Is AI Replacing UI/UX Designers? What Businesses Need to Know

Artificial intelligence is changing how websites, apps and digital platforms are researched, planned, designed and developed.

AI tools can now generate interface layouts, suggest user journeys, produce written content, create images and turn natural-language instructions into working prototypes.

Tasks that once required several hours of manual work can sometimes be completed in minutes.

This has led many businesses and designers to ask an important question:

Is UI/UX being replaced by AI?

AI is unlikely to replace the complete UI/UX design process. It is more likely to change how designers work, which tasks they complete manually and how quickly businesses can move from an idea to an early product concept.

AI is effective at generating options, processing information and accelerating repetitive production.

However, it does not automatically understand a company’s customers, commercial priorities, internal limitations, brand personality or long-term product strategy.

This guide explains how AI is being used in UI/UX and app design, which tasks it can support, which platforms are available and where experienced human designers remain essential.

TLDR: Is UI/UX being replaced by AI?

No. UI/UX is not being completely replaced by AI.

AI can support or automate tasks such as:

Research summaries
Competitor analysis
Sitemap generation
User-flow ideas
Wireframes
Interface concepts
Design variations
UX copy
Images
Prototypes
Front-end code

Human designers are still needed for:

Product strategy
Customer research
User empathy
Brand interpretation
Accessibility
Complex decision-making
Usability testing
Quality control
Design-system management
Collaboration with developers

The strongest approach is usually to combine AI speed with human direction and judgement.

AI can help a team generate and explore ideas quickly. A designer then evaluates whether those ideas solve the correct problem, support the business and provide a clear experience for users.

AI and human UI/UX design compared

Area	AI can support	Human designer remains responsible for
Research	Summarising interviews and reviews	Interpreting context and deciding what matters
Strategy	Suggesting possible features	Defining the product direction
User flows	Generating an initial journey	Assessing whether the journey matches real behaviour
Wireframes	Producing quick layouts	Prioritising the correct content and actions
UI design	Generating visual concepts	Creating a distinctive, usable brand experience
UX writing	Producing draft labels and messages	Ensuring accuracy, tone and situational relevance
Prototyping	Turning prompts into interactive concepts	Defining realistic interactions and test scenarios
Accessibility	Identifying some automated issues	Designing inclusively and testing with people
Development	Producing code and components	Reviewing architecture, security and maintainability
Testing	Summarising feedback	Observing behaviour and making design decisions

What is UI/UX design?

UI stands for user interface.

UX stands for user experience.

UI design focuses on the visual and interactive elements of a website, app or digital platform.

This includes:

Colours
Typography
Buttons
Forms
Menus
Icons
Cards
Page layouts
Animations
Interactive states
Error messages
Loading indicators

UX design focuses on the complete experience of using the product.

It considers whether users can understand the interface, find important information and complete their goals without unnecessary difficulty.

UX design may involve:

User research
Competitor analysis
Customer journeys
Information architecture
User flows
Wireframes
Prototypes
Usability testing
Accessibility
Conversion optimisation
Product improvement

UI and UX work together.

The interface controls what users see and interact with. The wider experience determines whether the product is useful, clear and easy to use.

UI vs UX

UI design	UX design
Focuses on visual and interactive elements	Focuses on the complete user journey
Defines colours, typography and components	Defines structure, steps and information
Creates screen layouts and component states	Creates flows, wireframes and prototypes
Helps the product feel consistent	Helps the product feel understandable
Answers “How should it look?”	Answers “How should it work?”

AI can support both disciplines, but it does not remove the need to connect them to customer needs and business goals.

Is UI/UX being replaced by AI?

UI/UX is not being completely replaced by AI, but many parts of the design process are becoming automated or AI-assisted.

AI can help designers and businesses create early ideas more quickly.

