AI in business: where it makes sense and where it does not
Where AI genuinely helps companies, how to spot a good use case, and why to start with a small validation.
AI is now pitched for everything, from marketing to warehousing. But it is not a universal solution, and in many cases it brings more problems than value. It makes sense where it replaces repetitive human work or processes data that people would miss.
The difference between a working deployment and a pointless one is usually how well the problem is defined — not which technology you pick.
Where AI helps most
Classifying and extracting data from documents, drafting text and replies, finding patterns in operational data, or a first level of customer support. Always tasks that are voluminous, repetitive and have a measurable outcome.
Where it does not
Where decisions are critical and cannot be checked, where data is incomplete or sensitive, or where an exact answer is essential. AI is statistical — it is occasionally wrong, and that has to be part of the design.
How to start
First validate the use case on a small scale with real data. Give it a measurable goal, clear ownership, and a plan for what to do if it does not work. Only then decide on a wider rollout.