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Accountability and audit evidence are incomplete

Make ownership, decisions, approvals and operating evidence inspectable before consequential AI work expands.

The problem

Important actions happen, but the organisation cannot reliably show who authorised them, what information was used or how an exception was handled.

What it looks like

  • Approvals live in inboxes or memory.
  • Teams cannot reconstruct why a consequential decision was made.
  • Control evidence is assembled manually after the event.

Why it happens

Decision rights, workflow state and evidence capture were designed separately. Automation then moves work faster without creating an accountable record.

What not to do yet

Do not add a reporting dashboard before defining the decisions, owners and evidence the dashboard must represent.

Diagnostic path

  • Which actions create material consequences?
  • Who may approve, override and recover them?
  • What evidence must exist at each transition?

Solution pathway

Define the decision record, connect it to workflow state, test exception evidence and only then automate collection or reporting.

AI accountability evidence model connecting source, authority, action, approval, exception, intervention, version and recovery.
Start hereAI for Business

Can your AI system be audited after a consequential decision?

Meaningful auditability begins with evidence that reconstructs information, authority, action, approval, exception, intervention and system state after the event.

Read the guide

Move from the visible constraint into the governed topic systems that explain and address it.

Connect the insight to the operating decision.