A practitioner's guide to data integration for AI

Your AI project is only as good as your data foundation

Most AI projects stall before reaching production, not because of the models, but because of the underlying data. This practical guide walks you through centralizing AI-ready data, choosing the right AI type for your use case, and bringing your first project from proof of concept to production.

Why data engineers spend 44% of their time on pipelines-and how to reclaim those hours for higher-value AI work.
The key architectural differences between traditional, generative, and agentic AI-and how to pick the right starting point.
A step-by-step framework for your first AI proof of concept, including what to test and how to validate results.
The three data readiness criteria every AI project depends on: clean, compliant, and fresh.

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