Many organizations struggle to scale AI beyond initial pilot projects due to fundamental architectural issues, not adoption problems. Pilots succeed by simplifying data and scope, which masks the complexities of real-world data estates, including conflicting definitions, stale information, and access control failures. This leads to AI systems providing plausible but incorrect answers, resulting in escalating costs and significant risks, particularly under regulations like the EU AI Act. AI
IMPACT Highlights critical challenges in scaling AI, emphasizing the need for robust architecture to avoid costly failures and regulatory non-compliance.
RANK_REASON Article discusses common challenges in AI implementation and scaling, drawing on survey data and expert opinion, rather than announcing a new product or research.
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