What assumptions are you making about our data?

Every AI vendor says their platform can connect to existing systems. Far fewer explain what must be true about the data flowing through those systems. Is sensor data complete? Are maintenance records consistent? Are operators entering information the same way across shifts and facilities?

Many AI failures are not technology failures at all. The model works as designed, but the underlying data does not. Manufacturers should insist that vendors identify every assumption being made about data quality, completeness and accessibility before deployment begins.

How does the model handle data drift?

Factories are not static environments. As conditions shift, AI models can gradually become less accurate. The danger is that degradation often happens silently and slowly so that a model that was 95% accurate six months ago may now be making significantly more mistakes without anyone noticing.

Manufacturers should ask how performance is monitored, how retraining is handled and whether ongoing model maintenance is included in the contract or billed separately.

What percentage of your customers have reached production scale?

Many AI pilots look impressive, but few become enterprise-wide deployments. Why did the users who did not successfully scale up choose to pull the plug on the project?

The answer often reveals the obstacles vendors rarely highlight during sales presentations, including integration challenges, organizational resistance, insufficient data quality and disappointing returns.

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