# Why Searching Company Knowledge with AI Is Not Enough

> When does making company documents searchable with AI actually help? The answer depends on how information reaches a decision and how the result is measured.

When preparing a sales proposal, you may need to find what you promised a similar customer last year. You locate the file, but it does not explain why the delivery time was accepted or which condition was attached to the discount. The right document is in front of you. You still need to ask the colleague who knows the work before making a decision.

An AI assistant with access to company documents can make the search easier. It can find old proposals, summarize correspondence, and bring related information together. That is useful. But a correct proposal also depends on understanding how far the earlier example applies to today’s customer.

That is why a knowledge-management project should begin by identifying which work is slowed down by missing information. Making the entire archive searchable is not an answer to that question.

## Where does the information sit, and where is it used?

In their [Harvard Data Science Review article on knowledge management](https://hdsr.mitpress.mit.edu/pub/jude8rkl/release/2), Faisal Hoque, Thomas Davenport, and Paul Scade distinguish between accumulated knowledge and knowledge flow. The first covers documents and records. The second concerns how that knowledge reaches the work where it is needed. Their recommendation is to connect the goal of improving access to a specific business problem.

In the proposal example, such a system could do more than rank old files. It could show the promises made before, their conditions, and the problems encountered in practice to the person preparing the proposal. That requires recording the reasons behind past decisions, separating current information, and making clear whom to ask when something is missing.

Without that preparation, a model can produce a plausible answer from incomplete information. The employee then returns to the files and colleagues to investigate its basis. A system that makes searching easier may not reduce the work required to decide by the same amount.

![Scattered documents are connected through AI and human judgment to a decision in a customer proposal](/images/inline-ai-knowledge-management/documents-need-decision-context.webp)

I discussed the human side of this knowledge in [my article about protecting employees who carry a company’s memory](/protecting-organizational-memory-during-ai-transformation). The next question is how that experience reaches everyday decisions.

## The distance between saving time and improving the work

In the Australian public sector’s [2024 evaluation of Microsoft 365 Copilot](https://www.digital.gov.au/initiatives/copilot-trial/microsoft-365-copilot-evaluation-report-full/productivity), participants reported saving time on summarizing, preparing first drafts, and finding information. Checking the answers and correcting them for the context of the work took back part of that saving. These were participant assessments; they should not be read as a directly measured company-wide productivity result.

This finding also shows an important measurement problem for knowledge management. Finding files faster does not prove that the next step became faster too. If the proposal still waits for the same approval, or another team has to verify the information from the beginning, the effect of the time saved in search needs to be measured separately.

![A document search and verification workflow compared with shorter time and less repeated work](/images/inline-ai-knowledge-management/search-speed-is-not-work-improvement.webp)

## Start with one piece of work

Start with a recurring task whose delays or errors have a cost. Customer proposals are one example. The people doing the work should help define what information is needed, where it is missing, and who is responsible for confirming it.

The required information can then be brought to the place where the employee makes the decision. Sometimes that calls for an AI assistant. Sometimes an up-to-date proposal template, clear discount rules, and a well-maintained record with a named owner are enough. The chosen solution should account for the effort of checking and correcting information, not only for collecting it.

When evaluating the trial, do not stop at how many files were added to the system. Completion time, returned corrections, and how often the same issue has to be researched again will tell you more about whether the work changed. Feedback from experienced employees can also show which information is still missing.

After such a trial, there is a concrete basis for expanding the scope: you can explain which decision each new information source is meant to improve and why it is needed.

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