It can generate:

Wireframes
Page layouts
App screens
Dashboard concepts
Written content
Design-system suggestions
Interactive prototypes
Front-end code

For example, a business might ask an AI tool to create a dashboard for managing customer enquiries.

The system could generate:

A sidebar
Customer table
Analytics cards
Search controls
Account settings
Notification area

This can provide a useful starting point.

However, the generated design may not understand:

Which information matters most to customers
How users currently complete the task
Which features support the business model
What makes the brand different
Whether the journey is accessible
How the product should grow
Which technical limitations apply
How sensitive information should be handled

AI can generate an interface, but creating an interface is only one part of UI/UX design.

Professional UI/UX design also involves understanding people, identifying problems, making strategic decisions and testing whether the proposed experience works in practice.

Which UI/UX tasks are most likely to be automated?

Tasks are more likely to be automated when they are:

Repetitive
Based on existing patterns
Easy to describe
Easy to compare
Low risk
Simple to review

Examples include:

Resizing designs
Generating placeholder copy
Removing image backgrounds
Producing layout variations
Converting a desktop concept to an initial mobile layout
Creating basic wireframes
Naming design layers
Generating documentation
Producing common interface components
Summarising research notes

Tasks are less likely to be fully automated when they require:

Human empathy
Accountability
Business judgement
Ethical consideration
Organisational context
Negotiation
Creative direction
Interpretation of ambiguous behaviour

How is AI being used in UI/UX?

AI can support almost every stage of the UI/UX process.

The value usually comes from helping teams complete tasks faster, explore more options and organise information more efficiently.

User research support

A designer may collect information through:

Customer interviews
Surveys
Reviews
Support tickets
Search data
Product analytics
Sales calls
Usability tests

Reviewing this information manually can take a considerable amount of time.

AI can help identify:

Repeated complaints
Common feature requests
Frequently mentioned frustrations
Similar answers across interviews
Potential user groups
Themes within large volumes of feedback
Recurring customer language

Research platforms such as Dovetail can help teams organise interviews, transcripts and research findings.

AI-generated summaries should still be reviewed carefully.

Important details can be lost when individual experiences are reduced to broad themes. The system may also overemphasise repeated comments without understanding their wider context.

Human judgement is required to decide which findings are reliable, relevant and worth acting on.

Customer-persona generation

AI can create early customer-persona drafts based on supplied research and business information.

A draft persona may include:

Goals
Frustrations
Behaviours
Technology use
Buying concerns
Preferred communication

This can help teams organise their thinking.

However, personas should not be created entirely from assumptions.

An AI-generated persona that is not based on real customer evidence may simply make unsupported ideas appear more formal.

Competitor analysis

AI can help organise information about competing websites and products.

It may compare:

Features
Pricing structures
Navigation
Messaging
Onboarding
Reviews
Positioning
Interface patterns

This can speed up the research process.

However, the team still needs to decide:

Which competitors are relevant
Which patterns are useful
What should not be copied
Where opportunities exist
How the product can be differentiated

Competitor analysis should inform the strategy rather than encourage the business to imitate every established product.

Information architecture

Information architecture determines how pages, content and features are grouped and labelled.

AI can suggest:

Sitemaps
Navigation categories
Page hierarchies
Content groups
Dashboard sections
Account areas

Relume can generate website sitemaps and wireframes from a project description.

These suggestions can accelerate early planning, but they still need to be checked against user expectations.

The business may use internal terms that customers do not understand. AI may also produce a standard website structure without recognising the organisation’s particular sales process.

User-flow generation

A user flow maps the sequence of steps someone takes to complete a task.

AI can create initial flows for tasks such as:

Account registration
Appointment booking
Ecommerce checkout
Subscription upgrades
File uploads
Customer support
Password recovery

A generated flow can help teams identify the major stages.

A designer must still review:

Whether every step is necessary
Whether the order is logical
Where errors may occur
Where users may abandon the journey
Which information should be requested
Whether the process works for different user groups

Wireframe generation

Wireframes are simplified layouts that show the structure of a page or screen.

