Incorporating AI into workflows presents challenges, particularly in building trust with users and ensuring that AI helps them progress rather than causing frustration.

The user experience of AI tools should be seamless and intuitive, focusing on taste rather than just technical challenges. This requires continuous iterations to improve usability.

Many AI agent assistants fail because they are built with incomplete features, leading to frustrating user experiences.

The expectation versus reality of AI adoption is often skewed; while expectations are high, the actual capabilities of current models may not meet those expectations, leading to a rapid adjustment in perception as models improve.

Trust in AI requires more than just improved accuracy metrics; it involves understanding the nuances of human trust and ensuring that AI technology is developed with user needs in mind.

To effectively use AI, users must clearly define the tasks and expectations for the AI, similar to how one would instruct an intern on their job responsibilities.

Feedback mechanisms in AI interfaces can help users refine their prompts and improve interaction with the AI, enhancing the overall experience.

As AI capabilities grow, product teams can still add significant value by focusing on user experience, ethical considerations, and strategic direction.

The disconnect between a well-developed frontend experience and the slower development of AI tools leads to user confusion about which features are supported.