How to Plan for Downtime in an AI-Assisted Laboratory Workflow

Map the tasks, integrations, data sources and decisions that rely on the AI-assisted workflow. Distinguish convenience from activities whose interruption could affect quality, safety or compliance.

Qualified owners must decide which work may continue, pause or move to an approved fallback.

Define how users recognise an outage, who confirms it, who communicates status and who can authorise a change in operating mode.

Do not let individual users improvise new tools or upload laboratory data to unapproved services because the normal system is unavailable.

Document the approved manual or alternative process, its data requirements, limitations and review points. Make the fallback accessible without depending on the failed service.

A fallback is not equivalent merely because it produces a similar-looking output; qualified human review remains essential.

Use controlled identifiers, timestamps and provenance so work completed during the outage can be distinguished and reconciled later.

Avoid duplicate entry, untracked local copies and uncontrolled transfers of confidential or regulated information.

Confirm service integrity, integrations, access controls and relevant versions before resuming normal use. Reconcile queued work and test representative cases before processing the full backlog.

Do not automatically re-run consequential tasks without checking whether inputs, decisions or circumstances changed during the outage.

Record duration, impact, decisions, workarounds, data issues and corrective actions. Assign owners and due dates for material gaps.

This operational guidance does not provide diagnosis, treatment or clinical advice; scientific and clinical decisions require appropriately qualified oversight.

Should users choose any available AI tool during an outage? No. Must downtime records be reconciled after recovery? Yes. Does a fallback remove the need for qualified oversight? No.