Getting Enterprise-Ready for Generative UI: What to Know Before You Build

Meta Description: Generative UI is the next step in AI-native software. Learn what it is, the gaps it fills, and how enterprise teams can leverage LLM-driven interfaces that adapt in real time.

Introduction

The rise of AI “co-pilots” and autonomous agents in business has been explosive in the past couple of years. Large language models (LLMs) are now planning marketing campaigns, triaging support tickets, even writing code alongside human developers. Enterprises are eager to infuse these AI capabilities into their products and workflows. However, there’s a critical bottleneck: the user interface. Too often, advanced AI systems are still presented to users through static dashboards or a simple chat box. The frontend hasn’t kept up with AI’s dynamic capabilities (AI Native Frontends). The result is a disconnect - powerful AI backends paired with clunky, one-size-fits-all interfaces that don’t do them justice. In short, the way users interact with software hasn’t evolved to match AI’s newfound intelligence.

This gap is driving interest in Generative UI (GenUI) as a key piece of the AI-native stack of technology. Generative UIs are essentially user interfaces that build themselves in real time, adapting to each user and situation. Instead of every user seeing the same fixed screens, an AI-driven frontend can assemble new UI components on the fly based on the context. Imagine an enterprise AI assistant that doesn’t just reply in text, but actually creates a form for you when it needs more info, or generates a live data chart when you ask for analytics. That’s the vision of GenUI: interfaces that are as smart and flexible as the AI agents behind them. Industry observers have noted the rise of AI agents and autonomous tools across enterprises (Rise of AI Agents), but unlocking their full potential will require a new approach to UI. In this article, we’ll explain what Generative UI is, why today’s frontend workflows struggle with LLM integration, and what architectures and best practices teams need to build AI-native, LLM-driven interfaces. By the end, you’ll understand how to get your enterprise ready for GenUI, and why it might be the missing piece to turn raw AI power into intuitive user experiences.

What is Generative UI?

Generative UI (GenUI) - short for Generative User Interface - refers to UIs that are dynamically generated by AI in real time, rather than fully designed and coded in advance (AI Native Frontends). In a GenUI system, the frontend can partially build itself on the fly based on the AI’s outputs and the user’s needs. This is a radical shift from traditional frontends, which are predetermined screens and components that only change when a human developer updates them. Generative UI essentially lets the AI take over some of the frontend work in real time.

Why is this important? Traditional enterprise UIs are slow to build and inherently rigid. Even with modern web frameworks, teams spend weeks crafting screens, forms, and buttons, and any new requirement means another development cycle. These static interfaces can’t easily accommodate every possible use case or adapt to each user’s specific needs. By contrast, an AI-driven interface can dynamically create the component it needs at runtime. Instead of showing every user the same layout, the UI can tailor itself to the task or question at hand.

For example, suppose a user asks an AI assistant in a sales application to “show our quarterly revenue breakdown.” A typical chatbot-style system might respond with a paragraph of text and numbers. In a Generative UI approach, the AI could generate a custom chart or table UI element to visualize the data on the spot (AI Native Frontends). If the user then asks to filter by region, the AI might conjure a dropdown or additional input fields right in the interface. Essentially, the AI agent becomes a UI creator: presenting information in the most effective format, not just describing it. Likewise, if more information is needed from the user, a GenUI could pop up a form with specific fields instead of forcing the user to type a long explanation. An AI agent might even assemble an entire dashboard on the fly based on a high-level prompt - acting as an AI dashboard builder that creates a tailored analytics view without any manual setup (AI Native Frontends).

Crucially, Generative UI isn’t about fancy visuals for their own sake. It’s about real-time adaptability. The UI can change moment to moment as the conversation or data evolves (AI Native Frontends). The AI essentially “interprets” the user’s intent and then renders an appropriate interface for that intent. If the context shifts or the user’s goal changes, the UI can morph accordingly - without waiting weeks for a human to redesign it. In other words, GenUI turns the frontend into something alive and context-aware, not a static set of screens. Users get a more intuitive, engaging experience because the interface continuously shapes itself around their needs. Generative UI essentially lets LLMs go beyond just generating text, to actually generating the elements of the user experience. It’s a key step toward truly AI-native software, where every layer of the application (from logic to interface) is designed around the AI’s capabilities.

