutions.
Jeff Cobb
Global Head of Product & Design, Chronosphere

AI OBSERVABILITY

Expect to see a rise in the need for AI-specific observability and application performance monitoring. Customers embedding GenAI into their own offerings will need to monitor these AI applications and AI factories, driving new requirements for observability platforms that can handle these specialized workloads.
Paul Appleby
CEO, Virtana

Pete Goldin is Editor and Publisher of APMdigest

Related Links

Hot Topics

The Latest

When the Database Is the Problem, Is APM Enough?

Production incidents rarely announce themselves as database problems. They appear as slow transactions, timeouts, rising response times, or an application struggling under a workload it previously handled. APM provides an essential starting point. It can identify a slow transaction path, highlight an affected service, and show that a database dependency is consuming more time than expected. But identifying the database as part of the problem is not the same as explaining what is happening inside it ...

From Cleanup to Prevention: The Future of Cloud-Native Cost Optimization

Cloud teams are under constant pressure to reduce spend without slowing development or increasing operational risk. They are deploying autoscalers, rightsizing workloads, enforcing resource requests, reviewing utilization dashboards, and building FinOps processes around cloud-native environments. Yet the results often disappoint ...

The Real Enterprise AI Risk Isn't Hallucinations

Ask most IT leaders about their biggest concern with AI and you'll hear the same answer: hallucinations ... Today, however, the conversation has shifted ... As organizations move beyond chatbots and experiments, they are increasingly deploying AI agents that perform multi-step tasks. These systems retrieve documents, query databases, call APIs, generate reports, write code, and make recommendations. The issue is not whether the model can reason. The issue is whether the organization can see, verify, and govern the decisions being made along the way ...

Beyond Buy vs. Build: The New Playbook for Data Pipelines

While organizations want to take control of their telemetry, building telemetry pipelines from scratch can be a very daunting, complicated task, even when leveraging open-source standards like OpenTelemetry. It requires specialized knowledge across distributed systems, data engineering, and security. This fragmented approach across systems causes higher operational costs; it puts a strain on resources and reduces efficiency as teams have to work with different interfaces and processes ...

The Corporate Network Is Fading - Here's What Replaces It

For decades, enterprise networks were designed around a simple assumption: work happened inside the office. Applications lived in centralized data centers, employees connected through internal infrastructure, and security focused on protecting the perimeter that surrounded everything ... But the way organizations operate today bears little resemblance to that environment. Cloud platforms host critical applications, employees connect from homes and airports as often as they do from offices, and partners collaborate through shared systems that exist far beyond corporate walls. In short, the corporate network no longer resembles the environment it was designed to protect ...

IT Leaders Must Establish a Network Data Architecture Practice

As an analyst who researches how IT organizations design, build, and operate their networks, I find that network data is a constant source of pain. Network teams struggle with data quality, fragmentation, authority, access, and trust. And these issues undermine everything they try to do. Here are the numbers: Only 45% of network teams are completely confident in the accuracy of their network source of truth, which documents the intent of their network ...

Data Center Outages Are Decreasing but More Costly

The 2026 Global Data Center Survey from Uptime Institute reveals an industry navigating workforce constraints, escalating outage expenses, even as rising costs remain the top concern for management teams ...

Observability Has a Physical Layer Now: What AI Infrastructure Teaches IT Operations

The next observability gap may not be in the code. It may be under the rack. That sounds strange until you think about how AI incidents actually feel in the middle of an investigation ... The application dashboard may be accurate. It may also be stopping at the wrong boundary. AI systems depend on software, but they also depend on a dense physical stack: racks, power paths, thermal margin, maintenance activity and, in many environments, liquid cooling. Those physical dependencies can change slowly before they look like a software incident ...

TLS Certificate Expiration Is Becoming an Observability Problem

Certificate expiration is the rare outage you can see coming. Every TLS certificate carries the date it stops working, so the moment it will begin breaking connections is knowable in advance. That's what makes an expired certificate such a frustrating way to lose a service. What's changing now is how often that date comes around ...

Enterprise Cloud Observability: Choosing the Right Starting Point

Enterprises operate different combinations of workloads across cloud, hybrid and multicloud environments. For business-critical workloads, teams need to consider monitoring and observability early so they can detect health issues, investigate failures, and understand operational impact. Organizations place workloads on cloud platforms based on a combination of technical requirements, economics, existing dependencies, organizational standards, and business priorities. Their monitoring priorities therefore depend on what they operate and where those systems run. Those priorities will not look the same for every organization ...

APM and Observability: Cutting Through the Confusion — Part 12

Pete Goldin
APMdigest

In Part 12, the final installment in the series, the experts present some final predictions about AI's future impact on APM and Observability.

Start with: APM and Observability - Cutting Through the Confusion - Part 11

AI-powered capabilities such as AI Assistants, zero-config ML-based multi-signal correlation, pattern analysis, failure detection, latency analysis, and more are enriching the APM experience and tightly integrating it with other observability signals, according to Bahubali Shetti, Senior Director, Product Marketing, Elastic. Users can solve problems holistically using all available signals and data, rather than relying on metrics, logs, or traces in isolation.

