The Reality: Internal Information Searches Waste 14,000 Hours a Year
“Where’s that document?” “Who has this data?”—Aren’t these exchanges a daily occurrence in your company too?
According to a report from Nikkei xTECH Active, one company achieved a reduction equivalent to 14,000 hours annually by streamlining internal information searches using generative AI. This isn’t just a case of introducing a “convenient tool.” It needs to be viewed from the perspective of “leveraging information assets,” which is fundamental to management.
From my own experience supporting IT implementation for over 38 clients, I can say that the loss from scattered internal information goes far beyond the surface-level “search time.” When information can’t be found, duplicate work occurs, decision-making slows down, and knowledge becomes siloed. Breaking this negative chain is what generative AI-powered information search revolutionizes.
Why Traditional Searches Fall Short
Many companies have implemented SharePoint, Google Drive, or internal wikis. However, these are fundamentally based on searches relying on “file names” or “folder structures.” They can barely handle vague queries like “the competitor analysis data mentioned in last month’s proposal for Company A.”
Generative AI is different. Ask a question in natural language, and it understands the document’s content, extracts relevant information, and provides an answer. You don’t need to remember file names or storage locations. This is a paradigm shift from “searching” to “asking.”
In the aforementioned case, by having AI cross-search and summarize a vast collection of internal documents, information gathering that previously took tens of minutes was completed in seconds. On a monthly basis, the figure of 14,000 hours becomes entirely plausible.
What’s Needed for Implementation Isn’t “Data Organization” but “Access Permission Design”
“We need to clean up our data before introducing AI”—many executives think this way. However, this is a misconception. The latest generative AI can extract target information even with some noise. What’s more important is designing permissions for who can access what information.
For example, there’s a risk that AI might mistakenly expose documents containing internal confidential information to all employees. To prevent this, a mechanism is needed to control the scope of AI search based on user permissions. Specifically, using cloud services like Azure OpenAI Service or Amazon Bedrock allows you to build AI searches linked to existing access permissions.
Key Points for Selecting Specific Tools
To streamline internal information searches with AI, there are broadly three approaches. Let’s organize their features and cost implications.
1. SaaS-type AI Search Tools
Representative examples include “Notion AI” and “Slack AI.” These are easy to introduce and relatively low-cost, at around $10–$30 per user per month. However, since the search scope is limited to data within that tool, covering all internal information requires using multiple tools in combination.
2. Enterprise AI Search Platforms
Dedicated platforms like “Glean” and “Coveo” can cross-search multiple data sources (email, chat, documents, CRM, etc.). Implementation costs are higher, ranging from several million to tens of millions of yen annually, but they are effective for large companies or organizations with large amounts of information.
3. Custom AI Search via In-House Development
This is the method I practice myself. By using APIs from Claude or ChatGPT, you build a search system linked to your internal database. Initial development costs are around $3,000–$13,000, and monthly API usage fees are just a few hundred dollars. Since it can be fully tailored to your company’s workflow, the ROI is the highest.
The Hurdles to Implementation Are Lower Than You Think
Hearing “in-house development” might seem daunting. However, with the evolution of no-code tools, environments are emerging where you can build an AI search system without programming knowledge. For example, using open-source platforms like “Dify” or “Langflow,” you can create AI agents with drag-and-drop functionality.
In fact, for one small-to-medium enterprise I supported, we built a system that could AI-search data from their internal Google Drive and Notion in just two weeks. The cost was about $200 per month, including API usage fees. In an organization of 30 employees, it reduced the time spent on information searches by an average of 2 hours per week. By simple calculation, this translates to an annual labor cost saving of approximately $30,000.
Start with a “Small Start” as the First Step
There’s no need to aim for company-wide implementation right away. I recommend starting with a pilot in a specific department or project to verify the effectiveness. For instance, targeting sales department proposal materials or FAQs for AI search makes the implementation benefits easy to visualize.
The trick is “not to aim for perfection.” Even if search accuracy is initially 80%, operational efficiency will improve dramatically. Then, by iterating improvements based on user feedback, accuracy can reach over 90% within a few months.
Actions Executives Should Take Now
AI-powered internal information search is no longer in the “whether to do it or not” phase but the “when to do it” phase. The figure of 14,000 hours a year is by no means an exceptional case. There’s a high probability that a similar amount of waste is lurking in your company as well.
First, start with the following three steps:
Step 1: Visualize the time spent on internal information searches. Just having members in each department record “how many minutes it took to find information” for one week will clarify the scale of the problem.
Step 2: Identify the data sources where the most impact can be expected. Understand where information is scattered—email, chat, documents, customer data, etc.
Step 3: Start small. Target one data source and use free trials of cloud services or API test environments to create a prototype of an AI search.
Generative AI is no longer a “technology of the future.” At this very moment, your company’s information assets are lying dormant, losing value over time. Whether to awaken them and leverage them as a management resource is up to you.
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