1.8 hours per day looking for things you already have

Employees waste an average of 1.8 hours every day searching for information they need to do their work (Iris.ai). That's not research time, not learning time, not exploring new topics. That's time spent looking for things the organisation already has: documents, decisions, reports, specifications, and reference material that exist somewhere in the company's tools but can't be found by the person who needs them.

Over a year, 1.8 hours per day is roughly 450 hours, or about 11 full working weeks, spent on search. For a 50-person company at an average loaded cost of $65/hour, that's nearly $1.5 million per year spent on finding things rather than using them.

Why search fails

The search fails because the information is distributed across tools that don't share context.

The typical search journey for a piece of information that should be easy to find: check Google Drive first (the most common file location). Not there. Search Slack (maybe it was shared in a thread). Find a reference to the document but not the document itself. Search email (maybe it was sent as an attachment). Find the email but the attachment is a link to a Google Doc that's been moved. Ask on Slack who has the current version. Wait for a response. The response points to a Notion page that has a different title from what you were searching for.

Total time: 15-20 minutes for a single document. This journey happens multiple times per day, and each failed search attempt doesn't just waste time. It wastes cognitive resources: the frustration of not finding something, the context switch from the work you were doing to the search and back, and the nagging question of whether the thing you're looking for even exists.

The semantic search fix

The structural fix is a search layer that spans all tools and finds information by meaning rather than by keyword.

Semantic search understands what you're looking for conceptually. "The analysis we did on customer retention" finds the relevant document whether it's titled "Q3 Churn Report," "Retention Analysis," or "Customer Lifecycle Metrics." The search works by meaning, which means you don't need to guess what the author called it, which folder they put it in, or which tool they saved it in.

When Google Drive, Slack, GitHub, Gmail, Notion, and your other tools all feed into one semantic search, the multi-tool search journey collapses into a single query. One search bar. All sources. Results ranked by relevance and recency.

The 1.8 hours doesn't drop to zero, because some searches are for entirely new information that the organisation doesn't have. But the portion of search time spent looking for things the organisation already has, which is the majority, drops dramatically when every source is searchable from one place by meaning.

Frequently asked questions

How is this different from just using better search within each tool? Each tool's native search only covers its own content. Google Drive search finds Google documents. Slack search finds Slack messages. Neither finds the relevant result in the other tool. The value of unified search is spanning the boundaries between tools, which is where most search time is wasted.

What about privacy and access controls? Search results respect the permissions of the underlying tools. You only see results from content you have access to in the source system.

Does this require moving our data? No. The data stays in the original tools. The search layer indexes and searches across them without requiring migration.

Related reading: The cost of scattered knowledge, Information silos are the default, Too many tools. Related pages: Search, One search, Connections.