Sarcouncil Journal of Multidisciplinary

Abstract: When pharmaceutical organizations utilize artificial intelligence in their operations, they must have datasets that are of sufficient quality and structure, yet there are no standardized means to assess data preparedness in practice. The Pharma-AI Readiness (PAIR) Framework addresses this shortfall with a quantitative assessment framework to assess datasets in the pharmaceutical space across five dimensions: quality, consistency, structure, compliance, and documentation. Each dimension contributes to an overall readiness score, which categorizes datasets for specific use based on readiness. This provides organizations a means to assess what datasets can be deployed immediately with a preferred level of preparation, to define what datasets require more extensive preparation before use, and to prioritize efforts in data preparation. Using the framework with clinical trial datasets, real-world evidence datasets, and commercial datasets showed that organizations have systematic readiness challenges, including the lack of consistent temporal data capture in clinical sources and inconsistent metadata standardization in commercial datasets. The framework enables cross-functional teams and business units to engage with one another based on the same set of criteria when it comes to assessing fitness for data usage, thus shortening timelines from concept to model competition and implementing the desired artificial intelligence. Once the PAIR Framework is implemented, pharmaceutical organizations gain clarity for their data governance strategy and can ensure regulatory compliance while increasing operational scalability to support digital initiatives. The structure and scoring within the PAIR framework allow benchmarking across different areas of data assets and enable evidence-based decision-making regarding an organization's AI transformation strategies.