The future of enterprise transformation lies at the intersection of data and AI. Nowhere is this more evident than in the insurance industry, where intelligent automation is revolutionizing how organizations process claims, assess risk, and engage customers. But to succeed at scale, these AI systems depend on robust data pipelines, quality governance, and analytics that deliver real-time insights.

In this session, we’ll explore how data teams at major insurers are powering AI-driven workflows using familiar data platform tools such as SQL Server, Azure Data Factory, Power BI, and modern data lakes. You’ll see how scalable architectures support machine learning, natural language processing (NLP), and robotic process automation (RPA)—cutting underwriting time from weeks to hours and triaging 85% of claims automatically.

We’ll walk through how structured and unstructured data from diverse sources is unified, transformed, and used to train and serve models across secure, auditable pipelines. Real-world implementations show over 60% improvements in fraud detection, 70% automation in routine operations, and sub-second predictive response times—all achieved through optimized data infrastructure.

The session will also detail how to embed AI insights directly into dashboards, API endpoints, and operational systems to deliver immediate business value. You'll learn strategies for model monitoring, feedback loops, and data observability to ensure trust, accuracy, and compliance in production.

Whether you're a data engineer enabling machine learning workflows or a BI professional looking to integrate predictive insights into business processes, this talk provides a practical, real-world blueprint for data-first AI transformation in highly regulated environments like insurance.

Attendees will leave with an understanding of how to build scalable, intelligent systems using modern data tools that bridge the gap between analytics and enterprise automation.