It is tempting to start with 'which AI tool do we need?' But choosing the tool gets much easier once you know which piece of work you want to improve, and why it is worth improving.
Having AI inside the company does not set the value of the investment on its own. Value shows up when repetitive work takes less time, when the quality of its output improves, or when the team gets information that helps it decide better.
Choose a specific process you can evaluate
'We want to automate sales' is too broad to start with. 'We want a draft summary and follow-up tasks prepared after each call, for someone to review' is a process whose inputs and outputs you can describe and compare against how you work today.
When you choose, look at how often the task recurs, how much time it takes, how clear the required result is, and whether suitable data is available. Then add one decisive question: what happens if the output is wrong?
Summarising internal notes for a person to check is very different from issuing a contractual promise or approving a financial decision automatically. Start with a scope that lets you catch and correct mistakes before they reach a customer.
Do you actually need AI?
Some problems only need a clear rule. Sending an alert when a task is overdue does not require understanding language or generating content. Connecting your tools, or switching on a step the system already supports, may be enough.
AI becomes a candidate to trial when the process has to handle varied text, such as drafting replies, summarising conversations, or suggesting a category for enquiries. Even then, you need to judge its accuracy in your own context.
Example: drafting a reply to enquiries
Imagine a team that answers recurring questions about services. A trial could start by suggesting a reply based on approved information, with a person reviewing it before it is sent. If the information is missing, the case goes to the right person instead of an invented answer.
Before you trial it, decide which data may be used, who can access it, and whether the tool fits your policies and your commitments to customers. Use cleaned data where you can, and update the source of information whenever the service changes.
Measure the benefit after review
Record the current time the task takes, then compare it against the time to prepare, review, and correct the output during the trial. Watch the errors, the rework, and how much the team actually relies on the result.
If the tool saves creation time but spends it on correction, revisit the scope, the inputs, or whether it is worth using at all. Expand the trial when the benefit is repeatable, with a clear owner for running it and reviewing it.