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The paper presents REDI, an open-source framework that automates the transformation of raw scientific datasets into AI-ready data through a unified five-stage pipeline, with companion tool SetGo for FAIR compliance, evaluated across multiple scientific domains.
AI has great potential in agriculture, but its effectiveness depends on clean and complete data foundations; the industry faces unique data challenges from IoT devices, weather feeds, and land-specific variables.
The article discusses how financial services companies must ensure data quality, security, and accessibility to successfully deploy agentic AI, emphasizing that the technology's effectiveness depends more on robust data foundations than on system sophistication.