Articolo
AI-powered tools for pricing: Key takeaways from ISPOR Europe 2025
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Leanna Baker Williams, MS, PharmD
The global pricing environment for medicines is becoming more complicated. Policies are changing, countries are influencing each other’s prices, and reimbursement systems are harder to navigate. Pharmaceutical companies need to adjust quickly so patients can get timely access to treatments. At the ISPOR Europe 2025 workshop, “Navigating the Global Pricing Policy Landscape and Leveraging AI for Strategic Insights," experts discussed how artificial intelligence (AI) can help pricing teams manage these challenges.
The workshop aimed to give pricing leaders tools to understand and respond to fast changing pricing conditions. Below are the main insights shared during the session, including how AI can support teams, practical steps for adoption, and what the future may look like.
The workshop aimed to give pricing leaders tools to understand and respond to fast changing pricing conditions. Below are the main insights shared during the session, including how AI can support teams, practical steps for adoption, and what the future may look like.
Understanding the workshop’s focus
The session explored how global pricing policies are shifting and how those shifts affect reimbursement and patient access. Pricing choices in one country often influence others through systems like reference pricing and cross-border trade. The workshop explained these links and offered advice on how companies can respond.
Pricing strategy affects how contracts are set, what evidence payers expect, and how fast patients receive new treatments. Missing key policy signals can delay launches or lead to tougher reimbursement terms. Speakers emphasized that AI can help pricing teams detect policy shifts earlier, summarize information faster, and test how changes could affect different markets, with the understanding that outputs should be reviewed by experienced pricing and market access professionals.
Figure 1: Poll results from the session
Pricing strategy affects how contracts are set, what evidence payers expect, and how fast patients receive new treatments. Missing key policy signals can delay launches or lead to tougher reimbursement terms. Speakers emphasized that AI can help pricing teams detect policy shifts earlier, summarize information faster, and test how changes could affect different markets, with the understanding that outputs should be reviewed by experienced pricing and market access professionals.
Figure 1: Poll results from the session
A poll from the session (Figure 1), conducted during the workshop with 246 participants, showed that 64 percent of organizations are not using AI in pricing work, and only 15 percent have adopted AI tools. This suggests that most companies are still in the early stages of exploring AI for pricing and are focusing their AI efforts elsewhere, such as patient engagement or medical affairs.
The role of AI in pricing strategies
AI tools can help pricing teams build and test scenarios much faster than manual methods. These tools can organize and analyze large datasets, including clinical data, payer rules, and market activity. They can uncover patterns, challenge assumptions, and turn information into insights that support decision making.
Figure 2: AI capabilities for pricing and scenario modeling
Figure 2: AI capabilities for pricing and scenario modeling
AI-powered tools (Figure 2) can be configured to ingest and structure inputs across clinical evidence, regulatory context, payer and HTA (health technology assessment) decisions, and economic and market signals. From there, analytics and modeling approaches are applied to surface patterns, test assumptions, and generate scenario outputs that can be reviewed and pressure-tested by pricing and access leaders as part of a governed process.
AI can mirror many of the steps that pricing teams use today, but it works at far greater speed and scale. For example, AI can model how a policy change—such as a Most Favored Nation rule or the launch of a biosimilar—could affect prices. This helps teams evaluate potential impacts and adjust assumptions more quickly.
One major advantage is that AI can reduce the time required to gather and summarize information. This lets teams spend more time checking assumptions and shaping strategies. With faster workflows, teams can explore more options and make better-informed decisions.
AI can mirror many of the steps that pricing teams use today, but it works at far greater speed and scale. For example, AI can model how a policy change—such as a Most Favored Nation rule or the launch of a biosimilar—could affect prices. This helps teams evaluate potential impacts and adjust assumptions more quickly.
One major advantage is that AI can reduce the time required to gather and summarize information. This lets teams spend more time checking assumptions and shaping strategies. With faster workflows, teams can explore more options and make better-informed decisions.
Overcoming barriers to AI adoption
Even with these advantages, many companies struggle to adopt AI tools. Common barriers include:
To overcome these challenges, the workshop recommended starting with small, well-defined use cases. It also stressed the importance of choosing tools with clear documentation and audit trails. AI should support—not replace—the expertise of pricing professionals. Teams should also validate outputs for accuracy, bias, and data coverage, especially when using generative AI to summarize source materials.
