AI systems have basic limits that affect when and how to use them. Unlike normal software, AI makes predictions that include uncertainty. Complex AI decisions often can't be explained in ways people understand, which causes problems when you need to show why something happened. AI learns from training data and picks up any unfair patterns in that data, which can lead to biased results if not managed well.

AI needs good data that matches real-world use. It struggles with rare events it hasn't seen before and doesn't understand context like humans do. It lacks common sense, so it makes mistakes that seem obvious to people. When real conditions change from training conditions, AI performs worse.

When AI can't handle something, good design admits the problem, explains why in simple terms, and gives users other options. Users need clear paths forward through different suggestions, helpful guides, or feedback channels. Always plan for AI failures. Include human oversight, backup options, and set honest expectations. Knowing these limits helps you choose when AI's benefits are worth its drawbacks.^[1]