“We need this by Friday.”
For 18 years, that sentence has triggered the same internal debate: Do I rush and compromise quality, or push back on the timeline and risk losing the project?
Now? I just say yes.
Not because I work longer hours. Not because I’ve lowered my standards. Because AI has fundamentally changed how fast I can work without cutting corners.
I’ve measured it: AI has reduced my overall design process time by approximately 40% while simultaneously improving the quality of my deliverables. That’s not hyperbole—it’s data from the last year of projects.
Here’s exactly how I’m doing it.
The Speed Problem in Design
Design has always been a time-intensive process:
- Research and ideation: Hours of exploring references, sketching concepts, considering options
- Execution: Translating ideas into high-fidelity designs
- Iteration: Making revisions based on feedback
- Quality control: Catching errors, inconsistencies, and oversights
- Documentation: Creating specs, style guides, and handoff materials
Even with 18 years of experience, a typical landing page redesign would take me 12-16 hours from concept to production-ready designs.
I could go faster by cutting corners. I could skip research. Limit iterations. Rush through QA.
But that’s not faster—that’s just worse work delivered sooner.
The question I’ve been asking for years: How do you maintain quality while increasing speed?
AI finally gave me an answer.
Where AI Actually Helps (And Where It Doesn’t)
Let me be clear upfront: AI doesn’t design for me.
It doesn’t make strategic decisions. It doesn’t understand user psychology. It doesn’t know when a design “feels right.”
What it does is eliminate the time-consuming, repetitive, and mechanical parts of design so I can focus on the strategic, creative, and judgment-based parts.
Here’s my current workflow and where AI fits in.
Phase 1: Brainstorming and Ideation (3 hours → 1 hour)
The old way:
I’d spend hours looking at design inspiration sites, competitor analysis, and reference materials. I’d sketch concepts. I’d explore different layout approaches. I’d consider multiple solutions to the same problem.
It was thorough, but slow.
The AI-assisted way:
Now I start every project with a brainstorming session with AI.
Me: “I’m redesigning a SaaS dashboard for a project management tool. The main user pain point is information overload—they’re drowning in data. The goal is to help them quickly identify what needs attention today. What are 10 different UI patterns or approaches I should consider?”
AI response:
- Priority-based card system with color coding
- Dashboard with collapsible sections
- Timeline view with flagged urgency items
- Kanban board with due date indicators
- List view with smart filtering
- [… and 5 more approaches]
What this gives me: A rapid exploration of possibilities. Some I’ve considered before. Some I haven’t. Some are terrible for this context, but trigger better ideas.
Time saved: 2 hours of manual research and initial exploration.
Quality impact: Actually better, because I’m considering more approaches faster. I’m not just defaulting to familiar patterns.
But Here’s the Key Part
AI generates the list. I evaluate it.
I know which approaches will actually work for project managers who are already overwhelmed. I know which patterns our developers can actually build. I know which solutions fit the technical constraints and brand aesthetic.
AI gives me options. My expertise makes the decisions.
Phase 2: Concept Development (4 hours → 2.5 hours)
Once I’ve selected a direction, AI helps accelerate the actual design work.
For layout exploration:
Instead of manually creating 3-4 layout variations, I use AI to rapidly generate layout concepts. These aren’t final designs—they’re structure explorations that I refine.
Me: “For a pricing page with three tiers (Basic, Pro, Enterprise), what are different ways to structure the layout that emphasize the middle tier without making the other options feel inferior?”
AI: Suggests layout structures, visual hierarchy approaches, and psychological positioning strategies.
I then design the actual page, but I’m starting from a more strategically considered foundation.
For content structure:
When designing a complex page (like a services page with six different offerings), AI helps me organize information architecture before I start designing.
Me: “I need to present six services on one page without overwhelming users. How should I structure this? What grouping strategies or progressive disclosure patterns would work?”
AI: Suggests multiple approaches with rationale.
I pick the one that fits my strategy and design accordingly.
Time saved: 1.5 hours of trial-and-error layout experimentation.
Quality impact: Higher, because I’m exploring more structural options before committing to visual design.
Phase 3: Design Execution (5 hours → 4 hours)
This is where AI has the least impact—because actual visual design still requires human judgment, aesthetic sense, and brand alignment.
But AI still helps in specific ways:
Microcopy and UX writing:
Instead of agonizing over button labels, error messages, or placeholder text, I ask AI to generate options.
Me: “Generate 5 different CTA options for a button that starts a free trial. Emphasize no credit card required. Keep it under 4 words.”
AI: Provides options like “Start Free Trial,” “Try Free—No Card,” “Get Started Free,” etc.
I pick the one that fits best. Saves 15-20 minutes per page.
Accessibility considerations:
Me: “What accessibility issues should I check for on a multi-step form with file uploads?”
AI: Reminds me of keyboard navigation, screen reader labels, error message clarity, upload status indicators, and WCAG requirements.
Saves 10-15 minutes I’d otherwise spend researching or remembering all accessibility requirements.
Time saved: 1 hour of small decisions and research.
Phase 4: Review and Quality Control (2 hours → 45 minutes)
This is where AI has made the biggest quality impact.
The problem with self-review:
When you’ve been staring at a design for hours, you stop seeing problems. You miss inconsistencies. You overlook accessibility issues. You don’t notice when spacing is off by 2 pixels.
You need fresh eyes. But you don’t always have another designer available.
AI as a second set of eyes:
After completing a design, I run a systematic review with AI.
Me: “Review this checkout flow design for UX issues. Focus on cognitive load, error prevention, clarity of next steps, and mobile usability.”
