2026 Observability Predictions - Part 7

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 7 covers Observability data.

PRIVACY-BY-DESIGN OBSERVABILITY

Privacy-by-Design Observability Becomes a Hard Requirement: In 2026, privacy-by-design observability will no longer be a nice to have — it will be a hard requirement for any enterprise that wants to safely analyze or automate decisions with operational data. Banks, healthcare organizations, insurance providers, and even consumer tech companies are being pushed to treat telemetry with the same level of caution they apply to financial or health records. They'll demand control over how data is collected, what gets masked, who can view sensitive fields, and whether that information stays in the cloud or inside their own walls. The companies that succeed will be the ones that build privacy choices into every layer of the platform. The ones that treat observability data casually will find themselves written out of RFPs before the conversation even starts.
David Jones
VP of NORAM Solution Engineering, Dynatrace

DATA DRIVES AI-FIRST OBSERVABILITY

Data Becomes the Enterprise Nervous System: By 2026, data will do more than power the business. It will be the business. The smartest CEOs won't just track performance. They'll feel it — like a coach who can read the momentum shift before it hits the scoreboard. With AI-first observability, enterprises will sense every operational signal, anticipate market pressure, and respond with the speed of a two-minute drill. AI will no longer just inform decisions. It will drive them, turning raw telemetry into game-changing moves that separate contenders from champions.
Christina Kosmowski
CEO, LogicMonitor

CURATED OBSERVABILITY

Curated observability becomes table stakes: teams will insist on dropping and shaping telemetry at ingest rather than paying surprise bills later. Observability shifts left and becomes policy — much like security — serving as a safety net that speeds development. 
Bill Hineline
Field CTO, Chronosphere

RICHER DATA

In 2026, observability and automation will take center stage in the evolution of AIOps. While today's AIOps tools help reduce noise and streamline root-cause analysis, the real breakthrough will come from richer, cleaner, and more contextual observability data. With richer data streams being collated, IT teams will be empowered to lean confidently into automation and advance their AIOps practices toward truly proactive, self-healing optimization. As these capabilities mature, we'll see intelligent agents continuously correlate data flows across applications, networks, and business outcomes — shifting operations from reactive firefighting to predictive insight. It's a pivotal step on the path to fully autonomous digital ecosystems. 
Douglas James
VP, Solutions & Ecosystem, ScienceLogic

ADAPTIVE TELEMETRY

Data value overtakes data volume: For years, teams have treated data collection as a contest of scale. Now they're realizing: more isn't better, better is better. Complexity has become the tax on innovation. In 2026, the winners will be those who pay it down. Adaptive Telemetry is leading that change, intelligently filtering data based on value, keeping 50-80% less while retaining what matters. When combined with autonomous investigation, teams can respond faster, cut costs, and focus on outcomes instead of overhead. The result? More reliable, cost-efficient systems with less overhead. The future of observability isn't about collecting everything. It's about keeping only the data worthy of attention.
Sean Porter
Distinguished Engineer, Grafana Labs

OPEN-SOURCE-DRIVEN PIPELINES

Observability moves to the edge: as workloads stretch across hybrid, multi-cloud, IoT, and edge locations, open source-driven pipelines (think Fluent Bit) will power local aggregation, filtering, and dynamic routing to cut bandwidth, latency, and cost. 
Eric Schabell
Director, Community and Developer Relations, Chronosphere

CUSTOMER EXPERIENCE DATA

Customer experience becomes a Board-level metric: observability data shifts from backend debugging to a visible measure of trust and customer health. 
Bill Hineline
Field CTO, Chronosphere