The Gap in Traditional Frontend Workflows

If Generative UI sounds transformative, it’s because it directly addresses a major gap in how we build frontends today - especially when integrating LLMs or other AI. In a traditional workflow, front-end developers must do a lot of heavy lifting to connect AI outputs to the user interface. Think about a typical enterprise app that adds an AI feature (say a chatbot or recommendation engine): developers would write glue code to take the model’s output (text, predictions, etc.) and embed it into existing web pages or components. Often this means weeks of work mapping every possible response to some UI element, building new pages for new features, and handling many edge cases manually (AI Native Frontends). The result is usually a static UI that can only present what it was explicitly programmed to - it can’t gracefully handle new types of outputs or interactions that weren’t anticipated in the design. This glue-code-heavy approach not only slows development, it also limits the AI’s usefulness because the interface can’t easily adapt to new capabilities.

For example, if your customer support bot suddenly gains the ability to parse invoices or charts, a static UI might have nowhere to display those except as plain text. Or if an AI co-pilot comes up with a multi-step workflow for the user, a traditional app would need a pre-built wizard or sequence of screens to walk the user through it. In most cases today, we end up shoehorning AI into a narrow chat window or a bolt-on panel within an existing app. This one-size-fits-all presentation often fails to showcase what the AI can really do (AI Native Frontends). It can also confuse users - imagine an AI giving you a complex JSON output in a text box because the UI has no way to render a proper table or form. The success of ChatGPT showed how providing a clean, conversational UI (just a chat box) made a powerful LLM widely accessible. But beyond chatbots, many AI-driven products haven’t yet found the equivalent UX that truly fits their capabilities.

The core issue is that traditional UI flows (menus, fixed dialogs, static forms) weren’t built for AI agents that can change behavior on the fly (AI Native Frontends). AI systems are probabilistic and adaptive, whereas most UIs have fixed pathways. This mismatch leads to either very constrained AI (dumbed down to fit the interface) or awkward user experiences (forcing users to adapt to the rigid UI). Every enterprise team integrating LLMs has felt this friction. You have an incredibly flexible AI model behind the scenes, but your front-end is rigid, so you either end up with a bare-bones chat interface or an explosion of new screens for each feature.

Generative UI closes this gap by making the interface itself dynamic and AI-driven. Instead of anticipating every possible interaction at design time, you let the AI decide what UI is needed next and generate it. In an AI-native workflow, much of that tedious wiring is eliminated (AI Native Frontends). The LLM can directly output instructions to create UI elements as needed, and the frontend just needs to know how to render those. Developers move from hand-crafting every element to orchestrating AI outputs and ensuring they display correctly. This not only speeds up development; it unlocks a more fluid user experience. The interface can evolve with the conversation or task, rather than forcing the user down a predetermined path. Enterprises that embrace this shift can deliver far more adaptive applications. As one industry article put it, companies should “stop patching” AI into legacy interfaces and instead start building new experiences that put LLMs at the core (Stop Patching, Start Building: Tech’s Future Runs on LLMs). In other words, retrofitting a chatbot into an old app is a short-term fix; the future belongs to AI-native products with frontends designed to harness AI from the ground up.

How Generative UI Works: LLMs as UI Creators

So, how can an AI actually create a user interface on the fly? The answer lies in a combination of LLM UI components and frontend automation (AI Native Frontends). The idea is not that the AI is drawing pixels or spontaneously coding in a vacuum. Rather, the AI model produces a structured description of what UI it wants, and the application knows how to render that description into real, interactive components. Think of it as the AI speaking a language that the frontend can interpret as UI.

In practice, developers set up a palette of predefined UI components that the AI is allowed to use - things like charts, tables, forms, buttons, text fields, etc. Each component is defined in advance (by the developers or a framework) with certain parameters. For instance, you might have a chart component that takes a dataset and labels, a form component that takes a list of fields, a table that takes rows of data, and so on. These become the building blocks of the generative UI. The LLM doesn’t generate the React code for a chart from scratch; instead, it generates a high-level instruction like “display a chart with title X and data Y.”

Concretely, an LLM’s text output might include a snippet of a structured format (like JSON or a function call) that corresponds to one of these components (AI Native Frontends). For example, when asked for sales by region, the LLM could return a JSON object such as:

jsonCopy{ "component": "Chart", "title": "Sales by Region (Q3)", "data": [ ... ] }

When the frontend application sees this, it recognizes that the AI isn’t just outputting text - it’s instructing the app to render a Chart component with the provided data. The app then uses the pre-built chart widget to display an actual chart in the UI. To the user, it feels like the AI magically “made” a chart appear in the app as part of its answer. Under the hood, the heavy lifting is done by the AI frontend infrastructure that links the LLM’s outputs to real UI elements.

