2026 Observability Predictions - Part 3

In APMdigest's 2026 Observability Predictions Series, industry experts — from analysts and consultants to the top vendors — offer predictions on how Observability and related technologies will evolve and impact business in 2026. Part 3 covers more predictions about Observability.

DEMOCRATIZATION OF OBSERVABILITY

AIOps and Observability are moving toward becoming proactive in identifying and correcting incidents before they impact the business. However, the transition will involve intermediate stages as organizations adapt and learn to trust the AI automation. Omdia believes that as these Observability tools become more autonomous and require less technical knowledge to use, the task of delivering this first line capability will move to line of business teams. IT Operations will become the level 2/3 domain experts.
Roy Illsley MBA CEng MIET
Chief Analyst, Omdia

OBSERVABILITY DRIVES BUSINESS GROWTH

Observability as a Direct Business Growth Driver: In 2026, observability will solidify its role as a direct business catalyst, moving beyond technical monitoring to actively drive revenue growth and customer satisfaction. We are already seeing this among early adopters in 2025. Organizations will increasingly leverage observability data to inform strategic business decisions and product roadmaps, demonstrably translating investments into tangible improvements. A major challenge lies in the careful selection of relevant data: it is essential to target pertinent information to limit costs and ensure a positive return on investment. This critical shift in observability is largely enabled by AI, which allows observability practitioners to prioritize innovation over maintenance, thereby fundamentally linking operational insights to business outcomes.
Jean-Sebastien Meurisse
Head of Product Marketing, Professional & Managed Services, Orange Business

UNIFIED OBSERVABILITY

Unified observability becomes the default operating model: In 2025, nearly three-quarters (73%) of executives reported that they had either adopted unified observability or were actively transitioning toward it. But the deeper story in the data wasn't about tool choices — it was about how organizations are restructuring teams, processes, and ownership to support a unified operating model. With only 3% lacking any strategy at all, the shift is clearly underway, even if execution remains uneven. Crucially, "unified" does not mean "fully consolidated." 
Dave Russell
Director, Voice of Customer, Grafana Labs

OBSERVABILITY TOOL CONSOLIDATION

Tool consolidation remains more aspiration than reality: 77% of leaders call it important, yet only 14% say their efforts have been strongly successful. Organizations are unifying how they work long before they've standardized what they use. By 2026, unified observability becomes the default operating model, not because companies have fully consolidated tools, but because they've aligned teams around shared data, workflows, and outcomes. Consolidation will continue, but pragmatically, as organizations prioritize openness and composability over forced standardization.
Dave Russell
Director, Voice of Customer, Grafana Labs

DECOUPLED OBSERVABILITY STACKS

The Rise of Decoupled Observability Stacks: In 2026, the era of the all-in-one observability black box will be over. AI is driving massive growth in logs, metrics, and traces, pushing tightly coupled observability platforms past their limits. Organizations are reaching a breaking point: they can no longer scale these monolithic systems without sacrificing data visibility or having to absorb runaway costs. The cost and complexity of scaling current observability stacks will become unsustainable. Forward-thinking teams are already starting to rethink architecture, pulling apart the data layer from the tools that sit on top of it. We've seen this movie play out before — business intelligence went through the same evolution over the last 40 years. It started as tightly coupled stacks in the 80s and exists today as a decoupled architecture that gives teams flexibility, choice, and control. Observability is next. The observability warehouse (i.e., specialized data stores for logs, metrics, and traces) will emerge as the new standard, serving as a central data layer that reduces dependence on any one monolithic platform, freeing teams from vendor lock-in and letting them choose the best tools for the job.
Eric Tschetter
Chief Architect, Imply

STRUCTURAL OBSERVABILITY

Structural observability will emerge as a core practice because most large-scale failures originate from changes that were never tracked. Runtime metrics cannot explain why systems fall out of alignment when the root cause is a schema edit, a permission shift, or a configuration update made early in the pipeline. Teams will recognize that understanding how a system evolved is often more important than how it behaves in the moment. Visibility into change itself will become a primary requirement for reliable software delivery.
Ryan McCurdy
VP, Liquibase

