The current AI gold rush has companies rushing to add machine learning to every product, often without asking if it actually improves anything. After leading design for several AI-powered products, I've learned that successful AI integration starts with a clear understanding of user problems, not with the technology itself.

The most common mistake is creating "black box" experiences where AI makes decisions without transparency or user control. This approach erodes trust even when the AI performs well, because users feel disempowered.

Instead, design AI features that augment human capabilities rather than replace them. Show users what the system is doing and why. Give them meaningful control over how much automation they want in different contexts.

AI excels at reducing tedious work, surfacing patterns humans might miss, and providing personalized experiences at scale. Focus AI efforts on these areas rather than trying to automate complex decisions that users want control over.

Remember that AI systems reflect their training data, including any biases. Regular auditing for fairness should be part of your design process, not an afterthought.

The most effective AI features make their capabilities and limitations clear to users. Don't overpromise - set realistic expectations about what the system can do.

Finally, collect feedback continuously. AI-powered features often evolve in surprising ways as they interact with real users. Design for this evolution by building in mechanisms to understand how people are actually using your AI tools.

The future belongs not to products that use AI most extensively, but to those that use it most thoughtfully.