AI can generate wireframes from:

A written prompt
A sitemap
Existing website content
A list of features
A screenshot
A sketch

For example, a designer could request a wireframe for a mobile banking app containing account balances, recent transactions and payment controls.

The AI may quickly create several arrangements.

Platforms such as Uizard, Relume and Figma Make can support different forms of prompt-led interface creation.

These outputs can accelerate early exploration, but the designer still needs to evaluate whether the hierarchy reflects real user priorities.

A wireframe can look organised while placing the wrong information in the wrong order.

UI concept generation

AI can generate interface concepts, visual styles and complete page layouts.

This is useful during early exploration when teams want to compare several directions.

AI may suggest:

Colour combinations
Page structures
Dashboard layouts
Card styles
Navigation patterns
Typography pairings
Icon ideas
Mobile-screen concepts

These outputs can provide inspiration, but they are often based on common patterns.

Without strong direction, AI-generated interfaces can resemble current templates and competitors rather than create something distinctive.

A designer needs to decide:

Which visual direction fits the brand
How the interface should feel
Which patterns are appropriate
Where consistency is required
Which concepts are realistic to develop

Content creation and UX writing

UI/UX design includes a considerable amount of written content.

This includes:

Button labels
Form instructions
Error messages
Confirmation messages
Onboarding steps
Notifications
Empty states
Help content
Tooltips
Permission requests

AI can create initial versions quickly.

It can also rewrite text to make it:

Shorter
Clearer
Friendlier
More formal
More suitable for a particular audience

However, interface content must match the user’s situation.

An error message should not only explain that something went wrong. It should help the user understand what happened and what they should do next.

For example:

Weak message:

An error occurred.

Better message:

We could not upload the file. Check that it is a PDF smaller than 10 MB and try again.

Human review helps ensure the wording is accurate, useful and consistent with the brand.

Image and asset generation

AI can support the creation of:

Illustrations
Backgrounds
Concept imagery
Icons
Placeholder photography
Product mock-ups
Decorative graphics

This can help teams explore visual directions before commissioning final assets.

Generated imagery still needs to be reviewed for:

Brand suitability
Accuracy
Consistency
Copyright and usage terms
Representation
Accessibility
Visual quality

Design-system support

A design system is a collection of reusable interface components, styles and rules.

AI may help teams:

Generate component descriptions
Suggest naming conventions
Create documentation
Produce design variations
Identify inconsistencies
Convert components into code
Draft accessibility guidance

The final system still requires clear ownership and governance.

Someone needs to decide:

Which components are approved
How they should be used
When they should change
How design and development versions remain aligned

Prototyping

AI can turn designs or written ideas into interactive prototypes.

These prototypes allow teams to demonstrate a concept before committing to full development.

For businesses, this can make it easier to:

Present an idea to stakeholders
Test an early product concept
Collect user feedback
Explore different features
Estimate development requirements
Support investment discussions

Figma Make, Framer AI, Uizard and Lovable can support different forms of prompt-led prototype or product creation.

The prototype still needs to be tested with real users.

A system may function correctly in a demonstration while remaining confusing in a realistic customer journey.

AI-assisted development

AI is also changing how products move from design into development.

A user can describe a component, page or feature and ask an AI platform to generate the initial code.

This can support:

Front-end components
Landing pages
Dashboards
Forms
Internal tools
Early web apps
Prototypes

Examples include:

Generated code still needs technical review before being relied upon in a live product.

What is app design AI?

App design AI refers to tools that help generate mobile or web application interfaces using artificial intelligence.

A user describes the app they want, and the tool generates:

Screens
Layouts
Components
User journeys
Prototypes
Code

App design AI can be used for:

Mobile apps
SaaS dashboards
Customer portals
Ecommerce experiences
Booking systems
Internal tools
Onboarding journeys
Account-management screens

These tools can help businesses visualise an idea before investing in complete product design and development.

