Before you start building with AI, make sure the product or feature that you have in mind requires AI, or would be enhanced by it.

AI is well-suited for applications like:

• Recommending different content to different users, such as movie suggestions
• Predicting future events, such as weather events or flight price changes
• Natural language understanding
• Image recognition

A rule or heuristic-based solution may be better when:

• Maintaining predictability is important
• Users, customers or developers need complete transparency
• People don’t want a task automated

See the User Needs chapter for more on when to use (or not) AI.

Because AI systems are probabilistic, your system will probably give an incorrect or unexpected output at some point.

This makes it critical that you help users calibrate their expectations about system functionality and output. Do this by being transparent about both its capabilities and limitations.

For example, indicating a prediction could be wrong may cause the user to trust that particular prediction less. However, in the long term, users may use or rely on your product more, because they’re less likely to over-trust your system and be disappointed.

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.

From early in the product development process, plan for the fact that your AI system will make bad predictions at some point. This is an important part of confirming that AI is the right technology for your project.

Think through the types of errors that your system could make, and their consequences. You should have an informed point of view about what's at stake for your user for a given error and expected impact of false positive and false negative predictions.

Plan to remediate for such errors using approaches like:

• Setting users expectations about your system with explanations
• Providing manual controls when the AI fails
• Offering high-touch customer support

AI-powered products can suffer without the right level of focus and resources on data from early on. We call such downstream effects “data cascades” and they can be hard to diagnose and detect until your product experience is impacted.

Good planning and scrutiny of your dataset can help you avoid issues downstream. Some actions that you can take include:

• Collect data in batches
• Embrace “noisy” data
• Plan for data maintenance
• Partner with domain experts