Enterprises are collecting large volumes of customer feedback, support interactions, and operational signals, but most teams still rely on manual analysis to identify patterns, risks, and improvement opportunities. In this session, I will share a practical enterprise AI workflow inspired by a real-world CSAT analytics use case, where AI was used to transform thousands of customer feedback responses into structured insights, leadership dashboards, and actionable signals. The talk will cover how to design an end-to-end AI workflow using trusted enterprise data, LLM-based classification and summarization, metadata capture, validation, human review, and self-service analytics. I will also discuss the lessons learned while moving from manual reporting to an operational AI pipeline, including data quality, prompt design, evaluation, governance, and how to build trust with business stakeholders. The goal of this session is to help data leaders, AI practitioners, and engineering teams understand how to move beyond GenAI demos and build AI workflows that deliver measurable business value in production environments.
Rajanikant Vellaturi is a Senior IEEE Member, author, and enterprise Data & AI architect with over two decades of experience building petabyte-scale platforms for Fortune 500 organizations across healthcare, CPG, and media. A pioneer of foundational architecture patterns such as the Medallion framework, Rajanikant specializes in transforming complex data ecosystems into real-time, AI-driven operational capabilities—powering everything from music recommendation engines for 100M+ users to enterprise-grade GenAI feedback pipelines. He serves on the Computer Science Advisory Board at East Carolina University, writes regularly on Data Lakehouses and AI Agents, and is a recognized 5× Top 50 Mentor on ADPList.
