That's why observability deserves renewed attention. Organizations need to understand problems faster, identify emerging issues earlier and, where appropriate, explore automated recovery.
But does that mean investing in more monitoring tools? Not necessarily.
Many Tools, One Question
Most large organizations don't rely on a single monitoring tool.
Different tools provide visibility into different layers: the network, the employee's device, applications, cloud services and the infrastructure underneath.
Each answers part of the question.
The harder question is what is actually happening to the employee, and why?
A service might appear healthy from an infrastructure perspective while employees continue to experience slow applications, connectivity issues or interruptions.
This is where Digital Employee Experience (DEX) and workplace observability intersect.
DEX focuses on understanding and improving employees' technology experience, while observability provides the underlying technical signals needed to investigate performance and reliability issues.
Together, they help organizations move beyond infrastructure availability toward understanding the experience employees actually receive.
That's why connecting signals across systems can be more valuable than simply collecting additional telemetry.
The objective isn't more dashboards. It's a clearer understanding of service health and its impact on users.
Observability as a Service: Worth Considering?
Rather than every team managing its own monitoring capabilities independently, organizations can consider delivering observability as a shared service, supported by common standards, centralized visibility and clearly defined ownership.
This could involve existing enterprise platforms, cloud-delivered services, managed providers or a combination of approaches.
The potential value lies in reducing fragmentation, improving consistency and making operational insights more accessible across teams.
Grafana Labs' 2026 Observability Survey reported that 77% of respondents had saved time or money through centralized observability. As a vendor-run survey, this is a useful indication of practitioner experience rather than proof of industry-wide outcomes.
However, shared observability also brings considerations around integration, data ownership, security and the cost of collecting, processing and retaining telemetry.
The question isn't whether every organization needs OaaS. It's whether its existing capabilities provide the visibility and coordination required to manage increasingly interconnected services.
From Detecting Problems to Fixing Them
Monitoring and scripted automation aren't new.
For years, organizations have used telemetry, alerts, scripts and workflows to detect and respond to operational issues.
What AI introduces is the potential to connect these capabilities more intelligently.
AI-assisted tools can help correlate signals across systems, identify patterns, support investigations and recommend corrective actions.
In some scenarios, they can also initiate approved remediation workflows.
Consider an employee experiencing repeated application failures.
Signals might exist across endpoint monitoring, application telemetry and service management systems. Individually, they may appear unrelated. Together, they could indicate a broader issue affecting multiple employees.
Better correlation can help teams investigate faster, while controlled automation may reduce repetitive troubleshooting and accelerate recovery.
But these capabilities have limitations. AI-generated findings may be inaccurate, integrations may be incomplete and automated actions can introduce additional risks.
Not every issue requires an AI agent, and not every remediation should happen without human approval.
The opportunity is worth exploring, but the outcomes must be validated.
Two More Areas Worth Understanding: AIOps and AI Observability
As observability evolves, two related concepts are also worth exploring.
AIOps (Artificial Intelligence for IT Operations) uses AI and analytics to help IT teams identify anomalies, correlate events, investigate incidents and support operational remediation.
AI Observability, on the other hand, focuses on understanding how AI applications and agents themselves perform.
Are they responding reliably? Are their outputs accurate and relevant? Are their actions succeeding? What are the latency and consumption costs?
The distinction is useful: AIOps applies AI to improve IT operations, while AI observability helps organizations monitor and evaluate their AI systems.
Both deserve consideration as enterprises introduce more AI into their technology environments. Neither removes the need for governance, validation or clear ownership.
What Changes, and What Doesn't
What changes:
- Detection: Moving beyond isolated alerts toward identifying patterns, anomalies and potential service degradation earlier.
- Investigation: Using AI-assisted analysis to connect operational signals and reduce repetitive manual triage.
- Remediation: Linking insights to approved workflows, established runbooks and, where appropriate, automated corrective actions.
What doesn't change: Ownership.
Someone still needs to determine which actions can be automated, define the boundaries, assess the risks and confirm that recovery has actually happened.
An automated response might restart a service, execute a script or initiate a recovery workflow.
But what happens if the action fails? What if the problem returns? What if the remediation creates another issue?
Service ownership, change management, operational governance and human oversight remain essential.
An automated fix isn't a resolved problem until recovery is validated. And it isn't governed until someone owns the outcome.
Questions Worth Asking
For organizations exploring observability and AI-driven remediation, a few questions are worth considering:
- Visibility: Do our existing monitoring capabilities provide an end-to-end understanding of service health and employee experience?
- Integration: Where are the gaps between tools, teams and operational data?
- Ownership: Who is accountable for correlating insights and coordinating service recovery?
- Automation: Which operational issues can be safely automated, and which require human approval?
- Validation: How will we confirm that automated remediation actually resolved the issue?
- Cost and value: What will telemetry collection, retention and automation cost, and how will we measure the benefits?
These questions matter just as much as selecting the technology.
Bottom Line
As enterprise environments become more interconnected, observability deserves greater attention.
Whether delivered through existing monitoring platforms, a shared observability service or a combination of capabilities, the objective should remain the same: better visibility into service health, earlier identification of issues and more informed operational decisions.
AI-assisted investigation and automated remediation introduce new possibilities, but they are not substitutes for sound service management, engineering practices or governance.
Organizations don't necessarily need more tools. They need to understand whether their existing capabilities provide the visibility, intelligence and response mechanisms their services require.
The opportunity is worth exploring. The outcomes still need to be demonstrated.
The way we address problems is changing. Accountability isn't.
Stay tuned for more updates...
Further Reading
- Grafana Labs — Observability Survey 2026
- OpenTelemetry — Documentation
- Microsoft Learn — Automation in Microsoft Sentinel
- Microsoft Learn — AIOps and Agentic Operations
- OpenTelemetry — Generative AI Observability
- Dynatrace — Observability
- Datadog — Observability
- Riverbed — Digital Experience Management
- Nexthink — What Is Digital Employee Experience (DEX)?
- Microsoft Learn — Endpoint Analytics Overview
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