AI can help with everyday work such as drafting replies, summarising documents and sorting enquiries. Whether it helps your business depends on how well it handles your tasks, including the time someone spends checking and correcting it.
A useful place to start is a job you know well enough to spot a mistake. Try it on a few examples and compare the result with how you work today. You don't need to settle the wider debate about AI to find out whether it saves your team an hour.
Tasks worth trying
Look for work where you can provide the source material and describe what a useful result should contain:
- Summarising notes. Turn meeting notes into a list of decisions and actions. Check that each action has the right owner and that nothing has been invented.
- Drafting routine replies. Give the tool an enquiry and an approved answer or policy. Review the draft for accuracy, tone and promises you haven't agreed to make.
- Finding information in documents. Ask a question about a handbook or report. Open the cited passage to check that it supports the answer.
- Sorting a batch of records. Suggest categories for support tickets or extract fields from documents. Compare the results with a sample someone has checked manually.
These are starting points for a trial. A tool that writes a good summary may still miss the most important sentence in the next document.
What can go wrong
AI can produce a confident answer that is wrong. It may invent a reference, overlook an exception or use an old document when a newer one exists. A fluent explanation doesn't tell you whether the underlying facts are sound.
It also needs access to the relevant information. If your prices changed yesterday, a tool without the updated price list cannot be relied on to quote them. Connecting it to your files helps, but you still need to check which files it used and whether it interpreted them correctly.
Errors become harder to catch when the tool handles a large batch or takes several steps. It might read an enquiry correctly but match it to the wrong customer before drafting the reply. Test the whole task, including the handoffs between systems.
Run a small, useful trial
Choose one recurring task and collect a handful of examples you are allowed to use. Include a straightforward case, one with missing information and one with an exception. Avoid uploading personal or confidential information until the tool and account have been approved for that use.
Write down what a good result looks like before you start. For a meeting summary, that might mean every agreed action is included, each owner is correct, and no new commitments appear.
Then record three things for each attempt:
- How long the task normally takes.
- How long it takes with AI, including checking and corrections.
- Whether the finished work meets the same quality standard.
For example, if a draft saves ten minutes but takes fifteen minutes to repair, it hasn't helped with that task. If it saves time on ordinary cases but misses exceptions, you may be able to use it for a narrower set of jobs.
When AI can take actions
Some tools can update records, create documents or send messages as well as answer questions. That can save copying between systems, but it also gives mistakes somewhere to go.
For an initial trial, ask the tool to prepare work for review. A draft invoice is easier to inspect than an invoice already sent to a customer. Check the permissions, the record of actions taken and how you would stop a run. Some actions, including sent messages, cannot simply be undone.
Only expand its access when the trial gives you a reason to. Keep a named person responsible for reviewing failures and checking that the process still works after changes to the tool or your business.
Deciding whether to keep it
At the end of the trial, ask the people doing the work whether they want to keep using it, and compare that with your timings and error notes. Both matter: a faster process that staff find confusing may create extra work elsewhere.
Keep the instructions and examples that worked so a colleague can repeat the task. If the results aren't useful, record why and stop or adjust the trial. Finding that out before a wider rollout is a useful result too.