High Signal: Data Science | Career | AI Episode 14: Why Most Companies Aren’t Actually AI Ready (and What to Do About It)
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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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Outdated Data Management
- Data and AI landscapes have evolved, but management practices remain outdated.
- Manual data validation methods haven't kept pace with the advancements in AI.
Prioritize Data Quality
- Prioritize data quality as a top-level company priority.
- Establish clear metrics and SLAs for data, similar to application availability.
Proactive Data Observability
- Shift from reactive data quality detection to proactive troubleshooting and triaging.
- Focus on four core reasons for data/AI issues: bad data, code changes, system failures, and model output.
