A successful generative AI strategy requires what type of data to enhance user engagement?

A successful generative AI strategy relies heavily on unstructured customer interaction data because this type of data captures the nuances and complexities of human communication, preferences, and behaviors. Unstructured data includes text from social media, chat logs, email correspondence, and other forms of interaction that can convey sentiment, intent, and context. This rich information allows generative AI models to better understand user needs, personalize interactions, and generate more relevant and engaging content.

While high-volume structured data is valuable for certain analytical tasks, it often lacks the depth required for understanding user engagement in a nuanced way. General data from internet sources may provide broad insights, but it is typically less relevant to specific customer interactions than detailed unstructured data. Similarly, relying solely on internal sources limits the breadth of understanding and may miss out on valuable external insights gained from customer interactions in diverse contexts. Thus, unstructured customer interaction data is essential for developing a comprehensive understanding of users, leading to enhanced engagement through generative AI applications.