Veeva AI 2025 in Action: Key Summit Demo & Insights (Part 2)

This video provides an in-depth exploration of Veeva's Agentic AI capabilities, showcasing how these intelligent agents are set to transform operations across the Veeva Vault platform and its various applications within the life sciences industry. The speaker, building on a previous discussion about Veeva Vault agents, focuses this session on practical demonstrations of how Veeva AI functions. The core message is that the Veeva Vault platform is evolving beyond mere data and content management to enable true intelligence through the integration of Agentic AI.

The presentation highlights Veeva's two-pronged approach to Agentic AI: standard agents integrated directly into the Vault platform for broad capabilities, and application-level agents designed for specific business processes within individual Veeva applications, offering tailored automation. The video progresses by presenting concrete examples of Veeva AI in action, primarily through an interactive chat window interface, which the speaker likens to ChatGPT. These examples illustrate how users can seamlessly interact with the AI to query both structured objects and unstructured content, and even initiate actions directly through conversational prompts.

Several compelling use cases are demonstrated. For object interaction, the AI chat window is shown integrated into a "classes object" within Veeva. Users can prompt questions such as "What is the current enrollment status?" or "Who is on the waiting list?" The AI efficiently analyzes the object's underlying data and provides immediate, accurate answers. A more advanced and impactful use case involves the AI performing actions: a user can simply type "enroll in the class," and the AI will process this command to create the necessary user record (e.g., enrolling "Bob Ng" into a biology course), significantly reducing manual effort and saving considerable time.

Beyond structured object data, the video demonstrates Veeva AI's ability to analyze unstructured document content. An example shows a user asking "What is the grading scale for this class?" while viewing a document, and the AI promptly extracts and presents the relevant information from the document's text. The presentation also delves into the configuration of these agents within Veeva Vault, illustrating how agents (e.g., a "classroom agent") are set up, tagged to specific objects (e.g., a "class object"), and how their behavior, objectives, context, and actions are defined within the platform's metadata section. The speaker concludes by outlining Veeva's roadmap, indicating a planned launch of Agentic AI in its commercial Vault by 2025, with subsequent integration across all Veeva applications.

Key Takeaways:

• Veeva's Strategic Shift to Agentic AI: The Veeva Vault platform is undergoing a significant evolution, moving beyond traditional data and content management to incorporate Agentic AI, enabling true intelligence and automation across its ecosystem. This represents a major advancement in how life sciences companies will interact with their data and processes.
• Dual AI Architecture: Veeva's Agentic AI strategy is built on two pillars: platform-level agents integrated into Veeva Vault for general intelligence, and application-specific agents tailored to optimize unique business processes within individual Veeva applications, ensuring both broad utility and specialized functionality.
• Intuitive Conversational Interface: Veeva AI introduces an interactive chat window, similar to large language model interfaces like ChatGPT, allowing users to engage with the AI through natural language prompts directly within Veeva applications. This conversational approach simplifies complex data retrieval and task execution.
• Enhanced Object Interaction: The AI can efficiently query and analyze data within specific Veeva objects. Users can ask questions about object attributes, such as enrollment statuses or waiting lists for a "classes object," and receive immediate, data-driven answers, improving data accessibility.
• AI-Driven Action Automation: A powerful feature demonstrated is the AI's capability to execute actions based on user commands. For example, a simple prompt like "enroll in the class" can trigger the AI to create a new user record, thereby automating administrative tasks and significantly reducing manual data entry and processing time.
• Comprehensive Content Analysis: Veeva AI is not limited to structured data; it can also analyze unstructured content within documents. Users can ask specific questions about a document's content, and the AI will extract and present the relevant information, such as a grading scale from a class document, enhancing information retrieval from diverse sources.
• Streamlined Record Management: The ability of the AI to generate and update records directly from chat prompts highlights its potential to streamline workflows and minimize user effort in record creation and management, leading to greater operational efficiency and data accuracy.
• Configurable Agent Behavior: Agents within Veeva Vault are highly configurable. The video illustrates how agents, such as a "classroom agent," are set up, tagged to specific objects (e.g., "class object"), and how their objectives, context, and actions are defined in the metadata, allowing for precise customization to meet specific business needs.
• Significant Time and Effort Reduction: A core benefit emphasized throughout the demonstration is the substantial reduction in user effort and time through AI automation, from querying information and analyzing content to executing actions and creating records, ultimately boosting overall productivity.
• Future Impact on Commercial Operations: Veeva's roadmap includes launching Agentic AI in its commercial Vault by 2025, with subsequent integration across all applications. This indicates a transformative impact on commercial operations, medical affairs, clinical processes, and regulatory compliance within the life sciences sector.
• Potential for Custom Agent Development: The mention of "custom agent" in the configuration section suggests that organizations may have the flexibility to develop or tailor agents to address unique operational challenges and integrate specific business logic, further extending the platform's adaptability.
• Empowering Faster Decision-Making: By providing quick access to accurate information and automating routine tasks, Agentic AI empowers users with smarter, faster insights, facilitating more informed and agile decision-making across various functions in the life sciences industry.

Key Concepts:

• Agentic AI: An advanced form of artificial intelligence where autonomous agents can understand context, make decisions, and perform actions to achieve specific goals, often interacting with users through natural language.
• Veeva Vault Platform: A cloud-based content and data management platform widely used in the life sciences industry for managing regulated content and processes across R&D, clinical, quality, and commercial operations.
• Application-level Agents: AI agents specifically designed and integrated into individual Veeva applications to automate tasks and processes unique to those applications.
• Object-level Interaction: The ability of AI to interact with and extract information from structured data objects within a system, such as a "classes object" in Veeva.
• Content Analysis: The capability of AI to process and understand unstructured data within documents and other content, extracting relevant information based on user queries.
• AI-driven Action Execution: The ability of AI to perform specific tasks or create records within a system based on user commands or predefined rules, automating workflows.
• Agent Configuration: The process of setting up, defining, and customizing the behavior, objectives, and actions of AI agents within a platform.

Examples/Case Studies:

• Querying Object Data: Demonstrating the Veeva AI chat window on a "classes object" to answer questions like "What is the current enrollment status?" or "Who is on the waiting list?"
• Automated Record Creation: Using a chat prompt "enroll in the class" to automatically create a user record for "Bob Ng" in a specific course section.
• Document Information Extraction: Analyzing a document to answer "What is the grading scale for this class?" and presenting the relevant grading criteria (e.g., A: 93-100%).
• Agent Setup: Illustrating the configuration of a "classroom agent" within Veeva Vault, showing its linkage to the "class object" and the definition of its metadata, objectives, context, and actions.