An AI system can draft a recommendation in seconds. Deciding whether to act on it is still a human responsibility — and in most organisations, the weakest part of the process. AI decision approval is the step where a person with the right to decide accepts, narrows, or rejects what the system proposed, on evidence they could actually inspect.
This guide covers who should approve, what they should see first, and how to record the approval so it holds up later. It draws on Planckchron's research on accountable decision workflows; HERA, referenced below, is in development and the example is synthetic.
Approval is a decision right, not a button
Many tools treat approval as "someone clicked accept." That records an action, not authority. A sound approval answers three questions: was this person entitled to decide, did they see the evidence, and what exactly did they agree to?
Separate the rights explicitly. Typical roles are:
- Recommend — propose an action (this can include the AI system).
- Review / object — examine evidence and raise concerns that stay on the record.
- Approve or reject — accept accountability for the decision within a defined scope.
- Release — authorise the action to take effect, often with conditions.
The AI may recommend. It should not hold the approve or release right for consequential decisions.
What an approver should see before signing off
- The sources behind the recommendation, with owner, version and the limits of what each supports.
- The rejected alternatives and why they were rejected.
- Open risks and unknowns, stated plainly.
- Any objections already raised by reviewers.
- The model and configuration that produced the output.
If the approver cannot open those things from the approval screen, the approval is relying on trust rather than evidence.
Conditional approval is the normal case
Real approvals are rarely all-or-nothing. A good record states the scope (where the decision applies), the conditions (what must stay true), and the expansion trigger (what evidence is needed before the scope grows). Without these, a narrow approval quietly becomes a general one.
Keep disagreement visible
If a reviewer objects and the approver proceeds, the objection should remain attached to the decision. Deleting or auto-resolving objections removes the most useful signal for anyone reviewing the decision later.
A worked example (synthetic)
In the HERA walkthrough, an AI recommends substituting a fastener on a non-primary bracket. Supplier quality objects because testing covered one production lot. The design authority approves the non-primary use only, the objection stays on the record, and the change board releases it with conditions. You can trace each step in the authority and governance record. It is illustrative, not a customer result.
A short approval checklist
- Is the approver's decision right recorded, not just their name?
- Could they open every source the recommendation relied on?
- Are scope, conditions and expansion triggers written down?
- Are objections retained after approval?
- Is the approval linked to the exact recommendation version?
For the full record that approvals sit inside, see our guide to the AI audit trail, and for how people stay in control throughout, AI human oversight.
Where Planckchron stands
HERA is a development-stage research architecture. We do not claim it is deployed, certified, or that it guarantees compliance; our proof page sets out what is evidenced today. If you run consequential reviews and want to test this on a real workflow, explore design partnerships.
Brian Adienge is founder & CEO of Planckchron, an early-stage deep-tech research company.
