Effective AI experiences depend on close collaboration between designers and data scientists, despite their different working methods. To make this partnership successful, teams should focus on several key practices:

• Establish clear communication channels early in the project. Create shared documentation where designers explain user scenarios and data scientists outline model capabilities. This helps both disciplines understand project parameters from the start.
• Integrate workflows rather than working in silos. Schedule regular joint working sessions, especially during problem framing. When designers present research, they should highlight findings that inform data requirements, while data scientists should emphasize insights relevant to user experience.
• Develop a common vocabulary. Terms like "model," "personalization," and "segments" often have different meanings across disciplines, leading to misunderstandings if not addressed early.
• Use balanced evaluation methods that combine qualitative measures (user satisfaction, trust) with quantitative metrics (prediction accuracy, engagement).

The strongest AI products emerge when design and data science perspectives complement each other throughout development.^[1]