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Servier Adopts Veeva OpenData Globally for AI

Servier, a French pharmaceutical company, is standardizing its customer reference data across more than 80 countries with Veeva OpenData, part of Veeva Data Cloud. This initiative builds on Servier's use of Veeva Link Key People and aims to create an AI-ready data foundation by harmonizing fragmented data silos. The centralized data will help commercial and medical teams gain faster insights and improve efficiency. Liam Hanrahan, Director of Global Operations Excellence at Servier, highlighted that the centralized data will improve processes and enable a semantic layer for better engagement with key healthcare professionals. Veeva's president, Kilian Weiss, emphasized that 89% of AI initiatives fail due to data challenges, and Servier's data-first approach demonstrates industry leadership. The adoption replaces fragmented silos and ensures consistency for scaling advanced analytics and AI.

• Servier standardizes customer reference data with Veeva OpenData across more than 80 countries.
• The initiative builds on Servier's use of Veeva Link Key People.
• Veeva OpenData is part of Veeva Data Cloud, which also includes Veeva Link and Veeva Compass.

New Veeva Study Builder Agent to Configure Clinical Studies in as Little as One Day

Veeva announces a new Study Builder Agent that can configure clinical studies in as little as one day, along with other updates including another Top 20 biopharma choosing Vault CRM and the introduction of Falcon Router for agentic triage.

Stop Anthropomorphizing LLMs, Treat Them as Tools

This article argues that marketers and tech professionals fundamentally misunderstand large language models (LLMs) by treating them as conscious entities. It explains that LLMs are statistical pattern engines that predict the next token based on probability, not logical reasoning. This leads to common failures like miscounting letters or clinging to incorrect answers. The author advises abandoning implicit logic by breaking tasks into single steps, providing tight constraints to reduce hallucinations, and not arguing with erroneous outputs. By reframing LLMs as tools rather than coworkers, marketing workflows can be made more effective and efficient.

• LLMs are predictive text engines based on statistical probability, not human logic.
• LLMs break text into tokens, which can obscure letter-level details and cause miscounting.
• Users should break tasks into single-purpose steps to improve LLM output accuracy.