A Framework for the AI Era
The AI-Ready
Data Manifesto
AI removes humans from the decision loop. Is your data ready?
Motivating Insight
AI-ready data is a systems design problem, not a data quality problem.
Data quality—accuracy, completeness, consistency—has always mattered. But treating AI-readiness as a data quality initiative misses the point.
The challenge isn't cleaning your data. The challenge is designing systems that can consistently deliver data meeting AI's specific requirements.
Data must be interpretable by machines, accessible at inference time, fresh enough to act on, observable before problems propagate, and compliant with new categories of risk.
You don't solve this with a data quality tool.
You solve it with architecture.
The Five Factors
What makes data AI-ready.
The Shift
AI has no institutional memory. If meaning isn't explicit in the data itself, AI will misinterpret it confidently.
The Shift
AI workloads have specific requirements: vectors for RAG, features for inference, real-time access for agents.
The Shift
AI acts on whatever it's given. A pricing agent doesn't wonder if inventory levels are stale. It prices based on the data it has.
The Shift
AI operates at machine speed. By the time you notice something's wrong, thousands of decisions have been made.
The Shift
AI introduces new compliance risks that traditional data governance doesn't address.
Prioritization Logic
Not every organization needs to solve all five factors at once.
Prioritization depends on the AI workloads you're deploying.
Use Case
RAG / Knowledge Assistants
Priority Factors
Rationale
Retrieval quality depends on semantic clarity; access patterns must support vector search and low-latency retrieval.
This prioritization is guidance, not prescription. Every organization's context is different.
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