Move from experiment to controlled use
Implementation is more than enabling accounts. It includes use-case design, approval, data controls, evaluation, workflow integration, training, monitoring, incident handling and a clear decision about whether to scale, restrict or stop.
Begin with one bounded, lower-consequence use case and a named owner. Test with realistic data, require human approval, measure reviewed value and set stop conditions before wider access.
A practical implementation sequence
- Name the accountable owner. Separate business, technical, privacy, security, legal, workforce and provider responsibilities.
- Approve the use case. Define the task, excluded uses, affected people, data, benefits, failures and risk level.
- Complete due diligence. Check the supplier, account, terms, security, privacy, administration, costs and exit.
- Build acceptance tests. Prepare representative cases, expected results, material failure categories and stop thresholds.
- Configure minimum access. Limit data, connectors, actions, roles, retention and usage to what the pilot needs.
- Train by real task. Cover approved use, prompt preparation, review, evidence, disclosure, records and incident reporting.
- Run a bounded pilot. Monitor outputs, review burden, user behaviour, cost, incidents and unexpected effects.
- Decide with evidence. Scale, change, restrict or stop based on net value and remaining risk.
- Monitor continuing use. Re-test after material model, provider, data, prompt, integration or workflow changes.
Pilot success measures
- Eligible work and actual use are recorded.
- Accepted outputs and material failures are measured.
- Preparation, review and correction time are included.
- Users understand prohibited data and escalation routes.
- Access, retention, logs and connectors match the approved design.
- Complaints, incidents and uneven effects are monitored.
- Provider and internal costs remain within the agreed scenario.
- A fallback process works if the service is stopped.
When to pause or stop
Material output failures
Critical errors exceed the threshold or reviewers cannot detect them reliably.
Information risk
Data is exposed, retained, connected or used outside the approved position.
Uncontrolled change
A model, feature, provider or workflow changes before proportionate re-evaluation.
No net benefit
Review, correction, administration or cost removes the expected improvement.
Check current obligations before deployment
Rules differ by country, sector, organisational role and use. As at this review date, the EU AI Act has phased application dates and specific obligations may already apply. Check the current European Commission AI Act page and obtain qualified advice where the planned use affects people or regulated activity.
AI implementation FAQs
How long does AI implementation take?
It depends on the use, consequence, information, integration, evaluation and approvals. A product account may be enabled quickly, but operational readiness requires evidence that the complete workflow is useful and controlled.
Should everyone receive an AI account during the pilot?
Usually not. Start with representative users and the minimum access needed to test the approved use. Wider access should follow evidence, training and adequate administration.
What should happen when an AI model changes?
Assess whether the change can affect outputs, data, controls, cost or obligations. Re-test proportionately before relying on the changed service for material work.
Continue your AI decision
Use the next guide that matches the question or risk you still need to resolve.