The integration of AI and machine learning will deepen, enabling faster, more accurate diagnostics and increasingly automated remediation, says Arun Balachandran, Senior Product Marketing Manager, ManageEngine APM Solutions.

"AI will become central, automating anomaly detection, root cause analysis, and performance optimization," adds Varma Kunaparaju, SVP and GM for Cloud Platform and OpsRamp Software, HPE, "making both APM and observability more proactive and predictive. This transformation will enable more agile and resilient IT operations, driving innovation and competitive advantage."

The following are more predictions from the experts:

ASSISTIVE OBSERVABILITY

Observability will move from being reactive to being assistive. As systems grow more complex, organizations will need observability platforms that don't just show what happened, but help explain why. That requires open, high-fidelity data, which is why the CNCF ecosystem is so critical. Projects like Thanos https://thanos.io/ (for scalable metrics), Fluent Bit (for log routing), and OpenTelemetry (for structured, correlated telemetry) are laying the foundation for AI-enhanced, team-centric observability that adapts as fast as the systems it observes.
Brian Douglas
Head of Ecosystem, Cloud Native Computing Foundation (CNCF)

AGENTIC WORKFLOWS AND CONVERSATIONAL EXPERIENCES

Agentic workflows and conversational experiences will completely change IT operations workflows, making it much more practical to find and resolve issues all through conversational experiences including code generation, patching, and deployment.
Bill Lobig
VP of Observability, IBM Automation

SMART DATA PIPELINES

Data pipelines will become smarter — filtering at the edge, routing to multiple destinations, and using AI to recommend what matters. 
Gurjeet Arora
CEO and Co-Founder, Observo AI

The implementation of AI engines is going to dramatically impact the scope and capabilities of APM solutions in a positive way. We will see the rise of tightly correlated data elements that are automatically traced, identified, and presented to IT Operations staff in real time, with targeted guidance on what they should be doing next to support the health of the application. Notably, this will be to a degree that will seem almost magical compared to current solutions. We're just seeing the very beginnings of it already, but I believe that vastly more data will be ingested and understood in real time, leading to what would be interpreted today as a near-perfect understanding of application state.
Bryan Cole
Director of Customer Engineering, Tricentis

OPENTELEMETRY

AI will clearly play a significant role in the future of observability, although it's unclear exactly which use cases will dominate. Some vendors today seem excited about the prospect of AI helping developers manage the overwhelming volume of data that comes from disparate logging, metrics, and APM tools. However, this data volume challenge isn't inherent to building systems — it's a consequence of emitting data in formats designed for previous generations of tooling. I hope that as tool makers bring AI into the observability landscape, they focus more on how AI can help us swiftly move into the OpenTelemetry future, e.g. by speeding the authoring and adoption of custom instrumentation, instead of providing an "intelligent" layer on top of a hodgepodge of existing logging, monitoring, and APM tooling. The higher up in the observability "funnel" we can deploy AI, the more powerful the results will be for our development teams.
Emily Nakashima
VP of Engineering, Honeycomb

SELF-LEARNING AI

In today's increasingly complex environments, visibility alone isn't enough. The next wave of AIOps solutions is being driven by self-learning AI platforms that unify and interpret data across operational domains, transforming it into predictive, prioritized, and actionable insights — without relying on static topologies or predefined rules. AIOps platforms built on a fully AI-native architecture are shifting the focus from simply monitoring systems to enabling intelligent, autonomous operations.

By applying predictive, causal, and generative AI, these platforms not only enhance the value of existing tools but increasingly have the potential to replace standalone observability solutions. They offer a single, intelligent layer that surfaces emerging issues, pinpoints root causes, and drives automated resolution — enabling a shift from fragmented monitoring to proactive, autonomous operations. Self-learning AI will ultimately replace traditional observability platforms by becoming the integrated, real-time source of operational truth. Rather than relying on topology and rules-based AIOps and Observability platforms, the next generation of platforms will continuously learn from live telemetry, historical incidents, human actions, and system behavior to proactively detect, diagnose, and even remediate issues. This real-time learning loop will reduce noise, surface meaningful patterns, and guide teams toward faster, more confident decisions — paving the way for predictive, autonomous, and eventually self-healing IT environments.
Josh Kindiger
President, Grokstream

SELF-HEALING SYSTEMS

It's likely we'll see more widely adopted "self-healing" application capabilities through the use of AI and the observability data that feeds it.
Justin Collier
Senior Director of Product Management, SmartBear

The next few years will be less about manually building SLAs, dashboards, and alerts and more centered on self-healing and adaptive systems. With the rise of AI and ML embedded into observability platforms, we'll see a shift toward systems that can detect anomalies, determine probable root causes, and even take corrective actions without (some) human intervention. The result is not just greater efficiency but a fundamentally more resilient and intelligent digital infrastructure.
Mimi Shalash
Observability Advisor at Splunk, a Cisco Company

