Before implementing a natural language interface, organizations need to collect, vectorize, and store data in a knowledge base. The system then uses large language models to generate answers to project-related questions asked through the interface. To protect sensitive data, it's recommended to roll out these systems on the client's on-premises infrastructure and establish rigid information security standards. The use of generative AI and natural language interfaces can enhance organizational effectiveness by up to 45%, according to McKinsey.
Key takeaways:
- Natural language interfaces can significantly enhance communication within organizations by making it more efficient, accessible and tailored to individual needs.
- Natural language interfaces can save time by swiftly fetching data from various sources, preserving crucial project knowledge, and establishing language-agnostic communication.
- Before implementing a natural language interface, it is necessary to collect data, vectorize it, and store it in a knowledge base.
- Companies should consider establishing rigid information security standards and clearly define data ownership and usage rights in contracts and agreements with LLM providers to avoid disputes and data breaches.