Output
Is the result fit for the task and its context?
Artificial intelligence can infer, draft, classify or recommend. The difficult question is what people do with that output, under which conditions, and who remains responsible when it is wrong.
The model can produce words quickly. The organisation still has to define review, escalation, records and the cost of error.
Ask what the person is trying to accomplish, what information they need, and what could happen when the system is wrong. Automation is one option among process change, training, better information or no change.
What must remain a human responsibility because the consequence is too important to outsource?
A useful AI-enabled workflow considers quality, review effort, repeat work, incidents, user understanding and the experience of people affected by the decision.
Is the result fit for the task and its context?
Can the responsible person check it with enough time, information and authority?
What happens to customers, staff, data and decisions when it fails?
Where is the error detected and who can stop the route?
Name the task, inputs, prohibited uses, review conditions, record, incident route and change owner. Confidence language alone does not create oversight.
What may the system do, for whom, and with which information?
Which signal, error or new condition requires a person to pause or change the system?
Competence, monitoring, incident response and change control keep the tool inside the organisation’s policy and the task owner’s real authority.
Continue to Practice →The sources below are primary management and regulatory references. Applicability depends on system, role, sector and jurisdiction; they do not make one universal AI workflow.
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