What Is GenUI (Generative UI) and How Can Your Business Use It?

Every interface you have ever shipped was designed in advance. A team decided which screens exist, what goes on each one and in what order, and every customer got the same screens. Generative UI changes that assumption. The interface is assembled at the moment someone asks for something, from what they asked, what they are trying to do and what your systems know.

This guide explains what GenUI is, how it works, where it is already live, and how a business can start using it without losing control of its brand, its data or its customer experience.

What is GenUI?

GenUI (generative UI) is an interface that an AI model composes at runtime for a specific user and request, instead of a fixed screen designed ahead of time. Ask a GenUI-powered product to compare three insurance plans and you get a comparison table with the plans you asked about. Ask it to plan a delivery route and you get a map with stops you can drag. The model chooses the layout, the components and the data to show, and the user can act on them directly.

It sits between two things most teams already know:

• A chatbot answers in text. Useful, but a wall of text is a poor way to compare options, fill in details or take an action.
• A traditional app has rich, reliable screens, but only the ones someone designed. Anything outside the plan needs a new release.

GenUI keeps the flexibility of the chatbot and gives the answer the shape of an app: cards, tables, charts, forms, maps and buttons, generated for the question in front of it.

The short version

• GenUI turns AI answers into interfaces people can scan, compare and act on, instead of paragraphs they have to read.
• It works best when the AI assembles screens from your own approved components, so every generated view still looks and behaves like your product.
• It is already live in consumer products: Google shipped generative UI in the Gemini app and in AI Mode in Search, and ChatGPT and Claude now render interactive app interfaces inside the conversation.
• Open standards are emerging (A2UI, MCP Apps, AG-UI), so this is no longer a single-vendor experiment.
• The hard part is not the model. It is designing the component system, the states, the guardrails and the measurement around it.

How GenUI works

Under the hood, every GenUI product does the same three things: it understands the request, decides what interface would serve it, and renders that interface on the user’s screen. The difference between implementations is how much freedom the model has in the middle step. The industry now describes three levels.

1. Controlled: the AI picks from screens you built

Your team designs a set of components, such as an order-status card, a pricing comparison or a booking form. The AI decides which one to show and fills it with data. This is the safest, most predictable form of GenUI and the right starting point for most businesses. Frameworks such as the Vercel AI SDK and CopilotKit are built around this pattern.

2. Declarative: the AI describes a screen, your app draws it

The AI returns a structured description of an interface (“a card with this heading, a chart of these values, two buttons”) and your app renders it using its own component library. The model can combine components in new ways, but it can only use the parts you have approved. Google’s open A2UI project, which reached version 0.9 in April 2026, standardises this approach so the same description can render natively on web, mobile and desktop.

3. Open-ended: the AI builds the whole interface

The model writes the interface itself, as HTML, CSS and JavaScript, and it runs in a sandbox. This gives the most freedom and the least control. Google’s research version of generative UI, which powers the “dynamic view” experiment in the Gemini app, works this way, as do MCP Apps, which let tools hand an interactive interface to ChatGPT, Claude and other AI clients.

Connecting all three is a newer layer of plumbing. AG-UI, an open protocol that started at CopilotKit, carries events between an AI agent and the front end, so the interface can stream in, update and send the user’s clicks back to the agent.

	Controlled	Declarative	Open-ended
Who designs the parts	Your team	Your team	The model
Brand consistency	High	High	Variable
Flexibility	Low to medium	Medium to high	Highest
Security risk	Low	Low	Needs sandboxing
Best for	Customer-facing products, regulated sectors	Multi-platform products, agents	Exploration, internal tools, prototypes

Where GenUI is already live

GenUI moved from research to mainstream products in under a year:

• Google Search and Gemini. In November 2025 Google Research published “Generative UI: LLMs are Effective UI Generators” and rolled generative UI into the Gemini app (as the dynamic view and visual layout experiments) and into AI Mode in Search. In Google’s evaluation, people preferred these generated interfaces to plain text answers, and rated them close to hand-built expert websites when generation time was ignored.
• ChatGPT and Claude. Both now render interactive apps inside the conversation through MCP Apps, so a travel, banking or retail brand can appear as a usable interface rather than a link.
• Developer frameworks. The Vercel AI SDK, CopilotKit, Google’s A2UI renderers (including Flutter’s GenUI SDK) and others make it practical to add generative interfaces to existing web and mobile products.

The consequence for businesses is simple. Your customers are getting used to software that responds with exactly the screen they need. Static flows and long forms will feel slower by comparison, the same way static websites felt slow once search and personalisation arrived.

