The Cost of Silent Errors in Automated Reporting
Practical guidance on the cost of silent errors in automated reporting and what to do about it.
The point
The Cost of Silent Errors in Automated Reporting is not a technology statement. It is a decision quality statement.
The decision lens for this topic
Ask: "What would a good decision look like without AI?" Then ask: "What does AI change - speed, coverage, consistency, or risk?" If you cannot answer, you are not ready to automate this decision.
Why this matters
AI systems can look compliant while still producing harmful outcomes. Compliance is often about process, while risk is about consequence.
Where organizations get surprised
- A model is "accurate" overall but unfair on important subgroups
- Reporting is consistent but wrong due to a silent data issue
- Controls exist on paper, but no one is accountable in practice
If an AI output can trigger money movement, customer impact, or regulatory exposure, you need controls, logs, and a sign-off chain - not just a prompt.
Practical guardrails
- Define prohibited uses (no automated approvals for high-stakes actions)
- Require traceability (inputs, prompts, outputs, approvals)
- Monitor drift (data changes, policy changes, edge cases)
- Run "red team" tests (try to break it intentionally)
Risk is reduced by governance and controls - not by confidence in the model.