They can also help experienced designers generate and evaluate early alternatives more quickly.

However, app design AI does not remove the need to define the product.

Before generating screens, the business still needs to know:

Who will use the app
What problem it solves
Which features are essential
Why users would return
How the app supports the business
How success will be measured
Which data it will process
Which systems it must connect to

Without these answers, AI may produce an attractive collection of screens without a clear product behind them.

What is vibe coding?

Vibe coding is an informal term for building digital products by describing the desired outcome to an AI system.

Instead of manually writing every line of code, the user enters instructions such as:

Create a customer dashboard with login functionality, subscription controls, support tickets and an analytics page.

The AI produces an initial interface or codebase that can be refined through further prompts or direct editing.

Vibe coding can make development more accessible and help teams create prototypes quickly.

It may be suitable for:

Internal tools
Early MVPs
Proofs of concept
Landing pages
Basic dashboards
Small applications
Product demonstrations

However, AI-generated code may require professional review before being used in a live product.

Businesses need to consider:

Security
Performance
Accessibility
Scalability
Data protection
Maintainability
Integrations
Code ownership
Ongoing support

A prototype that works for ten test users may not be suitable for thousands of paying customers.

Popular AI platforms for UI/UX and app design

Different AI design platforms support different parts of the process.

Some focus on research and planning. Others generate wireframes, websites, prototypes or code.

Figma AI and Figma Make

Figma is a collaborative interface-design platform used for websites, apps, SaaS products and design systems.

Its AI tools can support tasks such as:

Generating and rewriting text
Renaming layers
Removing image backgrounds
Producing design content
Searching for assets
Exploring interface ideas

Figma Make supports prompt-led creation of interactive product concepts and web-app experiences.

It can be useful when a team wants to move from an idea to an editable prototype quickly.

Best suited to

Existing Figma users
Product teams
UI/UX designers
Interactive prototypes
Early web-app concepts

Main consideration

AI-generated output still needs to be aligned with the product’s design system, research and technical requirements.

Framer AI

Framer AI helps users generate website structures and responsive layouts from written instructions.

The output can then be edited and published within Framer.

It may help with:

Landing pages
Marketing websites
Portfolio sites
Startup websites
Responsive wireframes
Initial website copy
Translation

Best suited to

Marketing websites
Landing pages
Startups
Designers who want to publish without a separate development handover

Main consideration

It is primarily website-focused and may not suit complex software products or advanced back-end requirements.

Relume

Relume helps teams plan and design marketing websites.

It can generate:

Sitemaps
Wireframes
Style guides
Website sections

Its outputs can be moved into design and development workflows including Figma and Webflow.

Best suited to

Website agencies
Marketing sites
Sitemap planning
Early wireframes
Webflow and Figma workflows

Main consideration

The generated website structure should still be reviewed against customer needs, SEO requirements and the business’s sales process.

Uizard

Uizard is an AI-assisted UI design platform aimed at making app and website concept creation more accessible.

It can help users:

Generate editable multi-screen designs
Turn text prompts into interfaces
Convert sketches into mock-ups
Create wireframes
Produce prototypes
Explore design variations

Best suited to

Early product concepts
Founders
Product managers
Non-designers
Rapid UI exploration

Main consideration

Generated layouts may require further refinement before they are ready for professional development.

Lovable

Lovable turns natural-language descriptions into editable web applications.

It can help users create:

Product prototypes
SaaS interfaces
Internal tools
Landing pages
Early MVPs

Best suited to

Startup concepts
Proofs of concept
MVP exploration
Founders with limited development resources

Main consideration

Businesses should review the resulting architecture, security and scalability before treating a generated project as a production-ready product.

Bolt

Bolt supports prompt-led creation and development of websites and web applications.

Users can describe the intended product, generate an initial implementation and continue editing it within the same environment.

