What are practical applications of generative AI in enterprise workflows?

Roborana embeds generative AI as steps within larger workflows rather than deploying standalone chatbots. The most practical applications today are:

‍

Summarisation. Case files or client dossiers with hundreds of pages get condensed into key facts, previous decisions, and outstanding questions. A caseworker reads the AI summary and refers to full documents only when needed, saving hours of reading time.

‍

Drafting. Customer complaints come in, and AI drafts an initial response acknowledging the issue and proposing next steps. Staff edit and send rather than writing from scratch. Responses become faster and more consistent.

‍

Knowledge retrieval (RAG). When a customer or employee asks a question, the system searches your internal knowledge base and drafts an answer grounded in your actual documents. This is more reliable than pure generative AI because the answers are based on your data, not the model's general training.

‍

Intent recognition with action. Incoming emails or requests get classified by intent (billing question, address change, complaint) and routed to the right team or workflow automatically. The AI labels the request and triggers the appropriate follow-up process.

‍

The key principle: generative AI handles language-based tasks within a controlled workflow. A human reviews AI-drafted output before it goes to customers. This captures the time savings while maintaining quality.