Unlocking AI Design: 23 Principles from Google & Microsoft for Smarter UX

In an era where AI is everywhere, intelligent technologies quietly integrate into daily life, but each impressive AI product is backed by a set of thoughtful design principles. Tech leaders like Google and Microsoft have distilled these principles from extensive product practice, offering a more rational and scientific approach to design that creates smarter, warmer user experiences.

Explanation is for understanding, not completeness

When explaining AI recommendations, focus on conveying the information users need to make decisions and avoid exposing every detail of the system. Often the exact reasons for a prediction are unknown or too complex, and users may not want unnecessary technical explanations that distract them.

User data privacy and security management

AI and machine learning improve user experience but also raise new privacy challenges, potentially exposing sensitive information such as addresses or locations. Beyond basic privacy safeguards, additional measures—like anonymizing data even when users consent to share their names—are needed to ensure users feel safe.

Emphasize AI‑driven value, not the technology itself

Don’t dwell on the underlying tech; instead, tell users how AI makes the product experience better and what advantages it brings. If users are curious, provide small hints that gradually reveal more details.

Provide AI services based on user scenarios

AI services should be precisely integrated into the user’s context, predicting and meeting needs without interrupting at inappropriate times, delivering personalized and natural interactions that boost satisfaction.

Convenient activation

When users need AI functionality, the system should offer a quick and easy way to activate or invoke the service—whether via a single click, voice command, or other intuitive interaction—while intelligently recognizing the user’s context.

Clearly explain why the product behaves a certain way

Design products with a clear, formal explanation mechanism that uses plain language (avoiding jargon) to help users understand the reasons behind specific behaviors, giving them a sense of control.

Google’s People + AI Guidebook, released by the PAIR team, explores human‑AI collaboration and includes 23 principles across six chapters that span the entire product development process, each paired with tools and case studies. Microsoft’s Human‑Centred AI guide, built on over two decades of research, offers 18 principles organized into four interaction stages: initial, during use, error handling, and over time.

The guide is currently not downloadable; readers can request the full version by leaving an email address in the comments.