Veeva Commercial Summit 25: On quality data foundations and industry AI adoption with Karl Goossens

This video provides an in-depth exploration of the critical role of robust data foundations in enabling the successful adoption and scaling of Artificial Intelligence (AI) in commercial biopharma. Featuring Karl Goossens, General Manager of OpenData Europe at Veeva Systems, the discussion is set against the backdrop of the Veeva Commercial Summit 2025 in Madrid. Goossens shares key findings from Veeva's "The State of Data, Analytics, and and AI in Commercial Biopharma" report, which highlights a significant industry paradox: while AI adoption is accelerating, a staggering 89% of companies struggle to scale more than half their AI initiatives due to inadequate data foundations. The report, based on a survey of 116 senior life sciences leaders overseeing commercial analytics and AI, underscores the urgent need for business leaders to prioritize building strong data infrastructure to truly unlock AI's potential.

The conversation delves into three core data issues identified by the report as hindering AI's impact: trust, speed, and consistency. Goossens explains that 73% of respondents reported insufficient data quality for AI applications, citing the need for highly accurate physician data (e.g., place of work) to generate meaningful insights. Speed is hampered by data fragmentation, with 63% of respondents noting that data scattered across disparate systems leads to lengthy "time to insight," often taking months to collect, map, and analyze data, rendering it too slow for timely decision-making. Consistency is another major challenge, as data about individuals often varies across different datasets, making global analytics and precise targeting (e.g., identifying doctors treating specific conditions) incredibly difficult, as illustrated by one customer having 30,000 different specialty values for physicians.

To address these foundational challenges, Goossens introduces Veeva's latest innovations, particularly OpenData 2.0, designed to unify and standardize HCP (Healthcare Professional) and HCO (Healthcare Organization) data. A key innovation is "agentic data stewardship," which leverages AI to enhance data quality for the 26 million individuals in OpenData. This involves using AI to monitor public information in real-time, detecting changes like a physician's place of work, thereby providing account teams with up-to-date, accurate data for better AI recommendations. OpenData 2.0 also features a simpler, globalized data model that seamlessly integrates with Veeva software like Vault CRM, enabling consistent global analytics. Furthermore, it promotes data democratization through a web application that allows any customer employee to easily explore data, akin to LinkedIn, and offers a direct data API for rapid data transfer to AI models and analytics platforms, facilitating real-time insights. The discussion concludes with a forward-looking perspective, emphasizing AI's transformative potential for patients through faster R&D and personalized treatments, as well as its role in reducing healthcare costs by connecting fragmented data systems.

Key Takeaways:

• Critical Data Foundation for AI Scaling: Despite accelerating AI adoption in biopharma, 89% of companies fail to scale more than half their AI initiatives due to poor data foundations. Building a robust data foundation is paramount for unlocking AI's full value.
• Three Core Data Issues: The report identifies Trust, Speed, and Consistency as the primary data challenges. Data quality is often insufficient (73% of respondents), data fragmentation hinders "time to insight" (63% experience delays of months), and inconsistent data across systems makes global analytics and precise targeting difficult.
• Importance of Unified HCP/HCO Data: Unlocking the full potential of AI, especially generative AI, requires connecting and standardizing HCP and HCO data from various sources (research, patient types, work locations) to provide comprehensive information for AI models.
• Agentic Data Stewardship: Veeva's innovation uses AI to proactively enhance data quality for millions of individuals in OpenData. This involves real-time monitoring of public information to detect changes (e.g., physician's workplace), ensuring data accuracy for better AI recommendations and tactical adjustments.
• OpenData 2.0 Innovations: This evolution focuses on three elements: a new, simpler, globalized data model for consistent analytics and seamless integration with Veeva software (e.g., Vault CRM); data democratization via an intuitive web application for easy data exploration by any customer employee; and a direct data API for rapid data transfer to AI models and analytics platforms, enabling real-time insights.
• Understanding Agentic AI: Agentic AI leverages Large Language Models (LLMs) to process both structured and unstructured data. It employs multiple specialized "agents" to perform distinct tasks, such as browsing the internet for information, answering questions (like chatbots), combining answers, and facilitating human quality checks, leading to more comprehensive and accurate data curation.
• Industry's AI Momentum: The biopharma industry is actively moving beyond discussions to real-world deployment of AI initiatives, recognizing that correct data is a critical prerequisite for scaling these efforts.
• More Data, Better Quality, Global Curation: The industry's current focus is on utilizing more data, ensuring higher data quality, and curating these datasets on a unified global data model to maximize AI's effectiveness.
• Patient-Centric AI Benefits: AI promises significant benefits for patients, including faster R&D for new treatments, the ability to find the right treatment for the right patients, and empowering doctors with better diagnostic tools.
• Healthcare Cost Reduction: Beyond patient care, AI has the potential to significantly reduce healthcare costs by connecting fragmented data systems, thereby minimizing duplicative procedures and improving overall system efficiency.

Tools/Resources Mentioned:

• Veeva Commercial Summit 2025
• Veeva's "The State of Data, Analytics, and AI in Commercial Biopharma" report
• Veeva OpenData
• Veeva OpenData 2.0
• Veeva Vault CRM
• ChatGPT, Gemini (as examples of chatbots/LLMs)
• LinkedIn (as an example of a web application for data exploration)

Key Concepts:

• Agentic AI: An advanced form of AI that utilizes multiple specialized "agents" (often powered by LLMs) to perform distinct tasks, process diverse data types (structured and unstructured), and collaborate to achieve complex goals, such as comprehensive data stewardship.
• Agentic Data Stewardship: A specific application of agentic AI where AI agents continuously monitor, collect, process, and verify data (e.g., HCP information from public sources) in real-time to ensure high quality, accuracy, and consistency, often with a final human review.
• Data Fragmentation: The issue of data being stored in multiple, disparate systems or silos within an organization, making it difficult to access, integrate, and analyze comprehensively.
• Time to Insight: The duration it takes from data collection to generating actionable insights, often hindered by data fragmentation and poor data quality.
• Data Democratization: Making data easily accessible and understandable to a wider range of users within an organization, empowering more employees to leverage data for decision-making without specialized technical skills.
• HCP (Healthcare Professional) and HCO (Healthcare Organization) Data: Critical data pertaining to individual medical professionals and healthcare institutions, essential for commercial operations, medical affairs, and clinical research in the life sciences.