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.

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

When designing your AI-powered product, evaluate the tradeoffs between having the set of recommendations your system gives to be broad (prioritizing recall) or accurate (prioritizing precision). This decision will have a big impact on the end user experience.

• Prioritize precision: If you are building a product in a high stakes domain (such as healthcare), and the risks stemming from an error are high, you will likely want to be more conservative with your model output, and only give recommendations for the things the system is most confident of. This means prioritizing precision.

• Prioritize recall: if your product is in a lower stakes domain and it’s not a problem for users to sort through a longer list of recommendations (for example, music recommendations), you might prefer to prioritize recall. This means your user will get a larger set of results, offering the chance for surprising or serendipitous discoveries, but some of them may be less relevant.