Agricultural AI Promises Big Yields, But Dirty Data Undermines Results

Executive Briefing

• Research shows AI-enabled models can improve crop yields 26%, cut water use 41%, and reduce chemical usage 33%
• Warns that fragmented, inconsistent agricultural data renders AI outputs misleading or actively counterproductive
• Highlights unique complexity: IoT sensors, autonomous equipment, GPS field mapping, and multi-source external data must be unified
• Stresses governance is critical, as outdated supplier or pricing data causes AI to recommend based on a business that no longer exists
• Advises organizations to build a single governed data foundation before investing in AI tools to avoid 'garbage in, garbage out' outcomes