Problem Solving

Problem solving is the work of reducing uncertainty until the next useful action is clear. Strong problem solvers do not rush to answers. They define the real gap, use evidence, test small changes, and learn from the result.

The working loop

Observe → Define → Explain → Choose → Test → Learn
   ↑                                          │
   └──────────────── repeat ──────────────────┘

1. Observe reality. What is happening, for whom, and how often?
2. Define the gap. What should happen instead?
3. Explain carefully. Which causes fit the evidence? What remains unknown?
4. Choose a response. Prefer the smallest useful and reversible step.
5. Test the result. Decide the success and failure signals before acting.
6. Learn and repeat. Keep, change, or stop based on what happened.

Questions that improve thinking

• What outcome matters?
• What do we know, and how do we know it?
• What are we assuming?
• Who sees the problem differently?
• What is the smallest test that could change our mind?
• What new risk could our solution create?
• How will we know the problem is actually better?

Problem solving with AI

AI can summarize evidence, challenge a theory, generate options, and draft an experiment. It can also repeat a false assumption or create a convincing answer without enough context.

Ask AI for alternatives and disconfirming evidence, not only agreement. Verify important claims against the system, users, data, and primary sources.

Learning path

References

• Lean Enterprise Institute: A3 problem solving
• DORA: State of AI-assisted Software Development 2025
• NIST AI Risk Management Framework