Do Not Approve the Conclusion Before Reviewing What Supports It
Do not approve the conclusion before reviewing what supports it. A ten-step workflow for checking AI-assisted evidence at the approval gate.
Who this is for
Reviewers and approvers — Reviewers, approvers, and decision owners who need a structured evidence-review gate immediately before recording an approval on an AI-assisted decision
The problem
Approval workflows are usually built around the conclusion, not the evidence behind it: does the recommendation look reasonable, does it match expectations, sign here. The specific evidence supporting the recommendation — where it came from, whether it's independently corroborated, whether it's contradicted elsewhere — is rarely reviewed as its own distinct step before the approval is recorded.
By the time a reviewer is looking at a polished recommendation, the underlying evidence has already been synthesized away. Reviewing the conclusion is not the same as reviewing what it rests on.
How ConvergePanel helps
ConvergePanel exposes the evidence behind a panel's conclusion directly — per-model claims, cited sources, and where models disagree or share a common source. A structured pre-approval evidence review uses that material deliberately, working through the underlying support before signing off on the conclusion it's meant to justify.
How it works
- 1Define the decision that approval will finalize
- 2Identify the material claims the decision actually depends on — not every claim the output contains
- 3Review the authority of each source behind those claims
- 4Confirm the source actually supports the specific claim made, not just a related topic
- 5Identify whether multiple sources share one underlying origin rather than independent corroboration
- 6Compare where the AI models disagreed on any of the material claims
- 7Test the assumptions the recommendation relies on, not just its stated conclusion
- 8Document any evidence that is missing but would normally be expected
- 9Assess what uncertainty remains after this review
- 10Approve, reject, or escalate based on the evidence review — not the recommendation's tone
Use cases
- Reviewing the evidence behind an AI-assisted claims payout recommendation before sign-off
- Checking source independence before approving a vendor-selection recommendation
- Building a consistent pre-approval evidence gate across a governance program
- Training new approvers to review evidence rather than just the polished conclusion
- Creating a documented step that distinguishes real evidence review from a formality
Worked Example: Approving a Claims Payout Recommendation
An insurance claims team receives an AI-assisted recommendation to approve a payout based on submitted documentation. Before signing, the reviewer works through the ten-step evidence review: the material claim is that the damage described matches the policy's covered causes. The source is the claimant's own submitted photos and a repair estimate — no independent inspection. The models agree on the recommendation, but that agreement is based on the same submitted documents, not independent verification.
The evidence review surfaces a gap the polished recommendation didn't highlight: no independent corroboration exists for the claimed cause of damage. The reviewer escalates for an independent inspection rather than approving on the strength of internally consistent, but single-source, documentation.
How This Differs from Reviewing an AI Recommendation Generally
Reviewing an AI-generated recommendation broadly checks whether to accept it at all — is the evidence real, is the recommendation complete, would a different framing change it. This page is a narrower, later-stage gate: the formal evidence check performed immediately before an approval is recorded, specifically for the material claims the decision depends on. The two overlap but serve different points in a workflow — one decides whether a recommendation is worth acting on; this one is the last check before that action is formally approved.
What to Do When Evidence Review Surfaces a Gap
- Escalate to primary-source verification if a material claim rests on a single, unverified origin
- Document the gap explicitly, even if you proceed with approval despite it
- Route to a subject-matter expert if the assumption being tested falls outside the reviewer's own competence
- Record the residual uncertainty rather than letting the approval imply the evidence was fully resolved
Frequently asked questions
Isn't this the same as checking whether the AI's evidence is sufficient?
It's a closely related but distinct step. Evidence sufficiency is the underlying judgment — is what was found enough for this decision. This page is the procedural workflow for performing that judgment specifically at the approval gate, immediately before a decision is recorded, working through material claims one at a time.
How many claims need this level of review?
Only the material ones — the claims the decision actually depends on. Reviewing every incidental detail in an AI-assisted output at this depth is not proportionate; identifying which claims are load-bearing for the decision, and focusing the review there, is the point of the second workflow step.
What if two models cite the same source — does that count as corroboration?
No. If multiple sources or multiple models trace back to one underlying origin, that is a single point of evidence regardless of how many times it's cited. Checking for this shared-source pattern is one of the specific steps in this workflow because it's easy to mistake for independent corroboration.
Should this review happen before or after the recommendation is finalized?
Before. The value of this workflow is reviewing the evidence as a distinct step prior to approval — not retroactively justifying a decision that's already been made. Treating it as a pre-approval gate, not a post-hoc rationale, is what makes the record meaningful.
Can this evidence review be skipped for low-stakes decisions?
For genuinely low-stakes, low-materiality decisions, a lighter check may be proportionate. The full ten-step review is intended for decisions where getting the evidence wrong carries real financial, legal, or reputational consequence.
Who is accountable if the evidence review misses something?
The reviewer who performed the approval-gate check, and the decision owner accountable for the final approval. ConvergePanel provides the material to review; it does not perform the review or bear responsibility for what a human reviewer did or didn't catch.
Explore related pages
ConvergePanel provides AI-assisted verification for informational purposes only. Not forensic analysis. Not legal evidence.
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