Apply the Lafley-Martin strategy cascade framework to AI initiatives. Define winning aspiration, diagnose competitive position, choose where to play and how to win, then cascade choices through teams.
By OperatorRadar Editorial
A.G. Lafley and Roger Martin's 'Playing to Win' framework, published in 2013, provides a five-step strategy cascade: (1) Winning Aspiration—what does winning look like?; (2) Where to Play—which markets, segments, or geographies?; (3) How to Win—what competitive advantage?; (4) Core Capabilities—what must we excel at?; (5) Management Systems—how do we measure and reinforce?. The model emphasizes that strategy is not a document but a cascading set of choices that align the entire organization toward a single definition of winning.
Lafley led Procter & Gamble's transformation in the 2000s by replacing vague mission statements with explicit strategic choices. Martin, a strategy theorist, codified this into a repeatable framework. The 'Playing to Win' approach gained traction in mature, multi-business organizations where strategy drift and misalignment were costly. It was designed for clarity in complex environments—exactly the condition many enterprises face with AI adoption today.
Lafley and Martin argued that strategy fails not from bad ideas but from poor cascade. A CEO might declare 'we will lead in AI,' but if that aspiration isn't translated into specific choices about where to compete, what capabilities to build, and how to measure success, teams optimize locally and contradict each other. The cascade ensures that every team's choices ladder up to the same winning definition. Strategy is a hierarchy of choices, not a hierarchy of people.
AI initiatives expose cascade failures faster than traditional business moves. A data science team might optimize for model accuracy (How to Win: best-in-class ML), while product teams optimize for speed to market (How to Win: rapid deployment), and finance optimizes for cost per inference. Without a cascaded winning aspiration, these teams pull in opposite directions. AI also compresses decision cycles—teams must make Where to Play and How to Win choices in weeks, not quarters. The cascade framework becomes a forcing function for alignment under time pressure.
The core insight holds: strategy is a set of explicit choices, not a vision statement. Winning Aspiration must be specific enough to guide trade-offs (e.g., 'become the fastest-to-insight analytics platform for mid-market retailers' beats 'be an AI leader'). Where to Play and How to Win remain the critical decision points—choosing which use cases, customer segments, or data domains to prioritize, and what competitive advantage you'll defend. The cascade still requires that each level of the organization translate the choice above into their own Where to Play and How to Win. Without this discipline, AI spending becomes a collection of unrelated experiments.
The original framework assumed multi-year strategic stability and clear organizational boundaries. AI initiatives often require rapid pivots based on model performance, emerging vendor capabilities, or competitive moves. A team's 'Where to Play' choice (e.g., customer churn prediction) may become obsolete in six months if a vendor releases a pre-built solution. The cascade also assumes that Core Capabilities are built internally; many AI strategies now rely on outsourced models, APIs, and third-party platforms, making the 'build vs. buy' decision more fluid. Finally, the original framework was designed for product and market strategy; applying it to AI requires translating abstract technical choices (model type, data architecture, governance) into business language.
Use the Playing to Win cascade to align your AI strategy, but compress and iterate the cycle. Start with Winning Aspiration: define what 'winning with AI' means for your business in 12 months (e.g., 'reduce customer acquisition cost by 15% through predictive targeting'). Next, choose Where to Play: which business function, customer segment, or data domain offers the highest ROI and lowest execution risk? Then, define How to Win: what specific AI capability or competitive advantage will you defend? (e.g., 'proprietary customer behavior models' or 'fastest inference latency'). Cascade these choices to your AI team, product teams, and data teams—each should translate the choice into their own Where to Play and How to Win. Finally, establish Management Systems: weekly reviews of model performance, cost per prediction, and business impact, tied to the winning aspiration. Revisit the cascade every quarter, not annually, because AI capabilities and competitive dynamics move faster.
Take your current AI roadmap and map it to the Playing to Win cascade. For each initiative, answer: (1) How does this ladder up to our Winning Aspiration? (2) Which customer segment or business function is this Where to Play choice? (3) What specific competitive advantage does this How to Win choice defend? (4) If you can't answer all three clearly, the initiative may be misaligned. Discuss with your leadership team and either clarify the cascade or deprioritize the initiative.
A.G. Lafley and Roger Martin
Playing to Win: How Strategy Really Works
“The Playing to Win framework defines strategy as a cascade of five choices: Winning Aspiration, Where to Play, How to Win, Core Capabilities, and Management Systems. Each choice must be explicit and aligned to ensure the entire organization pursues the same definition of winning.”
Roger Martin
The Design of Business: Why Design Thinking is the Next Competitive Advantage
“Strategy fails not from bad ideas but from poor cascade and misalignment. Teams must translate strategic choices into their own Where to Play and How to Win to ensure coherent execution.”
Operator interpretation
AI Strategy Cascade Application
“AI initiatives expose cascade failures faster than traditional business moves because decision cycles are compressed and team incentives (accuracy vs. speed vs. cost) often conflict. Weekly reviews and quarterly revisits are necessary to maintain alignment.”
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