"Which email did I attach that quote to when I sent it to the client last month? It took me ten minutes to find it." "A new hire asked me where the SOP was, but I couldn't remember which folder it was in either." When visiting a company with around forty employees, we heard comments like these one after another.
Each instance only takes a few minutes. However, when every employee repeats this daily, the time spent searching becomes a massive drain across the entire company. Moreover, work stops while searching, and if the file cannot be found, it leads to the worst kind of waste: creating it all over again. In June 2026, AI search using Gemini across Drive, Gmail, and Chat became widely available in Google Workspace. In this article, we discuss what is needed to transform into a company where information is truly findable, from our perspective as a custom development partner.
The true culprit behind "unfindable": Nothing shows up unless keywords match
First, why are internal documents so hard to find? Conventional search essentially runs on exact word matches. Unless the exact term you are looking for appears directly in the file name or email body, it will not be caught.
However, frontline memories are rarely preserved as exact words. People search with vague clues like, "I think it was a quote created for Company A around last autumn that included a discount." If the file name is "Quote_Final_v3.xlsx," you will almost never find it on the first try from that memory alone. This is where Gemini cross-app search proves powerful.
What makes Gemini cross-app search different?
The new AI search covers files in Drive, correspondence in Gmail, and chat conversations all together, allowing you to search by meaning. When you ask using natural phrasing such as "the quote for Company A with a discount," it gathers relevant candidates across applications and provides a concise summary of key points. A major advantage is that the searcher no longer needs to worry about whether the item is located in Drive or as an email attachment.
However, this is where many companies misunderstand. It is not the case that enabling the feature means everything can be found starting tomorrow. AI generates answers based on the information available within the company. If that information is disorganized, the AI can only return disorganized results. While the architecture for having AI handle internal knowledge was covered in our article on turning internal knowledge into RAG with NotebookLM, what they share is that how well the original information is organized directly determines search quality.
In custom development, we tackle how information is stored before touching search settings
That is why, when clients consult us, the first step we take is not flipping the switch on AI search. It is organizing how information is stored and configuring permissions.
When different versions of the same document exist across three separate places, when folders exist that only former employees know about, or when information is duplicated between shared drives and personal drives, the AI cannot determine which version is the newest and will confidently present an outdated quote. That is actually dangerous. Therefore, we eliminate duplication, establish rules for storage locations, and configure permissions governing who can see what. Only after this is cleaned up does cross-app search become a reliable, trustworthy tool.
Case study: A company that mapped out its folders before introducing AI search
Here is a specific example (company name withheld). A manufacturing company approached us wishing to roll out the new AI search company-wide. Looking at the actual state, however, Drive usage varied by department, and identical blueprints were scattered across multiple folders.
Therefore, instead of enabling search right away, we first inventoried the current folder structure to create a map, consolidated documents duplicated across departments, and established naming conventions and permission rules for storage locations. When we subsequently enabled Gemini cross-app search, complaints such as "I search but nothing shows up" or "outdated versions appear" virtually vanished. What worked was not the cleverness of the search engine, but cleaning up the information before feeding it to the AI. The concept of automating internal inquiry handling also connects to our article on automating internal help desks with AI.
Introducing AI search reveals things that should not be visible
Finally, there is one pitfall you must keep in mind. When cross-app search is enabled, any confidential files a user has permission to view will surface in search results, even if that person was never aware of them. Executive compensation documents, performance reviews, undisclosed contracts—if these are inadvertently shared too broadly, AI search will expose those gaps in plain sight.
In other words, introducing AI search must go hand-in-hand with an exhaustive review of permission designs. Exposing sensitive information in exchange for convenience defeats the purpose. Before enabling search, we always re-examine who can see what.
If your employees are losing time searching for materials, if you want to introduce AI search but worry about disorganized information, or if you want to properly structure everything down to access permissions, please feel free to reach out through GleamHub's free IT and Google Workspace consultation. From taking inventory of information to organizing storage structures, permissions, and safely rolling out AI cross-app search, we will work alongside you within a manageable scope.
