Despite the high adoption of AI in life sciences revenue management, companies are still losing over $50 billion annually due to systemic inefficiencies.
According to Model N’s 2026 State of Revenue Report, an impressive 97% of life sciences organizations are currently utilizing AI for revenue management, with projections indicating this will rise to over 99% in the next two years. However, the report reveals a paradox: while these companies are heavily investing in AI, they continue to grapple with fragmented data systems and manual processes that hinder effective revenue management.
The survey, which included over 400 leaders from pharmaceutical and medtech firms, highlights that many teams are deploying AI tools in isolation without a cohesive strategy. Notably, 83% of respondents identified significant barriers to automating workflows, primarily due to inadequate access to reliable data. This lack of integration contributes to substantial revenue leakage, particularly in managing gross-to-net (GTN) data, which is crucial for understanding the financial impacts of pricing, rebates, and compliance.
Looking ahead, industry leaders anticipate that AI will play a pivotal role in enhancing revenue management by 2028, particularly in areas like deal analytics and process automation. However, for these advancements to be realized, companies must prioritize the development of a unified data strategy that connects disparate systems and fosters collaboration across departments. Without this foundational change, the potential of AI in life sciences revenue management may remain unfulfilled.