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Implementation

The questions that begin after go-live

Deployment is a milestone, not the conclusion. Some of the most consequential questions emerge only after an AI system enters everyday use.

Go-live changes the kind of evidence available

Before deployment, an organisation can evaluate published research, technical documentation, local validation results, regulatory status, and procurement requirements.

After deployment, another form of evidence begins to emerge: experience from everyday use.

Teams start to see how the system interacts with workload, clinical judgement, patient populations, technical infrastructure, and existing responsibilities. These observations may reveal questions that were difficult to anticipate during procurement or validation.

Questions worth asking

A post-implementation conversation might begin with questions such as:

  • Are people using the system as expected?

  • Does it fit the actual workflow?

  • When do professionals disagree with its output?

  • Is the system creating additional work elsewhere?

  • Are the intended benefits visible?

  • Who reviews performance over time?

  • What happens when the data, population, workflow, or model changes?

  • What would cause the organisation to restrict or stop its use?

These are not questions that one person or profession can answer alone.

Technical monitoring may identify a change in system behaviour. Clinicians may notice a change in how outputs influence decisions. Operational teams may see delays or workarounds. Governance teams may identify unclear responsibilities.

Continuing beyond implementation

Implementation should therefore be understood as part of a lifecycle rather than a single event.

Need leads to planning and selection. Selection leads to preparation, validation, integration, and adoption. Use creates new information. Measurement and monitoring reveal what needs to improve. Improvement may lead back to a newly understood need.

Sharing practical experience does not replace local governance or formal evidence. It can, however, help another team recognise a question earlier and approach its own decision more carefully.

What question appeared only after your system entered practice?

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experience been?

Bring another perspective, ask what this means in practice, or describe what your team has learned.

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