AI Decision Maker: A Workflow for Checking Your Options

Use an AI decision maker as decision support: give it a specific question, agreed criteria, and evidence, then ask it to compare options and expose uncertainty. A person should choose the outcome, check consequential claims, and record the reasoning instead of treating a fluent recommendation as authority.

This works for choices such as which website approach to use or which internal workflow to trial. Tools like fenbs preserve the human decision and its reasons outside a single chat, so the next assistant can continue from what was actually agreed. Start with a real choice small enough to inspect.

For the broader principles behind human-led choices, see AI decision-making. Use the prompt and comparison structure below when you need a concrete worksheet for one choice.

How do you run the comparison step by step?

1. Define the decision in one sentence, name the human decision owner, and set a deadline for deciding.
2. List realistic options, including keeping the current approach when that is possible.
3. Agree on criteria and nonnegotiable constraints before asking AI to rank anything.
4. Provide authorized evidence, ask for a comparison, and have the assistant label gaps and assumptions.
5. Check the consequential claims, challenge the recommendation, and let the person record the final choice and reason.

For example, say a small design studio is deciding whether to revise its existing website or rebuild it. The decision is about a specific project and its requirements. It is not “What is the best website technology?” A narrow question makes it easier to identify the information needed to answer it.

Deciding and authorizing execution are separate steps. The AI assistant approval workflow covers how to control the action after a proposal is ready; keep that boundary visible when the comparison could lead to a consequential change.

What should you give the assistant before it recommends anything?

Supply the goal, constraints, options, evidence, and known unknowns. Identify which statements are verified facts and which are estimates. If two documents conflict, explain who will resolve the conflict rather than asking the assistant to quietly choose its preferred version.

Decision: revise the existing site or rebuild it for the approved project.
Human decision owner: the studio lead.
Goal: meet the approved site requirements by the agreed deadline.
Constraints: preserve required content and pass the acceptance checks.
Options: revise, rebuild, or defer nonessential changes.
Evidence: use only the supplied approved scope and technical assessment.
Compare against the agreed criteria. Separate facts from estimates.
Flag missing evidence. Explain what could reverse your recommendation.
Do not treat your recommendation as approval to act.

Add enough background for the task, rather than every conversation about the project. An AI brain keeps the work context available, but the current decision still needs a clear question and relevant evidence. More stored material does not remove the need to choose what matters.

How do you make the criteria fair and useful?

Distinguish a requirement that every acceptable option must meet from a preference that can be traded against another benefit. If preserving existing content is mandatory, an option that fails that requirement should not win because it receives a high convenience score.

If you use scores, define what each score means before filling the table. Avoid decimal precision when the inputs are rough judgments. Have the assistant show whether a reasonable change in priorities would change the preferred option; that matters more than a tiny difference in totals.

Save the criteria people adopt in Decisions and rules, so another session cannot silently substitute its own definition of the best option.

How do you challenge the AI recommendation?

Ask for the strongest case against the recommended option and the most important missing fact. Then check those points with evidence. A second generated opinion can reveal another question, but it cannot establish the accuracy of a claim by agreeing with the first answer.

NIST’s explainability principles↗ (opens in a new tab) distinguish an understandable explanation from one that accurately reflects the system’s process. For this workflow, readable reasoning is something to inspect. It is not proof that the recommendation is correct.

NIST’s Generative AI Profile↗ (opens in a new tab) also identifies confidently stated false content as a risk. Verify claimed deadlines, product capabilities, source references, and calculations against the original material before they drive your choice.

If the decisive evidence is missing, choose a next investigation rather than a final answer. In the studio example, a small technical check may resolve whether the existing site can meet the requirements. That is a better next step than asking for increasingly confident guesses.

What should the final decision record include?

Write the chosen option, decider, reason, evidence location, important alternatives, and conditions. Include what would justify revisiting the decision. A short record with those details is more useful than a long transcript that leaves readers guessing which suggestion became the actual agreement.

For the studio, the record might say that a person chose revision because the approved requirements were shown to fit the existing site, with a separate accessibility check still required. If that check fails, the team revisits the choice. The conditional part belongs in the record because it changes what happens next.

When a decision needs more investigation, the research workflow guide covers that nearby task. Keep evidence gathering connected to the decision without treating an unfinished investigation as approval.

How to do this with fenbs?

1. Create a free account and add the project containing the choice you need to make.
2. Add a task for the comparison with its question, criteria, evidence references, and checks.
3. Record the decision question as open while evidence is missing. Record a decided outcome only when a person actually chooses it.
4. Connect a supported assistant through Integrations. Ask it to read project context, compare options, and record the human decision with its reason.
5. Save progress with any conditions and next steps. Use History to see who changed the work record when the decision or supporting work changes.

fenbs does not choose for you, verify the evidence, or provide a mathematical decision engine. It is useful when the chosen decision must survive the current chat and guide later work. One person can start free, with no credit card.

When is this workflow too weak for the decision?

Use additional expertise and controls when a choice affects important rights, safety, or regulated activity. A general-purpose chat comparison cannot replace a qualified assessment or the required approval process. Scale the review to the consequence, not to how quickly the assistant can fill a table.

For ordinary work, finish by confirming the decision owner, the evidence checked, and the next action. The AI governance hub explores related review practices. A useful AI-supported choice leaves people able to explain both what they chose and why.