艾昆纬-生命科学中的人工智能商业化（英）-2025_15页_4mb

AI in Life Sciences Commercialization: Summary

Core Content

This white paper explores the current state of AI adoption in life sciences commercialization, based on a survey of 107 senior commercial leaders in May 2025. It highlights the strategic and operational shifts in how AI is being integrated into commercial functions, the challenges faced, and the recommended approaches for successful scaling.

Main Points

AI Adoption is Accelerating

• AI is no longer experimental but a core component of commercial strategy.
• Over 80% of organizations have advanced beyond AI pilots, with 36% classified as "AI Advanced," meaning they have widely adopted AI with a clear strategy and ongoing optimization.
• 44% of organizations allocate more than 20% of their commercial budget to AI initiatives, showing a significant increase in investment.
• 58% of leaders report achieving 2X ROI from AI initiatives within one year, with 7% seeing 3X or more.

ROI and Strategic Alignment

• AI is delivering real, measurable value when aligned with business goals and embedded across teams.
• The next phase of AI adoption requires disciplined execution, strong governance, and strategic partnerships.

Barriers to Scaling AI

• Data privacy, regulatory constraints, integration challenges, high implementation costs, and limited internal expertise are the top barriers to scaling AI in commercial functions.
• 41% of companies struggle with fragmented data, and only 11% have fully sufficient data for AI applications.
• The gap between strategic ambition and operational readiness is a key challenge, with many organizations still unable to fully leverage AI due to data infrastructure limitations.

Vendor Partnerships

• 89% of organizations co-develop AI solutions with external vendors, emphasizing the importance of strategic collaboration.
• 64% prefer large, established vendors with proven delivery models.
• 61% use centralized, executive-level decision-making for partner selection.
• Three key areas where vendors are creating the most impact: data integration and interoperability, model development and deployment, and ongoing performance monitoring and optimization.
• Emerging partnership models include joint governance structures, performance-linked contracts, and co-investment models.

AI Impact by Commercial Function

• Sales and Marketing are rated highest in strategic importance but show low adoption.
• Value and Access, and Compliance are overlooked opportunities with limited AI use.
• IT and Data Operations lead in AI adoption, focusing on data infrastructure and integration.

Key Insights

• AI Advanced organizations are more successful in ROI and implementation due to centralized governance, cross-functional teams, and faster pilot-to-production cycles.
• The shift from "How much are we investing?" to "How fast are we learning?" and "How broadly are we scaling?" is evident.
• Strategic partnerships are essential for scaling AI, especially for data integration, model development, and performance optimization.

Outlook

• Current AI use cases focus on operational automation, patient interaction tools, forecasting, and data integration.
• Future priorities include salesforce effectiveness, content personalization, patient adherence tracking, data analytics, supply chain optimization, and access and pricing.
• The next wave of value will come from integrated AI systems that drive cross-functional decisions and measurable outcomes.

Playbook for Scaling AI

To maximize AI impact, life sciences leaders should focus on the following six principles:

1. Commit to an enterprise AI strategy

   • Align AI with business goals from the start to ensure integration and continuous improvement.

2. Build a strong data foundation

   • Address data fragmentation and ensure data governance across all relevant functions.

3. Create AI-focused, cross-functional teams

   • Focused collaboration accelerates AI delivery and scaling across the organization.

4. Focus where value is shifting

   • Prioritize use cases that support agility, insights, and patient outcomes.

5. Share risk with partners

   • Link vendor compensation to business outcomes to enable more accountable and scalable delivery.

6. Embed compliance from the start

   • Ensure transparency, explainability, and bias checks in AI models during development.

Conclusion

AI is now a critical enabler for life sciences commercialization. Organizations that embed AI into their enterprise strategy, build resilient data infrastructure, and form strategic partnerships will be best positioned to realize its full potential and drive future growth.

Key Takeaways

• AI is essential for commercial execution, not just a nice-to-have.
• Data readiness remains a major bottleneck.
• Vendor collaboration is key to scaling AI effectively.
• Cross-functional integration and strategic alignment are critical for success.
• Future value will come from integrated AI systems and actionable analytics.

Acknowledgements

• The survey results are based on insights from 107 senior commercial leaders.
• The authors thank IQVIA's Commercial Solutions Strategic Operations team for their support.
• Special thanks to Katherine Brazer and Marlena Guthrie for their leadership in developing this white paper.

About the Authors

• Shravan Kotakonda is VP, Commercial Solutions, Strategic Operations at IQVIA, with over 15 years of experience in life sciences and healthcare.
• Dr. Pravindra Awasthi is Principal, Market & Competitive Intelligence at IQVIA, with over 16 years of experience in life sciences, specializing in cardiology and oncology.