Industry 5.0 readiness check for industrial SMEs.

1.Workers and end users help shape the AI use case.

Not only management, vendors or IT.

2.Human decision authority, override and escalation are explicitly defined.

People know when the AI advises, acts or must stop.

3.Training, role impact and time-to-competence are measured.

Adoption is treated as an operating outcome.

1.The project has a measurable resource or waste baseline.

For example energy, scrap, rework, water or travel.

2.The model and infrastructure are proportionate to the business problem.

Larger or cloud-hosted is not assumed to be better.

3.Resource outcomes are monitored after deployment.

Including unintended increases elsewhere in the process.

1.The workflow can operate safely when the AI is unavailable or wrong.

A tested fallback or manual path exists.

2.Critical data, model, network and supplier dependencies are documented.

The team knows what could interrupt the service.

3.Monitoring, incident response and update ownership are assigned.

Quality and security remain owned after launch.

1.The first use case is a specific workflow with a named decision owner.

It is not a broad request to 'add AI'.

2.Representative data or knowledge sources have been inspected.

Access, quality, sensitivity and gaps are understood.

3.Success, limitations and acceptance criteria are defined before the pilot.

The team can decide to stop, improve or scale using evidence.