Data quality is crucial in AI learning as inaccurate, incomplete, or biased data can hinder the model's ability to learn and make accurate predictions. Poor data quality can lead to subpar performance, erroneous conclusions, and unreliable AI systems. Data cleaning, proper preprocessing, and quality assurance measures are essential to ensure high-quality training data.