OBJECTIVES: Rising cost pressures and advancements in Machine Learning (ML) technologies accelerated the use of Artificial Intelligence (AI) in the pharmaceutical industry. By harnessing the power of AI, pharmaceutical companies can potentially address pricing and reimbursement challenges, while seeking innovative approaches to enhance Market Access (MA) and develop pricing strategies that optimize patient access. This research assesses how AI can drive peri-launch pricing strategies for pharmaceuticals, delving into the potential transformation of pricing frameworks within the healthcare industry.

METHODS: Secondary research from 2019 to 2024, including industry reports and whitepapers, was conducted to gather insights on AI's use in drug pricing.

RESULTS: We have identified five key areas where AI solutions have showcased benefits within pharmaceutical pricing:

• Dynamic Pricing Algorithms: adjust pricing in real-time based on market demand, competitor pricing, and product differentiation, optimizing competitiveness.
• Value-based Pricing Models: assess a drug's value through clinical efficacy, patient outcomes and economic impact, facilitating fair pricing.
• Segmented Pricing Strategies: analyze the macro-economic environment and market data to identify needs and willingness to pay, enabling customized pricing strategies.
• Optimized Negotiation Tools: analyze pricing scenarios, regulations, and market dynamics, providing recommendations for optimal pricing negotiations with payers.
• Real-time Competitive Intelligence Solutions: monitor competitors' pricing strategies in real-time, enabling proactive competitive pricing decisions.

CONCLUSIONS: AI-driven pricing strategies support pharmaceutical companies in optimizing pricing, considering market dynamics. However, the AI role in clinical benchmarking for pricing decisions is uncertain. Robust clinical evidence, including comparisons with standard of care is crucial in determining therapy prices, with Health Technology Assessment (HTA) agencies scrutinizing clinical data uncertainty prior to pricing negotiations. MA organizations can effectively support pricing optimization strategies, linking clinical data with economic value, by determining early in the development cycle the key parameters influencing the economic value, while maintaining regulatory compliance and transparency, to enhance patient access and meet payer needs.