Revenue Optimization: The Money Matters—That Matter Most

Jesse Mendelsohn, Senior Vice President, Model N

There are many forces at play in the pharmaceutical industry, but company executives believe technological advancements and disruption will have the most significant impact on their revenue management strategy in 2025.

This insight comes from Model N’s annual State of Revenue Report.¹ The survey of 400+ leaders in life sciences (including pharmaceutical companies) and high-tech manufacturing illuminates how organizations are embracing opportunities and overcoming challenges in revenue optimization and compliance.

Technology is a central focus for pharma manufacturers, ranking ahead of supply chain disruptions and regulatory pressure as a top influence on revenue management. Almost nine in 10 surveyed executives report their company’s innovation focus has shifted to automating revenue management operations. Organizations must become more data-driven to capitalize on improvement opportunities.

The future of technology in revenue management

More than half of pharma leaders believe data analytics offers the most significant opportunity to improve revenue operations, and 50% are actively prioritizing data integration and analytics in 2025. Improving these capabilities will contribute to operational automation by enabling companies to efficiently execute tasks, minimize human error, improve demand forecasts, and streamline decision-making. A comprehensive view and analysis of revenue data allows pharma companies to identify previously hidden opportunities for optimization and react more quickly to market changes.

Nine in 10 business leaders believe their tech investments have already had a measurable impact on revenue management outcomes.

Nearly everyone is optimistic about the potential of artificial intelligence (AI)—99% of all surveyed executives expect AI to drive revenue management value. Pharma leaders see immense potential in generative AI (GenAI) in particular. More than 60% use or plan to use the technology in revenue processes, compared to four in 10 that use or intend to implement AI and machine learning.

Executives’ top potential AI use cases for pharma revenue management include:

• Process automation (46%)
• Deal analytics and insights (45%)
• Quantitative revenue forecasting (42%)

Whether pharma organizations use AI or GenAI, or both, these technologies can enhance revenue management efficiency, accuracy, and decision-making. Process automation saves significant time and resources, and the ability to analyze vast datasets empowers companies to identify market trends, optimize pricing strategies, and improve demand and revenue predictions. However, strategic implementation is essential. Leaders must evaluate processes to determine where AI delivers the most value.

Unlocking the value of AI by improving data

Effective AI use requires quality data, which many pharma leaders find challenging. Model N’s report found only 55% of all surveyed leaders describe their companies as very data-driven, and 92% are concerned about the quality of data used for revenue management decision-making. For pharma organizations, incomplete data is the most frequently cited issue, followed by timeliness and accuracy.

Nearly 60% of surveyed companies use multiple revenue management solutions with little to no integration, which can result in missing information, delayed updates, mismatched data formats, and revenue leakage. These inefficiencies make it difficult to generate accurate revenue forecasts, ensure compliance, and derive actionable insights. To successfully harness the power of AI for any revenue management use case, organizations must unify organizational data, systems and processes.

Regulations will have a significant influence on revenue

Beyond technology, regulations weigh heavily on the minds of pharma executives. Four out of 10 leaders believe regulatory pressure will affect revenue in 2025. The Inflation Reduction Act (IRA)² is drawing the most concern, with 62% of executives worried about how the legislation will impact their pricing strategies.

Price negotiations under the IRA are currently underway for the next 15 Medicare Part D drugs, and price changes for the 10 original drugs will go into effect in 2026. Additional revenue pressure stems from other IRA provisions, including inflation-based rebate penalties and a shorter market exclusivity timeline.

We’re already seeing ripple effects from this legislation. In the Model N survey, 87% of pharma companies report that the IRA has altered their company’s launch plans for specific diseases or therapeutic areas.

In light of the IRA, drug manufacturers must overhaul their pricing models to adjust to net new problems created by the law. The current market necessitates sophisticated strategies that can only be derived from comprehensive data analytics. Recognizing this need, more than 50% of pharma executives plan to invest significantly in pricing strategies over the next two years.

Beyond the federal legislation, state-level regulations are also catching executives’ attention, including:

• State drug transparency regulation (40%)
• State drug affordability boards (37%)
• State activism related to the 340B program (37%)

State-level activity creates a patchwork of regulations that companies must navigate, increasing administrative complexity and compliance costs. Additionally, drug affordability board decisions and 340B expansion may strain margins.

The current White House administration’s tariff policies—which were not known when survey responses were collected—add additional uncertainty and challenges to the market. The policies break with established rules that exempt most medicines and active pharmaceutical ingredients from tariffs. As a result, experts believe the policy may increase drug production costs, disrupt supply chains, and exacerbate drug shortages.

Final thoughts

Technological advancements offer pharma companies tools to navigate the turbulent industry. However, successful implementation requires integration and high-quality data, which the survey found many companies struggle with. Executives must understand these ongoing challenges to strategically address the gaps in their data management processes.

Companies that build a strong data foundation and strategically apply AI will gain powerful revenue insights to drive business agility and resilience.

Jesse Mendelsohn is Senior Vice President at Model N

References

1. Steele, A. New Research Highlights the Impact of Data and Technology on Revenue Management. Model N. February 4, 2025. https://www.modeln.com/blog/new-research-highlights-the-impact-of-data-and-technology-on-revenue-management/

2. Inflation Reduction Act of 2022. IRS information/updates page. https://www.irs.gov/inflation-reduction-act-of-2022