Innovation & Growth

Playing to Win (Lafley & Martin)

Tests strategy coherence across five integrated choices: aspiration, where-to-play, how-to-win, capabilities, management systems

Rubric Type

qualitative-impact

Complexity

medium

Extractor

strategy

Required Inputs

SolveRight's AI extractor automatically derives these data points from your decision description:

  • ✓winning aspiration
  • ✓where to play
  • ✓how to win
  • ✓required capabilities
  • ✓management systems

Best For

ExecutivesConsultantsFounders

How Playing to Win (Lafley & Martin) Works in SolveRight

When you run a decision through SolveRight, Playing to Win (Lafley & Martin) is one of up to 155 frameworks that analyze your options simultaneously. The AI extractor identifies 5 key data points from your decision description, then the qualitative-impact rubric computes a normalized 0-100 score for each option. This score is combined with results from other frameworks to produce your overall ranking, with contradiction detection highlighting where Playing to Win (Lafley & Martin)disagrees with other methodologies.

Playing to Win (Lafley & Martin) — Frequently Asked Questions

What is Playing to Win (Lafley & Martin)?+
Tests strategy coherence across five integrated choices: aspiration, where-to-play, how-to-win, capabilities, management systems. In SolveRight, Playing to Win (Lafley & Martin) uses a qualitative-impact rubric to compute a normalized 0–100 score for each option.
When should I use Playing to Win (Lafley & Martin)?+
Playing to Win (Lafley & Martin) is best suited for Innovation & Growth decisions. It evaluates factors like winning aspiration, where to play, how to win, making it valuable when you need exploring new opportunities and creative problem-solving.
How does SolveRight use Playing to Win (Lafley & Martin)?+
SolveRight runs Playing to Win (Lafley & Martin) alongside up to 154 other frameworks simultaneously. The AI extractor identifies 5 key data points from your decision description, then the qualitative-impact rubric computes deterministic scores. If Playing to Win (Lafley & Martin) disagrees with other frameworks, contradiction detection highlights the divergence.
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