Why ERP Leaders Need to Understand Data Lakehouses Before Scaling AI Agents
As organizations explore the next wave of enterprise AI (artificial intelligence), the biggest challenge is often not the AI technology itself—it is the data behind it. A recent article on ERP Today explains why data lakehouses are becoming a critical foundation for organizations looking to safely expand AI agents across ERP (enterprise resource planning) and business operations. A data lakehouse combines the flexibility of a data lake with the structure, reliability, and governance of a traditional data warehouse. For ERP teams, this creates a centralized layer where data from systems like ERP, CRM (customer relationship management), human resources, supply chain, and finance can be connected, organized, and prepared for analytics and AI—without replacing the ERP system itself. ERP remains the system of record for transactions and business processes, while the lakehouse provides the broader data foundation AI needs.
The article highlights that AI agents require more than just access to information—they need context. Without a strong semantic layer that defines business terms, metrics, and data relationships, AI can produce answers that appear accurate but may not align with how the organization actually operates. For example, terms like revenue, customer, inventory, or margin can mean different things across different systems, making governance and data definitions essential. Security and control are also major considerations. As AI agents become capable of retrieving information and taking actions, organizations need clear rules around permissions, audit trails, data access, and cost management. Giving AI unrestricted access to enterprise data can create risks around accuracy, compliance, and decision-making.
The key takeaway is that AI readiness starts with data readiness. Organizations looking to scale AI agents should focus first on creating a governed, connected, and well-understood data environment. A strong ERP foundation combined with a modern data architecture will help businesses unlock AI’s potential while maintaining trust and control.
