AI did not end the data literacy crisis. It changed it. As machines move from providing information to interpreting it and recommending what to do, the scarce skill is becoming judgment: knowing what to trust, what to question, and what decisions are still ours to make.
Evidence does not make decisions. People interpret it, weigh it, challenge it, and make judgments. A courtroom reveals what leaders can learn about evidence, expertise, confidence, and data-informed decision-making.
Feeling sure doesn't mean you're right. Learn five simple ways to question what feels true, spot what might be influencing you, and make better decisions about what to believe.
The CTO fixed velocity. Metrics improved 4x. The board was pleased. Eight months later, the system collapsed. What happened? The dashboard showed improvement while invisible capacity degraded. This is why smart leaders fail. They're optimizing signals that lie.
Create a free account to get full access. No payment required.
Sign up for free
We teach data literacy as a ladder: data at the bottom, wisdom at the top, climb step by step. But the rungs are not where the work happens. The judgment lives in the gaps between them, and that is exactly where AI now carries us straight past.
Every organization keeps one scoreboard for mistakes and none for missed opportunities. So people optimize the one they can see, and the largest losses never appear as losses at all. They appear as things no one tried.
What a historic Red Sox winning streak reveals about the Two Clocks of decision-making. Every important decision is made between the deadline to act and the time required for evidence to mature. That's where judgment matters most.
When an institution cites a measurement you cannot see, it hands you a conclusion and calls it evidence. Two World Cup rulings show why that gap matters far beyond sport, and why the fix is not better technology but a different question.
