Market Access AI Readiness: Maximizing Strategic Alignment Through Organizational Insights
Author(s)
Jonas Jost1, Elaine J. Wright, higher diploma2, Stefan Walzer, MA, PhD1.
1MArS Market Access & Pricing Strategy GmbH, Weil am Rhein, Germany, 2WRIGHT Pharma Partnering ltd, Dunboyne, Ireland.
1MArS Market Access & Pricing Strategy GmbH, Weil am Rhein, Germany, 2WRIGHT Pharma Partnering ltd, Dunboyne, Ireland.
OBJECTIVES: Artificial Intelligence (AI) technologies are now integral across all stages of pharmaceutical development and commercialization, including market access, pricing and health economics and outcomes research (MA). Our preliminary analysis investigates company size relative to this AI readiness and takes the first steps to assess if similar trends exist within MA. An AI readiness framework specific to MA will was developed to ensure foundational elements of AI readiness are measurable, evident and actionable.
METHODS: We collected employee workforce data from 34 company websites and paired with Pharmaceutical AI readiness scores (CB Insights) applying regression models and a Pearson’s Correlation Coefficient (r) (R-package 4.4.1). To understand the specifics of MA functions’ AI readiness, we created a five-domain evaluation framework: Strategic Leadership, Data Excellence & Technology, People, Skills & Culture, Governance & Ethics, and Continuous Improvement & Adaptation. These domains reflect key enablers of AI integration tailored to MA.
RESULTS: Analysis confirmed a positive correlation (r = 0.82) between company size and AI readiness. Smaller firms had a lower AI Readiness score compared to larger companies. The MA AI Readiness score was > 62% for the largest 25% of companies compared to
< 18% for the smallest 25% of companies. Outliers exist which in themselves are of interest and explored.
CONCLUSIONS: This research indicates an AI readiness gap between larger and smaller pharmaceutical companies and small-to-mid-sized firms may benefit from a structured assessment to plan the first steps towards AI efficiencies and remain competitive. It is of interest to understand if this AI readiness gap also exists within pharma’s MA functions. The MA AI Readiness Score will allow for this analysis to be replicated for MA functions, and offers a novel, practical tool to guide MA organizations in their AI transformational journey.
METHODS: We collected employee workforce data from 34 company websites and paired with Pharmaceutical AI readiness scores (CB Insights) applying regression models and a Pearson’s Correlation Coefficient (r) (R-package 4.4.1). To understand the specifics of MA functions’ AI readiness, we created a five-domain evaluation framework: Strategic Leadership, Data Excellence & Technology, People, Skills & Culture, Governance & Ethics, and Continuous Improvement & Adaptation. These domains reflect key enablers of AI integration tailored to MA.
RESULTS: Analysis confirmed a positive correlation (r = 0.82) between company size and AI readiness. Smaller firms had a lower AI Readiness score compared to larger companies. The MA AI Readiness score was > 62% for the largest 25% of companies compared to
< 18% for the smallest 25% of companies. Outliers exist which in themselves are of interest and explored.
CONCLUSIONS: This research indicates an AI readiness gap between larger and smaller pharmaceutical companies and small-to-mid-sized firms may benefit from a structured assessment to plan the first steps towards AI efficiencies and remain competitive. It is of interest to understand if this AI readiness gap also exists within pharma’s MA functions. The MA AI Readiness Score will allow for this analysis to be replicated for MA functions, and offers a novel, practical tool to guide MA organizations in their AI transformational journey.
Conference/Value in Health Info
2025-11, ISPOR Europe 2025, Glasgow, Scotland
Value in Health, Volume 28, Issue S2
Code
OP15
Topic
Organizational Practices
Topic Subcategory
Industry
Disease
No Additional Disease & Conditions/Specialized Treatment Areas