Danger of AI: When Agentic AI Acts on Bad Master Data

With the rise of Agentic AI initiatives across organizations that now take action, the accuracy of its core business data becomes mission-critical for data strategy leaders and stewards.

Customer accounts, product records, 3rd party addresses, and contract terms are all examples of master data that enterprise AI agents rely on to make decisions, trigger workflows, or communicate your audience.

When that data is inconsistent or siloed, it can lead to faulty actions, compliance issues, and eroded trust. Master data management (MDM) ensures these foundational elements are standardized, connected, and governed so that AI operates with clarity and control.

In this session, we explore how MDM enables responsible and trustworthy Agentic AI, and why aligning it with enterprise governance is essential for trust and scale.

We will cover:

• How agentic AI initiatives depend on accurate and consistent master data and the consequences when it goes wrong
• How does MDM ensure accuracy and consistency across AI-initiatives
• Why MDM initiatives can stall without strong governance and how to fix it
• Where to start when aligning MDM and data governance into a single framework

If AI is making moves in your organization, this session will help you ensure it moves in the right direction.