Governed AI
Which AI tools help leaders prepare evidence-based reviews without letting AI approve decisions?
Use AI that works inside the decision record rather than beside it: it drafts and retrieves from permitted sources with citations, flags gaps and conflicts for reviewers, and leaves accepting evidence and approving the decision to named people. General-purpose assistants can help with drafting, but a review needs every AI contribution attributed, linked to its sources and reviewed by a person. Numbers should come from the analytical models, never from the AI’s explanation.
Name the jobs AI is allowed to do.
“Use AI for the review” is too vague to govern. Name the specific jobs instead. Useful ones in engineering and product reviews include:
- Drafting. A first version of the decision question, the study plan or an evidence summary, for the team to refine.
- Retrieval. Finding relevant material in permitted sources and citing the exact document and version.
- Challenge. Flagging missing alternatives, unsupported claims, conflicting assumptions and evidence that may not apply to the case at hand.
- Explanation. Describing analytical trade-offs, sensitivities and the conditions that could change a recommendation, in plain language.
- Scoped preparation. Named, reversible tasks such as assembling a package draft from approved material.
Each job has a different risk. Challenge and retrieval carry less risk, because a person reviews every finding. Drafting needs closer review because fluent text can hide gaps.
Set the boundaries before the work starts.
Decide in advance, and write down:
- Permitted sources. Which documents and data the AI may use, and which it may not.
- Data handling. Where data goes, which AI providers are acceptable, and what must stay inside your environment.
- Permitted actions. What the AI may do on its own (scoped, reversible preparation) and what it may never do: accept evidence, waive a requirement, approve a recommendation or commit the organization.
- A route without generative AI. For decisions where data rules prohibit it, the review process should still work.
Attribute and review every contribution.
For each AI contribution, the record should show what was produced, from which sources and versions, and what the reviewer did with it: accepted, revised, rejected or left open. This keeps AI output visible as AI output. It also means a reviewer’s acceptance of a draft is not mistaken for acceptance of the evidence in it.
Treat AI findings as questions, not verdicts.
A citation shows where a statement came from; it does not prove the statement applies. Suppose an AI summary cites a vibration test showing stable operation, and an installation specification calling for resilient mounting. If the test report does not describe how the unit was mounted, the right outcome is an open question and a request for evidence, not a pass. Good AI support makes that gap visible rather than smoothing it over.
Keep the numbers in the models.
Performance figures, costs and scores should come from the analytical model and its recorded run. AI can explain what a result means and what drives it. It should not generate the figures that a decision depends on.
Keep recommendation and approval human, and separate.
The review should end with two distinct records: a recommendation, with its reasoning and any dissent, and an authorized commitment by the person with authority, with its conditions. AI can help prepare the first. It should take no part in the second.
Questions to ask of any AI tool for reviews.
- Is every AI contribution attributed and linked to its sources?
- Can you restrict the sources it uses and the tasks it performs?
- Does the record show the reviewer’s disposition of each contribution?
- Are numbers taken from models rather than generated by the AI?
- Can the AI approve, accept evidence or commit? The answer should be no.
How DAOSoft supports this.
DAOSoft is an AI-assisted decision lifecycle management platform. AI finds the blind spots; decision science weighs the options; your leaders decide. AI works in three modes with different responsibilities. AI Assist proposes, retrieves, organizes and explains within the decision context. AI Review flags possible gaps or conflicts for reviewers to resolve, with findings attributed to their source. Delegated Execution performs named, authorized and reversible preparation tasks with recorded scope, inputs and outcomes. See how governed AI works.
Every AI contribution is attributed and reviewed by a person. AI never approves or commits. The decision record keeps the AI’s contribution, the source versions and the reviewer’s decision together, and numbers come from the analytical model and its run. Permitted sources, AI providers, data handling and delegated tasks are defined for each engagement, and critical decision workflows can operate without generative AI.
The recommendation and the authorized commitment are separate records. See how the platform works, follow the cold-weather EV pickup worked example (synthetic data), or request an AI workflow demo with a decision question and the constraints on using your data.
Common questions.
Can AI approve an engineering decision?
It should not. AI can draft, retrieve, challenge and explain, but accepting evidence, waiving a requirement, approving a recommendation and committing the organization should remain the responsibility of named people.
How do you keep AI accountable in a design or investment review?
Record each AI contribution with its sources and versions and the reviewer’s disposition: accepted, revised, rejected or left open. Restrict the sources and tasks the AI may use, and take all numbers from the analytical models.
Should AI-generated numbers be used in an engineering review?
No. Performance figures, costs and scores should come from the analytical model and its recorded run. AI can explain what a result means and what drives it.
Bring a decision your team is facing. We will work through it with you.
No prepared charter or data upload needed. We can use a representative example.