Novartis: Streamlining analytics & AI across the organization

Novartis found analyzing hundreds of 20-page healthcare-professional interview transcripts slow and tedious, and spent significant time on manual, Excel-based weekly updates to key metrics with high-stakes consequences for errors. Novartis used Dataiku's LLM Mesh, Prompt Studios, Dataiku Answers (a RAG chatbot), and LLM-powered classification/summarization recipes for GenAI work, plus a customized simulation dashboard, batch automation, real-time API scoring, and ML-based forecasting for the spreadsheet workflow. Time-to-insight on the GenAI use case fell 90% (21 days to two days), and data-ingestion time on the spreadsheet use case improved 600% (one week to six hours). Deepthi Sanam, Group Lead of Data Engineering, said the Dataiku LLM Mesh 'offers substantial strategic benefits, notably accelerating time to market for GenAI chatbot development, reducing costs, and mitigating risks.'

Dataiku is a low-code/no-code AI platform whose Prompt Studios and LLM Mesh let teams prototype and A/B-test candidate LLM approaches before committing to a production workflow.

This sits in the Design & Stress-Test stage of the AI use-case lifecycle: Named examples from the tools pharma teams use to scope, prototype and benchmark an AI use case before committing to build it — data-catalog, sandbox and evaluation platforms.