The Copilot Era
2024 is the year AI design copilots went mainstream. Every major design tool has AI features. New startups offer AI-first design capabilities. The landscape is evolving weekly.
After experimenting extensively with these tools, I want to share what I've learned: where they help, where they fall short, and how to integrate them into serious design workflows.
The Current Landscape
Figma AI
What it does: Rename layers, generate copy, suggest layouts, find similar components, auto-complete designs based on context.
Strengths:
- Native integration with existing workflows
- Leverages your design system context
- Layer renaming alone saves hours
- Copy generation that's reasonably good
Limitations:
- Creative generation is conservative
- Limited to what fits Figma's paradigm
- Still early -features feel incomplete
- No image/illustration generation
My take: Figma AI is less about revolutionary capability and more about removing friction from existing workflows. It won't design for you, but it'll make designing faster.
Galileo AI
What it does: Text-to-UI generation. Describe what you want; get editable Figma designs.
Strengths:
- Genuinely impressive generation quality
- Outputs are editable, not just images
- Fast for exploring directions
- Good for wireframing and concepting
Limitations:
- Results often need heavy editing
- Doesn't know your design system
- Consistency across generations is challenging
- Premium pricing limits experimentation
My take: Galileo is genuinely useful for early exploration. Describe a screen, get a starting point, iterate. It's not replacing design judgment, but it accelerates the early, exploratory phase.
Uizard
What it does: Screenshot-to-design, hand-drawn sketch-to-design, text-to-UI generation.
Strengths:
- Screenshot conversion is surprisingly good
- Sketch recognition enables paper → digital flow
- Low learning curve
- Good for quick prototyping
Limitations:
- Output quality varies significantly
- Design language feels generic
- Less sophisticated than specialized tools
- Better for wireframes than polished UI
My take: Uizard excels at speed over quality. It's useful for rough prototyping and converting inspiration screenshots, but results need significant refinement.
Diagram (by Diagram)
What it does: Figma plugins for UI generation, icon generation, copy assistance.
Strengths:
- Plugin-based means native Figma integration
- Magician plugin for AI text and icons is useful
- Generates directly in your file
- Good for micro-tasks
Limitations:
- Limited scope -specific features, not end-to-end design
- Quality varies
- Icon generation often misses the mark
- Text generation is hit-or-miss
My take: Diagram's plugins are worth having installed for occasional use. Not transformative, but occasionally very helpful.
v0 by Vercel
What it does: Text-to-code UI generation. Describe a component; get React/Tailwind code.
Strengths:
- Code output is usable, not just visual
- Integrates with modern stack (React, Tailwind)
- Good for developers who design
- Fast iteration on coded components
Limitations:
- Not visual design -it's coded components
- Requires technical comfort to evaluate output
- Design quality is basic
- Not a Figma alternative
My take: v0 is brilliant for designers who code. Describe a component, get code, refine. It's especially useful for prototyping in code rather than design tools.
Where Copilots Excel
Exploration and Ideation
AI copilots are excellent for quickly exploring solution spaces:
"Show me 5 different approaches to a pricing page."
Manual exploration takes hours. AI exploration takes minutes. Even if none of the results are final-worthy, they spark ideas.
First Drafts
The blank page problem is real. AI can produce "something to react to" faster than you can create it:
"Create a starting point for a user profile page with basic settings."
Now you're editing rather than creating. Many designers find editing easier than starting from scratch.
Repetitive Tasks
Some design tasks are tedious but necessary:
- Renaming layers meaningfully
- Generating copy variations
- Creating responsive versions
- Filling in placeholder content
AI handles these faster without the cognitive drain.
Speed Mockups
When you need to visualize an idea quickly -for stakeholder discussions, user testing stimuli, or internal alignment -AI-generated mockups are fast enough to be disposable.
Spending 10 minutes on AI generation beats spending 2 hours on a mockup you might throw away.
Where Copilots Struggle
Design System Consistency
AI tools don't understand your design system. They generate from general patterns, not your specific components, tokens, and rules.
Results often require heavy editing to match your system. This partially negates time savings.
Complex, Connected Flows
AI generates individual screens well. It struggles with connected experiences:
- How does this screen relate to the next?
- What state carries across the flow?
- How do edge cases manifest?
Design thinking across screens remains a human job.
Nuanced Quality
AI generates "good enough" design. It rarely generates excellent design. The nuances that elevate work -careful spacing, considered hierarchy, intentional restraint -often require human refinement.
Accessibility
AI-generated designs frequently have accessibility issues:
- Insufficient color contrast
- Missing semantic structure
- Problematic interaction patterns
- No consideration for screen readers
Always audit AI output for accessibility.
Brand and Voice
Unless extensively prompted, AI generates generic outputs. Capturing distinctive brand voice and visual personality remains challenging.
Integration Strategies
Strategy 1: AI for Exploration, Human for Execution
Use AI to generate concepts quickly. Select promising directions. Execute final designs manually with full control.
This maximizes AI's speed while preserving design quality.
Strategy 2: AI for First Pass, Human for Refinement
Let AI generate the first version. Spend your time refining, adjusting, and aligning with system and brand.
You're trading creation time for editing time -often a good trade.
Strategy 3: AI for Components, Human for Composition
Use AI to generate individual elements -icons, illustrations, copy snippets. Compose them manually into designed experiences.
This maintains control over the overall design while accelerating component creation.
Strategy 4: Parallel Processes
Generate with AI while also working manually. Compare results. Use whichever is better, or combine best elements from both.
This is expensive but produces best results when quality matters.
Workflow Tips
Batch Your AI Work
Context-switching between AI tools and manual work has costs. Batch AI generation -do all your AI exploration at once, then switch to refinement.
Document Your Prompts
When you get good results, save the prompts. Build a library. Prompt engineering knowledge compounds over time.
Always Edit Output
Never use AI output without editing. Even small adjustments improve quality and ensure outputs match your standards.
Check for Patterns
AI draws from common patterns. If your AI-generated design looks like every other app, add differentiation manually.
Verify Accessibility
Make accessibility checking a standard part of AI design review. Catch issues before they propagate.
The Human Advantage
Despite impressive capabilities, AI copilots have fundamental limitations:
No Context
AI doesn't know your users, your business, your constraints. It generates from general patterns, not specific understanding.
No Judgment
AI doesn't know if its output is good. It produces plausible designs without evaluating appropriateness. Judgment remains human.
No Taste
The refined aesthetic sensibility that distinguishes adequate design from excellent design isn't in the AI. It's in you.
No Relationships
Design happens in organizations with stakeholders, politics, and history. Navigating these is purely human.
Looking Forward
AI design copilots will improve rapidly:
Better design system integration - Tools that understand and use your components More contextual awareness - AI that knows what you're designing and why Improved quality ceiling - Generation that approaches professional quality Workflow integration - Seamless embedding in design processes
The designers who thrive will be those who:
- Learn to use these tools effectively
- Focus on the human elements AI can't replicate
- Develop judgment to know when AI helps vs. hinders
- Stay adaptable as capabilities evolve
The tools are here. The future is collaborative -human judgment and AI capability, together.
Which AI design tools have you tried? What's worked and what hasn't?