What are some common mistakes in AI integration?

What are some common mistakes in AI integration?

Some common mistakes include lack of clear objectives, insufficient data quality, and ignoring user feedback.

Can you elaborate on the importance of data quality?

Data quality is crucial because poor data can lead to inaccurate models and unreliable AI outputs.

What should I do to ensure data quality?

To ensure data quality, implement data validation processes, clean your data regularly, and involve domain experts.

How can I gather user feedback effectively?

You can gather user feedback through surveys, user testing sessions, and by monitoring user interactions with the AI.