{"id":25629,"date":"2025-09-29T15:14:44","date_gmt":"2025-09-29T19:14:44","guid":{"rendered":"https:\/\/enterprise-knowledge.com\/?p=25629"},"modified":"2025-09-29T15:23:07","modified_gmt":"2025-09-29T19:23:07","slug":"how-to-fill-your-knowledge-gaps-to-ensure-youre-ai-ready","status":"publish","type":"post","link":"https:\/\/enterprise-knowledge.com\/how-to-fill-your-knowledge-gaps-to-ensure-youre-ai-ready\/","title":{"rendered":"How to Fill Your Knowledge Gaps to Ensure You\u2019re AI-Ready"},"content":{"rendered":"

\u201cIf only our company knew what our company knows\u201d has been a longstanding lament for leaders: organizations are prevented from mobilizing their knowledge and capabilities towards their strategic priorities. Similarly, being able to locate <\/span>knowledge gaps <\/b>in the organization<\/span>, <\/b>whether we were initially aware of them (<\/span>known unknowns<\/span><\/i>), or initially unaware of them (<\/span>unknown unknowns<\/span><\/i>), represents opportunities to gain new capabilities, mitigate risks, and navigate the ever-accelerating business landscape more nimbly.\u00a0\u00a0<\/span><\/p>\n

AI implementations are already showing signs of knowledge gaps: hallucinations, wrong answers, incomplete answers, and even \u201cunanswerable\u201d questions. There are multiple causes for AI hallucinations, but an important one is not having the right knowledge to answer a question in the first place. While LLMs may have been trained on massive amounts of data, it doesn\u2019t mean that they know your business, your people, or your customers. This is a common problem when organizations make the leap from how they experience \u201cPublic AI\u201d tools like ChatGPT, Gemini, or Copilot, to attempting their own organization\u2019s AI solutions. <\/span>LLMs and agentic solutions<\/b><\/a> need knowledge\u2014your organization\u2019s <\/span>unique knowledge<\/b><\/a>\u2014 to produce results that are unique to your and your customers\u2019 needs, and help employees navigate and solve challenges they encounter in their day-to-day work.\u00a0<\/span><\/p>\n

In a <\/span>recent article<\/b><\/a>, EK outlined key strategies for preparing content and data for AI. This blog post builds on that foundation by providing a step-by-step process for identifying and closing knowledge gaps, ensuring a more robust AI implementation.<\/span><\/p>\n

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The Importance of Bridging Knowledge Gaps for AI Readiness<\/span><\/h2>\n

EK lays out a six-step path to getting your content, data, and other <\/span>knowledge assets<\/b><\/a> AI-ready, yielding assets that are correct, complete, consistent, contextual, and compliant. The diagram below provides an overview of these six steps:<\/span><\/p>\n

\"The<\/p>\n

Identifying and filling knowledge gaps, the third step of EK\u2019s path towards AI readiness, is crucial in ensuring that AI solutions have optimized inputs.\u00a0<\/span><\/p>\n

Prior to filling gaps, an organization will have defined its critical knowledge assets and conducted a content cleanup. A content cleanup not only ensures the correctness and reliability of the knowledge assets, but also reveals the specific topics, concepts, or capabilities that the organization cannot currently supply to AI solutions as inputs.<\/span><\/p>\n

This scenario presupposes that the organization has a clear idea of the AI use cases and purposes for its knowledge assets. Given the organization knows the questions AI needs to answer, an assessment to identify the location and state of knowledge assets can be targeted based on the inputs required. This assessment would be followed by efforts to collect the identified knowledge and optimize it for AI solutions.\u00a0<\/span><\/p>\n

A second, more complicated, scenario arises when an organization hasn\u2019t formulated a prioritized list of questions for AI to answer. The previously described approach, relying on drawing up a traditional knowledge inventory will face setbacks because it may prove difficult to scale, and won\u2019t always uncover the insights we need for AI readiness. Knowledge inventories may help us understand our <\/span>known unknowns<\/span><\/i>, but they will not be helpful in revealing our <\/span>unknown unknowns<\/span><\/i>.\u00a0<\/span><\/p>\n

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Identifying the Gap<\/span><\/h2>\n

How can we identify something that is missing? At this juncture, organizations will need to leverage analytics, introduce semantics, and if AI is already deployed in the organization, then use it as a resource as well. There are different techniques to identify these gaps, depending on whether your organization has already deployed an AI solution or is ramping up for one. Available options include:<\/span><\/p>\n

Before and After AI Deployment<\/b><\/h3>\n

Leveraging Analytics from Existing Systems<\/i><\/b><\/h4>\n

Monitoring and assessing different tools\u2019 analytics is an established practice to understand user behavior. In this instance, EK applies these same methods to understand critical questions about the availability of knowledge assets. We are particularly interested in analytics that reveal answers to the following questions:<\/span><\/p>\n