Artificial intelligence is transforming agriculture with promising use cases like improving crop yield and reducing water use, but its success relies on a solid data foundation, which is often lacking in the industry. Data accuracy, structure, and governance are crucial for effective AI solutions. If the data is inconsistent or incomplete, AI models can generate misleading outputs, leading to counterproductive decisions. Why it matters: The accuracy of agricultural AI outputs has significant implications for the industry’s ability to optimize resources and minimize waste, making data quality a critical factor in the adoption of AI technologies.