Best suited to

Full-stack prototypes
Internal tools
Early product builds
Developer-assisted experimentation

Main consideration

Complex products still require clear technical architecture, testing and maintenance.

v0

v0 generates interfaces and web components from natural-language instructions.

It can be useful for:

Dashboard concepts
Landing-page sections
Interface components
Front-end exploration
React-based product concepts

Best suited to

Developers
Product designers with technical knowledge
Component generation
Front-end prototypes

Main consideration

The generated component still needs to be integrated into the wider product and reviewed for accessibility, performance and consistency.

Galileo AI

Galileo AI focuses on creating interface concepts from text prompts.

It can support early exploration of:

Mobile apps
Websites
Dashboards
Product screens

Best suited to

Early visual concepts
UI inspiration
Rapid screen generation

Main consideration

Concept generation is not a substitute for complete product research, UX planning or usability testing.

AI UI/UX platforms compared

Platform	Best suited to	Main output	Can publish or build?
Figma Make	Product concepts and prototypes	Editable interactive experience	Partially
Framer AI	Marketing websites	Responsive website	Yes
Relume	Website planning	Sitemap, wireframe and style guide	Exports to other tools
Uizard	Rapid UI concepts	Editable screens and prototypes	Prototype
Lovable	Web-app MVPs	Editable web application	Yes
Bolt	Full-stack prototypes	Website or web application	Yes
v0	UI components	Front-end interface code	Requires integration
Galileo AI	Early visual ideas	UI concepts	No

Which AI design platform should a business use?

Choose Figma Make when the team already works in Figma and needs interactive concepts that can remain connected to the wider design process.

Choose Framer AI when the main goal is generating and publishing a marketing website.

Choose Relume when the team needs help creating a sitemap, website wireframes and an initial visual direction.

Choose Uizard when non-designers or product managers need to create editable UI concepts quickly.

Choose Lovable or Bolt when the goal is to explore a working web-app concept or early MVP.

Choose v0 when a designer or developer needs front-end components and has the technical ability to integrate them into a larger codebase.

Choose Galileo AI when the primary need is visual UI inspiration.

The best tool depends on the intended outcome.

A wireframe generator is not automatically the right platform for building a production application. A website builder may not be appropriate for a complex SaaS platform.

AI platform decision table

Business requirement	Suitable platform
Generate a website sitemap	Relume
Generate website wireframes	Relume or Framer AI
Create editable app screens	Uizard
Create a Figma-connected prototype	Figma Make
Publish an AI-generated marketing site	Framer AI
Build an early web-app MVP	Lovable or Bolt
Generate front-end components	v0
Explore visual UI directions	Galileo AI
Summarise customer research	Dovetail
Test a prototype	Maze

What UI/UX tasks can AI support?

AI is particularly effective when a task involves generating, organising or adapting information.

It can support designers with:

Research summaries
Competitor comparisons
Early personas
Basic user flows
Sitemap ideas
Wireframe concepts
Interface variations
Placeholder content
Button labels
Error messages
Colour suggestions
Image creation
Design documentation
Prototype generation
Front-end code
Usability-test summaries

These tasks can save time, especially during early exploration.

AI can also help smaller businesses communicate their ideas before speaking to a designer or developer.

For example, a founder may use AI to produce an initial app concept that demonstrates the core idea.

A professional team can then review, restructure and develop it properly.

Where are human UI/UX designers still needed?

Human designers remain important wherever context, judgement and understanding are required.

AI can produce options, but it cannot independently decide which option is right for a particular organisation.

Product strategy

Before designing an interface, a team needs to define what the product should achieve.

This involves understanding:

Target market
Business model
Customer problems
Commercial objectives
Product positioning
Technical constraints

A human designer can challenge assumptions and ask:

Does this feature solve a real problem?
Who is the primary user?
Why would customers choose this product?
What should be included in the first version?
What can be delayed?
How does the journey support revenue or retention?
How will the result be measured?

