Fable
After the accounting office removed the counter, orders finally completed the journey on their own
In the cloth shop, order taking, cloth measuring, dyeing, and account collection each had their own desk. At first the shopkeeper relied on paper notes passed hand to hand, with familiar people and familiar routes, and when things were small there were few mistakes. The cloth shop discovered that adding an AI button to each step of an old process would not automatically create a new way of working; value had to be measured by the flow result of the entire order.
Later, when orders were few, gaps could still be patched by running errands, but once peak season arrived, color changes, unpaid balances, and stockouts were often discovered only at the very end. During peak season, information still broke down between tables, and the more local automation there was, the later omissions were exposed; the process had not been redrawn, so efficiency only moved the waiting to a different place.
The first fix was rushed: the shopkeeper first had the clerks write prettier notes and stamp more reminder seals. Prettier notes and more reminder seals seemed to speed things up locally, but cross-step omissions only surfaced at delivery. Yet new problems followed—everyone watched only the most visible spots, and small deviations in the distance piled up deeper.
They reorganized work around order outcomes: AI handles identification, drafting and validation, people handle exception judgment, and inventory, payment and delivery status connect automatically at key points.
Later he rearranged the table positions according to the complete journey of a garment: check inventory when measuring cloth, check debts before dyeing, collect old samples before leaving the shop. This step was troublesome and initially slowed down the schedule; but after a few rounds, rework decreased, and the craftsmen no longer changed rules on a whim. Orders finally flowed automatically around results, and AI was no longer just an extra pair of hands beside the old counter.
From then on, evaluation no longer looked at how many actions a given tool completed, but at whether a job reached its outcome faster and more accurately; this is precisely the starting point of an AI-native workflow.
Workers no longer complete just their own small piece; they relay around the whole job. From then on, the new approach was not to polish the old table, but to make every step carry the information the next step needs.