AI Problems

Most companies have tried something with AI by now: a chatbot on the website, a ChatGPT subscription for the marketing team, a proof of concept that impressed the board. Far fewer have something running in daily operations. The blockers are rarely the models themselves. They are data spread across systems, no clear owner, unrealistic expectations and pilots built without a plan for how staff will use them.

The pages here cover those problems one at a time. Each explains how to tell whether it applies to you, what usually goes wrong and where we start when a client brings it to us. Most fixes begin small, with one process, one data source and a measurable outcome, before anything is scaled up.

If you cannot see your exact issue listed, a short call is usually enough to work out which category it falls into.