Yes, we’ve seen this pattern where standard metrics look fine but trust in the model slowly erodes. In our case, the issue wasn’t a sudden performance drop but rather small inconsistencies in specific segments that kept creeping in. A few examples:

• Certain user groups or data patterns started showing slightly worse predictions over time

• Edge cases that used to be rare became more common, leading to more manual overrides

• The model’s outputs stopped aligning with expected business outcomes even though accuracy/AUC stayed steady

What helped us was adding deeper segment-level monitoring rather than relying on aggregate metrics alone. Once we looked at performance broken down by key cohorts or feature buckets, we could see that some segments were drifting earlier than the overall metric.

We also found that clear communication with stakeholders about what the metrics actually mean was important. Sometimes people lose trust not because the model is failing badly, but because they see inconsistencies that aren’t reflected in high-level numbers.

From what I have seen, the issue is rarely a single broken model. It is usually a slow erosion of trust.

Aggregate metrics keep looking fine because they smooth over where the model is actually failing. The early signals tend to show up at the edges: specific segments, new behaviors, or moments where humans start overriding outputs “just to be safe.” That human intervention is often the first real monitoring signal.

What helped surface problems earlier was a combination of three shifts:

First, segment-level monitoring instead of global accuracy. Breaking performance down by customer type, geography, recency, or data source made drift visible long before top-line metrics moved.

Second, tracking human overrides and workarounds as first-class signals. When people stop trusting a model, they adapt quietly. Capturing where and why that happens reveals issues faster than dashboards.

Third, reframing success metrics from model performance to decision impact. Asking “Did this model change the decision in the right direction?” surfaced failures that pure ML metrics missed.

In hindsight, the models did not fail suddenly. The feedback loop did. Once monitoring was aligned to how decisions were actually made, the inconsistencies became much easier to catch early.