Why pilots stall
A familiar sequence: a data science team builds a model on historical data, the results look convincing, and a presentation follows. Then nothing happens. The pilot never touched the production system, the data pipeline was manual, and no one owns the outcome on the shop floor or at sea.
The model was the easy part. The hard parts are reliable data, integration with existing control and IT systems, validation and someone accountable for using the output.
Start with a decision, not a dataset
Good industrial AI use cases are anchored in a specific decision: when to service a component, which parts to inspect, how to adjust a process parameter. Identify who makes that decision today, with what information, and what a better decision is worth.
- ·Who acts on the output, and how often?
- ·What happens when the model is wrong, and is that acceptable?
- ·Which data is available in real time, not just historically?
Resolve data ownership early
In industrial settings, data is often spread across machine suppliers, automation systems, MES, ERP and partner systems. Access may be contractually restricted or technically difficult. In international setups, data may also be subject to local rules.
Map data sources, owners and access rights before committing to a use case. Many pilots that look technically ready are blocked by a single unresolved data agreement.
Engineer for production
Running AI in production requires the same discipline as any other industrial system: versioning, monitoring, defined fallback behaviour and clear responsibility. Models drift as equipment, materials and operating conditions change.
Plan for edge or on-premise deployment where latency, connectivity or security require it, and make sure operators can understand and override the system.
Validate under real conditions
Validation on a historical test set says little about behaviour in rare but important situations. Agree validation criteria with operations and safety stakeholders, include edge cases, and run a supervised shadow period before the system influences real decisions.