Many AI projects fail to reach production due to data silos, which create inconsistencies and gaps in data. To address this, organizations should unify their data workloads across on-premises and cloud environments, as 60% of IT leaders identify data silos as a significant barrier to digital transformation.

Organizations can enhance AI security by embedding protection at the data layer. This includes implementing immutable protection, integrated detection, and automated recovery to safeguard against ransomware and data exfiltration. Currently, many organizations lack foundational security practices, making it crucial to prioritize security in AI initiatives.

Modernizing data infrastructure is essential for AI success as it accelerates data pipelines and eliminates bottlenecks, enabling real-time insights. Organizations that streamline their IT infrastructure can achieve reliable AI performance and maximize ROI, with studies showing that IT modernization can reduce maintenance costs by up to 50% and improve system reliability by 74%.