{"id":22332,"date":"2024-10-31T10:07:18","date_gmt":"2024-10-31T14:07:18","guid":{"rendered":"https:\/\/enterprise-knowledge.com\/?p=22332"},"modified":"2025-10-28T15:04:03","modified_gmt":"2025-10-28T19:04:03","slug":"inject-organizational-knowledge-in-ai","status":"publish","type":"post","link":"https:\/\/enterprise-knowledge.com\/inject-organizational-knowledge-in-ai\/","title":{"rendered":"How to Inject Organizational Knowledge in AI: 3 Proven Strategies to Achieve Knowledge Intelligence"},"content":{"rendered":"\r\n

Generative AI (GenAI) has made Artificial Intelligence (AI) more accessible to the business, specifically by empowering organizations to leverage large language models (LLMs) for a wide range of applications. From enhancing customer support to automating content creation and operational processes, the investment in AI has surged in the past two years – primarily through proofs of concept (POCs) and pilot projects.<\/span><\/p>\r\n\r\n\r\n\r\n

For many organizations, however, these efforts have failed to yield the anticipated results proportional to their investments. According to Gartner\u2019s recently published \u201c<\/span>Hype Cycle for Artificial Intelligence, 2024<\/a><\/strong>\u201d, AI has entered the \u201c<\/span>trough of disillusionment<\/span><\/i>.\u201d\u00a0<\/span><\/p>\r\n\r\n\r\n\r\n

We’re witnessing this firsthand as organizations are hiring EK to address AI projects that have stalled due to content and data challenges. Many are still grappling with how to ensure quality and diversity of their AI products\u00a0 – with the biggest hurdle being the lack of institutional and domain knowledge that AI requires to deliver meaningful results tailored to a specific organization.\u00a0<\/span><\/p>\r\n\r\n\r\n\r\n

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The reality is that algorithms trained in one company or public data sets may not work well on organization and domain-specific problems, especially in domains where industry preferences are relevant. Thus, organizational knowledge is a prerequisite for success, not just for generative AI, but for all applications of enterprise AI and data science solutions. This is where experience and best practices in knowledge and data management are lending the AI space effective and proven approaches to how domain and institutional knowledge can be shared effectively. Below, I have picked the top three ways we have been working to embed domain and organizational knowledge and empower enterprise AI solutions.<\/span><\/p>\r\n

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1. Semantic Layer<\/b><\/h2>\r\n\r\n\r\n\r\n

Without context, raw data or text is often messy, outdated, redundant, and unstructured, making it difficult for AI algorithms to extract meaningful use. The key step to addressing this AI problem involves the ability to <\/span>connect <\/span><\/i>all types of organizational knowledge assets, i.e., using shared language, involving experts, related data, content, videos, best practices, and operational insights from across the organization. In other words,<\/span> to fully benefit from an organization\u2019s knowledge and information, both structured and unstructured information as well as expertise knowledge must be represented and understood by machines.<\/b><\/p>\r\n\r\n\r\n\r\n

A Semantic Layer<\/a><\/strong> provides AI with a programmatic framework to make organizational context, content, and domain knowledge machine readable. Techniques such as data labeling, taxonomy development, business glossaries, ontology and knowledge graph creation make up the semantic layer to facilitate this process.\u00a0\u00a0<\/span><\/p>\r\n

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How a Semantic Layer Provides Context for Structured Data<\/b><\/b><\/h3>\r\n\r\n\r\n\r\n