The first question in an AI meeting is often, “Which model should we use?” A more useful question is, “Which decision are we trying to improve, and can we trust the information behind it?” A retailer might want to predict returns; a manufacturer might want to anticipate machine stoppages. Both ambitions depend on definitions, access and ownership that are easy to overlook when a prototype looks impressive.
Start with a decision, not a platform
Write down the decision in ordinary language: who makes it, how often, what information they use now and what a better decision would change. “Improve forecasting” is too broad. “Help the purchasing team decide next week’s replenishment quantity for seasonal products” is testable. The second statement exposes which sales, stock and supplier records you actually need.
Then trace those records back to their sources. An order may be cancelled, refunded or split across shipments. A customer may have several identifiers. A machine may be offline for reasons the sensor stream does not record. If those distinctions are missing, an AI model can learn a neat pattern that fails in operations.
Build the minimum dependable foundation
Choose a small set of data products around the decision. Give each one an owner, a business definition, a refresh expectation and a way to report exceptions. Document who can access it and why. That is more useful than collecting every available field into one large repository and hoping a data science team will make sense of it later.
For an initial pilot, examine a representative sample manually with the people who understand the process. Ask which records are wrong, which events arrive late and whether past decisions changed the data you are treating as objective truth. A fraud team, for example, investigates only some alerts; uninvestigated transactions are not automatically “clean” examples.
Define success before model selection
Agree on a baseline that a simpler rule or existing workflow can achieve. Measure decision quality alongside operational effort, response time and unwanted outcomes. Decide who reviews questionable recommendations and how feedback returns to the dataset. A pilot is ready for modeling when the business question, usable data, owner and evaluation method fit together. The model choice then becomes a technical decision within a clear business plan.