This approach puts some important guardrails in place. Since the AI is limited to using the components the developers allow, you maintain control over the look, feel, and safety of the UI. The model can’t arbitrarily execute code or invent totally new UI widgets that haven’t been vetted. It’s selecting from a known toolkit. In essence, these LLM UI components act as a bridge between the model and the interface (AI Native Frontends). Developers define the pieces, and the AI decides which pieces to use and how to configure them. By designing your application to accept structured outputs (like that JSON example or function calls) from the LLM, you let the model drive parts of the UI within safe, predefined bounds (AI Native Frontends).

Thanks to recent advances, this concept has quickly moved from theory into practice. A number of tools and frameworks have emerged to help implement Generative UI patterns. For example, CopilotKit is an open-source project that lets an AI agent “take control” of a React application, communicating what it’s doing and generating custom UI components on the fly (AI Native Frontends). The popular LangChain framework, known for orchestrating LLM “agents,” has introduced support for streaming LLM outputs as React components, effectively letting an agent continuously update a web UI as it thinks (AI Native Frontends). Even major AI platforms are evolving in this direction: OpenAI’s ChatGPT now supports function calling, which allows the model to output a JSON object that can trigger external actions or renderings - a primitive form of generating UI-like elements instead of just raw text (AI Native Frontends). All of this points to a new layer of frontend automation: we’re moving beyond automating code generation to automating interface generation (AI Native Frontends).

Developers building AI-driven products can leverage these patterns by using an AI frontend API or runtime that handles the rendering of LLM-driven UI. One approach is to integrate a service like C1 by Thesys, which is a Generative UI API designed for live applications. C1 by Thesys enables your application to pass LLM outputs (like that JSON spec for a chart or form) and get back actual UI components ready to render. In other words, it manages the heavy lifting of turning the model’s ideas into interactive React components on the screen. Using an API like this, you don’t have to write a bunch of adapters or custom parsers for the model’s outputs - the platform takes care of it. This greatly reduces the glue code burden on your team. By giving the model the power to directly render UI components, you automate a huge portion of the frontend work for AI-driven applications (AI Native Frontends). Instead of a developer hand-coding every dialog, form, and chart, the AI (via the GenUI system) creates them as needed.

The end result is a more fluid architecture: the AI agent, the backend logic, and the frontend are in constant communication. The user’s input goes to the LLM/agent, which returns both answers and UI update instructions. The frontend then immediately reflects the AI’s “thoughts” by generating new interface elements. The user can interact with those elements (click buttons, fill forms), which feeds back into the AI’s context for the next step. This tight loop makes the UI real-time adaptive - it’s always in sync with the AI’s state and the user’s needs. In an agent-powered app, you’re not browsing through static pages; you’re collaborating with an AI that’s assembling the interface as you go (AI Native Frontends). When done right, the technology becomes almost invisible - users just feel like the application is highly responsive and tailored to them.

Benefits of Generative UI for Enterprise Teams

Adopting Generative UI isn’t just a cool tech experiment; it comes with very tangible benefits for both users and developers. Here are some of the key advantages, especially relevant to enterprise scenarios:

