Embedded intelligence

The weaving of machine intelligence into familiar workflows, transforming standalone AI tools into extensions of the actor’s existing ways of thinking and acting.

Understanding intent

Translating user’s fuzzy, high-level goal into a precise, structured command the system can execute. This process involves two key steps:

Intent Detection

The system identifies signals that indicate a user’s goal. These signals can be:

• Keywords and natural language: Direct text input.
• User actions: Selecting text, dragging an element, hovering over an item for a certain duration.
• Navigation paths: The sequence of pages or views a user visits can imply a larger goal.
• Contextual cues: The current state of the application, the type of data being viewed, or the user’s role.
• User history

Intent interpretation

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To-do

• Continuous feedback loop — the actor’s reaction to the embedded intelligence (accepting or rejecting a suggestion) provides feedback for improving the underlying model.

Resources & references

• Heer, J. (2019). Agency plus automation: Designing artificial intelligence into interactive systems. PNAS.
• Salovaara, A. (2004). Six modes of proactive resource management.
• Noessel, C. (2017). Designing agentive technology.
• Tennenhouse, D. (2000). Proactive computing.
• Nardi, B., Miller, J. R., & Wright, D. J. (1998). Collaborative, programmable intelligent agents. Communications of the ACM, 41(3), 96–104.