AI products complicate traditional design roles in ways most teams don’t anticipate. While brand identity typically focuses on visual consistency across touchpoints, AI introduces unpredictability that challenges standard brand identity design approaches.
A 2025 Figma study found that UI designers now handle interactive elements that adapt based on user behavior, while UX designers manage experiences where outcomes vary probabilistically. This shift affects corporate brand identity because AI systems can’t guarantee consistent results the way traditional software could. Modern brand identity design services must account for this reality, where visual brand identity design extends beyond static assets into dynamic, learning systems that evolve with use.
Understanding UI Design in AI Context
UI design handles what users see and touch. Colors, buttons, layouts, typography, icons — these are UI elements. But AI changes the game.
Traditional UI design assumes predictable states. Click a button and the same action happens every time. AI breaks this assumption because outputs vary based on training data, context, and probability.
Your UI must communicate this uncertainty visually. Show confidence scores through progress bars or color coding. Display alternative options when AI isn’t certain. Make the interface reveal what’s happening behind the scenes.
ChatGPT does this well. It shows a “thinking” animation before responses, setting expectations about processing time. The UI makes AI’s internal state visible rather than hiding it.
What UX Design Means for AI Products
UX design shapes the entire user journey. It’s about how products feel and whether users accomplish goals smoothly.
Peter Morville’s seven UX criteria ask: Is your product useful, usable, desirable, findable, accessible, credible, and valuable? AI products must answer “yes” to all seven while managing unique challenges.
AI introduces unpredictable behavior that traditional UX patterns don’t address. A recommendation engine might suggest different products for identical queries. A chatbot could give varying responses to the same question.
UX designers must create frameworks that make this variability feel natural instead of broken. Users need to understand why outputs differ without feeling the product is unreliable.
How AI Blurs the UI/UX Boundary
Traditionally, UI is a specialized subset of UX. Figma’s Hugo Raymond notes that “engaging UI lays the foundation for positive overall user experience.” This hierarchy holds true, but AI creates new overlap areas.
Consider an AI-powered search interface. The UI designer chooses how results display visually. The UX designer maps the overall search flow. But who decides how to show AI confidence levels? How to explain why certain results ranked higher? When to surface alternative interpretations?
These questions sit between disciplines because they’re simultaneously visual (UI) and experiential (UX). AI forces designers to collaborate more tightly than traditional products required.
Product Design’s Expanded Role
Product design oversees the entire product lifecycle, connecting user needs with business goals and technical feasibility. AI amplifies this responsibility.
Product designers now ask questions that didn’t exist five years ago:
- What happens when our AI makes mistakes?
- How do we communicate model limitations honestly?
- Which decisions should AI handle versus requiring human judgment?
- How do we design for continuous learning and improvement?
These aren’t just UX questions. They’re strategic decisions affecting brand identity design at fundamental levels. Your corporate brand identity gets tested when AI behaves unexpectedly.
Transparency as a Design Challenge
Users distrust what they can’t understand. AI’s “black box” nature creates immediate credibility problems that affect both UI and UX.
UI designers handle transparency visually through:
- Confidence indicators showing AI certainty levels
- Source attribution revealing what data informed outputs
- Progress animations making processing visible
- Color coding distinguishing high versus low confidence results
UX designers handle transparency structurally through:
- Explanation flows letting users dig deeper into reasoning
- Override mechanisms giving users final control
- Feedback loops improving AI through corrections
- Error states acknowledging failures gracefully
Your brand identity design services should include transparency mechanisms from day one. They’re not nice-to-haves; they’re trust-builders that determine adoption rates.
Designing for Two User Types
Here’s something traditional product design never considered: AI agents are now users too.
A 2024 industry report showed some companies saw AI-driven traffic jump 5000% when ChatGPT started browsing websites on users’ behalf. Your product now serves:
- Human users who need intuitive visual interfaces
- AI agents that parse structured data to help humans
This creates dual design requirements. Your UI must look beautiful to humans while remaining machine-readable for AI agents. Your UX must flow naturally for people while providing clear navigation paths for bots.