OBSERVABILITY AND HIGH AVAILABILITY

Observability Becomes Essential for Complex IT Environments: As IT infrastructures expand across on-premises, cloud, hybrid, and multi-cloud environments, visibility into application performance and health and interdependencies of the elements of the IT stack will become mission-critical. In 2026, observability will emerge as a key differentiator for HA solutions, allowing IT teams to identify and resolve issues before they impact uptime. The most successful HA platforms will provide deep insights across the full stack—from hardware to application layer.
Cassius Rhue
VP of Customer Experience, SIOS Technology

OBSERVABILITY DASHBOARD EVOLUTION

Dashboards don't disappear; they graduate. As AI agents take over detecting incidents and diagnosing root cause (RCA), the dashboard evolves from an operational crutch to a source of trust, verification and compliance. Investigation and pattern identification is owned by the agents.
Tucker Callaway
CEO, Mezmo

INCIDENT COMMUNICATIONS

Faster and more transparent incident communications will become table stakes for customers: Given access to AI and improved tech for incident response and comms, customers will expect real-time visibility into incidents affecting them, not just a status page that turns red after the fact. The industry will shift from the customer seeking out the status of the services they use to those services proactively helping them see if and how they are impacted. The companies that do this well will turn incidents from trust-destroying events into trust-building moments of transparency.
Kat Gaines
Senior Manager, Developer Relations, PagerDuty

OBSERVABILITY FOR TEAMS

In 2026 the real multiplier is the 10x software team, not the 10x developer. Teams that share context across production signals, traces, prompts, agent actions and all the observability data around them will move dramatically faster. It's no longer about one engineer grinding through tasks, or one super performer carrying the team on their back; it's about everyone operating from the same real-time reasoning and feedback loops. That context becomes incredibly powerful because it captures the full picture of what the code and agents were doing, not just the final result. When you pair this with agents that can take action, the entire team accelerates. The teams that don't work this way will feel painfully slow by comparison.
Milin Desai
CEO, Sentry

Go to: 2026 Observability Predictions - Part 4, covering user experience, website performance and ITSM

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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 ...

The Governance Gap Behind AI's Infrastructure Problem

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 ... 

2026 Observability Predictions - Part 3

In APMdigest's 2026 Observability Predictions Series, industry experts — from analysts and consultants to the top vendors — offer predictions on how Observability and related technologies will evolve and impact business in 2026. Part 3 covers more predictions about Observability.

DEMOCRATIZATION OF OBSERVABILITY

AIOps and Observability are moving toward becoming proactive in identifying and correcting incidents before they impact the business. However, the transition will involve intermediate stages as organizations adapt and learn to trust the AI automation. Omdia believes that as these Observability tools become more autonomous and require less technical knowledge to use, the task of delivering this first line capability will move to line of business teams. IT Operations will become the level 2/3 domain experts.
Roy Illsley MBA CEng MIET
Chief Analyst, Omdia

OBSERVABILITY DRIVES BUSINESS GROWTH

Observability as a Direct Business Growth Driver: In 2026, observability will solidify its role as a direct business catalyst, moving beyond technical monitoring to actively drive revenue growth and customer satisfaction. We are already seeing this among early adopters in 2025. Organizations will increasingly leverage observability data to inform strategic business decisions and product roadmaps, demonstrably translating investments into tangible improvements. A major challenge lies in the careful selection of relevant data: it is essential to target pertinent information to limit costs and ensure a positive return on investment. This critical shift in observability is largely enabled by AI, which allows observability practitioners to prioritize innovation over maintenance, thereby fundamentally linking operational insights to business outcomes.
Jean-Sebastien Meurisse
Head of Product Marketing, Professional & Managed Services, Orange Business

UNIFIED OBSERVABILITY

Unified observability becomes the default operating model: In 2025, nearly three-quarters (73%) of executives reported that they had either adopted unified observability or were actively transitioning toward it. But the deeper story in the data wasn't about tool choices — it was about how organizations are restructuring teams, processes, and ownership to support a unified operating model. With only 3% lacking any strategy at all, the shift is clearly underway, even if execution remains uneven. Crucially, "unified" does not mean "fully consolidated." 
Dave Russell
Director, Voice of Customer, Grafana Labs

