THE CASE: Misinterpreted AI Insights
Kunal's team gets AI insight: "Feature X drives 23% more engagement." They build it. Huge investment. Later they discover: biased sample, misleading insight. The 23% was correlation, not causation. ₹15L wasted.
65% lack basic data literacy (PwC). They can't spot biased samples, correlation vs. causation, or statistical significance. Misinterpreted insights cause 40% of failed launches (Gartner).
The Evidence
65% lack data literacy (PwC)
Misinterpreted insights: 40% of failed launches (Gartner)
Data literacy training: 50% fewer bad decisions (IBM)
The 3 Critical Questions Framework
Train Your Team on These Questions
- Sample size: How much data? Is it statistically significant?
- Bias: Does the data reflect reality or a skewed subset?
- Causation: Is this correlation or causation?
Assign a "Data Skeptic" to challenge AI insight interpretation before acting.
The Experiment
Before acting on AI insights for 4 weeks, require "Data Skeptic" review. Track decision quality improvement.
Sources
- PwC. Data Literacy in the AI Era. 2023.
- Gartner. The Cost of Misinterpreted Data. 2023.
Key Takeaways
- 65% of employees can't properly interpret AI-generated insights
- Always ask: Sample size? Bias? Causation?
- Assign "Data Skeptics" to challenge interpretation