AI use cases are rippling across commercial biopharma, helping companies make faster, more informed decisions. Yet almost 70% of top generative AI (GenAI) users cite poor data quality as their most significant obstacle in unlocking AI’s full potential. As the adoption of applications grows, the true competitive edge lies in the quality of the data fuelling them. 

To fully harness AI, commercial leaders are establishing a scalable, seamlessly connected data foundation across markets, functions, and disease areas. Without it, companies’ AI pilots could amount to isolated experiments. Those who focus on creating standardised and well-integrated data can unlock AI’s full potential to gain a competitive advantage and drive long-term success. 

Data Consistency and Connectivity: The Foundation of AI
Commercial biopharma teams are uniquely positioned to strategically leverage AI as they collect vast amounts of data, including customer, sales, medical engagement, and social media activity. The next step is to harmonise the data – essentially to “speak the same language” to generate accurate and scalable insights. 

Consider a common scenario: One system lists a healthcare professional (HCP) as “John Smith” and another as “J. Smith.” Or perhaps “cardiology” is recorded in one database while “heart medicine” appears in another. AI may fail to connect the variations, leading to errors, duplication, and unreliable insights. These inconsistencies often stem from diverse data sources that don’t speak to each other, creating friction for AI and significantly reducing its ability to provide value.