Across Europe, regulators are emphasizing a human-centric approach. The European Medicines Agency (EMA) encourages risk management and explainability when AI is used in the drug development or market access process, ensuring decisions remain ethical, traceable, and grounded in scientific rigor. (https://www.ema.europa.eu/en/documents/scientific-guideline/reflection-paper-use-artificial-intelligence-ai-medicinal-product-lifecycle_en.pdf? )

2) Real-world evidence moves from “supporting” to “decisive” with AI doing the heavy lifting

Health Technology Assessment (HTA) agencies are placing greater emphasis on understanding how treatments perform in real-world settings, not just clinical trials. AI is becoming the key to turning real-world data (RWD) such as electronic health records, claims data, or patient registries into real-world evidence (RWE) that informs payer and pricing decisions.

AI can help identify patient groups, fill data gaps, and even build external control arms (virtual comparators) when traditional trials are not feasible. What’s important is that these analyses are transparent and reproducible, with built-in bias checks and validation steps.

Organizations like ISPOR and journals such as Value in Health have seen a rapid increase in research showcasing how AI can enhance RWE quality and credibility, particularly in oncology and chronic diseases. NICE has also begun outlining how AI can be safely and responsibly incorporated into RWE studies. (https://www.ispor.org/heor-resources/presentations-database/presentation/euro2024-4013/145012?)  (https://www.valueinhealthjournal.com/article/S1098-3015%2825%2900090-7/fulltext?)

3) Pricing strategy becomes more predictive and more personalized

AI is also transforming how pricing and market access teams plan launches and manage payer negotiations. Instead of relying solely on historical data or manual forecasts, AI tools can now:

• Predict how price changes may affect demandand payer acceptance.
  • Identify different payer segmentsand tailor value messages accordingly.
  • Simulate risk-sharing or outcome-based contracts to better anticipate performance and cost impacts.

By integrating epidemiology, payer data, and real-world outcomes, these models allow for more data-driven, scenario-based pricing strategies. Leading life sciences companies are already moving from pilot programs to full-scale implementation, demonstrating measurable returns on investment. (https://www.iqvia.com/-/media/iqvia/pdfs/library/white-papers/ai-in-life-sciences-commercialization-white-paper.pdf?)

Bottom line

AI will not replace the fundamentals of PRMA such as strategy, evidence, and stakeholder relationships, but it will make them faster, more consistent, and more predictive. The future belongs to organizations that treat AI as a trusted copilot, not a black box, one that enhances evidence quality, improves pricing precision, and strengthens payer partnerships.

Just as importantly, putting AI to work in PRMA can reduce operational costs by automating manual data analysis, dossier preparation, and payer research. This efficiency allows teams to accelerate decision-making, reach reimbursement milestones faster, and optimize revenue sooner — turning AI from a technological advantage into a measurable business benefit.

As regulators clarify the ground rules for AI-enabled evidence, the key question is no longer if you use AI, but how responsibly and reproducibly you do it.