5 data strategies to break the AI pilot loop and scale AI

AI is no longer experimental. Nearly 9 out of 10 organizations are using AI in at least one function. Yet almost 60% of innovation efforts stall after proof of concept. At the same time, 91% of data leaders report that data reliability blocks GenAI from moving into production.

Scaling AI requires more than models. It demands alignment across your architecture, workflows, governance and team capabilities. This executive workbook outlines five strategies to help you learn how to:

Reinforce data integration and engineering for scalable AI
Enhance model monitoring and observability across pipelines
Build trust through scaled data and AI governance
Advance agentic data management and AI readiness

5 Strategies to Scale AI Beyond Pilots with Trusted Data

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