• Personalization at Scale: Generative UIs can tailor themselves to each individual user’s context and needs without manual configuration. The interface one user sees could be completely different from another’s, generated on the fly to suit each scenario. This level of personalization was impractical with static UIs, but with an AI assembling the UI, every app session can be “just for you, in that moment.” For enterprises, this means software that adapts to different departments, roles, or customer segments automatically, improving relevance and satisfaction.
• Real-Time Adaptability: Because the UI is created in response to the current context, it stays in sync with the underlying AI’s capabilities and the user’s goals. The interface can evolve instantly as the AI’s answers or the data change. Users get an application that reconfigures itself as they interact, instead of hitting dead-ends or waiting for the next software update. In fast-paced business environments, this real-time adaptability allows tools to keep up with shifting requirements. An analyst can ask new questions of the data and see the interface reshape to explore answers immediately.
• Faster Development & Iteration: For development teams, AI-native frontends promise huge efficiency gains. When a big chunk of the UI can be generated by the model, developers spend less time coding routine forms and pages and more time on high-level logic. Companies embracing Generative UI have found they can roll out new features or interface changes much faster, since the AI handles a lot of the UI updates on the fly (AI Native Frontends). A recent report highlighted that some startups are launching features 2× faster, and major enterprises are automating 70-80% of the tedious frontend work, saving millions in development costs (Louise, 2025). Routine interface changes - like adding a new field or supporting a new data view - no longer require weeks of work; the AI generates what’s needed guided by simple prompts or configuration. This dramatically shortens development cycles and lets teams iterate rapidly based on user feedback.
• Reduced Maintenance and Glue Code: Generative UI can significantly reduce the long-term maintenance burden on frontend teams. Instead of constantly tweaking UI code to support changing requirements or new AI outputs, developers can focus on refining the AI’s behavior and the component library. The AI, via the GenUI system, takes care of many UI adjustments automatically. This minimizes the boilerplate “glue” code needed to connect backend AI outputs with frontend components (AI Native Frontends). With an AI frontend API handling the translation from model output to UI, teams avoid writing countless adapters and update routines. In effect, the front-end becomes more declarative - engineers specify what they want the user to see (or let the AI infer it), and the generative system figures out how to display it. Less hard-coded UI means fewer bugs and less effort every time something changes.
• Improved User Experience and Engagement: A well-implemented generative frontend can deliver a richer, more intuitive UX than traditional interfaces. Instead of forcing users to interact through a narrow chat window or navigate a rigid menu, the UI can present information in whatever format is most useful - be it an interactive chart, a map, a form with follow-up questions, or a step-by-step workflow. The AI can guide the user proactively by showing suggestions, visualizing its reasoning, or adjusting the interface based on the user’s actions. This leads to interfaces that feel more like a collaborative tool and less like a black box. Users can see what the AI is doing (through the UI changes) and have more control (by directly manipulating UI elements rather than only typing commands) (AI Native Frontends). All of this builds trust and engagement. Users are more likely to embrace an AI-driven system if it’s transparent and responsive to their input. In enterprise settings, improved UX can translate to higher productivity and fewer errors, as users get the information they need in the clearest form.
• Scalability and Future-Proofing: An AI-driven UI can scale with the complexity of your AI backend and adapt to new requirements without a complete redesign. If you add new capabilities to your LLM or integrate a new data source, a generative interface can start incorporating new types of outputs or interactions on the fly. This makes your application more resilient to the rapid advances in AI technology. Your user experience can continuously improve as the AI and its prompts improve - without waiting for big front-end releases each time (AI Native Frontends). For enterprises, this is a strategic advantage. In an environment where AI capabilities are evolving quickly, having a frontend that can keep pace dynamically means your product can leverage the latest innovations immediately. It also means serving a wide variety of use cases from the same application: the UI can flex to handle everything from a simple Q&A to a complex multi-step analysis, guided by the AI. In summary, Generative UI helps ensure your app’s frontend is never the limiting factor in deploying new AI-driven features.

In short, Generative UI aligns the user experience with the full power of modern AI. It turns what might have been a static or confusing interaction into something engaging and continuously optimized. For developers and product teams, it enables building AI-native software that delivers personalized, intelligent experiences while actually simplifying the development process. It’s a rare win-win: better UX for users, and less grunt work for engineers.

Key Considerations Before You Build GenUI

Moving to a generative, AI-driven frontend is a big shift. Before you dive in, enterprise teams should keep a few important considerations in mind. Building a GenUI isn’t just a technical implementation - it also requires rethinking some design and workflow conventions. Here are some best practices and things to know before you build:

