In the automotive industry, the ability to quickly turn data and real-world experience into industrial decisions is becoming a major factor in competition. Modern cars are highly complex, containing more than 150 electronic control units (ECUs), tens of millions of lines of code, and generating large amounts of data. While this complexity supports innovation, it also raises the costs of developing, testing, and maintaining vehicles. For manufacturers, the challenge is to manage this complexity without slowing down the time it takes to bring new models to market. Artificial intelligence (AI) is helping engineering teams navigate this complexity by providing better insights into complex systems. Traditional tools have focused on structured data, such as signals from car networks, analog measurements, and error codes. However, the root causes of problems are rarely found in these sources alone. Engineers also need to analyze technical specifications, test reports, repair records, and software updates, which are often scattered across different tools, teams, and suppliers. A significant amount of knowledge remains spread across documents, business systems, and individual expertise. When an engineer leaves a project or company, much of that knowledge can be lost with them. AI can help by connecting these scattered sources, restoring context, and allowing engineers to form more relevant hypotheses more quickly. This reduces the time spent searching, comparing, and interpreting information, thereby shortening the time needed to diagnose and validate issues. On a large scale, such improvements can directly impact engineering costs, the number of prototypes used, and the deadlines for production. The effectiveness of an AI system depends less on the sheer amount of data available and more on its ability to access the right information at the right time with enough context to interpret it. A single signal on its own may not provide much value, but its significance becomes clear when it is connected to the conditions under which it was recorded, the vehicle’s state, its history, or other events that occurred simultaneously. The challenge for manufacturers is to develop systems that can collect and dynamically contextualize relevant data, rather than trying to centralize all information systematically. AI can integrate vehicle data, diagnostic information, logs, technical documentation, and maintenance history to create a more complete picture of an event, guiding the analysis toward the most likely causes. It can process and link a volume of information far greater than what a human could handle manually, uncovering more correlations, hypotheses, and possible solutions. This allows engineers to gain a deeper understanding of the problem and focus their expertise on interpreting results and making decisions, rather than on the tedious task of finding the information needed for diagnosis. Ultimately, in the competitive automotive industry, the advantage will go to companies that can transform their data and experience into industrial decisions more quickly. AI can contribute to this goal, provided it is viewed as a tool for increasing productivity and capturing knowledge, rather than just a technological promise.