AI IN BUSINESS · 6 MIN READ
Where to start with AI in your business so it does not end at testing tools
Many businesses start by choosing a tool: they buy access to a model, test a few prompts and a few weeks later go back to their old ways of working. An AI implementation has a better chance of working when it starts with the process, the problem and the way it will be controlled — and only then with the technology.
Choose one process, not the whole company
To begin, choose a task that is repetitive, takes up a lot of the team's time and has a clear output. This could be, for example, replying to similar enquiries, organising information from documents or preparing a weekly summary.
Describe who performs the task today, how often, on what data and where errors or delays occur. This description lets you assess whether AI is the right solution at all — sometimes a simple automation or a change of procedure is enough.
Check what data the solution will have access to
An agent or model works only as well as the data it works on. Establish where the information comes from — email, CRM, files or the sales system — and who has access to it.
Before implementation, you also need to decide what data may be processed and where. This applies especially to customers' personal data and confidential information. It is worth agreeing the scope and rules with the person responsible for privacy.
Decide where a person makes the call
How independent the solution is should depend on the consequences of an error. Drafting a reply or a summary can be done automatically, but sending an offer, a financial decision or a reply on an important customer matter should go through human approval.
- What can the solution do on its own?
- What does it only prepare as a proposal for approval?
- Who approves the output, and how quickly?
- What happens when the solution is not sure of the answer?
Test on real but safe cases
Instead of rolling the solution out to everyone at once, test it on an agreed set of cases. Compare the output with the team's work: accuracy, time, the number of exceptions and the points where a correction was needed.
The test also shows whether the team understands the new way of working. A tool that nobody uses does not solve the problem, even if it works correctly.
Measure the effect on the work, not the mere use of AI
The right question after implementation is not “are we using AI?” but “does the process work better?”. Decide in advance what you want to observe: task completion time, the number of errors, the number of cases needing correction or the response time to customers.
Not every process should be automated. If the test shows that the benefit is small or the risk too high, that is also a valuable result — it saves the team money and time.