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AI user design

As explained in First-Party Data Activation

A product-design approach that supports collaborative iteration between human users and AI through natural interaction patterns.

AI user design treats product use as collaborative iteration between people and AI. Instead of requiring users to configure every linkage or action manually, the system proposes work that users can refine through natural language. section 9.3.2

A practical AI user design emphasizes:

  • Showing the evolving work product alongside a dialogue between the human user and AI. section 9.3.2
  • Letting users adjust inferred results, such as unified data models, with natural language. section 9.3.2
  • Combining AI-directed workflows or agents with governance and controls. section 9.3.2

Split-screen campaign workspace

A marketing platform can place the campaign or audience artifact on a canvas while a separate panel lets the user ask for changes, review suggestions, and iteratively refine the result. section 9.3.2

How First-Party Data Activation covers it

How deep it goes
Explains the interaction pattern and split-screen example but stops short of implementation details.
Examples
Uses inferred customer data models and a split-screen canvas with an AI dialogue panel as design examples.
What you can do after
Apply it by designing interfaces where users review, direct, and refine AI-produced work through natural dialogue.
How it connects
Connects natural language interaction with AI-native products, AI-driven workflows, AI agents, and unified data models.
Cover of First-Party Data Activation
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This explainer was generated by Manning's knowledge graph from the text of First-Party Data Activation. The section links point to the parts of the book it draws on.