Interactive Designer
Design a 5Ps Loop
Turn a real strategic challenge into a living loop: 〰 perceive the system, ▲ perform with intent, ● build a portfolio, ●〰 choose what matters, and ▲● build learning architecture.
〰 Cultural sensing · Sensing intelligence
Begin not with answers, but with attention. Perceive combines lived experience, weak signals, prospective sensemaking, and AI pattern recognition to understand what truly matters before solution-building begins.
Chapter example
In the chapter, a radiologist and AI system together detect early-stage cancer that either might have missed alone. Human clinical intuition plus AI pattern recognition creates integrated perception.
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▲ Intentional execution · Execution intelligence
Insight without action is delay. Perform converts strategic clarity into concrete moves, using AI to scale execution while preserving human intent, values, and strategic boundaries.
Chapter example
The Marimekko example shows performance architecture: AI executes many customer-facing decisions, but human creative intent and brand principles define what the system optimises for.
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● Adaptive resilience · Option intelligence
Don’t seek one perfect strategy. Build an option system that gets stronger from uncertainty — safe bets, bold experiments, and learning loops that turn volatility into capability.
Chapter example
The chapter uses Taleb’s barbell strategy and the clay pot lesson: quality often emerges from quantity, variation, and learning — not from perfecting one fragile plan.
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●〰 Strategic judgement · Discernment intelligence
In the AI age, the problem is not too few options — it is too many. Pause/Promote designs filtering architectures that combine AI pattern recognition with human values, context, and judgement.
Chapter example
The hiring example shows AI filtering thousands of applications while humans judge cultural fit, team dynamics, and mission alignment — better scale without losing discernment.
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▲● Systematic learning · Learning intelligence
Progress is not making one thing better. It is building systems that keep getting better — learning architecture that captures outcomes, synthesises meaning, and transfers capability across the organisation.
Chapter example
Shell’s predictive maintenance and JPMorgan’s COiN show learning systems at scale: machines detect patterns continuously while humans guide what improvement actually means.
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