Why GenAI Fails Without Clean Data

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https://www.microsoftinnovationpodcast.com/705   

What if the biggest barrier to AI success isn’t the model—but your data? In this episode, Sedarius Tekara Perrotta, CEO of Shelf, reveals why unstructured data is sabotaging enterprise AI efforts and how to fix it. From SharePoint chaos to hallucinating copilots, Sedarius shares a practical framework for transforming messy data into clean, actionable fuel for generative AI. If you're a business or tech leader navigating AI adoption, this conversation is your roadmap to clarity, control, and competitive edge.
 
🔑 KEY TAKEAWAYS
Unstructured data is the silent killer of AI accuracy. Most organizations have millions of files riddled with micro-errors that confuse AI models and lead to hallucinations. 
A successful AI strategy starts with a data strategy. Clean, enriched, and refined data is essential before plugging into any AI system. 
Use the “Reduce, Enhance, Refine” framework. Focus on a specific use case, eliminate irrelevant data, enrich with metadata, and refine to a trusted set of documents. 
Automation is key—but not enough. Human oversight and iterative monitoring are still critical to ensure AI outputs remain accurate and relevant. 
AI agents will transform job roles incrementally. Expect gradual automation of tasks, with agents augmenting—not replacing—human expertise.

🧰 RESOURCES MENTIONED
👉 Shelf – AI-powered unstructured data management platform: https://www.shelf.io  
👉 Microsoft Copilot Studio – Customizable AI interface for enterprise use: https://learn.microsoft.com/en-us/power-platform/copilot-studio  
👉 Gartner Research – Identified poor data quality as the #1 barrier to scaling GenAI - Read the full Gartner press release

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Thanks for listening 🚀 - Mark Smith