DATACON 2026: Building AI-Ready Microsoft Data Estates
Most organizations don’t struggle with AI because of the model, but because of everything that comes before it.
Point AI at a mess and you get the mess at machine speed.
After working with hundreds of organizations and managing hundreds of thousands of data platforms, Datavail has seen the same challenges appear again and again: hidden technical debt, poor visibility into data estates, weak governance, conflicting business definitions, and infrastructure that isn’t ready to support modern analytics and AI initiatives.
In this DATACON 2026 session, Datavail Solution Principals Steve Wise and David Hay share real-world lessons from the field and the practical steps organizations are taking to build trusted, AI-ready data estates.
What You’ll Learn
- Why data modernization projects often take longer than expected
- How poor data quality and governance quietly increase costs
- How leading organizations are using trusted data to improve business outcomes and AI readiness
Watch “Lessons from the Field: Building AI-Ready Microsoft Data Estates” and start preparing your organization for AI success.
Frequently Asked Questions About AI-Ready Data Estates
What does it mean to be AI-ready?
Being AI-ready means more than having access to AI tools. Organizations need trusted, governed, and accessible data, modern platforms, clear ownership, and consistent business definitions before AI can deliver reliable results.
Why do data modernization projects often take longer than expected?
Many organizations underestimate the complexity of their data environments. Legacy systems, undocumented dependencies, duplicate data sources, manual processes, and unowned assets can surface during discovery and significantly extend timelines.
How important is data governance for AI initiatives?
Data governance is foundational. Poor data quality, duplicate records, inconsistent definitions, and a lack of ownership can lead to inaccurate reporting, wasted spend, and unreliable AI outputs. Successful organizations build governance and data quality processes into their ongoing operations rather than treating them as one-time projects.
How can organizations measure whether their data initiatives are successful?
Focus on business outcomes, including reduced decision-making time, increased trust in data, less manual reconciliation, and measurable business impact from specific use cases. Success is ultimately measured by better decisions and business results, not by technology deployments alone.