We're heading toward Autonomous Service Reliability: systems that not only observe themselves, but also understand, diagnose, and even self-heal with minimal human intervention.
Severin Neumann
Head of Community & Developer Relations, Causely

AI WILL NOT REPLACE HUMANS

AI's role in APM and observability will be to assist and guide, not replace, human expertise. AI will be used to narrow down search spaces, prioritize issues, and support human operators in diagnosis rather than providing fully automated solutions.
Jeff Cobb
Global Head of Product & Design, Chronosphere

AI OBSERVABILITY

Expect to see a rise in the need for AI-specific observability and application performance monitoring. Customers embedding GenAI into their own offerings will need to monitor these AI applications and AI factories, driving new requirements for observability platforms that can handle these specialized workloads.
Paul Appleby
CEO, Virtana

Pete Goldin is Editor and Publisher of APMdigest

Related Links

Hot Topics

The Latest

When the Database Is the Problem, Is APM Enough?

Production incidents rarely announce themselves as database problems. They appear as slow transactions, timeouts, rising response times, or an application struggling under a workload it previously handled. APM provides an essential starting point. It can identify a slow transaction path, highlight an affected service, and show that a database dependency is consuming more time than expected. But identifying the database as part of the problem is not the same as explaining what is happening inside it ...

From Cleanup to Prevention: The Future of Cloud-Native Cost Optimization

Cloud teams are under constant pressure to reduce spend without slowing development or increasing operational risk. They are deploying autoscalers, rightsizing workloads, enforcing resource requests, reviewing utilization dashboards, and building FinOps processes around cloud-native environments. Yet the results often disappoint ...

The Real Enterprise AI Risk Isn't Hallucinations

Ask most IT leaders about their biggest concern with AI and you'll hear the same answer: hallucinations ... Today, however, the conversation has shifted ... As organizations move beyond chatbots and experiments, they are increasingly deploying AI agents that perform multi-step tasks. These systems retrieve documents, query databases, call APIs, generate reports, write code, and make recommendations. The issue is not whether the model can reason. The issue is whether the organization can see, verify, and govern the decisions being made along the way ...

Beyond Buy vs. Build: The New Playbook for Data Pipelines

While organizations want to take control of their telemetry, building telemetry pipelines from scratch can be a very daunting, complicated task, even when leveraging open-source standards like OpenTelemetry. It requires specialized knowledge across distributed systems, data engineering, and security. This fragmented approach across systems causes higher operational costs; it puts a strain on resources and reduces efficiency as teams have to work with different interfaces and processes ...

The Corporate Network Is Fading - Here's What Replaces It

For decades, enterprise networks were designed around a simple assumption: work happened inside the office. Applications lived in centralized data centers, employees connected through internal infrastructure, and security focused on protecting the perimeter that surrounded everything ... But the way organizations operate today bears little resemblance to that environment. Cloud platforms host critical applications, employees connect from homes and airports as often as they do from offices, and partners collaborate through shared systems that exist far beyond corporate walls. In short, the corporate network no longer resembles the environment it was designed to protect ...

IT Leaders Must Establish a Network Data Architecture Practice

As an analyst who researches how IT organizations design, build, and operate their networks, I find that network data is a constant source of pain. Network teams struggle with data quality, fragmentation, authority, access, and trust. And these issues undermine everything they try to do. Here are the numbers: Only 45% of network teams are completely confident in the accuracy of their network source of truth, which documents the intent of their network ...

Data Center Outages Are Decreasing but More Costly

The 2026 Global Data Center Survey from Uptime Institute reveals an industry navigating workforce constraints, escalating outage expenses, even as rising costs remain the top concern for management teams ...

Observability Has a Physical Layer Now: What AI Infrastructure Teaches IT Operations

The next observability gap may not be in the code. It may be under the rack. That sounds strange until you think about how AI incidents actually feel in the middle of an investigation ... The application dashboard may be accurate. It may also be stopping at the wrong boundary. AI systems depend on software, but they also depend on a dense physical stack: racks, power paths, thermal margin, maintenance activity and, in many environments, liquid cooling. Those physical dependencies can change slowly before they look like a software incident ...

TLS Certificate Expiration Is Becoming an Observability Problem

Certificate expiration is the rare outage you can see coming. Every TLS certificate carries the date it stops working, so the moment it will begin breaking connections is knowable in advance. That's what makes an expired certificate such a frustrating way to lose a service. What's changing now is how often that date comes around ...

Enterprise Cloud Observability: Choosing the Right Starting Point

Enterprises operate different combinations of workloads across cloud, hybrid and multicloud environments. For business-critical workloads, teams need to consider monitoring and observability early so they can detect health issues, investigate failures, and understand operational impact. Organizations place workloads on cloud platforms based on a combination of technical requirements, economics, existing dependencies, organizational standards, and business priorities. Their monitoring priorities therefore depend on what they operate and where those systems run. Those priorities will not look the same for every organization ...