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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

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 ...

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

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 ...