THE CASE: Blind Trust in AI
Rajesh's AI lead-scoring tool ranked a major enterprise lead as "low priority." Sales team ignored it. Months later they discovered it was a ₹50L opportunity. AI was wrong. But no one questioned it.
Automation bias: Humans over-rely on AI without critical evaluation. 35% of AI decisions are suboptimal (Gartner). Critical questioning reduces AI-driven errors by 50% (IBM).
The Evidence
Automation bias: Over-reliance on AI (MIT)
35% of AI decisions suboptimal (Gartner)
Critical questioning: 50% fewer AI errors (IBM)
The AI Red Team Framework
How to Question AI Effectively
- What data did it use?
- What could it miss?
- Does this make intuitive sense?
- What's the cost of being wrong?
Assign one person to challenge AI output on critical decisions. Document "AI Oops" moments.
The Experiment
"AI Red Team" for 4 weeks on critical AI decisions. Track: Better outcomes? Fewer errors?
Sources
- MIT. Automation Bias in Human-AI Collaboration. 2023.
- Gartner. AI Decision-Making. 2023.
Key Takeaways
- Blind trust in AI = disasters
- Assign "AI Red Team" to challenge critical recommendations
- Ask: What data? What's missing? Does this make sense?