AI improves data management by automating and optimizing various components of the data lifecycle. This includes tasks like data capture, de-duplication, anomaly detection, and data validation. By leveraging metadata analysis, AI can detect deviations in data usage and correct them automatically, allowing organizations to focus more on building core business value rather than spending excessive time on manual data management.

Organizations benefit from AI-driven techniques by achieving significant improvements in speed and cost-efficiency. For example, a Fortune 500 company was able to analyze over 75 datasets with 100 million rows of data at a total compute cost of just $80, which is considerably lower than traditional methods. Additionally, AI helps surface unseen patterns and trends, providing actionable insights that aid in decision-making.

One challenge organizations face is the lack of a dedicated team of expert data scientists who can build responsible and compliant AI models. Additionally, companies may struggle with the complexity of AI implementation for tasks like data preparation and forecasting. To address these issues, organizations can utilize second-generation tools that simplify AI integration and adopt a gradual approach to build trust in AI systems.