AI for Design Review: Check Against Your Brief

Design review is the step where a completed or in-progress design is checked against your brief, the requirements, and the usability standards. AI can speed up that check significantly: given your brief and the design output, it can flag gaps, inconsistencies, and missing states before a human reviewer touches the work. Tools like fenbs keep the approved findings, decisions, and follow-up tasks in a shared record outside any single chat, so the next session picks up exactly where the review left off.

This guide covers how to structure an AI-assisted design review, how to record what it finds, and how to hand review tasks on to the next person or assistant.

What a design review needs to check

A design review covers four things: does the design meet the stated requirements, does it handle all the interaction states, does it follow the agreed visual and component standards, and are there usability problems that a typical user would encounter. An AI can check all four if you give it the right inputs.

The right inputs are: the original brief, the requirements list, the component library or design system reference, and the design output. Without your brief, the AI can only check internal consistency, not whether the design solves the stated problem.

For a broader look at AI review in development workflows, AI code review tools covers the parallel question in code.

Building a review checklist

A review checklist makes the process repeatable. Ask an AI to generate a checklist from your brief and requirements before the review starts. The checklist covers: requirements traceability (which requirement does each screen element address), interaction states (loading, error, empty, success), accessibility basics, and visual consistency.

Save the approved checklist as a reusable prompt or template. The next review for the same product or client starts with the same standard rather than regenerating it. A checklist that improves each cycle is more valuable than one generated fresh each time.

For the ai-at-work topic hub, similar checklist approaches appear in code review, writing, and research workflows.

How to run an AI-assisted review

Give the AI the checklist, your brief, and a description of the design output. Walk through the checklist systematically. Ask the AI to flag items where the design does not clearly meet the requirement, not to approve items where it does.

Treat AI findings as candidates for human review, not as confirmed problems. The AI may flag an interaction state as missing when it is handled in a separate component. The flag is still useful: it prompts you to verify rather than assume.

Recording findings and decisions

Record every finding that a person reviews and acts on. The record should say: what the AI flagged, what the human reviewer decided about it, and what follow-up task (if any) was created. A finding that was reviewed and dismissed with a reason is not the same as one that was never reviewed.

When the reviewer decides that a particular pattern is acceptable, record that decision so the next review does not flag it again. A decision like "error states do not need an icon in this product's design system" applies to all future reviews of the same product.

Handing review tasks to the next person or session

A review rarely ends in one session. Some findings go back to the designer. Some need a stakeholder decision. Some are recorded as acceptable. The record needs to say where each finding stands when the session ends.

Keep a clear status for each finding: reviewed and resolved, reviewed and dismissed with reason, escalated for decision, or not yet reviewed. The next session reads the status and continues from there rather than re-running the entire review.

How to do this with fenbs

1. Create a free account and add a project for the product or design cycle being reviewed.
2. Add the review checklist as a project note. Add each open finding from the AI review as a task with the relevant requirement.
3. Connect your AI tool over MCP from Integrations and ask it to read the project context before starting each review session.
4. After review, record approved findings as tasks in the To Do lane. Record accepted exceptions and review decisions in Decisions and rules.
5. Update Where we left off with which findings are resolved and which are waiting on a designer or stakeholder decision before the session ends.

For adjacent review workflows, reviewing AI-generated code covers the code side of the same handoff. For security review questions that arise when AI agents touch a product, security review AI agent access covers what to check.