Before You Buy an AI Tool, Name the Owner of Your Item Master
Ask a room of managers who owns the item master and watch what happens. Finance points at operations. Operations points at IT. IT says it only keeps the server running. That silence tells you more about your AI readiness than any vendor demo. The quiet file that runs everything The item master is t
Posted on Oct 21 • Originally published at sevenbillion.co
Ask a room of managers who owns the item master and watch what happens. Finance points at operations. Operations points at IT. IT says it only keeps the server running.
That silence tells you more about your AI readiness than any vendor demo.
The item master is the list of every product you buy, make or sell. Each line has a code, a unit of measure, a supplier, a lead time. Forecasts read it. Reorder logic reads it. Dashboards group by it. If one product has three codes, the forecast sees three weak products instead of one strong one. If one plant counts in boxes and another in units, every total is wrong before any model runs.
Nobody notices this while people plan in spreadsheets, because people fix it by hand. They know that "SNK-01" and "SNACK 1" are the same thing. An AI system does not know that. It takes the data at its word.
Clean data is not a one-time project. New items arrive every month. Suppliers change. Someone creates a duplicate code on a busy Friday. Without a named owner, the clean-up decays and the AI layer slowly starts giving confident wrong answers.
So the first readiness question is not "which model?" It is "who approves a new item code, and who fixes a bad one?" Give each master, items, customers and suppliers, one person with the authority to say no.
If step four is hard, you have found your first project. It is usually smaller than an AI project, and it pays back faster.
We saw this with a food-service distributor in India on Microsoft Dynamics NAV. Once live stock data could be trusted, forecasts could raise reorders inside the ERP. The distributor saw 18% less perishable wastage, a 96% fill rate and Rs 1.2 crore in added margin. The ERP was not replaced. The data in it was made reliable.
The full post covers the rest: a readiness checklist, how to tell a system of record from a system of conversation, and when to extend, upgrade or replace.
Read the full guide: ERP Readiness for AI: Why ROI Needs a Data Backbone
Abhijit Singh is CEO of Seven Billion Analytics, which builds enterprise AI, analytics and ERP systems for mid-size manufacturers and distributors.