Is Your Data Actually AI-Ready?

DateJune 4, 2026 Time11 a.m.-12 p.m. ET HOSTED BYAtorus

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Speakers

Host<br>Aga Rasińska

Host
Aga Rasińska

Associate Director of Strategy

Christine Kanalis

Christine Kanalis

Executive Director, Clinical Data Management

Michael Collins

Michael Collins

Associate Data Solutions Engineer

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Everyone is asking whether their data is AI-ready. Not many are asking what that actually means, or being honest about what happens when the answer is no. 

In this unscripted conversation, Aga, Christine, and Michael explore why high-quality data is the essential foundation for AI – a requirement that most teams acknowledge but few have actually addressed. Three practitioners with three different vantage points:  

  • The person building AI tools – for clinical teams and beyond 
  • The person managing clinical data and operations day to day  
  • The strategist connecting the two 

No demos. No sales pitch. Just an honest conversation about a problem most teams are either avoiding or getting wrong. 

You’ll leave with a clear view of what AI-readiness really requires and who owns it, plus what to do when your data isn’t there yet. 

  • What “data as a product” means in practice and why the buzzword gets in the way 
  • Assess AI readiness as a data quality question  
  • Why data quality is a continuous process and what that means for your team 
  • When to use AI and when to wait  
  • Who owns data quality when AI is in the picture 
  • Linking data quality signals to downstream analytics and AI-driven decisions  
  • A working definition of AI-ready data. Not the marketing version, but a practical framework for assessing whether your data is ready for AI to use. 
  • The product mindset shift. What treating data as a product changes about how teams operate, and why that matters more than the technology. 
  • Data quality as a process, not a deliverable. Why one-time cleanup doesn’t work, and what ongoing quality ownership looks like. 
  • Ownership clarity. A direct conversation about who is responsible for data quality when AI enters the picture, and what happens when that responsibility isn’t clear. 
  • When not to use AI. Practical guidance on recognizing when your data isn’t ready, and what to do instead.