AI and Analytics

Scaling AI enterprise-wide requires a unified, governed data foundation, as well as infrastructure built for AI-scale performance across hybrid, edge, and cloud. Additionally, strong governance and sovereignty controls are important for scaling AI, paired with low-code agent builders and model context protocols that close the skills gap to move teams from experimentation to production faster.

Assess your organization’s AI readiness by first evaluating data quality and accessibility. Second, use the Hitachi AI Discovery Service to identify use cases and ROI. Third, assess infrastructure, governance, and skills together as an integrated system. These steps will enable your organizations to build a staged roadmap rather than jumping straight to deployment.

Hitachi iQ M Series is an excellent option for mission-critical, high performance AI workloads with the trusted VSP One as the unifying data layer underneath. 

Preparing data for AI/ML means acknowledging that fragmented, multi-platform data is the core obstacle. Organizations can use a modern data lakehouse (like EverFlex AI Data Hub) to connect and analyze that data in place rather than physically migrating it, then build it on high-performance, governed infrastructure like VSP One. Organizations can also prepare data for AI/ML by consuming it in a flexible, pay-for-what-you-use model so that cost and complexity don't stall the effort before it starts.

Effectively deploying AI in business operations begins with assessing and scoping through a formal discovery engagement that pinpoints the right pipelines and workloads. Hitachi iQ then provides fast, scalable, cost-optimized infrastructure with Hitachi iQ Studio as the control plane for enterprise AI, providing governance, operations, and data readiness capabilities to reduce reliance on scarce skills, support data and AI governance and progress deliberately from pilot to production with agent orchestration.