AI and the New Rules of Observability

Observability has shifted from a niche engineering concern to a core competency for enterprises running cloud-native, distributed and AI-driven systems. Yet many organizations remain trapped in a reactive posture—staring at static dashboards, chasing alerts and stitching together siloed data—when the real mandate is proactive, predictive insight.

Leonard Bertelli, senior vice president of enterprise and AI solutions at FPT Americas, argues that the next era of observability blends smarter telemetry with cultural change. With two decades of experience in modernization and large-scale architectures, he explains how AI alters the stakes, why legacy practices fall short and what it takes to move from monitoring to understanding.

From Monitoring to Predictive Insight

The gap between “monitoring” and true observability is as much about people as it is about platforms. Many enterprises still bolt on dashboards and alerts after launch, training teams to react to incidents rather than design systems that can explain themselves. In siloed environments, operations teams are tasked with uptime while developers ship features, creating a handoff where the builders aren’t always the debuggers.

Modern stacks complicate things further. Microservices and AI pipelines emit high-dimensional, high-cardinality telemetry that overwhelms threshold-based tools. Rule-driven alerts catch “known knowns” but miss nuanced patterns and emergent failures. Organizations that are maturing embed observability into CI/CD, push for shared ownership of reliability and invest in platforms that correlate logs, traces and metrics in real time—shifting the focus from red lights to root causes.

How We Got Here: Early Roadblocks

• Siloed signals: Logs, metrics and traces historically lived in separate systems, letting engineers see symptoms without causality. The industry’s embrace of distributed tracing (inspired by internal systems such as Google’s Dapper and later standardized via OpenTelemetry) emerged to close this gap.
• High cardinality: Time-series backends and early monitoring stacks struggled as labels and dimensions exploded, obscuring insight under a deluge of unique values. Newer observability tools were built specifically to query and reason over this complexity.
• Static dashboards: Fixed views couldn’t keep up with elastic, failure-prone topologies. As chaos engineering made clear, dashboards alone rarely surface emergent, cross-service failure modes.

AI’s New Blind Spots

AI systems introduce failure modes that infrastructure graphs won’t catch:

• Model and data drift: When input distributions shift, models degrade—even though the infrastructure looks healthy. High-profile chatbot failures and content moderation breakdowns underscore that semantic quality needs its own observability.
• Hidden technical debt: Feature stores, retraining jobs and feedback loops create brittle dependencies. Pipelines can fail “quietly,” producing stale or corrupted features without tripping uptime monitors.
• Opaque decisions and bias: A model can be “up” and still make unfair or unsafe choices. Without monitoring outputs and outcomes—not just resources—organizations miss systemic issues like bias from skewed training data.

When Observability Becomes a Burden

More telemetry isn’t always better. Bertelli points to three common inflection points where observability flips from enabler to cost center:

• Runaway storage and egress: Retaining high-cardinality data indefinitely drives costs and slows queries.
• Query and compute pressure: Ad hoc investigations and long-range correlations can starve production resources.
• Signal-to-noise collapse: Excessive, redundant or low-value metrics bury meaningful anomalies.

The remedies are pragmatic: tiered retention (hot, warm, cold), adaptive sampling for traces, cardinality budgets, edge aggregation, and cost-aware query policies. In short, measure what matters—and prove it.

AI-Enhanced Detection vs. Traditional Rules

Classic monitoring keys off static thresholds and signatures. It’s effective for predictable failure modes—“CPU above 80% for five minutes”—but brittle when signals are subtle, interacting or novel.

AI-driven detection learns baselines that evolve with workload seasonality, user behavior and deployment changes. By correlating across logs, metrics, traces and even unstructured text, these models surface weak signals that rules miss and can predict incipient failures—latency drift, anomalous dependency calls, memory pressure—before they trigger outages. The result is a shift from rear-view alerts to forward-looking prevention.

Solving the “Garbage In” Paradox

AI is only as good as the telemetry it consumes. If the data is noisy, biased or incomplete, the models echo those weaknesses. Bertelli’s guidance:

• Treat telemetry as a product: Define schemas, ownership and SLAs for observability data. Validate, dedupe and enrich at ingest.
• Beware bias amplification: If teams historically over-index on CPU or network metrics, AI will too—overlooking emerging signals like service-to-service latency or memory leaks. Rebalance training data to reflect current systems, not yesterday’s priorities.
• Human-in-the-loop: Close the feedback loop. Engineers should label false positives, confirm root causes and feed those outcomes back into the detection models.
• Correlate multiple sources: Cross-validate logs, traces, metrics and external cues to reduce blind spots from any single stream.

Governance for the Observability Era

Just as data governance matured for analytics and AI, observability needs its own guardrails. Define the metrics that matter, enforce naming and cardinality standards, monitor drift in telemetry itself and ensure team-level parity so one service’s rich data doesn’t drown out another’s sparse signals. Clear ownership and budgets prevent sprawl; published runbooks and SLOs keep responses consistent.

The Road Ahead

The future of observability is predictive, correlated and explainable. Technology must unify signals and scale economically; culture must reward shared reliability and design-for-debuggability. For enterprises embracing AI, that also means watching the models as closely as the machines—observing not just uptime, but outcomes.

The payoff is profound: fewer firefights, faster resolution and systems that tell you what’s going wrong—before your customers do.