DATA CHALLENGE: VENDOR LOCK-IN

Vendor Lock-In Will Threaten the AIOps Promise: As enterprises invest in AIOps platforms for vendor-agnostic observability across their technology stacks, a countertrend is emerging that threatens this fundamental value proposition. Major enterprise software vendors are increasingly restricting access to operational data, effectively forcing customers towards their own proprietary AI tools. This represents a new battleground in enterprise software economics. Where vendors once competed on features and performance, they're now competing on data access and control. The logic is simple: if customers can't extract operational data to feed into their AIOps platforms, they must rely on the vendor's own AI capabilities, regardless of whether those tools deliver comparable value. The logic is simple: controlling data means controlling AI outcomes. For CIOs and CTOs, this demands renewed vigilance in contract negotiations, explicit data access guarantees, and potentially reconsidering vendor relationships where observability is compromised. That includes making data portability and telemetry access non-negotiable in your contracts.
Efrain Ruh
Regional CTO, Digitate

DATA CHALLENGE: AUTHENTICITY

In 2026, observability and AIOps teams will face a new performance bottleneck: verifying the authenticity of the data flowing through their systems. As synthetic and machine-generated content increasingly blends with legitimate telemetry, IT operations will struggle to maintain reliable alerts, model accuracy, and automated decisioning. This shift will drive demand for built-in data provenance and integrity checks across monitoring pipelines, giving organizations that can validate their operational data a meaningful advantage in speed, stability, and AI-driven resilience.
Ryan Steelberg
CEO, Veritone

DATA CHALLENGE: GATEKEEPING MCP DATA QUERIES

Observability vendors will start gatekeeping MCP's ability to query data out of their systems in an attempt to limit commoditization of their platforms.  Customers will want to adopt AI-powered observability tooling that looks past dashboards and manual queries to automated diagnostics and human-like crafted RCA.  Incumbent vendors will want you to adopt their AI-powered tooling, not become a datastore for another vendor's AI analysis — like Slack limiting access to your messages to train other tools.
Ian Smith
Head of Strategy, PlayerZero

CONVERGENCE OF METRICS, LOGS AND TRACES

Observability will evolve into a full-system intelligence layer that blends telemetry with stateful operational data. Rather than treating metrics, logs, and traces as separate pillars, firms will unify them with contextual datasets using flexible pipelines and interactive analysis tools. Platforms that can ingest and process all of this data in real time will give teams the ability to diagnose anomalies before outcomes are affected. This convergence will be especially valuable in finance, where small latency shifts or dependency failures can have immediate business impact.
Robert Cooke
CEO, 3forge

GENAI TRANSFORMS LOG ANALYSIS

Generative AI will transform the way we store, retrieve, and read the essence of issues by analyzing logs. In 2026 and beyond, logs will be analyzed using natural language querying, and narrow LLMs will be trained to summarize, contextualize, and suggest steps to find the root cause of an issue and fix it. No-sampling, full-fidelity log ingestion is also replacing traditional sampling and storage techniques, which will further be improved using AI's ability to correlate. Logs will see deeper integration into observability platforms, which will provide the backup to achieve near-real-time incident detection and resolution.
Srinivasa Raghavan Santhanam
Director of Product Management, ManageEngine

ANALYST REPORT: 2025 Gartner® Magic Quadrant™ for Digital Experience Monitoring

AGENTIC LOG ANALYSIS

Log analysis for app and IT performance: By 2026 logs will be refined and consumed entirely by agents. As agents take over analysis, the log becomes a richer and more powerful data source because LLMs can interpret and correlate patterns at a scale and speed humans cannot match.
Tucker Callaway
CEO, Mezmo

LOG ANALYSIS BECOMES SEMANTIC

Log analysis will become truly semantic. AI models will interpret logs as structured narratives rather than token streams, enabling deep correlation across logs, traces, and metrics without rigid schemas. Unstructured log data will finally become reliably actionable at scale.
Vladimir Mihailenco 
CEO, Uptrace

Go to: 2026 Observability Predictions - Part 8, covering outages and downtime.

Related Links

Hot Topics

The Latest

5 Takeaways from the Observability Forecast for Retail and eCommerce

Seeing is believing, or in this case, seeing is understanding, according to New Relic's 2025 Observability Forecast for Retail and eCommerce report. Retailers who want to provide exceptional customer experiences while improving IT operations efficiency are leaning on observability ... Here are five key takeaways from the report ...

