"Talk to My Documents": A Quick Win for Enterprise AI

Traditional enterprise work systems are primarily based on search as their core interface. Users must input the appropriate search terms into the search bar, then manually review the search results to extract an answer. If the search results span hundreds of pages, the process becomes even more painful. From a practitioner's perspective, this wastes significant time and often makes it difficult to find the exact answer when needed. One of the major issues with search is that users must already know which keywords are used in the document they are searching for. This is paradoxical for someone searching because they lack that knowledge in the first place. In the end, calling the relevant person becomes the fastest and most convenient option. However, apart from core functions like HR or IT, very few departments have someone available to provide quick and helpful answers.

In theory, a chat interface provides instant responses to user questions, directly referencing internal company documents to filter and deliver the necessary information. This mimics the effect of speaking with a knowledgeable person, significantly contributing to time and cost savings while improving operational efficiency. However, until now, chatbots have been rule-based, meaning they often struggled to understand various forms of questions (making search more convenient), or maintaining AI capable of processing natural language was prohibitively expensive. With the introduction of LLMs (Large Language Models), which have dramatically improved natural language processing performance, and affordable access to them like a utility, the core interface of future enterprise systems is expected to shift from search to chat.

Implementing a chat interface based on LLMs in enterprise systems represents a complete departure from traditional chatbot implementations, and can be described as a "Talk to My Documents" approach. There are several key elements for successfully building this system. One important aspect is digitizing internal documents and organizing them so the chatbot can easily access them. Additionally, since multiple organizations will likely be using the system, it is essential to ensure that the chatbot has the appropriate permissions to access and reference documents.

An LLM-based chat system can be implemented within a few weeks at the team level and within 3 to 4 months at the division level. Compared to traditional IT system development, implementing a chatbot powered by LLMs is much faster and more affordable. This is primarily because AI development and operations are outsourced. LLM-based chatbot systems leverage AI like electricity, enabling rapid and efficient results, with the cost of building such a system reduced to one-fifth or even one-tenth of what it used to be.

To successfully adopt AI, companies need to establish a framework that delivers quick wins. A LLM-based chatbot is an excellent way for companies to experience early success rapidly.