Utilizing internal knowledge has been a long-standing challenge for many companies. Problems such as "not being able to find the necessary information," "the same questions being asked repeatedly," and "there being tasks that only certain people understand" are common in any organization.<\/p>\n\n\n\n
In conclusion, these challenges can be significantly improved by utilizing AI. In particular, by using chat-based AI and search AI, which have been attracting attention in recent years, internal knowledge can evolve from something that is "accumulated" to something that is "immediately usable."<\/p>\n\n\n\n
This is because AI is not just a search tool; it can understand and integrate multiple pieces of information and present them in the most optimal way. This makes it possible to solve problems such as searching, sharing, and reliance on individual expertise all at once.<\/p>\n\n\n\n
This article systematically explains specific methods for utilizing internal company knowledge with AI, its effects, and key points for implementation.<\/p>\n\n\n\n
Internal knowledge refers to the knowledge and information accumulated within a company. Examples include operational manuals, sales pitches, past troubleshooting records, and customer interaction histories. These are naturally accumulated through daily operations and are important assets for the company.<\/p>\n\n\n\n
However, despite its importance, knowledge is often not being used effectively. One reason for this is "knowledge dispersion." Information is scattered across file servers, Google Drive, internal chat, email, and other platforms, making it difficult to know where anything is located.<\/p>\n\n\n\n
The key concepts here are "explicit knowledge" and "tacit knowledge." Explicit knowledge is documented knowledge, such as manuals and documents. Tacit knowledge, on the other hand, is knowledge based on individual experience and intuition, and is not put into words. In many companies, this tacit knowledge is the cause of reliance on individual expertise.<\/p>\n\n\n\n
AI plays a role in bridging the gap between explicit and tacit knowledge. In other words, the essence of AI utilization is not simply data management, but "making knowledge usable."<\/p>\n\n\n\n
To understand how AI can leverage internal company knowledge, it's helpful to know about a technology called "RAG (Retrieval-Augmented Generation)."<\/p>\n\n\n\n
RAG is a system that combines "Retrieval" and "Generation." Simply put, it's a technology where AI searches for relevant information from an internal company database and generates the optimal answer based on that information.<\/p>\n\n\n\n
Traditional search systems required users to enter keywords and find relevant information. However, with RAG, users simply ask questions in natural language, and the AI collects relevant information and presents a summary of the key points.<\/p>\n\n\n\n
For example, if you input "Tell me the rules for expense reimbursement," the AI will refer to multiple regulations and manuals and return a clearly organized answer. This significantly reduces the time spent searching for information.<\/p>\n\n\n\n
In this way, AI functions not merely as a search engine, but as an entity that "understands and answers."<\/p>\n\n\n\n
Traditional knowledge management relied on the assumption that "people would search for the information." However, by utilizing AI, this changes to a model where "AI searches for and presents the information." This difference is significant and directly impacts work efficiency.<\/p>\n\n\n\n
Traditionally, it was necessary to think of appropriate keywords, open multiple documents, and extract and understand the necessary information. On the other hand, with AI, you can get a well-organized answer simply by asking a question.<\/p>\n\n\n\n
Furthermore, while conventional systems could only handle information in "units," AI understands "context." This means it can handle ambiguous questions and intermediate exchanges.<\/p>\n\n\n\n
This change will significantly lower the barrier to knowledge utilization, making it easier for anyone to access the information they need.<\/p>\n\n\n\n
In conclusion, the biggest benefit of implementing AI is the reduction of search time. In many companies, employees spend a significant portion of their day searching for information.<\/p>\n\n\n\n
By introducing AI, this time can be dramatically reduced because AI can instantly organize and present the necessary information. This allows employees to focus on their core tasks.<\/p>\n\n\n\n
For example, the support department will be able to respond to inquiries more quickly, and the sales department will be able to reduce the time it takes to create proposals. As a result, the productivity of the entire organization will improve.<\/p>\n\n\n\n
Personalization poses a significant risk for many companies. The more information that only certain employees know, the more unstable the continuity of operations becomes.<\/p>\n\n\n\n
By utilizing AI, knowledge is centralized and accessible to everyone. Furthermore, because the AI provides answers based on past interactions and documents, the knowledge of veteran employees is indirectly shared.<\/p>\n\n\n\n
This system naturally promotes knowledge sharing and raises the skill level across the entire organization.<\/p>\n\n\n\n
AI will also make a significant contribution to training new employees. Traditionally, if new employees had questions, they had to ask senior employees, but AI will take on that role.<\/p>\n\n\n\n
This creates an environment where employees can ask questions at any time, enabling them to solve problems independently. As a result, training costs are reduced and employees become productive more quickly.<\/p>\n\n\n\n
Furthermore, because AI consistently provides answers of the same quality, it also helps prevent inconsistencies in educational content.<\/p>\n\n\n\n
The most important aspect of AI implementation is knowledge organization. If the data is not organized, the AI cannot provide the correct answers.<\/p>\n\n\n\n
Therefore, the first step is to organize existing documents and remove unnecessary information. Furthermore, classifying and tagging the information will make it easier for AI to understand.<\/p>\n\n\n\n
Neglecting this step will significantly reduce the effectiveness of AI implementation, so caution is necessary.<\/p>\n\n\n\n
Security is a crucial point when handling internal company data. Companies that handle confidential information, in particular, often consider "on-premises" solutions rather than cloud-based ones.<\/p>\n\n\n\n
On-premise refers to a system configuration where the system is operated within the company's own servers. This helps to reduce the risk of data leakage to external parties.<\/p>\n\n\n\n
On the other hand, cloud computing offers the advantages of being easy to implement and highly scalable. It's important to choose the appropriate configuration according to your company's requirements.<\/p>\n\n\n\n
The key to successful AI implementation is not to aim for perfection all at once, but to start small and continuously make improvements.<\/p>\n\n\n\n
First, we will conduct a pilot program in a specific department or task to verify its effectiveness. Then, we will gradually roll it out while making improvements based on the feedback.<\/p>\n\n\n\n
This process allows you to achieve results while minimizing risk.<\/p>\n\n\n\n
By leveraging AI to manage internal knowledge, challenges such as searching, sharing, and reliance on individual expertise can be significantly improved. Evolving from traditional "searching" knowledge management to "interactively acquiring" knowledge utilization dramatically improves operational efficiency and productivity.<\/p>\n\n\n\n
On the other hand, organizing knowledge and establishing an operational system are essential for success. By properly addressing these issues, the effectiveness of AI can be maximized.<\/p>\n\n\n\n
In the future, AI will become a core element of business activities. Early adoption will enable companies to establish a competitive advantage.<\/p>","protected":false},"excerpt":{"rendered":"
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