AI can support these discussions, but it should not make strategic decisions without human direction.

User empathy

UX design requires an understanding of how people think, behave and feel.

Users may be:

Impatient
Uncertain
Distracted
Unfamiliar with the product
Using assistive technology
Experiencing stress
Working with limited time

A designer must consider these conditions.

AI can identify patterns in information, but it does not experience confusion, anxiety or frustration in the same way a person does.

Human empathy helps designers recognise when an experience feels demanding, unclear or insensitive.

Creativity

AI can generate a large number of ideas, but those ideas are based on existing patterns.

This makes AI effective at producing familiar interface styles.

It is less reliable when a business needs a distinctive experience that expresses a new concept or challenges standard patterns.

Human creativity involves more than producing something visually different.

It involves understanding when to follow familiar conventions and when to introduce something new.

The best designers create experiences that feel original without becoming difficult to use.

Brand understanding

A strong digital product should feel consistent with the organisation behind it.

The interface should reflect the brand’s:

Personality
Values
Audience
Market position
Tone of voice
Visual identity
Level of professionalism

AI can follow clear brand guidelines.

However, it may struggle with the subtle decisions that make a product feel genuinely connected to the organisation.

A human designer can interpret what the brand should feel like across different screens, messages and interactions.

A financial platform may need to feel secure and dependable.

A gaming product may need to feel energetic and playful.

A healthcare service may need to feel calm and reassuring.

These qualities are created through a combination of visual design, content and interaction decisions.

Complex decision-making

Digital product design often requires balancing competing needs.

Users may want a simple experience while the business needs to collect information.

Developers may need to reduce technical complexity while stakeholders want additional features.

A designer helps find a suitable balance.

This may involve deciding:

Which information should appear first
Which features belong in the main navigation
How much onboarding is necessary
When users should register
Which actions need confirmation
How advanced settings should be organised
Which compromises are acceptable

AI can suggest solutions, but it does not take responsibility for the outcome.

Usability testing

A design should be tested with real or representative users.

During usability testing, a designer observes how people interact with a prototype or working product.

They may notice that users:

Hesitate before selecting a button
Misunderstand a label
Ignore an important message
Follow an unexpected route
Become frustrated by a form
Fail to notice a feature

These behaviours can reveal problems that were not obvious during the design stage.

AI can help summarise test results, but a human researcher is better placed to interpret hesitation, body language and emotional reactions.

Accessibility

Accessible design helps make digital products usable for people with different abilities and needs.

Designers should consider:

Colour contrast
Font sizes
Keyboard navigation
Screen readers
Focus states
Form labels
Motion sensitivity
Touch-target sizes
Alternative text
Clear language
Captions
Error recovery

AI can identify some accessibility issues, but it should not be treated as a replacement for professional review and testing.

Accessibility involves understanding how design decisions affect real people, not simply passing an automated audit.

Does UI/UX need coding?

UI/UX design does not always require coding.

Most UI/UX designers use visual tools to create:

User flows
Wireframes
Interface designs
Interactive prototypes
Design systems
Developer specifications

Their main responsibility is normally to define how the product should work and communicate that experience to developers.

However, coding knowledge can still be valuable.

Understanding HTML, CSS and JavaScript can help a designer understand:

How interfaces are structured
How responsive layouts behave
Which interactions are realistic
How designs affect performance
How reusable components work
Why certain features take longer to build

Some designers also use no-code, low-code and AI platforms to build working products.

The amount of coding required depends on the role.

A UX researcher may not need to code.

A UI designer may benefit from basic front-end knowledge.

A product designer working within a small startup may be expected to create advanced prototypes or working interfaces.

AI may reduce the amount of manual coding needed, but designers still need to understand technical limitations and collaborate with developers.

Will AI reduce the need for designers?

AI may reduce the time required for certain design tasks.

