According to IEEE Spectrum, in late‑stage testing of a distributed AI platform, dashboards flash “healthy” while users report decisions gradually slipping. Engineers find no crashes or sensor failures, yet the system’s output drifts from its design. The silent drift challenges conventional monitoring, forcing teams to rethink reliability metrics. Practitioners must add drift‑detection checks to keep AI trustworthy.