AIOps adoption is accelerating, but many organizations are finding that automation alone does not deliver meaningful operational gains without a consistent observability foundation.
Platform engineering teams are emerging as critical enablers, standardizing how metrics, logs, traces, and system health signals are collected and correlated across increasingly complex, distributed environments.
This unification enables AIOps tools to move beyond alert noise reduction toward more advanced capabilities, such as intelligent incident grouping, root cause analysis, and, ultimately, self-healing systems.
Without that baseline, efforts to automate operations risk adding complexity rather than reducing it, reinforcing the need for platform-led approaches that turn fragmented telemetry into actionable, AI-driven insight.
Jim Mercer, IDC program vice president, software development, DevOps and DevSecOps, says organizations often struggle with “organic” telemetry debt, where inconsistent schemas and fragmented naming conventions create a data “Tower of Babel” that cripples AIOps.
“ML works best when there is consistency, completeness, and correlation. So, these data gaps lead to noise and to RCA failures,” he says. “Otel can help provide a vendor-neutral semantic layer that standardizes attributes and structures across the stack.”
He points out that some platform teams are adopting a two-tier telemetry pipeline architecture utilizing the Otel Collector to normalize schemas, enrich data, and enforce cardinality and governance before signals reach storage backends.
Donnie Page, solutions engineer, Itential, says the role of platform teams is evolving from building dashboards to engineering the data foundation that makes AIOps viable.
Rather than simply collecting telemetry, these teams are now responsible for ensuring that metrics, logs and traces are standardized, contextualized and reliable enough for AI systems to act on.
“The role of the platform team has shifted from ‘dashboard builders’ to ‘data architects,’” Page says, emphasizing that the priority is creating a trusted data supply chain in increasingly noisy, distributed environments.
To achieve that, platform teams are adopting structured approaches to unify telemetry, including defining “golden paths” to standardize outputs, establishing a single source of truth to reduce false positives, and using dynamic topology mapping to create real-time dependency graphs across services.
These strategies allow AI systems to better understand how incidents propagate across hybrid and microservices-based architectures.
“The challenge isn’t just collecting data—it’s engineering a data supply chain that AI can actually trust,” Page says.
Organizations are moving beyond reactive alerting toward automated remediation by codifying operational responses into repeatable, testable workflows embedded directly into the platform.
Instead of relying on manual intervention, teams are implementing versioned remediation procedures—such as pod restarts or deployment rollbacks—through automation frameworks like Kubernetes controllers and workflow engines, with built-in safety controls to limit blast radius and prevent unintended impact.
“The journey to self-healing will likely require a mature progression from reactive alerting to the execution of well-defined, versioned remediation procedures,” Mercer says, highlighting the need for disciplined, auditable automation.
At the same time, the shift to AIOps-driven environments requires both architectural and organizational change, moving from siloed monitoring toward unified platform observability.
Teams are adopting GitOps principles for traceable remediation and using internal developer platforms as a centralized source of truth, while SREs transition from manual responders to automation engineers who codify and test runbooks.
This evolution is enabling systems to reason over dependencies, execute remediation actions and connect observability directly to DevOps workflows, particularly as AI-driven and agentic workloads increase operational complexity.
“AIOps is no longer just about surfacing alerts; it is increasingly about systems that can reason, act and bridge observability with execution,” Mercer says.
Page says transitioning to AIOps is an evolution from deterministic operations (where we tell the machine exactly what to do) to probabilistic operations (where we tell the machine what the desired outcome is).
“This requires an architecture that is contextualized and a culture that prioritizes ‘learning from data’ over ‘following a manual,” he explains. “Organizationally, this requires a shift from firefighting to orchestration.”
In this mode, teams stop managing individual alerts and start managing the AI models and automation logic that handle those alerts.
“AIOps isn’t about replacing the engineer; it’s about replacing the toil,” Page says. “Platform engineering provides the standardized foundation that allows engineers to stop being the glue between systems and start being the architects of them.”