Leaders are often trapped "inside the box" of their own assumptions when making critical decisions. By providing AI with context and assigning it an expert role (e.g., "world-class chief product officer"), you can prompt it to ask probing questions that reveal your biases and lead to more objective, defensible outcomes.

For big, uncertain choices like schooling, use a formal process: Frame the question, Fact-find without deciding, set a time for a Final decision, and schedule a Follow-up. This structure prevents endless deliberation by acknowledging you can't be 100% certain but can still move forward confidently and revisit the choice later.

When faced with imperfect choices, treat the decision like a standardized test question: gather the best available information and choose the option you believe is the *most* correct, even if it's not perfect. This mindset accepts ambiguity and focuses on making the best possible choice in the moment.

The goal of testing multiple AI models isn't to crown a universal winner, but to build your own subjective "rule of thumb" for which model works best for the specific tasks you frequently perform. This personal topography is more valuable than any generic benchmark.

Instead of complex prioritization frameworks like RICE, designers can use a more intuitive model based on Value, Cost, and Risk. This mirrors the mental calculation humans use for everyday decisions, allowing for a more holistic and natural conversation about project trade-offs.

Elite decision-making transcends pure analytics. The optimal process involves rigorously completing a checklist of objective criteria (the 'mind') and then closing your eyes to assess your intuitive feeling (the 'gut'). This 'educated intuition' framework balances systematic analysis with the nuanced pattern recognition of experience.

A strong gut feeling or intuition should be treated as a critical decision-making tool. For many entrepreneurs, this intuitive 'knowing' consistently leads to the right choices, even when it contradicts logical analysis, making it a superpower to be trusted and honed.

A common cognitive error is justifying a decision with a long list of minor benefits ("blended reasons"). A robust decision should be justifiable based on one single, strong reason. If that primary reason isn't compelling enough on its own, the decision is likely weak.

Intuition is often overridden in professional settings because it's intangible. A bad decision backed by a rational explanation is often more acceptable than a good one based on a "gut feeling," which can feel professionally risky.