The semantics, governance, and boundaries that define trustworthy AI
Your lakehouse centralizes storage, governance, and compute, but running agentic AI takes more.
When an autonomous agent reads a term like “top sales rep” or “year to date,” it acts on whatever definition it finds first. Get that definition wrong at machine speed, and you’re just confidently working with wrong answers at scale.
In this digital dialogue, research fellow Donald Farmer sits down with data and analytics leaders to map the foundation your lakehouse needs for accountable, production-ready AI.
You’ll discover:
Where organizations overestimate their AI readiness, and the semantic gap that trips agents up
How governed self-service widens who can shape business logic while IT keeps the full picture
Why clear boundaries let an agent act freely and stay auditable
How to scope what an agent should do vs. what it should never touch, before production
If agentic AI is a priority for your organization, the architecture underneath matters more than ever.
I agree that the data I provide may be used to contact me for marketing purposes. I may opt out at any time.
By submitting this form, I acknowledge my data will be processed in accordance with the Alteryx Privacy Policy
Recommended Resources
Blog Post
Evaluating Business Intelligence Tools That Scale Without Increasing Complexity
2026 Executive Insights on AI, Agentic AI, and Enterprise Readiness
Learn how to empower your organization to accelerate insights, improve productivity, and drive innovation with a unified, AI-powered analytics platform.
See what S&P Global Market Intelligence finds in the shift to unified analytics, cloud access, and AI-ready data, with Alteryx One as an example inside the analysis.
Discover why most enterprises are falling short on cloud data platform ROI and how a business interface layer like Alteryx One can help you maximize your data.
