Why Decisions Go Wrong

The Decision Problem

A McKinsey study found that executives spend 37% of their time making decisions — and more than half of that time is spent ineffectively. Not because the decisions are impossible, but because the decision process is broken.

Most people approach decisions the same way: think about it, talk to a few people, weigh pros and cons in their head, and go with what feels right. This works for everyday choices. For high-stakes decisions, it’s a recipe for regret.

What You’ll Learn

This course gives you a systematic approach to decisions that matter:

• Frameworks that structure your thinking instead of leaving it to chance
• Bias detection that catches the mental traps you don’t know you’re falling into
• Risk analysis that quantifies uncertainty instead of ignoring it
• Speed calibration that tells you when to decide fast and when to slow down

What to Expect

Each lesson pairs decision science with practical AI prompts. You’ll practice on real decisions — not theoretical exercises. By the final lesson, you’ll have a personal decision system you can apply to any significant choice.

Why Decisions Fail

Decisions go wrong for predictable, preventable reasons:

1. Framing effects. How a problem is presented changes the solution. “This surgery has a 90% survival rate” and “This surgery has a 10% mortality rate” are identical — but people choose differently depending on which frame they see.

2. Confirmation bias. Once you lean toward an option, you unconsciously seek information that supports it and dismiss information that contradicts it. You’re not analyzing — you’re building a case.

3. Anchoring. The first number you hear dominates your thinking. In salary negotiations, the first offer sets the range. In pricing, the first number you see becomes the reference point.

4. Status quo bias. Staying put feels safer than changing, even when the evidence supports change. The pain of potential loss looms larger than the benefit of potential gain.

5. Emotional reasoning. Fear, excitement, and fatigue all warp judgment. You reject a good opportunity because you’re anxious, or accept a bad one because you’re excited.

✅ Quick Check: Which of these biases would make you overvalue your current job when considering a new offer?

Status quo bias and loss aversion. Your current job is known — you know the people, the routines, the commute. A new job is uncertain. Your brain amplifies what you’d lose (certainty, relationships, familiarity) and discounts what you’d gain (growth, salary, challenge). AI helps by making you list and weigh both sides equally, without the emotional thumb on the scale.

Where AI Fits In

AI doesn’t eliminate biases — you still have them. But AI creates a structured process that makes biases visible and manageable:

Exercise: Identify Your Decision

Choose a real decision you’re currently facing (or recently made) to use throughout this course:

I need help thinking through a decision. Here's my situation:

Decision: [what you're deciding — career move, purchase, business choice, etc.]
Options I'm considering: [list your current options]
Timeline: [when do I need to decide?]
Stakes: [what happens if I choose wrong? Is this reversible?]
What's making it hard: [why am I struggling with this?]

Help me understand:
1. Is this a reversible or irreversible decision?
2. What category does this fall into (career, financial, relationship, health, business)?
3. What information would make this decision easier?
4. What biases might be affecting my thinking?
5. On a scale of 1-10, how much analysis does this decision deserve?

Key Takeaways

• Smart people make bad decisions because cognitive biases operate automatically — intelligence doesn’t protect against anchoring, confirmation bias, or status quo bias
• Categorize decisions first: reversible decisions should be made quickly; irreversible decisions deserve careful analysis
• AI serves as a structured thinking partner — it organizes options, challenges assumptions, and surfaces blind spots, while you retain final judgment
• Most decision failures come from process problems (how you decide) not intelligence problems (who’s deciding)
• The five predictable failure patterns are: framing effects, confirmation bias, anchoring, status quo bias, and emotional reasoning
• Throughout this course, you’ll apply every framework to a real decision — not theoretical exercises

Up Next: In the next lesson, you’ll learn structured decision frameworks — weighted matrices, pros-cons-mitigations, and decision trees that bring clarity to complex choices.