Imagine a data scientist tasked with improving customer segmentation for a marketing campaign. Typically, they might ask an AI, "What is the best clustering algorithm to use for customer segmentation based on purchase history?" This question, while precise, limits the scope of the AI's response to just selecting an algorithm.

Instead, the data scientist decides to use a more open-ended approach: "In what innovative ways can we use data science to understand our customers' behavior and improve our marketing strategies?" This broader question doesn't just seek an algorithm; it opens the door to a wider range of data-driven insights and strategies.

The AI's response suggests not only using clustering algorithms like K-means for segmentation but also incorporating sentiment analysis of customer reviews and feedback to add another layer to understanding customer preferences. It also proposes predictive modeling to forecast future purchasing behaviors based on a combination of historical purchase data and external factors like market trends and seasonal impacts.