In Episode 8 of the Intentional AI series, Cole, Virgil, and Chad explore one of the most tempting uses of AI in digital work: wireframing and page layout. With AI now able to generate full wireframes in minutes or even seconds, the promise of speed is undeniable. But speed alone is not the point.

The conversation focuses on where AI genuinely helps in the wireframing process and where it introduces new risks. Wireframes are meant to establish structure, hierarchy, and intent, not just visual output. While AI can quickly generate layouts, components, and patterns, it still requires strong human judgment to evaluate what is correct, what is missing, and what could cause problems downstream.

A key theme of the episode is escalation of responsibility. As AI reduces the time required to create wireframes, the importance of human review, direction, and decision making increases. Treating AI generated wireframes as finished work can introduce serious risks, especially around accessibility, content fidelity, maintainability, and overall project direction.

Virgil shares an experiment where he used AI to first generate a detailed prompt for wireframing, then tested that prompt across three tools: Claude, Google Gemini 3, and Figma Make. The results reveal clear differences in layout quality, accessibility handling, content retention, and how easily the outputs could be integrated into real workflows.

Claude produced the strongest layout and structural patterns but failed badly on accessibility and removed large portions of content. Gemini generated simpler wireframes with clearer structure, but used even less content and still struggled with accessibility. Figma Make stood out for workflow integration, retaining all content and allowing direct editing inside Figma, though it also failed accessibility requirements and relied heavily on generic styling and placeholder imagery.

Throughout the episode, the group returns to the same conclusion. AI is extremely effective at getting the first portion of wireframing done quickly. It is far less effective at making judgment calls, enforcing standards, or understanding context without guidance.

In this episode, they explore:

1. How wireframing fits into the content lifecycle
2. Why speed changes the risk profile of design work
3. Using AI to generate prompts instead of starting from scratch
4. Where AI wireframes succeed and where they fail
5. Accessibility and content risks in AI generated layouts
6. A wireframing comparison of Claude, Gemini 3, and Figma Make

A downloadable Episode Companion Guide is available below with tool comparisons and key takeaways.

DS-S3-E8-CompanionDoc.pdf

Previously in the Intentional AI series:

1. Episode 1: Intentional AI and the Content Lifecycle
2. Episode 2: Maximizing AI for Research & Analysis
3. Episode 3: Smarter Content Creation with AI
4. Episode 4: The role of AI in content management
5. Episode 5: How much can you trust AI for accessibility?
6. Episode 6: You’re asking AI to solve the wrong problems for SEO/GEO/AEO
7. Episode 7: Why AI can make your content personalization worse

New episodes every other Tuesday.

For more conversations about AI, design, and digital strategy, visit www.discussingstupid.com and subscribe on your favorite podcast platform.

(0:00) - Intro

(1:12) - Why wireframing belongs in the content lifecycle

(2:24) - Wireframing is hard / The appeal of AI here

(4:08) - Using AI to create the prompt for wireframing

(5:27) - Why prompt creation unlocks the real value

(7:15) - AI wireframing = filling in blanks & reacting

(10:34) - Risks for teams without wireframing expertise

(12:21) - Using AI to ask better questions, not skip thinking

(13:57) - Iterating prompts and adding constraints

(15:24) - We tested 3 AI tools for wireframing

(15:56) - Testing Claude

(19:41) - Testing Gemini

(21:05) - Testing Figma Make

(24:56) - Practical takeaways and best use cases

(26:50) - Outro

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