The regulatory environment is placing extreme pricing pressure on pharmaceutical manufacturers. Their success in the market depends on mounting an agile response.
The past few years, and particularly recent months, have brought an onslaught of regulatory headwinds for the biopharma industry. Managing the impacts of executive orders, the Inflation Reduction Act (IRA) and tariff policies will require companies to reinvent their pricing processes to maintain revenue streams.
As senior vice president of Model N’s Center of Excellence, my team and I have been working closely with pharmaceutical manufacturers to navigate these policy shifts. We’ve seen firsthand how legislative mandates and market dynamics are reshaping the industry’s revenue strategies. Artificial intelligence (AI) is key to responding adeptly to these shifting dynamics.
Policy Pressures
Even before President Donald Trump took office, pharma lobbyists were pushing for changes to the IRA’s drug price negotiation provisions. However, Trump reaffirmed the federal government’s commitment to the process via an April executive order calling for greater transparency into the Medicare drug price negotiations. The directive also includes an initiative to remove the so-called “pill penalty” by equalizing the timeframe for large and small molecule drugs to become eligible for negotiations, a favorable shift for the industry. But any speculation that the administration will strive to eliminate price negotiations has been laid to rest.
The industry was already grappling with IRA implications before these directives. In Model N’s 2025 State of Revenue Report, compiled before the second Trump presidency, 62% of pharmaceutical executives surveyed expressed concern about the IRA’s impact on their pricing strategies, and 87% said they’ve already altered their launch plans for specific diseases or therapeutic areas.
In May, Trump signed a sweeping executive order renewing efforts to implement the “most favored nation” (MFN) policy. The mandate aims to reduce U.S. drug costs by tying certain medication prices to significantly lower ones abroad. Industry groups estimate that this regulation could cost the pharmaceutical industry $1 trillion.
This proposal mirrors an earlier version introduced during Trump’s first term, which stalled amid legal challenges and industry pushback. The new EO lacks specifics on how the benchmark prices would be calculated and relies on voluntary compliance with no immediate regulatory authority. For now, the industry is in a holding pattern.
Meanwhile, tariffs could throw an additional wrench into pharma companies’ activities. If enacted, the trade policies will increase drug production costs and further strain margins. However, President Trump has walked back portions of his tariff threats, leaving their future uncertain.
Taken together, we gauge the current risk level of the MFN and tariffs as moderate—significant enough to warrant planning and scenario modeling but not enough to create an immediate crisis.
These potential policy changes, combined with IRA mandates, will require pharma companies to fundamentally restructure their pricing frameworks to maintain margins. More than half of the executives in the Model N survey indicated they plan to make significant investments in pricing strategies over the next two years, even before the latest executive orders. Many are turning to AI and data analytics for revenue optimization.
AI’s Value in Revenue Management
Responding to continuous market and regulatory shifts with speed and confidence demands a level of pricing agility that only intelligent data systems can provide. If implemented, the MFN executive order will add to the complexity of this response, requiring manufacturers to rapidly obtain new visibility into their global pricing and product volume in comparable international markets—an area previously irrelevant to U.S.-based pricing and market access teams.
Achieving transparency and flexibility hinges on comprehensive, up-to-date data from many different sources. Manually collecting and preparing this information takes extensive time and resources and often results in mistakes, incomplete information and delays. And teams cannot keep up with regulatory policies that change weekly, if not more often.
When companies build an integrated system connecting internal and external data sources, AI will automate the collection, standardization, enrichment and management of information from drug portfolios, current contracts, sales and supply chain metrics, formularies, regulations, competitor prices and market trends.
AI can quickly analyze this data to detect emerging patterns and opportunities. The resulting insights allow manufacturers to build dynamic, differentiated pricing frameworks for targeted customer segments that account for changing policy implications and fluctuating demand. The appetite for AI-powered tools is growing: 62% of leaders surveyed by Model N say they use or plan to use generative AI to manage revenue, and 56% use or plan to use advanced analytics.
Shifting government policies make predictive analytics especially valuable to pharma companies. AI can model how different scenarios impact revenue, such as:
- The effect of varying tariff rates on production costs.
- Whether IRA inflation penalties, which require manufacturers to rebate the difference when Medicare drug prices rise faster than inflation, cancel out any potential gains from higher prices in the commercial market.
- How varying pricing and rebate structures alter revenue and sales.
When list prices are capped—whether by Medicare negotiation, inflation-based penalties or MFN policies—the room for maneuvering narrows. In these conditions, data strategy is less about pushing prices and more about driving sales volume and extracting maximum value from contracts and portfolios. Our customers have renewed their focus on rebate modeling to help them determine if discounts designed to drive more sales will pay off. AI and analytics thus help companies adapt within existing boundaries to optimize net revenue through better contract management, dynamic pricing strategies and alternate distribution models.
It’s impossible to predict every policy change and market shift ahead. What pharma companies can do is prepare flexible, data-informed pricing strategies that allow for fast pivots. By adopting AI-driven revenue management, manufacturers can maintain compliance, patient drug access and margins while sustaining the R&D investments that drive long-term innovation. As more executives prioritize advanced pricing strategies and analytics to maximize portfolio value, the industry has an opportunity to become more efficient, responsive and innovative.
