What signs suggest it will pay off?
The strongest sign is a pattern of repeated interruptions. If one or two experienced people field the same questions about procedures, product specifications, contract terms or past jobs, and the answers exist in writing, retrieval can return those answers with a link to the source. Other signs include new staff who rely on colleagues to find documents on the shared drive, field staff who ring the office for information that is in a manual, and tender teams that rebuild answers from old submissions.
- The same questions come up every week, from different people.
- The answers exist in documents that someone owns and keeps current.
- Finding them takes time from people whose time is scarce.
- A wrong answer causes rework, a complaint or a compliance problem.
When is it not worth it?
If staff ask a handful of document questions a month, a well-organised folder and a one-page index will serve them. If the answers live in a senior person's memory, the first job is writing them down, because search can only return what exists. If three departments keep three versions of the same procedure, a search tool will return all three, so someone has to decide which is authoritative before any build. Where every answer needs a manager's judgement, such as approving an exception, search can find the policy but cannot make the call.
How do you estimate the time it could give back?
Measure before you estimate. For two weeks, ask the people who field questions to tally each document question they answer and roughly how long it took. Note which document held the answer, or that none did. That tally gives you a baseline of volume, time and source coverage. Then estimate, conservatively, what share of those questions a tested system could answer correctly with a citation. Treat that share as an assumption until a pilot measures it on your own documents.
Hypothetical example: an operations manager in a 60-person business answers 15 document questions a day at about 6 minutes each, or 90 minutes a day. If a tested system answered half of them correctly, that assumption gives back about 45 minutes a day, before the time staff spend checking answers. The figures are illustrative assumptions, not a forecast.
What does a wrong answer cost you?
Value depends on the cost of errors as well as the time saved. A wrong answer about where to find a form costs a minute. A wrong answer about a contract notice period, a safety procedure or a product tolerance can cost far more. Microsoft's own Copilot documentation says generative AI responses are not always factual and asks users to review output before sending it to others. For high-stakes questions, plan for a person to check the cited source, and count that checking time in your estimate.
What should you try before buying anything?
Cheaper fixes solve some of these problems. Give key documents titles people would search for, archive superseded versions, keep one current procedure library in a single place, and add an owner and review date to each document. Check whether your existing tools already cover the need: Microsoft 365 and Google Workspace both include search, and your current subscriptions may include AI features worth testing first. If these steps remove most of the repeated questions, you may not need a separate system.
How do you prove the value before committing?
Run a bounded pilot. Choose one document set and the questions it should answer, write down the correct answers and sources, and agree the accuracy that would justify wider use. The OAIC advises organisations to do due diligence on AI products, including whether a product has been tested for the intended use, and Australia's National AI Centre recommends asking suppliers for proof of testing and testing before deployment. Measure the pilot against your baseline tally, and stop if it does not reach the agreed result.