Most writing about AI in business is aimed at companies with a data team, a budget line, and a year to spend. A ten person company has none of those, and needs an answer that fits inside a working week.
Here is the honest version. AI is good today at work that is text-heavy, repetitive, and reviewable. Drafting a first version of something. Summarising a long thread into what was actually decided. Pulling specific fields out of a stack of documents. Sorting incoming messages into categories. Searching your own material and answering from it.
Those five cover most of what a small company would want. Reading forty invoices and extracting the amounts, dates, and vendors is a genuine hour back every week. Turning a messy email chain into a task list is another. Neither is glamorous, and both are real.
What AI is not good at yet is anything that has to be right without a person looking, where being wrong is expensive and silent. Filing statutory returns. Computing statutory contributions. Moving money. Those failures do not announce themselves; they sit in a record until someone audits it.
So the useful test is not whether AI can do a task. It is whether a person can check the output faster than they could have done the work. If checking is quick, it is a good candidate. If checking is as slow as doing, it is not, whatever the demo showed.
The other thing worth saying plainly is that the tool is rarely the problem. Teams buy a subscription before naming the task, then conclude that AI does not work for them. Naming the task first usually reveals that you needed one workflow, not a platform.
Start with a single task somebody on the team genuinely dislikes. Run it for a month. Keep it if the hour comes back, drop it if it does not. That is the whole method, and it is more useful than any roadmap.