Artificial intelligence in practice
Today artificial intelligence (AI) mostly means models that understand and produce text and can interpret images and documents. Unlike a traditional script, which follows rules written one by one, an AI model has learned from huge amounts of examples and gives likely answers, not certain ones.
For a business it is useful where fixed rules aren't enough: sorting incoming emails by subject, summarising a long quote request, pulling data from documents that are worded differently every time, drafting a reply. Built into an automation, it takes care of the reading and the first sorting.
An example: requests come in through your website form. The AI reads them, recognises whether each one is a quote request, a complaint or a general question, and assigns it to the right person with a short summary. Whoever replies starts with a clear picture.
The limits must be stated clearly. AI can be wrong, even convincingly so, making up details that don't exist: for anything that matters (amounts, deadlines, replies to clients, legal documents) a person must check the output. Also, the data you send to an AI service leaves your systems: check where it is processed, don't send sensitive data unless you need to and comply with the GDPR. Often the best solution combines AI with simple rules, as in well-designed chatbots.