Maximizing Impact Amid Constraints: The Role of Automation and Orchestration in Federal IT Modernization

Technology leaders across the federal landscape are facing, and will continue to face, an uphill battle when it comes to fortifying their digital environments against hostile and persistent threat actors. On one hand, they are being asked to push digital transformation ... On the other hand, they are facing the fiscal uncertainty of continuing resolutions (CR) and government shutdowns looming near and far. In the face of these challenges, CIOs, CTOs, and CISOs must figure out how to modernize legacy systems and infrastructure while doing more with less and still defending against external and internal threats ...

The SRE Report 2026: Reliability Is Being Redefined

Reliability is no longer proven by uptime alone, according to the The SRE Report 2026 from LogicMonitor. In the AI era, it is experienced through speed, consistency, and user trust, and increasingly judged by business impact. As digital services grow more complex and AI systems move into production, traditional monitoring approaches are struggling to keep pace, increasing the need for AI-first observability that spans applications, infrastructure, and the Internet ...

The Hidden Data Engineering Work Required to Make Enterprise AI Viable

If AI is the engine of a modern organization, then data engineering is the road system beneath it. You can build the most powerful engine in the world, but without paved roads, traffic signals, and bridges that can support its weight, it will stall. In many enterprises, the engine is ready. The roads are not ...

Why AI Is the Differentiator for Operationally Resilient Organizations

In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...

Escaping Pilot Purgatory: How AI Becomes an Operational Advantage

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Closing the Gap in Modern Tech and the Tools Meant to Monitor Them

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 ...

MEAN TIME TO INSIGHT Podcast - Episode 23: NetOps Labor Shortage

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Why FinOps Rewrote Its Mission and What It Signals for Technology Management

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 ...

Clearing the Path to AI: Why Vendor Consolidation Matters Now

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 ...

2026 Observability Predictions - Part 7

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 7 covers Observability data.

PRIVACY-BY-DESIGN OBSERVABILITY

Privacy-by-Design Observability Becomes a Hard Requirement: In 2026, privacy-by-design observability will no longer be a nice to have — it will be a hard requirement for any enterprise that wants to safely analyze or automate decisions with operational data. Banks, healthcare organizations, insurance providers, and even consumer tech companies are being pushed to treat telemetry with the same level of caution they apply to financial or health records. They'll demand control over how data is collected, what gets masked, who can view sensitive fields, and whether that information stays in the cloud or inside their own walls. The companies that succeed will be the ones that build privacy choices into every layer of the platform. The ones that treat observability data casually will find themselves written out of RFPs before the conversation even starts.
David Jones
VP of NORAM Solution Engineering, Dynatrace

DATA DRIVES AI-FIRST OBSERVABILITY

Data Becomes the Enterprise Nervous System: By 2026, data will do more than power the business. It will be the business. The smartest CEOs won't just track performance. They'll feel it — like a coach who can read the momentum shift before it hits the scoreboard. With AI-first observability, enterprises will sense every operational signal, anticipate market pressure, and respond with the speed of a two-minute drill. AI will no longer just inform decisions. It will drive them, turning raw telemetry into game-changing moves that separate contenders from champions.
Christina Kosmowski
CEO, LogicMonitor

CURATED OBSERVABILITY

Curated observability becomes table stakes: teams will insist on dropping and shaping telemetry at ingest rather than paying surprise bills later. Observability shifts left and becomes policy — much like security — serving as a safety net that speeds development. 
Bill Hineline
Field CTO, Chronosphere

