From Business Problem to AI Workflow: A Practical Framework

The gap between recognising a problem and deploying a working AI workflow is usually filled with either paralysis or a rushed pilot. Neither is necessary. The path in between has a small, repeatable shape:

First, name the problem in operational terms — not “we need AI,” but “this specific task takes this many hours and produces this many errors.” Second, check feasibility: does the data exist, is it accessible, is the task actually judgment-light enough to automate well. Third, prioritise against every other candidate workflow, because most businesses can only run two or three of these well at once. Fourth, design the workflow with a human-in-the-loop checkpoint appropriate to the risk. Fifth, roll it out with a defined review point, not an indefinite “set and forget.”

Treated this way, “AI strategy” stops being a mystery and becomes a repeatable operating habit — one prioritised workflow at a time.