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Trust AI Outputs or Require Human Review? Binary Business - BB-13

A hospital put a human review layer on every AI-generated patient summary. Six weeks later, reviewers were approving 97% without changes. One told me, "I stopped reading them after day three." That's not human oversight. That's a screensaver with a pulse.

In this episode, I break down when to trust AI outputs and when to require human review, including a four-rung "Trust Ladder" framework that tells you exactly how to scale oversight as AI performance improves.

What You'll Learn:
• Why humans are terrible reviewers of AI output (and the 4% error rate that ends careers)
• The editor getting paid $85/hour to change 11 words per article — "That's not quality control. That's an expensive ego edit."
• How an AI-generated contract clause got discovered during a deposition (the worst time to find out your AI made up law)
• Happy birthday, Karen — your compliance report is late because we were reviewing your cake emoji
• The senior manager who held proposals 48 hours so his "fingerprints" were on everything
• The Trust Ladder: Full review → Sampled → Exception → Audit (and how to define the criteria for climbing)

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Timestamps:
0:20 - Context: The Trust Question Nobody Wants to Answer
2:30 - Binary 1: Trust AI Outputs
5:15 - Binary 0: Require Human Review
7:45 - ABCD Breakdown
8:00 - Audience: Segment by Error Tolerance
10:15 - Build: Confidence Scoring and Exception-Based Review
12:30 - Convert: When Review Becomes a Power Position
14:30 - Deliver: The Trust Ladder Framework
16:30 - The Call: Catching Errors or Catching Feelings?

About William Guidry:
Will Guidry is CEO and Founder of EntreNova AI, a Houston-based Microsoft Cloud Solutions Partner. He helps operators build review architectures that match risk instead of defaulting to oversight theater, using the Binary Decision Scorecard framework.

Previous Episode: BB-12 - Change Culture or Change Tools First?
Next Episode: BB-14 - Optimize Individuals or Optimize Teams?

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