Title

Architecting AI-Ready Enterprise Data

About

In recent enterprise settings, the effectiveness of AI systems is increasingly constrained by data readiness rather than model capability. Although modern enterprises possess substantial data assets, they are often not organized, governed, or operationalized for reliable AI consumption. This session introduces a comprehensive layered framework that moves enterprise infrastructure from traditional pipelines into a Data for AI paradigm. We will explore how to architecturally organize data across six conceptual layers, ensuring that enterprise data is discoverable, governed, and perfectly formatted for machine consumption.

To make this actionable, the talk will walk through the implementation of four complementary systems: the System of Record, the System of Understanding, the System of Discovery, and the System of Action. We will also showcase a real-world case study in the out of home advertising industry, demonstrating how introducing these robust data foundations rescued a multi-agent planning assistant from query time latency, hallucination, and reliability issues.

Key Takeaways

• Architectural Blueprint for AI Readiness: Attendees will learn how to build a unified discovery plane by interconnecting lakehouses, vector stores, and enterprise knowledge graphs.
• Bidirectional AI for Data Pipelines: Discover how to leverage AI within the data pipeline itself to automate data quality validation, metadata enrichment, and governance controls.

Audience

Data Engineers, AI/ML Platform Engineers, Data Architects, and technical leaders who are tasked with upgrading legacy data infrastructures to support reliable GenAI applications, complex agentic workflows, and scalable semantic retrieval systems.

Bio

Sujit Kamthe is a Solution Consultant at Sahaj Software based in Pune, India. He focuses on data engineering, large scale data processing, and architecting robust data platforms. He has previously presented at The Fifth Elephant on pragmatic guides to robust data quality checks, and his work centers on building enterprise systems that are scalable, governed, and ready for modern AI consumption.