There are two experiential forms of AI. Reactive AI relies on people knowing how to interact with AI. Proactive AI has been common historically, suggesting results and adapting the interface without telling you it’s AI. The potential of combining both remain under-explored, & Designers are the right innovators. Here’s why and a first step towards how.

AI has generally operated in the background. It follows best practice that people care about outcomes; they shouldn’t worry about technology mechanisms. Lately, AI has been increasingly central cast into product experiences, setting expectations upfront & telling you AI is here.

AI and UX have grown from different disciplines. AI concerns itself with data and algorithms; UX concerns itself with usability and aesthetics. Overlaps become increasingly apparent when AI is called to attention in interfaces.

Human-Centered Machine Learning brings focus to users, but ML has already been chosen as the enabling technology. In these cases, a common trap is AI-led problem framing, similar to a hammer searching for nails.

Understanding technology allows for innovation in different layers of complexity, from shaping the underlying model to applications people use. But towards discovering valuable applications for AI, consider starting from experience, not technology.

This is a case for re-focusing on pleasurable experiences and desirable outcomes, not technology itself. We create experiences for users and technology is leveraged in service of that goal… the point of design is solving the problems that linear, mechanical thinking can’t.

Approaching design from a level of abstraction beyond methods and mechanisms,

A promising approach involves hybrid AI experiences, blending proactive and reactive forms of intelligence

Hybrid experiences rely less on understanding how to interact with AI mechanisms, help people make choices, and feel natural within the user’s flow of actions.

Generally, products have quietly integrated AI without requiring user interaction. Emergent AI, particularly content generation and language understanding, mostly position AI as a central product experience, encouraging people to actively participate and make choices. The distinction is subtle, but crucial when defining experience.

In combination, I categorize AI into two overarching experiential types and provide a rare collection of AI examples demonstrating these experience types.