High Signal: Data Science | Career | AI
Episode 14: Barr Moses on Why Most Companies Aren’t Actually AI Ready (and What to Do About It)
Apr 10, 2025
Barr Moses, co-founder and CEO of Monte Carlo, shares insights on the AI readiness crisis many companies face. She reveals high-stakes data disasters, including a shocking $100M schema change. The discussion emphasizes the necessity of data quality and observability, as organizations struggle to align their ambitions with reality. Barr also highlights the transformative role of LLM agents in improving data debugging. Overall, it's a sharp critique of the disconnect between current data practices and the demands of AI.
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Quick takeaways
- Despite the rapid advancement in AI technology, most companies still manage their data with outdated practices, leading to significant financial risks.
- The gap between the perceived pressure to adopt AI and actual data readiness highlights the need for improved data management strategies.
Deep dives
The Evolution of Data Management
The landscape of data management has transformed significantly over the past decade, with the stakes now much higher than before. Previously, only a few individuals handled data within organizations, allowing ample time for ensuring its accuracy. Today, however, nearly everyone in an organization relies on data and AI, making it crucial to address any data issues promptly. The implications of poor data practices can be severe, as highlighted by notable incidents like Unity’s $100 million loss and Citibank's $400 million fine for data quality failures.
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