Artificial intelligence has been accelerating for years—and interest exploded again with the rise of generative AI in late 2022 and through 2026. Search interest, investment, and real-world adoption have all continued to climb.

AI is now embedded in marketing, product, support, analytics, and operations. I’ve written about the marketing skills you need to thrive alongside AI, but its impact goes far beyond ad targeting or content generation.

Brands and websites use AI chatbots to improve communication with visitors, and they use machine learning to analyze large datasets for faster, better decisions. AI gives teams leverage—automating repetitive work, personalizing experiences, and surfacing insights that used to be hard to find.

Today, AI is also reshaping how websites are designed and developed—and it will keep changing the way digital experiences get shipped going forward.

Traditionally, websites have been built with HTML, CSS, and JavaScript—hand-coded or assembled with CMSs and site builders. The front-end stack has matured, and design systems, components, and no-code tools have made sites more consistent and visually polished.

Although still maturing, ADI (artificial design intelligence) is increasingly used in web design and development.

The idea behind ADI is that much of web design can be automated with AI—making solid, on-brand layouts and assets faster, while humans focus on strategy, messaging, and quality control. Conversation around ADI has grown steadily, which inspired this guide.

My goal is to help you understand where ADI stands today and how to prepare your site for what’s next. That doesn’t mean every site should immediately lean on AI for design—it’s powerful, but you’ll get the best results when you pair AI with clear goals, brand guidelines, and editorial judgment.

I’ll look at how this technology works, where it helps, where it falls short, and how to use it responsibly.

Growth and benefits of AI

Before diving into ADI specifically, here’s a quick refresher on how AI works at a high level.

AI systems let computers perform tasks that typically require human intelligence—understanding language, recognizing patterns, making predictions, and creating text or images. With techniques like deep learning and large language models, systems learn from data and improve as they’re used.

For AI to work well, it needs quality data, strong training, and feedback loops. Models learn patterns in the data they process and generalize those patterns to new inputs.

Adoption and investment have surged, and industry revenue has scaled alongside practical use cases in customer support, analytics, design, content, and engineering.

Early forecasts in the late 2010s projected a dramatic rise in enterprise AI spend by the mid-2020s. The reality has matched—and in many sectors exceeded—those trajectories as generative AI went mainstream.

Bottom line: AI’s momentum isn’t theoretical anymore.

Design and development are natural places to apply it—because patterns, components, and predictable user tasks are everywhere.

And while automation is part of the story, the real promise is scalable augmentation: using data to produce options, test variations, and optimize for measurable outcomes like speed, accessibility, conversions, and usability.

As models improve with feedback, the systems get better at proposing layouts, copy, and asset choices that align with your constraints and goals.

How ADI works

Applied to web design, ADI uses machine learning to recognize successful design patterns and generate customized layouts, styles, and content. The system crunches your inputs—brand cues, industry, goals, content—and assembles a site starting point that you can refine.

In theory, websites can be generated end-to-end by AI. In practice, today’s best results come from a hybrid workflow: AI proposes; humans review, edit, and implement.

Historically, you had three choices:

Learn to code and build from scratch (maximum control, more time), use website builders (faster, no code, still manual), or hire designers/developers (expertise at a cost). All three require significant human effort.

AI adds a fourth option: generate a structured starting point—with layouts, navigation, color systems, and content placeholders—then iterate quickly.

Wix helped popularize the approach—first with ADI (2016) and, more recently, with its AI Site Generator and AI Website Builder that create full, customizable sites from a prompt.

The concept is simple: answer a few questions about your business and goals, and the system creates on-brand sections and pages you can customize.

Modern tools can also propose copy and imagery to speed up the first draft—useful when you have limited assets and want to see options quickly.

The “brain” of ADI is a model trained on real websites and best practices. It assembles layouts, components, and styles that align with your inputs, and it can propose variations for testing.

Over time, this reduces reliance on rigid templates. Instead of picking a theme and retrofitting content, you start with a generated layout tailored to your intent—then refine.

Applying technology to web design

Take creativity out of it for a moment and focus on outcomes. Great web design is about clarity, speed, and helping users complete tasks.

The best designers don’t rely solely on inspiration—they apply patterns that consistently work: clean information architecture, readable typography, accessible contrast, sensible spacing, and obvious calls to action.

Consider the most important factors of web design from the visitor’s perspective.

Most people value finding what they need quickly far more than ornamental visuals. Navigation clarity, scannable content, and accessible components beat flashy effects.

That’s where AI thrives. A system can analyze a draft and compare it to large pattern libraries, flag accessibility issues, and suggest structural improvements—like simplifying menus, improving button labels, or adjusting contrast.

Think of it like an SEO assistant for design. Where SEO tools benchmark content against what ranks, ADI tools benchmark layouts and components against proven patterns.

