AI, Customer, and Delivery
Domain knowledge is the only moat. AI can write the code, but it cannot own the mess it creates.
I see this in mid-size food distributors trying to ship a new warehouse scanning app or customer portal. The “vibe coding” path looks fast until week two, when returns spike because the agent did not understand catch weights, lot codes, or how substitutions really work. With a human babysitting the agent - a warehouse lead, dispatcher, or AR manager checking screens, edge cases, and data rules - teams have shipped usable software in 10-14 days and cut pick errors by 15-20% in the first month.
👉 The hidden win is not speed. It is fewer wrong turns. A human-in-the-loop can kill bad approaches early, reuse what already exists in the ERP or WMS, and keep QA focused on the real failure points: credits, recalls, short ships, and proof of delivery. That is where margin gets lost right now, under cost pressure and tighter service expectations.
Pick one painful workflow and write a one-page “definition of done” with the person who owns it on the floor, then have them review every AI-generated screen and rule before anything gets merged.