OBSERVABILITY TOOL CONSOLIDATION

Tool consolidation remains more aspiration than reality: 77% of leaders call it important, yet only 14% say their efforts have been strongly successful. Organizations are unifying how they work long before they've standardized what they use. By 2026, unified observability becomes the default operating model, not because companies have fully consolidated tools, but because they've aligned teams around shared data, workflows, and outcomes. Consolidation will continue, but pragmatically, as organizations prioritize openness and composability over forced standardization.
Dave Russell
Director, Voice of Customer, Grafana Labs

DECOUPLED OBSERVABILITY STACKS

The Rise of Decoupled Observability Stacks: In 2026, the era of the all-in-one observability black box will be over. AI is driving massive growth in logs, metrics, and traces, pushing tightly coupled observability platforms past their limits. Organizations are reaching a breaking point: they can no longer scale these monolithic systems without sacrificing data visibility or having to absorb runaway costs. The cost and complexity of scaling current observability stacks will become unsustainable. Forward-thinking teams are already starting to rethink architecture, pulling apart the data layer from the tools that sit on top of it. We've seen this movie play out before — business intelligence went through the same evolution over the last 40 years. It started as tightly coupled stacks in the 80s and exists today as a decoupled architecture that gives teams flexibility, choice, and control. Observability is next. The observability warehouse (i.e., specialized data stores for logs, metrics, and traces) will emerge as the new standard, serving as a central data layer that reduces dependence on any one monolithic platform, freeing teams from vendor lock-in and letting them choose the best tools for the job.
Eric Tschetter
Chief Architect, Imply

STRUCTURAL OBSERVABILITY

Structural observability will emerge as a core practice because most large-scale failures originate from changes that were never tracked. Runtime metrics cannot explain why systems fall out of alignment when the root cause is a schema edit, a permission shift, or a configuration update made early in the pipeline. Teams will recognize that understanding how a system evolved is often more important than how it behaves in the moment. Visibility into change itself will become a primary requirement for reliable software delivery.
Ryan McCurdy
VP, Liquibase

OBSERVABILITY AND HIGH AVAILABILITY

Observability Becomes Essential for Complex IT Environments: As IT infrastructures expand across on-premises, cloud, hybrid, and multi-cloud environments, visibility into application performance and health and interdependencies of the elements of the IT stack will become mission-critical. In 2026, observability will emerge as a key differentiator for HA solutions, allowing IT teams to identify and resolve issues before they impact uptime. The most successful HA platforms will provide deep insights across the full stack—from hardware to application layer.
Cassius Rhue
VP of Customer Experience, SIOS Technology

OBSERVABILITY DASHBOARD EVOLUTION

Dashboards don't disappear; they graduate. As AI agents take over detecting incidents and diagnosing root cause (RCA), the dashboard evolves from an operational crutch to a source of trust, verification and compliance. Investigation and pattern identification is owned by the agents.
Tucker Callaway
CEO, Mezmo

INCIDENT COMMUNICATIONS

Faster and more transparent incident communications will become table stakes for customers: Given access to AI and improved tech for incident response and comms, customers will expect real-time visibility into incidents affecting them, not just a status page that turns red after the fact. The industry will shift from the customer seeking out the status of the services they use to those services proactively helping them see if and how they are impacted. The companies that do this well will turn incidents from trust-destroying events into trust-building moments of transparency.
Kat Gaines
Senior Manager, Developer Relations, PagerDuty

OBSERVABILITY FOR TEAMS

In 2026 the real multiplier is the 10x software team, not the 10x developer. Teams that share context across production signals, traces, prompts, agent actions and all the observability data around them will move dramatically faster. It's no longer about one engineer grinding through tasks, or one super performer carrying the team on their back; it's about everyone operating from the same real-time reasoning and feedback loops. That context becomes incredibly powerful because it captures the full picture of what the code and agents were doing, not just the final result. When you pair this with agents that can take action, the entire team accelerates. The teams that don't work this way will feel painfully slow by comparison.
Milin Desai
CEO, Sentry

Go to: 2026 Observability Predictions - Part 4, covering user experience, website performance and ITSM

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 ...

The Governance Gap Behind AI's Infrastructure Problem

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 ...