During this process, the GenAI integration identifies several customers named Robert Smith and suggests a way to combine all Robert Smiths without an active address on file. Because the bank implements this integration rapidly and without proper training, these erroneous merges are approved by human moderators. Now, the GenAI has absorbed misleading guidance about when and why to merge customer information – meaning this error will be committed in perpetuity, at least, until flagged by a system moderator and addressed.
This simplified example demonstrates the problem leaders will face if they use GenAI to drive down data disorganization: AI systems require human oversight and robust guidelines on which to operate. Without switzerland whatsapp number data data in its correct place and format, AI systems will generate inaccurate outputs, creating headaches for human moderators and hampering progress.
MDM Is a Long-Term Solution for a Long-Term Problem
MDM systems are the perfect partner for GenAI because these systems restore data integrity, providing AI with the prior knowledge it needs to generate correct outputs. In our banking example, MDM could have cleansed, analyzed, and sorted the bank’s data before GenAI integration. With a comprehensive view of customer data, GenAI could focus on creating more useful outputs, such as providing insights into customer behavior or identifying candidates for special rates and offers.