艾昆纬-生命科学中的人工智能商业化（英）

White PaperAI in Life Sciences Commercialization Strategic insights and practical recommendations from 2025 survey of Commercial LeadersSHRAVAN KOTAKONDA, VP, Commercial Solutions, Strategic Operations, IQVIA DR. PRAVINDRA AWASTHI, Principal, Market & Competitive Intelligence, IQVIATable of contentsExecutive summary 1Survey methodology 2State of AI adoption 3AI investment, maturity, and organizational readiness to scale AI 3AI impact by commercial function 5Barriers to scale 6Vendor strategy and partnership models 7Outlook 8Conclusion: From ambition to advantage 9Playbook for scaling AI 10Acknowledgements 11About the authors 12 iqvia.com | 1Artificial Intelligence (AI) has become an indispensable component of life sciences commercialization, reshaping how companies allocate resources, engage customers, and drive growth. To understand this transformation, IQVIA surveyed 107 senior commercial leaders across five critical areas: adoption and maturity, investment and ROI, impact by function, barriers to scale, and partnership strategy. Our goal was to capture the full journey from AI ambition to real-world execution to help leaders benchmark their progress, understand how peers are adapting their strategies, and make informed decisions about where to focus next.Survey findings show that AI is moving rapidly from experimentation to execution. Over 80% of organizations have advanced beyond pilots, and more than a third now describe themselves as “AI Advanced.” AI Advanced organizations are defined as those that have widely adopted AI technologies, with a clear strategy and ongoing optimization.Investment in AI is rising, and the returns are real. Nearly half of the surveyed companies dedicate more than 20% of their commercial budgets to AI, with another quarter investing between 11% and 20%. This level of commitment reflects AI’s growing role in commercial strategy with proven results. The returns are compelling: 58% of leaders report achieving 2X ROI from their AI initiatives within a single year, with 7% seeing 3X or more. However, scaling AI — and realizing its full value —remains challenging. Data privacy, legacy systems, and talent shortages are the most common barriers. Notably, 36% of organizations say their data is either “mostly insufficient” or only “moderately sufficient” to support AI, highlighting a persistent gap in data readiness. Just over half (52%) rate their data as “mostly sufficient,” and only 11% say it is “fully sufficient.” Because many organizations still face data readiness gaps, partnerships are helping accelerate progress. 89% of those surveyed are already co-developing or actively considering co-development of AI solutions with external vendors, with selection driven by criteria such as domain expertise, data security, and proven innovation.All of these insights begin to tell the story of what life sciences organizations need to successfully adopt, and utilize, AI moving forward. The survey results reveal that successf