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.
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.
Evidence can tell us what happened, but it can't decide what that means, how much it matters, or what we should do next.
Evidence does not interpret itself. Two capable people can accept the same facts and still reach opposite conclusions, because the real work happens in the gap between the evidence and the conclusion, and that gap is judgment. A courtroom stages that gap in public. The same moves run quietly in every meeting where someone presents a recommendation.
If you decide from data, you have watched two readings of the same numbers point in opposite directions and assumed one side had it wrong. Usually neither did. Treating "the data said so" as the end of the argument hides the judgment calls that actually drive the decision, which is exactly where the errors live and where they can be caught.
Anyone who decides from evidence they did not gather themselves. The executive weighing a recommendation, the leader reading a dashboard, the manager choosing between two expert opinions. You do not need to out-analyze the expert. You need to question the chain between the evidence and the conclusion.
A set of questions that separate strong evidence from a confident story: how to weigh evidence instead of counting it, how to spot the pull that bends an honest expert toward whoever retained them, how to read a blank in the data, and how to raise real confidence by seeking the strongest case against your own conclusion. It ends on the one question worth carrying into any decision. What does the evidence actually earn us the right to believe?
I am sure you all have seen a situation where two capable people look at the same set of facts and walk away certain of opposite things. It happens between friends, inside families, and in courtrooms.
I have been following one of those courtroom versions here in Massachusetts, the Lindsay Clancy case. I have no intention of arguing what the verdict should be. Three children are dead, and a family is living through something no family should. That is not what this piece is about. What holds my attention, as someone who studies how people use evidence to make decisions, is something different.
The experts are not simply disagreeing about the facts. They are disagreeing about what the facts mean. That distinction reaches well past a courtroom. You have porbably seen a version of this in your workplace. Maybe when two analysts read the same customer data and recommend opposite moves.
In this case, qualified people retained by different sides have examined overlapping records, timelines, behaviors, and interviews, and arrived at materially different conclusions. It is not unusual for two experts to agree on what a record contains and still disagree on what it establishes. None of that requires anyone to lie. Reasonable people can build different interpretations from the same body of evidence. Bias can shape a conclusion without anyone deciding to distort a thing.
A trial makes visible a sequence that organizations blur together every day.
Evidence becomes interpretation. Interpretation becomes explanation. Explanation becomes judgment. Judgment becomes a decision. We talk as though the first thing determines the last thing but it doesn't.
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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.
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.
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.