RICHER DATA

In 2026, observability and automation will take center stage in the evolution of AIOps. While today's AIOps tools help reduce noise and streamline root-cause analysis, the real breakthrough will come from richer, cleaner, and more contextual observability data. With richer data streams being collated, IT teams will be empowered to lean confidently into automation and advance their AIOps practices toward truly proactive, self-healing optimization. As these capabilities mature, we'll see intelligent agents continuously correlate data flows across applications, networks, and business outcomes — shifting operations from reactive firefighting to predictive insight. It's a pivotal step on the path to fully autonomous digital ecosystems. 
Douglas James
VP, Solutions & Ecosystem, ScienceLogic

ADAPTIVE TELEMETRY

Data value overtakes data volume: For years, teams have treated data collection as a contest of scale. Now they're realizing: more isn't better, better is better. Complexity has become the tax on innovation. In 2026, the winners will be those who pay it down. Adaptive Telemetry is leading that change, intelligently filtering data based on value, keeping 50-80% less while retaining what matters. When combined with autonomous investigation, teams can respond faster, cut costs, and focus on outcomes instead of overhead. The result? More reliable, cost-efficient systems with less overhead. The future of observability isn't about collecting everything. It's about keeping only the data worthy of attention.
Sean Porter
Distinguished Engineer, Grafana Labs

OPEN-SOURCE-DRIVEN PIPELINES

Observability moves to the edge: as workloads stretch across hybrid, multi-cloud, IoT, and edge locations, open source-driven pipelines (think Fluent Bit) will power local aggregation, filtering, and dynamic routing to cut bandwidth, latency, and cost. 
Eric Schabell
Director, Community and Developer Relations, Chronosphere

CUSTOMER EXPERIENCE DATA

Customer experience becomes a Board-level metric: observability data shifts from backend debugging to a visible measure of trust and customer health. 
Bill Hineline
Field CTO, Chronosphere

DATA CHALLENGE: VENDOR LOCK-IN

Vendor Lock-In Will Threaten the AIOps Promise: As enterprises invest in AIOps platforms for vendor-agnostic observability across their technology stacks, a countertrend is emerging that threatens this fundamental value proposition. Major enterprise software vendors are increasingly restricting access to operational data, effectively forcing customers towards their own proprietary AI tools. This represents a new battleground in enterprise software economics. Where vendors once competed on features and performance, they're now competing on data access and control. The logic is simple: if customers can't extract operational data to feed into their AIOps platforms, they must rely on the vendor's own AI capabilities, regardless of whether those tools deliver comparable value. The logic is simple: controlling data means controlling AI outcomes. For CIOs and CTOs, this demands renewed vigilance in contract negotiations, explicit data access guarantees, and potentially reconsidering vendor relationships where observability is compromised. That includes making data portability and telemetry access non-negotiable in your contracts.
Efrain Ruh
Regional CTO, Digitate

DATA CHALLENGE: AUTHENTICITY

In 2026, observability and AIOps teams will face a new performance bottleneck: verifying the authenticity of the data flowing through their systems. As synthetic and machine-generated content increasingly blends with legitimate telemetry, IT operations will struggle to maintain reliable alerts, model accuracy, and automated decisioning. This shift will drive demand for built-in data provenance and integrity checks across monitoring pipelines, giving organizations that can validate their operational data a meaningful advantage in speed, stability, and AI-driven resilience.
Ryan Steelberg
CEO, Veritone

DATA CHALLENGE: GATEKEEPING MCP DATA QUERIES

Observability vendors will start gatekeeping MCP's ability to query data out of their systems in an attempt to limit commoditization of their platforms.  Customers will want to adopt AI-powered observability tooling that looks past dashboards and manual queries to automated diagnostics and human-like crafted RCA.  Incumbent vendors will want you to adopt their AI-powered tooling, not become a datastore for another vendor's AI analysis — like Slack limiting access to your messages to train other tools.
Ian Smith
Head of Strategy, PlayerZero