• Conversation as the Backbone: Embrace natural language interaction as a first-class input method. In many generative UIs, a chat or dialogue serves as the spine of the experience, with the AI generating UI elements as needed around it. Let users express intent in plain language (text or voice), then respond by showing them the appropriate UI. Even as visual elements appear, maintain the conversational thread. This makes the experience feel intuitive - users can always ask for what they want, and the AI will handle how to show it. Designing your UI around a back-and-forth dialogue also helps structure the AI’s reasoning process in a way users understand.
• Maintain Context and Memory: Ensure that the system remembers context between interactions so the UI can adapt coherently. If the user has already provided certain information or made a selection, the generative UI should not ask for it again - it should incorporate that context into subsequent content. This might mean your AI agent keeps a memory of the conversation or the application state, and when rendering new UI, it uses what’s already known. From a technical perspective, you’ll need to feed relevant conversation history or state back into the LLM for each turn. The payoff is a smoother experience where the AI feels truly attentive and the UI progressively refines itself based on prior inputs.
• Transparency and Feedback: Users need to trust an AI-driven interface, so design for clear feedback about what the AI is doing. When the AI takes an action - for example, switching to a different view or suggesting a next step - provide visual cues or brief explanations. This could be as simple as a subtitle like “Analyzing results…” while a chart is being generated, or highlighting which data the AI used to create a graph. If an operation will take time (e.g. calling an external API), show a loading indicator or status message. The AI’s reasoning should not be a complete black box. By surfacing a bit of context (“Filtering results by region…”) you help the user follow along and trust the system’s actions. Similarly, give feedback for user inputs: if the user presses a button or fills a form the AI generated, make it clear that the input was received and what will happen next.
• User Control and Overrides: Generative UI does not mean the AI has free rein to do anything without oversight. Always keep the human user in the driver’s seat. Include mechanisms for the user to correct or steer the AI’s actions. For instance, if the AI creates a form for additional info, the user should be able to edit or skip fields if they aren’t applicable. Provide options like “undo” or “refine” whenever the AI makes a big change - maybe the AI built a dashboard but the user wants a different chart, so allow them to adjust the parameters or ask the AI to change it. The interface should make it easy to backtrack or modify AI-generated content. This not only prevents user frustration, it also gives valuable feedback to the AI (which you can capture in prompts or fine-tuning data). The goal is a collaborative UI, not a domineering one.
• Consistency and Design Standards: Just because the UI is generated by AI doesn’t mean it should look random or inconsistent. In fact, consistency is even more crucial when content is dynamically created. Establish a design system or style guide that all AI-generated components will follow. This might involve providing the AI with templates or examples of each component type so it knows how they should appear. Ensure the color schemes, typography, padding, etc., of generated elements match your brand and the rest of the application. Many GenUI implementations use a set of “primitive” components styled to the app’s theme, so whether a chart or form is generated, it feels native to the product. This way, as the AI assembles UIs, everything still adheres to a cohesive look and feel. Consistency builds user confidence that the AI’s outputs are trustworthy and part of a professional tool, not just random content thrown on the screen.
• Testing and Validation: Finally, treat your generative UI components and prompts as part of the software that needs testing. AI output can be unpredictable, so invest in a thorough QA process. This might include validating that all component specifications from the LLM are complete and within allowed ranges before rendering (for example, if the AI tries to create a table with 10,000 rows, maybe you cap it or ask for confirmation). Use sandbox or staging environments to simulate the AI-driven interface with various scenarios and make sure the results are acceptable. It’s also wise to log AI outputs and user interactions extensively in production - this helps in diagnosing issues and continuously improving the prompt design or component set. As you roll out generative UIs in an enterprise setting, start with pilot projects and limited domains so you can learn and refine the system before scaling up. With good monitoring and an iterative mindset, you can mitigate risks and steadily expand the AI’s UI-generating responsibilities.

By keeping these considerations in mind, enterprise teams can build GenUI solutions that are not only innovative but also reliable and user-friendly. Generative UI development is as much about UX design and system design as it is about ML models. When done thoughtfully, it leads to powerful tools that users will love - and that your team can maintain and improve with confidence.

Build Generative UIs with Thesys

Generative UI is poised to redefine how users interact with software, especially in the enterprise. Forward-thinking teams are already exploring AI-driven frontends to stay ahead of the curve. At Thesys, we’re building the core AI frontend infrastructure to make this transition easier. Our flagship product, C1 by Thesys, is a Generative UI API that enables LLMs and AI agents to generate live, interactive UIs directly from their outputs. In essence, C1 by Thesys lets your AI not just talk about results, but actually show them - from forms and buttons to charts and entire dashboards - all rendered in real time. This means your enterprise can harness LLMs to create interfaces on demand, without rebuilding your frontend from scratch for every new AI feature.

C1 by Thesys is designed with enterprise needs in mind: it provides a robust way to translate model outputs into safe, consistent UI components that you control. With C1 by Thesys integrated, your developers define the UI building blocks and design constraints, and the API handles the rest - turning the AI’s instructions into front-end code and state automatically. Thesys is a company focused on this AI-native frontend layer, so that you don’t have to patch together custom solutions. We believe that the interface is where the power of AI truly meets the user, and it should be as intelligent and adaptive as the AI itself.

If you’re ready to explore how Generative UI can elevate your software, we invite you to learn more about what we’re building. Check out our website at Thesys to see our vision for AI-native tools, and visit the official Thesys documentation for a deeper dive into C1 by Thesys and how it works. With the right infrastructure, any enterprise can enable their AI systems to generate live, interactive UIs from LLM outputs, ushering in a new era of software that builds itself around the user. The era of static frontends is giving way to adaptive, AI-powered interfaces - and with platforms like C1 by Thesys, you can start building that future today.