Think of it like designing a classroom. You optimize for students (humans) but also need to support the teaching assistant (AI agents). If you ignore either audience, the learning experience suffers for both.
Information Architecture for Intelligent Systems
Information architecture (IA) maps navigation, content hierarchy, features, and interactions. AI products need IA that accommodates learning and adaptation.
Traditional IA assumes static structures. AI products need IA that:
- Supports personalization without fragmenting experience
- Maintains consistency while adapting to individual users
- Scales as AI capabilities expand
- Handles edge cases where AI struggles
Flowcharts and wireframes remain useful, but they must account for branching based on AI confidence, user preferences, and model evolution. Your IA documentation should show both typical paths and exception handling.
Visual Consistency Meets Dynamic Behavior
Your visual brand identity design typically demands consistency. Same colors, same typography, same interaction patterns across all touchpoints. AI challenges this by introducing dynamic elements that change based on context.
How do you maintain brand identity when:
- Recommendations vary between sessions?
- Interfaces adapt to individual users?
- Confidence levels require different visual treatments?
- Errors happen unpredictably?
The answer lies in designing flexible systems rather than rigid templates. Create design tokens that adapt while maintaining family resemblance. Use animation and motion to signal when AI is working versus when content is static.
Notion handles this smartly. Their AI features have distinct visual styling (gradient highlights) that clearly separates AI-generated content from user-created content. You always know which parts come from the algorithm.
Testing What You Can’t Fully Control
Usability testing for AI products requires new approaches. You can’t script every scenario because AI outputs vary.
Traditional testing asks: “Can users complete task X?” AI testing asks: “Can users complete task X when the AI gives response Y, Z, or fails entirely?”
Test across multiple scenarios:
- Best case: AI performs perfectly
- Average case: AI provides decent but imperfect results
- Worst case: AI fails or produces nonsensical output
- Edge case: AI encounters situations outside training data
Your brand identity design gets tested during failures. How you handle mistakes reveals whether your brand treats users as partners or subjects. Does your error messaging apologize? Explain what happened? Offer alternatives? These choices shape brand perception more than perfect-state visuals.
Iteration Never Stops
Traditional products ship with defined feature sets. AI products evolve continuously through data and user feedback. This changes the designer’s relationship with their work.
UX designers must build feedback collection into every interaction. Not just “thumbs up/down” but contextual ways users can correct and teach the AI without disrupting their workflow.
UI designers must create visual systems that accommodate new features without redesigns. Modular design systems become essential because your product changes more frequently than traditional software.
Product designers must plan roadmaps that balance new capabilities with refinement of existing features. You’re never “done” in the same way you finish traditional products.
Frequently Asked Questions
What’s the main difference between UI and UX in AI products?
UI handles how AI outputs look visually (buttons, colors, layouts). UX handles the entire user journey including how people understand, trust, and interact with unpredictable AI behavior.
How does product design differ from UI/UX design?
Product design oversees the complete product lifecycle, connecting user needs, business goals, and technical constraints. UI/UX are specialized roles within product design’s broader scope.
Why does AI make UI/UX design more complex?
AI introduces unpredictability, opacity, and continuous evolution that traditional design patterns don’t address. Designers must create experiences for variability instead of consistency.
Can one person handle both UI and UX for AI products?
Smaller teams often combine roles, but AI’s complexity makes specialization valuable. The overlap requires tight collaboration regardless of team structure.
How do you maintain brand identity when AI behaves unpredictably?
Design flexible systems with clear visual distinctions between AI-generated and static content. Use consistent error handling, transparency mechanisms, and user control to reinforce brand values.
About Legit Design Studio
Legit Design Studio specializes in brand identity design and brand identity design services for AI-powered products. Our corporate brand identity expertise helps teams navigate the unique challenges of designing for intelligent systems. We combine visual brand identity design with UX research and product strategy to create cohesive experiences across predictable and unpredictable touchpoints. Our work has helped 86+ companies raise over $42M by building AI products that maintain strong brand identity while embracing algorithmic complexity. Contact us to discuss how thoughtful design can make your AI product more trustworthy and engaging.