Be able to understand design patterns and use cases for AI products, services, and platforms.
Understand AI design & validation methods, such as a confusion matrix, and how to apply them.
Have a toolkit with frameworks for AI-focused ideation and overcoming technical barriers (toolkit includes several canvas tools, ideation deck, and program workbook).
Gain confidence speaking about AI and its use cases (machine learning, deep learning, narrow vs. general).
Understand the ethical and business impacts of data bias on AI & machine learning algorithms.