Thread 9: AI Metrics Visibility and Trust Gap
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Full Post Text (Key Excerpt)
“We realized the ‘AI-assisted’ metric is just guessing based on IDE plugin telemetry…”
Why This Matches Ryva ICP
This is an engineering-manager signal quality problem: dashboards look precise, but actual team state remains unclear. It maps to “we don’t know what’s going on” despite reporting layers.
Underlying Problem
The team is using proxy metrics that are disconnected from real delivery decisions and ownership.
Suggested Public Reply (Copy)
If the metric is inferred telemetry, it can’t carry operational decisions. Teams need evidence tied to outcomes: what changed, who owned it, and what risk moved. Otherwise AI reporting becomes theater, not visibility.Suggested DM Idea (Copy)
Your point about guessed telemetry is spot on. If useful, I can share a practical measurement model focused on delivery decisions and risk movement, not raw “AI-assisted” activity counts.