Sandeep Pawar demonstrates functionality:
Most enterprise data lives in free text – tickets, contracts, feedback, clinical notes, and more. It holds critical information but doesn’t fit into the structured tables that pipelines expect. Traditionally, extracting structure meant rule-based parsers that break with every format to change, or custom NLP models that take weeks to build. LLMs opened new possibilities, but on their own they bring inconsistent outputs, no type of enforcement, and results that vary between runs. What production workflows need is LLM intelligence with structured-output guarantees, delivered inside the data platform teams already use.
Microsoft Fabric AI Functions deliver exactly that. Functions like ai.summarize, ai.classify, ai.translate, and ai.extract let you transform and enrich unstructured data at scale with a single line of code – no model deployment or ML infrastructure needed. For the full list, see Transform and enrich data with AI functions.
Click through for an example. The tricky part of this is, because answers won’t be deterministic, you have to do a lot of testing and verification to ensure things are working reasonably well.