Pharmaceutical manufacturers are facing mounting pressures to protect margins and optimize revenue amid an increasingly complex pricing and reimbursement landscape. At the core of this challenge is gross-to-net (GTN) erosion—the difference between a drug’s official list price and the net revenue that remains after all rebates, discounts, chargebacks and fees.
The stakes are high: GTN leakage directly affects revenue forecasts, profit margins and a company’s ability to reinvest in R&D and growth. Optimizing GTN outcomes has therefore shifted from a back-office operations and accounting exercise to a C-suite strategic imperative. Modern approaches, especially those leveraging AI, are now crucial to identifying and plugging revenue leaks that traditional methods miss. AI-driven analytics can sift through vast datasets to find patterns and anomalies far beyond human capacity, offering new ways to safeguard revenue. Coupled with automation-driven productivity gains, contract managers can evolve their roles from back-office processors to account managers, spending more time managing disputes with customers and reinforcing contract execution intricacies during the deal life cycle.
Equally important is adopting an “agentic thinking” mindset—a proactive, forward-looking approach where organizations and their AI tools act as agents on the company’s behalf to anticipate and address GTN issues before they impact the bottom line. In contrast to reactive analysis, agentic thinking is about continuous, autonomous monitoring and intervention.
In this white paper we explore how, by combining AI capabilities with agentic thinking, pharmaceutical companies can move from passively reporting GTN outcomes to actively protecting and optimizing revenue.
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