- Complex data needs: Pricing relies on many types of information, including policies, evidence, and contracts.
- Concerns about transparency: Teams worry about how AI reaches conclusions and whether outputs comply with regulations.
- Limited resources: Smaller companies may lack the expertise to build or maintain AI systems, making them dependent on partners.
To overcome these challenges, the workshop recommended starting with small, well-defined use cases. It also stressed the importance of choosing tools with clear documentation and audit trails. AI should support—not replace—the expertise of pricing professionals. Teams should also validate outputs for accuracy, bias, and data coverage, especially when using generative AI to summarize source materials.
Generative AI and natural language processing in pricing
Two technologies—generative AI and natural language processing (NLP)—play key roles in modern pricing tools. NLP can scan large sets of regulatory documents, published HTA outcomes, and market reports to identify trends that teams might miss. Generative AI can turn complex information into clear summaries, helping teams communicate quickly and consistently when used with appropriate controls and review.
Figure 3: End-to-end policy intelligence workflow for pricing models
Figure 3: End-to-end policy intelligence workflow for pricing models
These tools (Figure 3) can help pricing teams create scenarios and assess how policy changes or new products may affect the market, and tailor recommendations using both current and historical data. They can shorten decision-making time and increase visibility into pricing rationale by providing a structured starting point for analysis.
However, AI is only as strong as the data and assumptions used. For new or unfamiliar policies, AI is best used to structure possibilities, while human experts must still validate the outputs. In addition, teams should retain references to underlying source documents to support auditability and traceability.
However, AI is only as strong as the data and assumptions used. For new or unfamiliar policies, AI is best used to structure possibilities, while human experts must still validate the outputs. In addition, teams should retain references to underlying source documents to support auditability and traceability.
Practical steps for adopting AI in pricing
For companies that want to begin integrating AI but feel unsure about complexity or compliance, the workshop recommended:
Hybrid models—where AI speeds up data processing and experts make the final decisions—are expected to become the standard. Organizations that build repeatable, well-governed workflows will be better prepared for future policy shifts.
- Start small: Use AI on narrow tasks such as tracking policy signals or summarizing data.
- Prioritize transparency: Select tools with strong documentation and built in audit trails.
- Use human expertise: Ensure skilled experts review and validate all AI outputs.
- Invest in training: Make sure pricing teams understand how to apply AI in their work.
Hybrid models—where AI speeds up data processing and experts make the final decisions—are expected to become the standard. Organizations that build repeatable, well-governed workflows will be better prepared for future policy shifts.
The future of AI in pricing strategies
AI driven pricing is still in its early stages within the biopharma industry, but the potential benefits are strong. AI can help pricing teams make decisions more quickly, reduce delays in analysis and planning, and create more effective strategies when paired with strong governance and experienced oversight.
For companies wanting to use AI in their pricing efforts, Cencora offers guidance and tools backed by decades of experience. Our approach blends deep market knowledge with modern technology to help teams navigate complex global pricing environments.
AI is not a stand alone solution, but when combined with expert judgment and a clear plan, it can help pricing teams make better, faster decisions.
Our teams of science and research experts utilize decades of experience and relationships with global health authorities to provide guidance and strategic advice. See how we help organizations like yours make better market access decisions
For companies wanting to use AI in their pricing efforts, Cencora offers guidance and tools backed by decades of experience. Our approach blends deep market knowledge with modern technology to help teams navigate complex global pricing environments.
AI is not a stand alone solution, but when combined with expert judgment and a clear plan, it can help pricing teams make better, faster decisions.
Our teams of science and research experts utilize decades of experience and relationships with global health authorities to provide guidance and strategic advice. See how we help organizations like yours make better market access decisions
The information provided herein contains marketing statements and does not constitute legal advice. AI-enabled analyses should be based on appropriately sourced data and used within a documented governance framework, with expert review to confirm accuracy and compliance. Cencora strongly encourages readers to review available information related to the topics discussed herein and to rely on their own experience and expertise in making decisions related thereto.
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