Businesses may no longer need a designer to create every basic layout, placeholder page or early concept manually.

However, this does not mean designers will become unnecessary.

The role is likely to shift towards:

Product strategy
Research
AI direction
Output review
Design systems
Complex journeys
Brand development
Accessibility
Testing
Cross-team collaboration

Designers who use AI effectively may be able to complete projects faster and explore more possibilities.

The value of the designer will increasingly come from the quality of their judgement rather than the speed at which they manually move elements around a screen.

AI-assisted designer vs traditional workflow

Traditional workflow	AI-assisted workflow
Begins with a blank canvas	Begins with generated options
More manual production	More review and refinement
Fewer early variations	More rapid exploration
Designer creates most draft content	AI drafts and designer approves
Developer writes most initial code	AI may generate initial components
More time spent on repetitive work	More time available for strategy and testing

The goal should not be to remove human involvement.

It should be to spend less time on repetitive production and more time solving meaningful problems.

The risks of relying entirely on AI design

Businesses should be careful when using AI-generated designs without professional review.

Generic visual design

AI tools often produce interfaces based on popular patterns.

This can result in websites and apps that look similar to competitors.

Weak usability

A generated interface may appear polished but include:

Unclear navigation
Unnecessary steps
Poor hierarchy
Confusing labels
Inconsistent interactions

Accessibility problems

AI may produce:

Low colour contrast
Small text
Weak keyboard states
Poor form labels
Inaccessible motion
Difficult touch controls

Inaccurate content

Generated copy may include:

Incorrect service information
Unsupported claims
Invented pricing
Unclear instructions
Inappropriate language

Security vulnerabilities

Generated code may contain weak data handling, unsuitable dependencies or insecure implementation.

Limited scalability

A quick prototype may not have an architecture suitable for a growing product.

Difficult maintenance

Generated code may be inconsistent, poorly documented or difficult for another developer to manage.

Ownership uncertainty

Businesses should review the platform’s terms concerning generated assets, code, uploaded information and commercial usage.

Incorrect assumptions

AI may produce a product based on common patterns rather than verified user needs.

AI design risks compared

Risk	Possible result	Recommended response
Generic output	Weak differentiation	Apply human creative direction
Poor hierarchy	Users miss important actions	Review with UX expertise
Inaccessible design	Some users cannot complete tasks	Test against accessibility requirements
Incorrect copy	Customers receive misleading information	Apply content review
Weak code	Security and performance problems	Use professional technical review
Unclear ownership	Commercial restrictions	Review provider terms
No user research	Product solves the wrong problem	Test with representative users

How businesses should use AI in UI/UX

Businesses can gain the most value from AI by combining it with a structured design process.

A practical process may include:

Define the customer problem and business goal.
Research the target audience.
Use AI to explore early concepts.
Review and refine the strongest options.
Create structured user flows and wireframes.
Develop a consistent visual system.
Test the experience with real users.
Work with developers to build the product.
Review accessibility, security and performance.
Monitor the product after launch.
Improve it using evidence and feedback.

This approach uses AI for speed without allowing it to replace strategy and judgement.

Where AI works best in the design process

Design stage	Recommended AI role	Human role
Discovery	Organise ideas and questions	Define the real problem
Research	Summarise supplied evidence	Conduct and interpret research
Structure	Suggest sitemaps and flows	Approve hierarchy and terminology
Wireframes	Generate initial options	Refine priorities and journeys
UI design	Explore visual directions	Apply brand and accessibility
Content	Produce first drafts	Check tone and accuracy
Prototyping	Create interactive concepts	Define realistic behaviour
Development	Generate initial code	Review architecture and security
Testing	Summarise findings	Observe and prioritise issues
Optimisation	Identify patterns	Decide what to change

Questions to ask before using an AI design platform

Before using an AI platform for a website or app, businesses should ask:

Is this being created as a prototype or final product?
Who will review the generated output?
Does the platform support our required features?
How will customer data be protected?
Can the design be edited and expanded?
Is the generated code maintainable?
Does the output meet accessibility requirements?
Who owns the generated design and assets?
Can the product support future growth?
Can the data and project be exported?
Which third-party services are used?
Will we need a designer or developer after generation?
What happens if the platform closes or changes pricing?
Is there a clear human approval process?

