In this conversation, Jay Stansell and Conor Joyce delve into the significance of feature definitions in AI applications, emphasizing the need for clear definitions to drive meaningful outcomes. They discuss the transition from product-led growth to behavior-led growth, highlighting the importance of understanding user behavior and the role of behavioral science in product development. Conor shares insights on practical frameworks and resources for improving feature definitions and the overall impact of AI in product design.

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Takeaways

* Feature definitions are crucial for effective AI applications.

* Understanding user outcomes is more important than just outputs.

* Behavioral science can enhance product development processes.

* The tech industry needs to focus more on research and understanding user behavior.

* There is a shift from product-led growth to behavior-led growth.

* AI should solve real problems rather than just follow trends.

* Organizations should not overlook established ML solutions for new AI trends.

* Practical frameworks can help improve feature definitions and product outcomes.

* Investing time in research can uncover significant growth opportunities.

* Career advancement can be achieved by focusing on impactful AI features.

Chapters

* 00:00 Introduction to AI and Impactful Features

* 08:03 The Importance of Feature Definitions

* 12:12 Outcomes vs Outputs in AI

* 21:18 Behavioral Science in Product Development

* 30:03 The Shift from Product-Led to Behavior-Led Growth

* 33:14 AI's Role in Feature Definitions

* 39:28 Practical Frameworks and Resources for Improvement

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