CONVERGENCE OF METRICS, LOGS AND TRACES

Observability will evolve into a full-system intelligence layer that blends telemetry with stateful operational data. Rather than treating metrics, logs, and traces as separate pillars, firms will unify them with contextual datasets using flexible pipelines and interactive analysis tools. Platforms that can ingest and process all of this data in real time will give teams the ability to diagnose anomalies before outcomes are affected. This convergence will be especially valuable in finance, where small latency shifts or dependency failures can have immediate business impact.
Robert Cooke
CEO, 3forge

GENAI TRANSFORMS LOG ANALYSIS

Generative AI will transform the way we store, retrieve, and read the essence of issues by analyzing logs. In 2026 and beyond, logs will be analyzed using natural language querying, and narrow LLMs will be trained to summarize, contextualize, and suggest steps to find the root cause of an issue and fix it. No-sampling, full-fidelity log ingestion is also replacing traditional sampling and storage techniques, which will further be improved using AI's ability to correlate. Logs will see deeper integration into observability platforms, which will provide the backup to achieve near-real-time incident detection and resolution.
Srinivasa Raghavan Santhanam
Director of Product Management, ManageEngine

ANALYST REPORT: 2025 Gartner® Magic Quadrant™ for Digital Experience Monitoring

AGENTIC LOG ANALYSIS

Log analysis for app and IT performance: By 2026 logs will be refined and consumed entirely by agents. As agents take over analysis, the log becomes a richer and more powerful data source because LLMs can interpret and correlate patterns at a scale and speed humans cannot match.
Tucker Callaway
CEO, Mezmo

LOG ANALYSIS BECOMES SEMANTIC

Log analysis will become truly semantic. AI models will interpret logs as structured narratives rather than token streams, enabling deep correlation across logs, traces, and metrics without rigid schemas. Unstructured log data will finally become reliably actionable at scale.
Vladimir Mihailenco 
CEO, Uptrace

Go to: 2026 Observability Predictions - Part 8, covering outages and downtime.

Related Links

Hot Topics

The Latest

5 Takeaways from the Observability Forecast for Retail and eCommerce

Seeing is believing, or in this case, seeing is understanding, according to New Relic's 2025 Observability Forecast for Retail and eCommerce report. Retailers who want to provide exceptional customer experiences while improving IT operations efficiency are leaning on observability ... Here are five key takeaways from the report ...

Maximizing Impact Amid Constraints: The Role of Automation and Orchestration in Federal IT Modernization

Technology leaders across the federal landscape are facing, and will continue to face, an uphill battle when it comes to fortifying their digital environments against hostile and persistent threat actors. On one hand, they are being asked to push digital transformation ... On the other hand, they are facing the fiscal uncertainty of continuing resolutions (CR) and government shutdowns looming near and far. In the face of these challenges, CIOs, CTOs, and CISOs must figure out how to modernize legacy systems and infrastructure while doing more with less and still defending against external and internal threats ...

The SRE Report 2026: Reliability Is Being Redefined

Reliability is no longer proven by uptime alone, according to the The SRE Report 2026 from LogicMonitor. In the AI era, it is experienced through speed, consistency, and user trust, and increasingly judged by business impact. As digital services grow more complex and AI systems move into production, traditional monitoring approaches are struggling to keep pace, increasing the need for AI-first observability that spans applications, infrastructure, and the Internet ...

The Hidden Data Engineering Work Required to Make Enterprise AI Viable

If AI is the engine of a modern organization, then data engineering is the road system beneath it. You can build the most powerful engine in the world, but without paved roads, traffic signals, and bridges that can support its weight, it will stall. In many enterprises, the engine is ready. The roads are not ...

Why AI Is the Differentiator for Operationally Resilient Organizations

In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...

Escaping Pilot Purgatory: How AI Becomes an Operational Advantage

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Closing the Gap in Modern Tech and the Tools Meant to Monitor Them

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 ...

MEAN TIME TO INSIGHT Podcast - Episode 23: NetOps Labor Shortage

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Why FinOps Rewrote Its Mission and What It Signals for Technology Management

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 ...

Clearing the Path to AI: Why Vendor Consolidation Matters Now

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 ...