Testing AI Presentation Workflows: 10 Excel Traps Expose 3 Failure Modes
North_Teacher_7522 · reddit · 2026-07-22
The author tested an AI presentation workflow (using their product Julius AI) against messy data, creating a synthetic SaaS workbook with 10 planted errors. The most critical trap was a -$65,000 churn adjustment entered in a $000 column, which would falsely translate to a $65M loss if read literally.
The experiment revealed three distinct failure classes in AI data processing:
- Deterministic errors: Issues like unit mismatches or duplicates that the AI can concretely correct based on evidence.
- Semantic conflicts: Disagreements that can't be solved by arithmetic alone (e.g., conflicting definitions of 'active customer'). The AI displayed both figures and highlighted the gap rather than silently picking one.
- Non-decision-grade data: For unvalidated anomalies (like a 10x marketing outlier), the AI explicitly refused to treat the metric as settled, correctly deciding 'do not use this metric yet.'
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