You don’t need to be the best at AI. You need to be clear about where you’re useful. People trust clarity more than complexity. If someone can explain what you do in one sentence, you’re doing it right. More clarity going about our work is more trust!! :-- more suggestions to be good at work and being more trusted ??

Built document extraction system. 99.4% accuracy on test data. Client lost $42,000 in first month. The 0.6% errors hit their highest-value transactions. WHAT WENT WRONG: System accuracy breakdown: - 99.9% accurate on standard invoices - 96% accurate on invoices $5,000-$10,000 - 91% accurate on invoices $10,000-$50,000 - 87% accurate on invoices over $50,000 The bigger the transaction, the worse the accuracy. And that's where the money was. THE $42,000 INCIDENT: Invoice: $420,000 construction vendor payment Extracted amount: $42,000 (missed a zero) Payment processed: $42,000 Vendor called: "Where's the other $378,000?" Bank reversal fees: $850 Staff time to fix: 12 hours Client trust damage: Severe WHY HIGH-VALUE INVOICES FAIL: - Custom contract formats - Handwritten notes and exceptions - Multi-page complex layouts - Non-standard payment terms - Executive approval signatures All the edge cases. All on the expensive invoices. THE FIX - THREE VALIDATION LAYERS: LAYER 1: Format Validation - Extracted amount matches currency format? - Dates within valid business ranges? - Line items actually sum to total? - All required fields present? LAYER 2: Business Rule Validation - Amount within vendor's 6-month history range? - Payment terms match vendor profile? - Missing PO number for amounts over $5,000? - Unusual discounts or adjustments flagged? LAYER 3: Confidence-Based Routing - High confidence (>95%) + Low value (<$5,000) = Auto-process - Medium confidence (85-95%) OR Medium value ($5-20K) = Review queue - Low confidence (<85%) OR High value (>$20K) = Always human approval RESULTS AFTER IMPLEMENTING LAYERS: - Zero high-value errors: 8 months and counting - Human review required: Only 8% of documents (was 100%) - Processing speed: Still 82% faster than full manual - Errors caught before processing: 23 incidents - Estimated damage prevented: $187,000 THE LESSON: Accuracy percentage means nothing without context. What matters: What happens when it fails? Does it fail safely?