Answering these questions can help prevent a quick prototype from becoming a long-term technical problem.

AI as a design partner, not a replacement

The strongest way to view AI is as a design partner.

It can help teams:

Move more quickly
Explore more ideas
Reduce repetitive work
Communicate concepts
Test assumptions earlier
Create working prototypes

It can also make digital-product design more accessible to founders and smaller businesses.

However, AI still needs:

Clear instructions
Relevant evidence
Accurate business information
Professional review
User testing
Technical oversight

A poorly defined prompt is likely to create a poorly defined product.

Human designers provide the context AI lacks.

They talk to customers, challenge assumptions, interpret behaviour and connect design decisions to the organisation’s goals.

How AI affects design costs

AI may reduce the time required for:

Early wireframes
Initial layouts
Placeholder content
Basic prototypes
Repetitive component work

This may make early-stage design and validation more accessible.

However, businesses should not assume that AI removes the wider costs associated with:

Research
Strategy
Professional design
Accessibility
Content
Development
Testing
Security
Maintenance

A cheaper initial build may become expensive later when the product needs to be redesigned or rebuilt.

The most useful financial benefit of AI may be the ability to test an idea before investing in complete development.

How AI affects design agencies and internal teams

Agencies and internal product teams can use AI to accelerate:

Workshops
Research analysis
Concept creation
Content production
Prototypes
Documentation
Development handover

This may allow teams to spend more time on:

Customer understanding
Strategic planning
Design quality
Product testing
Client communication
Post-launch improvement

Businesses should ask prospective designers and agencies how they use AI and how generated output is reviewed.

A responsible partner should be transparent about the role AI plays within its process.

The future of UI/UX and AI

UI/UX design will continue to change as AI becomes more capable.

Designers may spend less time creating basic screens manually.

They may use AI to generate:

Layouts
Components
Content
Images
Prototypes
Code

They will then refine and test those outputs.

Businesses may be able to test product ideas sooner and launch early versions at a lower initial cost.

However, the need for strong design thinking will remain.

As it becomes easier to generate digital products, businesses will need to work harder to create experiences that are:

Useful
Trustworthy
Accessible
Secure
Distinctive
Maintainable

The main competitive advantage will not be the ability to generate an interface.

Many businesses will be able to do that.

The advantage will come from understanding customers better, solving the right problems and creating products people want to continue using.

Use AI without losing the human side of design

AI is replacing and accelerating certain tasks within UI/UX, but it is not replacing the complete role of the designer.

It can support:

Research
Wireframing
App design
Content creation
Prototyping
Development

Human designers are still needed for:

Strategy
Creativity
Brand understanding
User empathy
Accessibility
Testing
Complex decisions

Businesses should not treat AI and human design as opposing choices.

The strongest approach uses both.

AI provides speed, exploration and scale.

Human designers provide direction, context and judgement.

Together, they can create digital products more efficiently without losing sight of the people who will ultimately use them.

Create a digital experience built around real users

AI can help your business move from an idea to an early concept faster.

However, a successful website or digital product still needs a clear strategy, useful content and an experience designed around real customers.

Nertia designs and develops websites, SaaS interfaces, dashboards and digital customer journeys that combine modern tools with human-led product thinking.

Our work can include:

Product discovery
User journeys
Wireframes
UI/UX design
Responsive layouts
Interactive prototypes
Design systems
Website development
AI-assisted workflows
Post-launch improvement

Explore Nertia’s Website Design and Development service

Share With Others

Contents