Because many best practices are repeatable, they’re teachable to machines. For example, clear hero messaging with a primary CTA, visible contact options, predictable navigation, and mobile-first spacing can all be programmatically assessed.

Full automation isn’t the goal right now—quality is. Use AI to get to a strong draft faster, then apply judgment, brand guardrails, and user testing.

Even platforms with AI assistance still benefit from human inputs—answering setup questions, curating imagery, editing copy, and validating accessibility and performance.

Examples of AI web design

Most major builders and CMSs now ship AI features that draft sections, generate copy and images, or audit accessibility and speed. Here are current, widely used options:

  • Wix AI Website Builder / AI Site Generator: Generate a full, editable site—including structure, text, and images—from a prompt, then refine in the editor.
  • Webflow AI Assistant: In-context help to generate sections and copy as you build, plus guidance while editing inside the Designer.
  • Framer AI: Create responsive page drafts and starter content from chat-style prompts, then polish visually and ship.
  • Squarespace Blueprint AI: Creates a personalized, mobile-ready site based on your brand inputs, with AI-assisted sections and styling.
  • WordPress.com AI Website Builder: Generates layouts, content, and designs in minutes directly on WordPress.com, ready to edit and publish.

Practical ways to use AI in web design today

  1. Generate a first draft quickly. Provide brand details, goals, and content. Let ADI create a starting layout with sections (hero, features, pricing, FAQ, contact) that you’ll refine.
  2. Create multiple variations. Ask for alternative hero sections, navigation structures, or pricing layouts. Compare with real users and analytics and keep the winner.
  3. Audit accessibility and readability. Use AI checks for color contrast, focus order, alt text prompts, heading hierarchy, and font size/line length.
  4. Improve performance. Get suggestions to compress images, lazy-load media, limit blocking scripts, and simplify layout to improve Core Web Vitals (LCP, INP, CLS).
  5. Personalize responsibly. Draft copy variants for segments (first-time vs. returning users), but gate with analytics and privacy-aware settings.
  6. Accelerate asset creation. Generate placeholder images or icons to explore directions faster—then replace with final, brand-approved assets.
  7. Document a design system. Ask AI to summarize token names, spacing rules, and component usage into a concise style guide for your team.

Limitations, risks, and how to avoid them

  • Generic outputs. AI can converge on “average” design. Counter this with strong brand inputs (voice, imagery, palette) and real examples of what you like.
  • Accessibility gaps. Don’t assume compliance. Manually test keyboard navigation, screen reader experience, and color contrast.
  • Performance regressions. Autogenerated sections can bloat pages. Watch bundle size, third-party scripts, image dimensions, and animation cost.
  • Content accuracy. Treat AI copy as a draft. Fact-check, add first-hand details, cite sources where appropriate, and align with your product reality.
  • Privacy and bias. Avoid feeding sensitive data into prompts. Review outputs for biased language or exclusionary patterns.

Design, SEO, and conversions: what to prioritize

  1. Clarity first. Clear headlines that match visitor intent. Keep primary CTAs above the fold and descriptive (“Get a demo,” “Start free trial”).
  2. Information architecture. Group navigation by tasks, not org charts. Use descriptive labels and limit top-level items.
  3. Readability. Use generous spacing, 16–18px+ body text, and a scannable hierarchy (H1–H3). Keep paragraphs short.
  4. Accessibility. Ensure sufficient color contrast, visible focus states, alt text prompts for images, and logical heading order.
  5. Core Web Vitals. Optimize images (modern formats), minimize render-blocking resources, and test on real devices. Prioritize INP (which replaced FID in 2024) alongside LCP and CLS.
  6. Trust and proof. Add testimonials, logos, data points, and screenshots that demonstrate real-world use—ideally from your own experience.
  7. Unique value. Include original insights, examples, and visuals specific to your product and audience—what AI can’t infer on its own.

A simple AI-assisted workflow

  1. Define constraints. Brand voice, palette, type scale, components, audience jobs, and one primary page goal.
  2. Generate a draft. Use an ADI tool to create a full page with sections tailored to your inputs.
  3. Edit ruthlessly. Tighten headlines and body copy. Remove fluff sections that don’t support the goal.
  4. Run checks. Accessibility pass, performance pass, mobile pass. Fix what the tool flags and what real testing reveals.
  5. Ship, measure, iterate. A/B test critical elements. Keep improving based on analytics and user feedback.

Conclusion

AI isn’t a fad—it’s a durable shift in how digital products get built. Web design is no exception.

Expect AI features to keep threading through your toolchain—helping you draft faster, check quality, and iterate with data. Your edge comes from pairing those tools with a strong brand, first-hand expertise, and clear goals.

There are plenty of platforms using AI to assist design and development today. While the tech hasn’t replaced designers, it’s already raising the floor—and the ceiling—for teams that embrace it thoughtfully.