Enterprise systems have evolved significantly, but many changes have been superficial, failing to address fundamental issues. Creating new applications often requires re-establishing a shared understanding of key concepts and data, which leads to complexity rather than agility. AI, which relies on understanding intelligent data, complicates this further if the reasoning and rules are embedded in code instead of a common, accessible layer. By structuring data with a shared semantic layer, organizations eliminate repetitive and confusing integrations across systems and allow for more intelligent data interactions.
The importance of a shared semantic layer cannot be overstated. It acts as a foundation for AI agents, enabling them to communicate more effectively and collaborate without being hindered by disparate logic or definitions across systems. This setup not only simplifies application development but also enhances the overall architecture by making it transparent and easily adaptable to changes in business logic. Companies need to transition from complex applications to smart data solutions that facilitate significant efficiency gains while reducing redundancy in their AI strategies.
👉 Pročitaj original: CIO Magazine
