Enterprise AI rollouts face data quality and security hurdles, prompting temporary halts

Summary

The article discusses how enterprises rushing into agentic and generative AI rollouts are encountering significant roadblocks due to poor data quality, outdated information assets, and security risks. Veterans of enterprise AI implementations at a recent conference warned that long-forgotten data assets suddenly become valuable—and risky—when used in AI systems. These issues have led to temporary halts in AI deployments as executives reassess their data strategies, highlighting the critical need for proper data governance, cleaning, and security measures before scaling AI initiatives.