If AI Were Your Employee, You’d Fire It — Here’s How to Rebuild Trust

If AI was your employee, you'd probably fire it. You use AI daily. You know it’s powerful, but are you really going to hand it the keys to your most sensitive systems? Would you trust an LLM with your customer’s personally identifiable information? Would you give it administrative access to your Snowflake warehouse? Would you let it touch production payroll data, financial transactions, or regulated records? Of course not. Because AI doesn't understand your enterprise data...it just guesses with alarming confidence. Here's the problem: connecting enterprise data to AI is difficult and risky. LLMs hallucinate. They misread column relationships, join the wrong tables, and return outputs that look correct but are fundamentally wrong. What if your AI could actually be trusted with production data? Join us for a technical session on solving AI's data problem at the source. We'll walk through:

1. Governed connectivity to enterprise data in place: Whether your data lives in Snowflake, Trino, OneLake, or across fragmented systems, we'll show you how to connect it securely without data copies or brittle pipelines.

2. Eliminating hallucinations with deterministic results: Learn how a semantic layer stops hallucinations before they start by giving AI the context it needs to return consistent, verifiable answers every time.

3. Production-ready governance for AI: Implement role-based access, audit trails, and compliance controls that scale with your AI systems (not against them.)

You wouldn't trust ChatGPT or Claude with your HR data, payroll, or production systems. But with the right architecture, you don't have to choose between innovation and control. See you there.