Companies manage vast amounts of knowledge across both commercial and clinical areas including regulatory filings, product information, sales materials, and clinical documentation. Making this knowledge accessible across the global company landscape is no small task. Making this knowledge accessible across the global company structure is no small task. Traditional search often falls short, and employees struggle to find what they need.
Retrieval-Augmented Generation (RAG) chatbots promise a breakthrough: one central interface where employees can query documents in natural language. But deploying such a solution across a global organization brings specific challenges.
Here are the key challenges we’ve encountered in rollouts and the best practices that make scaling possible.
The situation: Different Commercial Market Units often manage their own SharePoint repositories, each with unique structures and taxonomies. Sales affiliates may store thousands of product documents, while regulatory teams manage highly sensitive submissions.
Best practice: Create a document ingestion pipeline that connects to SharePoint via API. Documents are processed into a standardized central repository structure while still preserving the original source folder structures. Each Businnes Units pipeline is configured once and then runs automatically. Based on the standardized document storage, embeddings are generated and stored in a vector database. This ensures the chatbot accesses a consistent knowledge base while allowing local teams to continue maintaining their own systems.
The situation: Business Units delete, update or create new documents on a daily basis. This forms the knowledge foundation for a chatbot. If the chatbot delivers outdated content, it risks losing user trust immediately.
Best practice: Automate synchronization between local repositories and the central knowledge base. Continuous updates ensure employees always receive the latest information by the RAG system. This builds user trust, important for adoption of a new system, but also safeguards compliance.
The situation: Not every employee should have access to every document. Access requirements can vary by market, product type, or business function and permissions may change over time.
Best practice: Implement Role-Based Access Control (RBAC) directly at the vector store level. Each chatbot instance dynamically enforces permissions, so users can only access documents they are entitled to.
The situation: Business Units often need specific chatbot functions tailored to their workflows. Without structure, this can lead to dozens of custom versions that are difficult to manage.
Best practice: Co-develop local use cases during onboarding and add them to a shared global marketplace. This allows markets to adopt proven use cases from other affiliates, ensuring scale and reusability while still meeting local needs.
The situation: A global rollout across dozens of Business Units sounds resource-intensive. Many organizations fear they will need large local support teams.
Best practice: With the right architecture, operational effort after onboarding is minimal. Document ingestion is automated, permissions are managed locally, and use cases can be activated centrally. A small global team can maintain the platform effectively.
For companies, GenAI chatbots are not just a trend, they are a necessity for handling complex and distributed knowledge. Success at scale requires a careful balance: centralized structure for consistency, local flexibility for relevance, and automation for sustainability.
By addressing these challenges proactively, organizations can roll out RAG chatbots that adapt to local needs, operate with minimal overhead, and provide employees worldwide with reliable, compliant access to critical information.
