The complexity of modern consumer products, such as cars, is staggering, with numerous communication buses, control units, software components, and mechanical parts working together to create a product that meets regulatory and safety requirements. To manage this complexity, manufacturers rely on data silos, but these can lead to partial views of the product's information. The next step in managing data effectively is through AI and knowledge graphs, which create knowledge context from relevant information, enabling companies to make better decisions and automate processes. Knowledge graphs are dynamic, linked heterogeneous data, and intelligent metadata enable data mining and simplify navigation, allowing for deeper searches and answering new questions. By mapping the product's DNA using a knowledge graph, manufacturers can design a map of functions and trace dependencies, identifying clusters and loops in the system design, optimizing project planning, and maintaining overall functionality. Ultimately, AI and knowledge graphs provide increasingly dynamic, relational, and connected representations of data, allowing for improved expediency in deciphering the product's DNA.