Where should a company start with AI implementation?
Start by listing processes that are repetitive, time-consuming and have a measurable outcome, such as answering enquiries or processing documents. For each one, estimate the current cost, data availability and the risk of errors. Pick a single process for a pilot, define one success metric, and only then choose the model and tools.
A good first step is a workshop with the people who actually run the process. They know where time is lost, which cases are unusual and what data really exists. Capture each candidate in a simple table: how often it happens, how long it takes, who does it, which system holds the data and what happens when someone gets it wrong.
Next, assess data readiness. Large language models do not need thousands of labelled examples, but they do need current, well-organised knowledge: price lists, procedures, terms and ticket history. If that knowledge is scattered across inboxes and shared drives, organising it becomes task number one.
Finally, agree on the one metric that will decide whether the pilot succeeded – for example the share of enquiries resolved without a human, or the time it takes to prepare a quote.
