3 Common Pain Points in Transitioning from Semantic Strategy to Implementation and How to Avoid Them

At EK, we work with many organizations that are looking to connect, standardize, and enrich knowledge assets (both structured data and unstructured content) for their enterprise through the implementation of a Semantic Layer.  While traditionally, Semantic Layer implementation was in large part driven by the need to provide high quality, trusted, and richly labeled data for analytics and business intelligence needs, AI has intensified this momentum. By connecting isolated information sources through a unified framework of taxonomies, metadata, ontologies and business glossaries, a Semantic Layer transforms data into actionable knowledge. It enables organizations to unlock the full value of their knowledge assets, ensuring that AI solutions deliver contextual, relevant, and accurate answers. Despite having deep technical capabilities, many organizations are finding the transition from strategy to execution fraught with challenges, leading to initiatives that fizzle past their pilot stage, Proof of Concepts (PoCs) that fail to scale to enterprise-wide deployments, and AI efforts that fail to deliver on ROI.