How AI supercharges your data platform

AI doesn’t replace your data platform; it amplifies its value when the platform is designed for trusted access, governance, and operational scale. In this webinar, we show decision-makers how to turn an existing data platform into an AI enablement layer that delivers measurable outcomes: faster insight, better self-service, reduced manual effort, and new “AI-first” experiences for business teams.

We’ll walk through a practical set of use cases and the platform capabilities they require:

• Chat With Your Data for decision support: build ChatGPT-like experiences over internal documents and curated datasets.
• Embeddings, vectors, and semantic search: why vectors matter, what “embedding pipelines” look like, and how vector databases/indexes enable semantic discovery, recommendations, and better retrieval for RAG. We’ll compare platform-native options (e.g., Fabric vector database concepts) with lakehouse-grade implementations.
• AI-assisted development for data teams: how copilots accelerate notebooks, transformations, and analytics delivery—while still requiring guardrails (standards, code review, and testing) to stay reliable.
• Classification & enrichment at scale: using AI to label, route, and enrich records (tickets, emails, customer feedback, contracts), then pushing results back into governed models so BI and operational workflows benefit.
   

What you’ll leave with: a clear decision framework for where AI fits in your platform, what to prioritize first (governance, lineage, security, evaluation), and a realistic roadmap to move from pilots to production. We’ll also highlight where targeted consultancy typically delivers the fastest ROI; use-case selection, reference architecture, embedding/vector design, RAG quality evaluation, and operating model setup (so your team can scale sustainably).
 

Who should attend

Data & analytics decision-makers, data platform and engineering leaders working with Microsoft Fabric, Azure, or Databricks, analytics & BI managers driving self-service and operational value, and anyone who wants to scale AI on top of a trusted, governed data platform.