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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The $600 Billion Wake Up Call

In 2026, the cost of downtime or an outage is no longer just a technical inconvenience; it's a $600 billion wake up call for global businesses. As our digital ecosystems become  more interconnected, each touchpoint introduces new risks and multiplies the consequences when things go wrong. And the data is clear: aggregate downtime costs  for Global 2,000 companies have surged 50% since 2024, reaching a staggering $600 billion ...

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Cloud Spend Is Rising and Cloud Optimization Is Key to Funding AI and Protecting Margins

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Building AI Agents That Build Trust

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I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

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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

Related Links

Hot Topics

The Latest

When AI Becomes the Corporate OS

AI is becoming the operating system of the enterprise. It acts as an invisible coordination layer that understands intent, connects systems, and executes work across complex SaaS environments. Previously, employees had to click through multiple systems — CRM, ERP, support tools, collaboration platforms — to complete a single task. Now, instead of navigating each application manually, they can simply state what they need to accomplish ...

The $600 Billion Wake Up Call

In 2026, the cost of downtime or an outage is no longer just a technical inconvenience; it's a $600 billion wake up call for global businesses. As our digital ecosystems become  more interconnected, each touchpoint introduces new risks and multiplies the consequences when things go wrong. And the data is clear: aggregate downtime costs  for Global 2,000 companies have surged 50% since 2024, reaching a staggering $600 billion ...

Breaking Down Agentic AI Fragmentation and Complexity with Governance

Deloitte found that 74% of enterprises expect to deploy agentic AI solutions in the next 24 months. However, the rush to deployment is outpacing foundational work, though. Only 21% of enterprises have fully formed agent governance models in place. The result? AI agents deployed without guidance or governance begin to function as fragmented islands of complexity ...

Cloud Spend Is Rising and Cloud Optimization Is Key to Funding AI and Protecting Margins

Cloud spending is no longer viewed as a passthrough IT expense, but as a strategic financial lever that directly impacts innovation capacity, profitability and enterprise resilience, according to the CFO Cloud Cost Optimization Report from Azul ...

Building AI Agents That Build Trust

As AI moves from generating responses to performing actions, the need for trust increases exponentially. And as organizations enlist AI agents for increasingly sophisticated business processes, trust is going to be the single most important theme for spurring adoption. What can organizations do to build trustworthy AI agents? ...

Keys to Building a Partner Ecosystem That Scales

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

How AI Agents Are Reshaping DataOps for the Always-On Enterprise

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

AI Deepfakes: Rethinking Trust in the Workplace

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Nine in Ten Enterprises Plan Cloud Data Repatriation amid Rising Cloud Costs and Data Sovereignty Mandates

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Why ITOps Need Right-Sized AI, Not Bigger Models

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...