An AI pilot is useful when it addresses a specific operational question and can be evaluated on relevant data. Its purpose is not simply to show that a model runs, but to establish whether it helps a team make a better decision under real working conditions.
Start with the decision you want to improve
Before choosing an algorithm or software, describe who makes the decision, when they need to make it and what they do today. A useful question is specific: which batch needs an additional check, which fault is more likely, or which production option could reduce energy use? Define what success means and who will review the result.
Check the data before the model
List the data sources, time period and units of measurement. Check whether the data actually relates to the decision. For example, can an energy reading be linked to the right batch and alloy? Look for missing values, duplicate records, changes in definitions and outcomes recorded late. These issues often affect reliability more than the choice of algorithm.
Design a test that resembles real use
Split the data so evaluation simulates future use. For time-based forecasting, this usually means training on earlier periods and checking performance on later ones. For image inspection, include representative examples from different cameras, shifts and production conditions. Good performance on an easy or narrow sample does not guarantee similar behaviour on the production line.
Measure value and errors together
Choose measures that connect to the work. Alongside overall accuracy, examine missed defects, false alerts, how early a forecast is available and time saved. Discuss the cost of each type of error with the people who will use the system. A single average score cannot show whether the tool is useful.
Include people and define limits
The pilot should show how users see a suggestion, inspect the evidence behind it and correct or reject an output. For decisions with significant consequences, the model can help prioritise cases or provide a second check, while an authorised person makes the final decision. Record when the system should abstain, such as when data is incomplete or outside the tested scope.
When is it ready to move forward?
Agree on continuation criteria before work starts: acceptable performance on representative data, a clear workflow, an accountable owner and a realistic operating cost. If the pilot succeeds, the next step is controlled integration and ongoing performance monitoring as products, data or conditions change. If it does not, analysing the errors and limitations still helps determine what needs to change.