AI as a technology has unlocked a wide array of new, and often exciting, products and services. But no matter how novel your use of AI, when explaining your AI-powered product to your users, focus primarily on conveying how it makes part of the experience better or delivers new value, versus explaining how the underlying technology works.

Run user studies to answer questions like:

• What do users need to know about how the system works to understand and use your product?
• Why is this knowledge useful to the users?

The level of technical detail needed in your explanation will vary depending on the product and users.

AI systems often require access to some data or input from their users to provide them with personalized recommendations.

Before asking new users what and how much data they are willing to share, give them the opportunity to test your system. For example, this can be done by offering an initial experimentation experience.

This will help new users who are eager to get started, or who don’t have time to fully consider and configure their preferences before diving in. It will also support users who are wary of sharing this information before they fully understand what the system offers in return.

Even after onboarding is complete, continue to make user actions and decisions reversible where possible. User preferences and context are always changing. Giving piecemeal undo options allows them to manipulate preferences recommendations without needing to resort to a nuclear reset option.

With AI-driven products, there can be a temptation to communicate the “newness” or “magic” of the system’s predictions through its UI metaphors.

However, unfamiliar UI touchpoints can make it harder for users to learn to use your system, potentially leading to degraded understanding of, or trust in, your product, no matter the quality of your AI output.

Instead, anchor new users with familiar UI patterns and features. This will make it easier for them to focus on the key task at hand, which is building comfort with, and calibrating their trust in, your system’s recommendations.

As you design your product, think critically about the balance of automation and control that you need to offer your users for them to use your product successfully.

In some cases, it may make sense to offer multiple “levels” of automation in your product (for example, partial automation and full automation), to help users with varying levels of comfort or familiarity with your product use it successfully.

In such cases, start users with the lowest level of automation, and progressively increase automation. Make sure that users can easily adjust their choice, including dialing up or down their level of automation, and that the steps between levels are small and land well.

Choosing the right level of automation depends heavily on your user and product needs and context. Research from Sheridan et al has demonstrated 10 levels of automation to consider.