How important is data for AI to be efficient? Is the pharmaceutical industry data-ready to exploit AI? Is the industry aware of the importance of well-governed information? Are efforts being made to have clean data that AI can exploit/use? Is there coordination between departments for proper data management? Past and present Artificial intelligence (AI) is transforming the pharmaceutical industry by driving progress in drug research and development and optimising clinical trials to personalise treatments and improve business operational efficiency. Before a company can successfully carry out an AI-based project, it is necessary to understand that data quality and organisation are fundamental to building these systems. An artificial intelligence model reaches its full potential when it is nourished by soundly managed data; without an efficient strategy in this regard, the capabilities of these technologies are severely limited. For many years the pharmaceutical industry has relied on disorganised systems and separate databases to manage crucial information. In the beginning, clinical records were paper-based and processes were manual; there was little integration between laboratories, hospitals and manufacturers. As digitisation has progressed, there has been a qualitative change in data collection; however, the same main challenges such as data quality, interoperability and security remain. Artificial intelligence is now emerging as a key agent of transformation; however, its impact depends on our ability to manage data in this new digital era. The critical points that define success In the pharmaceutical industry, artificial intelligence developments need to have a large set of organised and structured data to operate efficiently; however, there are companies that face difficulties in data management which would delay or even prevent the implementation of AI-based solutions. Collecting large amounts of information is not enough One of the main difficulties companies encounter when implementing artificial intelligence is the lack of quality in the data used. Inconsistent, duplicate, incomplete or unstructured data can lead to inaccurate and biased modelling. To prevent this, it is essential to carry out clean-up and standardisation processes before introducing any artificial intelligence system. In addition, it is crucial to have a detailed record of the origin of the data to ensure its reliability and proper compliance with regulations. Collecting large amounts of information is not enough; it is crucial to organise it strategically for optimal benefits. Establishing proper data governance involves setting clear quality standards and ensuring secure access to information to comply with regulations such as the GDPR in Europe or the FDA in the US. In addition to this, having an efficient and secure storage infrastructure enables agile real-time data processing to improve responsiveness and support AI-based decision making. One of the most important challenges in the pharmaceutical field is the dispersion of information in different systems, even within the same work team. To make artificial intelligence projects successful, it is crucial to ensure compatibility between these data sources. This implies the adoption of integration standards, such as FHIR (Fast Healthcare Interoperability Rapid Resources) in clinical settings, and the use of flexible structures to facilitate efficient data exchange. Due to the pharmaceutical industry's handling of sensitive information such as patient and clinical trial data, security and compliance become critical in the management of such sensitive data, making it crucial to implement robust cybersecurity and access control systems, as well as ensuring traceability through blockchain or other currently available technologies. Adhering to regulations such as HIPAA, GDPR and FDA guidelines not only protects information, but also contributes to the establishment of trust between participants in the medical sector. (Gobierno de España, 2024) The role of technology companies: architects of the future To achieve success in an artificial intelligence project, it is advisable to adopt an organised approach to information management: First assessment - Examine the current data situation and identify possible gaps. Standardisation and normalisation of data: Application of techniques for cleaning and structuring information. Development of a robust and reliable technological infrastructure to ensure the implementation of scalable storage and processing solutions. Data organisation - Define internal functions and rules for information management. Interoperability means ensuring that data can be effectively shared and integrated between different systems. Technology companies must provide not only advanced data management tools but also lead digital change in the industry in a holistic way. From creating systems that facilitate seamless data integration to using artificial intelligence algorithms to improve clinical and business analysis, the role of technology is essential in this new chapter of the healthcare sector. The future of data governance: beyond information The future of data management in the pharmaceutical industry is not just about organising information; it is also about making it a strategic pillar for innovation. Companies that are able to structure their data efficiently will not only be ready to adopt artificial intelligence technologies, but will also transform their business model by optimising every link in their value chain. We are entering an era in which information is no longer simply a passive asset; it stands as the driving force behind an extraordinary medical revolution. Without direction there is no change; without reliable information there is no real wisdom; without a robustly designed digital strategy there is no promise for the future in this modernised environment; mastery of data will be the key to leading emerging industry sectors in this era of imminent innovation and technological advancement.