References

• Firestorm Consulting. Rise of AI Agents. Firestorm Consulting, 2025.
• Firestorm Consulting. Stop Patching, Start Building: Tech’s Future Runs on LLMs. Firestorm Consulting, 2025.
• Krill, Paul. “Thesys introduces generative UI API for building AI apps.” InfoWorld, 25 Apr. 2025.
• Louise, Nickie. “Cutting Dev Time in Half: The Power of AI-Driven Frontend Automation.” TechStartups, 30 Apr. 2025.
• Thesys. What Are Agentic UIs? A Beginner’s Guide to AI-Powered Interfaces. Thesys Blog, 2025.
• Nielsen Norman Group. “Generative UI and Adaptive UX.” Nielsen Norman Group, 2024.

FAQ

What is Generative UI (GenUI)?
Generative UI is a type of user interface that is created dynamically by an AI (often an LLM) in real time, instead of being pre-built by developers. In a GenUI, the AI can generate new UI components or layouts on the fly based on the user’s needs and context. This means the interface can adapt and change as the conversation or data evolves. It’s essentially an interface that builds itself in response to high-level instructions from the AI, rather than remaining fixed or manually coded for every scenario.

How is Generative UI different from a traditional UI?
Traditional UIs are static and predetermined - developers design every screen and interaction upfront, and the UI only changes when the app is updated. Generative UI, on the other hand, is dynamic and adaptive. It allows the software to present different components or information depending on the situation. For example, a traditional app might have a fixed dashboard with set charts, whereas a generative UI app could create a new chart or form at runtime because the user asked an AI a question that wasn’t anticipated during design. In short, traditional UI is like a set menu, while generative UI is more like an AI chef that can cook up a custom dish for each request.

What is an AI frontend API?
An AI frontend API is a tool or platform that connects an AI’s outputs to the user interface. It provides a bridge between the language model and the visual components. Essentially, it takes structured instructions from an AI (such as “show a table of X data” or “create a button labeled Y”) and renders the corresponding UI element in the application. The API handles the translation of AI output into actual interface code, which saves developers from writing a lot of boilerplate. For example, C1 by Thesys API is an AI frontend API: developers send the model’s UI instructions to C1 by Thesys, and it returns ready-to-render UI components. This lets the AI effectively control the frontend in a safe, controlled manner.

Can an LLM really generate a user interface from a prompt?
Yes - with the right setup. By using predefined components and structured outputs (like JSON or function calls), an LLM can specify interface elements as part of its response to a prompt. For instance, if you prompt an LLM with “Show me the sales data for this week,” a properly instructed model could return not just text but a data table or chart specification. That spec is then turned into an actual UI element by the application. The LLM isn’t drawing the UI directly; it’s deciding what UI to show and providing details, which the frontend can use to generate the element. This is what we mean by Generative UI - the LLM’s answer includes the UI layout or component info needed to present the answer visually. With frameworks like LangChain, CopilotKit, or APIs like C1 by Thesys, this process is becoming more accessible, allowing UIs to be generated from model prompts in real products.

How can enterprise teams implement Generative UI in their applications?
To implement Generative UI, enterprise teams should start by identifying parts of the user experience that would benefit from AI-driven adaptability (for example, dashboards, forms, or reports that frequently change). Next, they need to define a set of UI components that the AI will be allowed to generate - essentially building a component library or using an existing one. Then, integrate an approach for the AI to output structured instructions for those components. This could be done by prompt design (getting the LLM to output JSON for components) or by using an AI frontend API that handles the communication. Teams will also need to maintain context (so the AI knows what’s currently on screen or what the user did) and put guardrails in place (ensuring the AI doesn’t produce unsupported UI or insecure content). It’s wise to start with a pilot project, using a tool like C1 by Thesys or similar frameworks, to test how the AI-generated UI performs. From there, developers can refine the prompts, component set, and UX based on testing. With careful iteration and collaboration between frontend developers, designers, and ML engineers, enterprises can gradually fold generative UI capabilities into their apps. Importantly, they should monitor user feedback and model behavior closely - treating the AI part of the interface as something that’s continually learned and improved. When executed well, this will allow the company’s software to deliver far more dynamic and personalized experiences without a proportional increase in development burden.