Defining success for AI requires carefully designing your reward function. This is the formula that tells your system what to optimize for and shapes the user experience. Teams from UX, Product, and Engineering must work together on this.

Think about different types of mistakes your AI might make. A running app might suggest runs users don't want, or miss runs they would enjoy. Deciding which mistake is worse shapes how your system develops over time.

Consider the balance between being careful and being thorough. Being careful (precision) means you're confident in what you recommend, but might miss good options. Being thorough (recall) means you catch more possibilities but include more mistakes. Your roadmap should plan how this balance changes.

Look at the long-term effects of your choices. A simple goal applied broadly can create problems later. Making users share more content might seem good at first but could make the app annoying over time. Watch for unexpected consequences. Ask yourself: "What happens if our system perfectly achieves its goal?" The answer should help users. Keeping people's attention all day might boost engagement numbers but not actually help them.^[1]