Artificial intelligence operates as a transformative force that transforms various industries, including healthcare, together with finance and all other sectors. AI systems achieve their highest performance through data that has been properly prepared for training purposes. AI success depends on high-quality data because inaccurate all-inclusive or duplicated data or conflicting records lead to both diminished performance and higher operational costs, biased decisions, and flawed insights. AI developers understate the true impact of dirty data-related expenses because these factors directly affect business performance levels together with user trust and project achievement.The Financial Burden of Poor Data QualityThe financial costs represent one direct expense related to using dirty data during AI development processes. Organizations that depend on AI systems for decision automation need to budget sizable expenses toward cleaning data, preparing it for processing, and validating existing datasets. Studies show poor data quality annually creates millions of dollars of financial losses through several efficiency issues, prediction mistakes, and resource ineffectiveness. Faulty data that train AI models sometimes leads businesses to make mistakes involving resource wastage and incorrect targeting of customers, followed by incorrect healthcare diagnoses of patients.