"It wasn't incompetence, but rather a loss of clarity in the rush to deliver - a failure of judgment, not execution. I've been in that room before - on the other side. I've watched teams I led ship features that solved problems no one had previously identified. The hard lesson: executing speed without clear judgment gets you to failure faster. Of course, timely execution remains vital, but discovery judgment has become the actual constraint."
"Teams emphasize that judgment can't be automated. Yet, AI clearly performs tasks that resemble judgment, such as identifying patterns, flagging contradictions, and synthesizing insights across dozens of interviews. So what's the distinction that matters? AI excels at pattern-based reasoning: recognizing correlations in data, clustering similar themes, and optimizing within defined parameters. Humans provide meaning-based judgment: interpreting what patterns signify about real needs, deciding which correlations reveal causation, and determining what's worth pursuing given purpose and values."
A software team shipped major features on time and met velocity metrics yet could not articulate which customer problems were solved. Rapid execution without clear discovery judgment leads teams to build features that address no identified need. AI handles pattern-based reasoning—finding correlations, clustering themes, and optimizing within parameters—but cannot assign meaning, infer causation, or prioritize by value and purpose. Human meaning-based judgment interprets patterns, decides which insights matter, and determines strategic direction. The Discovery Judgment Framework aims to develop that capability, with a first component enumerating 19 judgment points where human interpretation guides product decisions.
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