Among the main challenges in nutrition research are development of strategies for providing dietary solutions that help people adjust their dietary needs and behavior at every stage of their life. An appropriate diet will maintain the body in good health and therefore prevent chronic diseases associated with dietary excess. Personalized nutrition is a novel approach that recommends food choices and eating patterns that meet individual needs and follow personal preferences. Over the last century, nutrition research has progressively incorporated small bodies of knowledge into the puzzle of personalization, including considering diet as a treatment for different diseases, biochemical markers, anthropometric markers, food frequency questionnaires, nutrigenetic and nutrigenomic information, and incipient nutritional genetic risk scores. Other factors will also need consideration, such as food sustainability, environmental protection, food security, cultural variations, allergies and intolerances, among others. This greatly complicates the matter of promoting personalized nutrition. Recent research aimed at predicting individual response to a nutrient includes use of deep phenotyping (i.e., through continuous postprandial monitoring), microbiota, and epigenetic data that will shape future precision nutrition approaches. Despite advances in personalized nutrition, many obstacles and challenges remain before its full benefits can transition from bench side to bedside. For instance, it requires specialized healthcare professionals, competitive costing, and potential customers ready to understand and accept new nutritional approaches. This chapter is an overview of how individualization has been shaping approaches to personalized nutrition including its social impact, business and value creation, social concerns, ethical and legal concerns, communication, and consumer attitudes toward personalized nutrition. Overall, developing precision nutrition must integrate biology, environment, and lifestyle. Although biology may remain fairly constant throughout life, both environment and lifestyle change constantly through epigenetic mechanisms. Moreover, integrating these data for every period of life will require new resources for large-scale data analysis, such as artificial intelligence and machine learning algorithms.