AI response:
- Points out that the “Continue” button doesn’t indicate what comes next
- Notes that the error state for credit card validation isn’t designed
- Suggests that the multi-step indicator could be more prominent
- Identifies that mobile spacing between form fields might be too tight
- Recommends adding inline validation to reduce errors
These aren’t all issues I would have missed, but some I definitely would have. And AI catches them in seconds, not after deployment.
For conversion optimization:
Me: “Analyze this landing page for conversion optimization. What friction points exist? What trust signals are missing?”
AI: Provides a detailed analysis of psychological triggers, trust elements, friction reduction opportunities, and CTA optimization suggestions.
Again—I don’t implement all suggestions blindly. But I evaluate each one, and often find 2-3 genuinely useful improvements I’d overlooked.
For brand consistency:
Me: “Compare this design to our brand guidelines [paste guidelines]. Where does it deviate? What inconsistencies exist?”
AI: Catches color deviations, spacing inconsistencies, typography issues, and tone mismatches.
Time saved: 1 hour 15 minutes of manual QA.
Quality impact: Significantly higher. I catch 30-40% more issues before client review.
Phase 5: Documentation and Handoff (3 hours → 2 hours)
Developer handoff used to be tedious:
- Writing specs for every interaction
- Documenting all states (hover, active, disabled, error, etc.)
- Creating style guides
- Explaining complex interactions
AI-assisted documentation:
Now I use AI to generate first drafts of documentation.
Me: “Based on this design, create developer documentation for this modal component. Include all states, accessibility requirements, and interaction details.”
AI: Generates comprehensive documentation that I review and refine.
Not perfect—I always edit it—but 80% accurate, which means I spend 20% of the time I used to on documentation.
Time saved: 1 hour of mechanical writing.
The Compound Effect
Here’s how the time savings add up for a typical landing page redesign:
| Phase | Old Process | AI-Assisted | Time Saved |
|---|---|---|---|
| Brainstorming | 3 hours | 1 hour | 2 hours |
| Concept Development | 4 hours | 2.5 hours | 1.5 hours |
| Design Execution | 5 hours | 4 hours | 1 hour |
| Review & QA | 2 hours | 45 minutes | 1.25 hours |
| Documentation | 3 hours | 2 hours | 1 hour |
| Total | 17 hours | 10.25 hours | 6.75 hours (40%) |
That’s 40% faster with better quality.
What This Means for My Business
More iterations = better outcomes:
With the time I save, I can do more revision rounds within the same budget. Instead of presenting one concept, I can explore three. Instead of two revision rounds, I can do four.
More iterations = better final product.
Better work-life balance:
I used to work 50-60 hour weeks to meet deadlines. Now I work 40-45 hours and deliver the same amount of work at higher quality.
Competitive pricing:
Because I’m more efficient, I can either:
- Charge the same and make better margins
- Charge less and win more projects
- Deliver faster and take on more clients
I usually do a combination of all three.
Fewer errors reaching clients:
The AI-assisted QA process means I catch issues earlier. Clients see fewer problems. Revisions are based on preferences, not mistakes.
This builds trust and leads to repeat business.
What AI Doesn’t Replace
Let me be clear about what still requires human judgment:
Strategic decisions:
- Which user pain points to prioritize
- What brand personality to convey
- How aggressive or conservative the design should be
- What trade-offs to make between competing goals
Creative vision:
- Visual aesthetic that feels right for the brand
- Emotional impact of design choices
- When to break conventions vs. follow patterns
- Nuanced understanding of target audience preferences
Context understanding:
- Client’s actual (vs. stated) needs
- Political dynamics affecting decision-making
- Industry-specific constraints and expectations
- Cultural considerations for target markets
Relationship management:
- Presenting work persuasively
- Handling client feedback diplomatically
- Negotiating scope and timeline
- Building trust through communication
AI can’t do any of that. Those skills matter more than ever.
How to Implement This Yourself
If you’re a designer curious about working with AI:
1. Start with brainstorming
Before jumping into design, ask AI to help you explore approaches. Get 10 ideas. Evaluate them with your expertise. Pick the best direction.
2. Use AI for QA, not design
Let AI review your work, not create it. Ask it to find issues, suggest improvements, identify inconsistencies. You decide what to change.
3. Automate documentation
Use AI to generate first drafts of specs, documentation, and style guides. You edit and refine. Saves hours of mechanical writing.
4. Build a prompt library
Save effective prompts for recurring tasks. Refine them over time. Build your own custom workflow.
5. Measure the impact
Track your time before and after implementing AI assistance. You’ll likely see 30-50% time savings on mechanical tasks, which translates to 20-40% overall project time savings.
The Future of Design Work
I don’t think AI will replace designers. But designers who use AI will replace designers who don’t.
The profession isn’t going away. It’s evolving. The value is shifting from mechanical execution to strategic thinking, creative vision, and expert judgment.
AI handles the grunt work. Designers handle the decisions that matter.
And frankly, this is more interesting work. I spend less time on tedious tasks and more time solving complex problems. I’m a better designer because of it.
Bottom Line
AI has made me 40% faster without compromising quality. Actually, quality has improved because I catch more issues and explore more options.
This isn’t about working less—it’s about working smarter. The time I save gets reinvested into iteration, refinement, and strategic thinking.
Clients get better work, faster.
I get better margins, happier clients, and less stress.
If you’re still designing the old way—without AI assistance—you’re not just slower. You’re working harder for results that aren’t as good.
The technology is here. The only question is whether you’re willing to adapt.
I did. And it’s transformed my practice.