GenUI vs chatbots vs personalisation

	Chatbot	Personalisation	GenUI
What changes per user	The text answer	Content inside a fixed layout	The layout, components and content
Can the user act on it	Mostly by typing more	Yes, on the pre-built screen	Yes, on the generated screen
Handles unplanned requests	Yes, in text	No	Yes, with a usable interface

Why it matters to a business

For a CMO, CTO or product head, the value of GenUI shows up in four places:

1. Shorter paths to an answer. A buyer comparing plans, specifications or configurations gets the comparison directly, rather than clicking through five pages to build it in their head.
2. Fewer dead ends. Requests your product team never planned for still get a usable screen instead of “I can’t help with that”.
3. Self-service that actually completes. A generated form that only asks for the missing details converts better than a 20-field form or a support chat that ends with “please email us”.
4. Faster iteration. Once the component system exists, new journeys can be added by changing instructions and data, not by designing and shipping new screens every time.

The risks are just as real: inconsistent screens that don’t look like your brand, interfaces that show wrong or unsupported data, actions taken without clear confirmation, accessibility gaps, and higher latency and running costs. Each of these is a design and engineering problem, which is why GenUI projects succeed or fail on the system around the model rather than the model itself.

GenUI use cases across industries

The strongest use cases share a pattern: users with varied questions, data that lives across several systems, and a decision or action at the end. A few examples:

• B2B SaaS: dashboards and reports generated from a plain-language question, instead of a fixed report builder.
• Manufacturing and industrial: product selectors and specification comparisons built from a buyer’s requirements, ending in a pre-filled RFQ.
• Financial services: portfolio views, loan scenarios and plan comparisons composed for the customer’s own numbers.
• Healthcare and pharma: clinician-facing summaries and patient journeys that adapt to the question, with strict guardrails on what can be shown.
• Retail and ecommerce: shoppable comparison tables, bundles and guided buying flows generated from a conversation.

We cover ten sectors in detail, with an example interaction and the guardrails each one needs, in GenUI use cases for different industries.

Designing interfaces for GenUI

GenUI does not remove the need for design. It moves design up a level. Instead of drawing every screen, designers define the parts the AI can use, the rules for combining them, and how the interface behaves while it is being generated, when it is wrong, and when the user wants to change it. The essentials:

• Treat your design system as the AI’s vocabulary: well-named, documented components with clear rules.
• Design the in-between states: streaming, partial results, loading, errors and empty answers.
• Keep a fixed frame (navigation, brand, key actions) around the generated content so the product still feels like one product.
• Make every generated view editable and reversible, and ask for confirmation before anything consequential happens.
• Show where data comes from, so users can trust what they see.

The full checklist, with examples, is in our guide how to design interfaces for GenUI.

How to start with GenUI

1. Pick one journey where users get stuck. Look for places where people ask varied questions, compare options or abandon long forms. Support logs and site search data are good sources.
2. Audit your design system. GenUI is only as good as the components it can use. If your components are inconsistent or undocumented, fix that first.
3. Start controlled. Let the AI choose between a small set of well-designed components before giving it more freedom.
4. Define guardrails and success metrics up front: what the AI may show, what needs confirmation, and how you will measure completion, accuracy and time to answer.
5. Prototype, test with real users, then widen. Add components and freedom only where testing shows the value.

How Supercode helps

Supercode designs and builds generative interfaces for B2B companies: the component system the AI assembles from, the conversational and agent flows around it, and the front end that renders it on your website or product. If you are exploring GenUI, see our GenUI design services or talk to our team.

Frequently asked questions

What does GenUI stand for?

GenUI stands for generative user interface, or generative UI: an interface that an AI model creates at runtime for a specific user and request, rather than a screen designed in advance.

Is GenUI the same as AI-generated design?

No. AI design tools generate mock-ups for designers to refine before launch. GenUI generates the live interface your customer uses, while they use it.

Does GenUI replace designers?

No. It changes what they design. Designers define the components, rules, states and guardrails the AI works within. Without that work, generated interfaces drift off-brand and become hard to trust.

Is generative UI safe for regulated industries?

It can be, with the controlled or declarative approach. The AI only uses approved components, sensitive data stays behind your existing permissions, and any action that matters asks for confirmation. Open-ended generation is better suited to internal tools and prototypes.

What technology is used to build GenUI?

Common choices include the Vercel AI SDK and CopilotKit for web apps, Google’s A2UI for declarative cross-platform interfaces, AG-UI for agent-to-front-end communication, and MCP Apps for interfaces that appear inside ChatGPT, Claude and other AI clients.

How long does a GenUI project take?

A focused pilot on one journey, using an existing design system, usually takes 6 to 10 weeks from discovery to a tested release. Building or rebuilding the design system first adds time.

Sources: Google Research: Generative UI, a rich, custom, visual interactive user experience for any prompt; Google Developers Blog: A2UI v0.9; Google Developers Blog: A2UI and MCP Apps; CopilotKit: The developer’s guide to generative UI in 2026.