An AI project doesn’t have to start with a transformation programme and a budget line. A better place to begin is a specific, annoying part of the week where somebody has to read something, work out what it means, and decide what happens next.
Starting there keeps you from buying a tool and then hunting for a problem to point it at. It also gives you something you can genuinely test: a real process, real inputs, and an outcome you can check.
#Map the work first
Walk the process as it runs today. Who kicks it off? What turns up, and in what state? Where does someone have to decide something? Where does it stall? The moments where people interpret, retype or chase are where this technology tends to help.
#Keep the first one small
A good first project has a known audience, a source of information you control, and a result somebody can look at and say yes or no to. Sorting inbound support email. Summarising a stack of documents. Researching a lead before the call.
Pick a real job, put the output where someone will see it, and agree what “working” means before anyone builds anything.
#Sort the foundations while it’s still small
Data access, security, who owns what. These are much easier to settle on a project with three moving parts than on one with thirty. Get the habits right early and the next build inherits them.
The aim isn’t to have AI. It’s to give the people who understand your business more room to use that understanding. The technology comes second, and it should.
Recognise any of this in your own business? Tell us about it and we’ll say whether it’s worth automating.
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