What this guide contains
- Why 60% of AI projects without AI-ready data get abandoned (Gartner)
- The 95% no-ROI finding and what the 5% do differently (MIT)
- How AI amplifies bad data, and hallucination on stale inputs
- The AI-ready data checklist
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Why most AI projects fail on the data not the model, what AI-ready actually means, and the data layer that separates the 5% who win.
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What you'll learn
- Why the model is almost never the problem
- What 'AI-ready data' actually means
- Why verified-at-use beats a static snapshot for AI
- How to audit your data layer before blaming the model
Who it's for
- RevOps and data teams deploying AI
- Sales and marketing leaders using AI outreach
- Founders building AI workflows
- Anyone whose AI runs on CRM data