Infrastructure dashboards offer a sense of control: CPU utilization, error rate, request latency, queue depth. These measures tell engineers whether systems behave as designed. They do not necessarily tell the business whether those systems produce value efficiently. An API may return HTTP 200 in twenty milliseconds while its caller retries six times, a human corrects the result and the customer never completes the intended task. Operational health is necessary, but economic usefulness is a different dimension.
The missing link is a meaningful unit of work. A booking finalized without manual repair, a payment successfully reconciled, a license renewal completed, a report whose definitions are accepted or a notification demonstrably delivered. Each unit has a journey across services, and each journey has costs: compute, storage, provider fees, retries, human interventions and recovery. When metrics stop at service boundaries, the cost of the real outcome disappears.
This does not require exposing customer-sensitive business content inside observability tools. Correlation identifiers, tenant-scoped aggregates, status transitions and audited links to restricted evidence can describe the lifecycle without copying entire records into logs. In fact, indiscriminate logging can create privacy and security liabilities. A mature observability design is strict about what it collects and deliberate about what decisions its measurements support.
Build a small scorecard for a few high-consequence workflows. Include successful outcomes, cost per outcome, time to outcome, recovery time and unexplained exceptions. Then ask a harder question: are teams rewarded for optimizing a service metric that makes the business outcome worse? A reliable platform needs engineering visibility and economic context together. Otherwise, it can become faster at doing work nobody should be paying for.