Sell better with AI

· Source: AI + IQ · Field: Business & Management — Sales & Commercial Development, Marketing, Branding & Advertising, Project & Product Management · Depth: Intermediate, short

Summary

This article introduces a narrative-based approach to human decision-making, asserting that individuals primarily make important choices by constructing and favoring specific stories. It challenges the notion of purely rational decision-making, highlighting that most significant decisions, from hiring to personal choices, are driven by underlying narratives rather than simple cost-benefit analyses. The author proposes that to influence someone, one must craft a compelling story in which the desired outcome is embraced. The article then addresses the limitations of current AI "digital twin simulations" for persuasion, which often produce overly rational and unrealistic models. It offers a "megaprompt" designed to help AI create a more human-like digital twin by interviewing the user, modeling the target person's decision-making story, and identifying their deeper narrative to generate concrete influence strategies, including better framings and language.

Key takeaway

For AI Product Managers developing persuasion tools, recognize that generic "digital twin" simulations fail because humans are not purely rational. Your focus should shift to AI models capable of understanding and generating narrative-driven influence strategies. Implement systems that interview users to build a target's decision-making story, surfacing deeper narratives to create more effective and human-centric persuasion prompts.

Key insights

Human decisions are primarily narrative-driven, requiring compelling stories to influence outcomes.

Principles

Method

The proposed method involves using a "megaprompt" to train AI to build a human-like digital twin by modeling a target's decision-making story and underlying narrative, then generating influence strategies.

In practice

Topics

Best for: Prompt Engineer, AI Product Manager, Consultant

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Editorial summary, takeaway, and curation by AIssential. Original article published by AI + IQ.