Software teams building AI-powered applications often encounter a frustrating bottleneck: their AI models are smart and dynamic, but the user interfaces are slow to develop and largely static. Teams might spend months crafting a traditional frontend for an AI agent, only to end up with a one-size-fits-all interface that feels like a basic dashboard or chat box. This disconnect between advanced AI and inflexible UI isn’t just a user experience problem; it’s a drag on return on investment (ROI). Every extra week spent hand-coding an interface is time not spent delivering new features or improving the AI. The rise of autonomous AI agents in workflows (What is Generative UI) has only amplified the need for smarter interfaces. In the fast-paced world of AI-native software, Generative UI (GenUI) has emerged as the solution to reclaim that lost time and maximize team velocity.

I explain in What is Generative UI that Generative UI means letting AI create the interface in real time instead of engineers hand-coding it in advance. Instead of explicitly programming every button or form, the AI (for example, a large language model, or LLM) generates UI components on the fly based on context, user input, and prompts. In effect, you can build UI with AI, allowing the model to decide how to present information to the user. This paradigm makes the UI fluid and adaptive, directly linked to the AI’s capabilities. The promise is huge: if your app’s interface can evolve as quickly as your AI does, you remove bottlenecks. GenUI is unlocking that promise, translating into real ROI by boosting development productivity and speeding up AI product delivery.

What ROI Means in the Context of GenUI
In software, ROI measures how much value a team gets for the effort and cost invested. With generative UI, the returns come in multiple forms. On the efficiency side, development is much faster - a frontend that might take ten weeks to build manually could be generated in one week or less. Faster development cycles mean quicker time-to-market, which can directly drive revenue and competitive advantage. Also, a small team can accomplish what previously required many engineers; you’re getting more output for the same (or less) input (What is Generative UI). On the value side, GenUI enables better user experiences that drive adoption. An AI product with a dynamic, intuitive interface will attract and retain more users than one with a clunky static UI. Higher user engagement means the investment in AI is actually realized through regular use and business outcomes. In short, generative UI increases the “return” (more features, faster delivery, happier users) while lowering the “investment” needed (fewer development hours and less maintenance). That combination is the essence of ROI for GenUI.

The High Cost of Traditional AI Frontends
Today, building the user interface for an AI-driven application is often a huge undertaking that eats into project ROI. Front-end developers must anticipate myriad user needs and design screens or web pages for every possible interaction an AI might support. This either forces teams to limit what the AI can do (so the UI stays simple), or to build a very generic interface that isn’t particularly user-friendly. On top of that, engineers write glue code to connect AI outputs to UI elements and backend systems, the unglamorous plumbing that can dominate development time. One analysis found about 70% of development effort in enterprise applications goes into this kind of integration and wiring, rather than core features (What is Generative UI). All that work slows down AI projects and drives up costs. An AI feature might take a week to implement in the model, but then six more weeks to integrate into a usable UI, delaying any real user value. Moreover, static and rigid UIs often lead to poor user adoption. In fact, after heavy investment, 74% of companies have yet to see tangible value from their AI initiatives, largely due to low user uptake and workflow issues (Glue Code is Killing Your Velocity). Users won’t embrace AI tools that have confusing or insufficient interfaces, so the promised ROI of those AI projects never materializes. Rather than continuously patching old interfaces to fit new AI capabilities (Firestorm Consulting, Stop Patching, Start Building, 2025), it’s clear that a new approach is needed to break this cycle.

How Generative UI Unlocks Team Velocity
Generative UI turns the traditional development process on its head. Instead of manually coding every interface element, developers can rely on an AI frontend API (such as C1 by Thesys) to generate the UI as needed. When an AI model needs to display information or get user input, it can essentially say “here is what I want to show” and the generative UI system creates the interface on the spot. This eliminates entire classes of glue code and drastically reduces the custom UI code developers must write.

The impact on team velocity is dramatic. Imagine adding a new feature to an AI-powered dashboard. In the past, you’d create new UI components, go through design and review, and integrate everything - a process that could take weeks. With GenUI, you might just update the AI’s prompt or output to indicate a new chart or form, and the system will generate it in real time. Features that once took weeks of front-end work can be delivered in days or hours. Teams have reported standing up workable interfaces in a single afternoon using generative UI, once the LLM logic was in place (What is Generative UI). Instead of front-end development being the gating item, it nearly keeps pace with back-end AI development.

Equally important, a generative UI is dynamic and adaptable. Changes in the AI’s behavior or the data don’t require scrapping and rebuilding the interface; the UI will adjust on the fly. If your LLM gains a new capability tomorrow, you don’t have to manually redesign the UI to surface it - the generative system will include the new elements as appropriate. This means the UI is no longer a bottleneck when the product evolves. Developers and product managers can also iterate faster because they can tweak the AI’s instructions to change the UI, rather than writing new code from scratch. Even non-engineers can experiment with UI changes by adjusting prompts or high-level settings, instead of waiting through a full development cycle. All of this leads to a more agile workflow: faster experiments, faster feedback, and faster delivery. And when you deliver value to users sooner, you start reaping ROI sooner.

Productivity Gains and Cost Savings with GenUI
Generative UI doesn’t just save time; it also saves money and developer effort, which directly contributes to ROI. By automating large portions of UI creation, teams dramatically cut down on the engineering hours required for a project. Gartner, for instance, predicts roughly a 30% reduction in UI development costs for organizations embracing AI-assisted design tools by 2026 (Glue Code is Killing Your Velocity). This cost reduction comes from developers writing far less boilerplate code and spending less time on maintenance. An interface that assembles itself can also fix or update itself as the product changes, which means fewer expensive redesigns down the road.

For startups or small teams, GenUI is a force multiplier. It allows a lean team to deliver a polished AI UI without hiring a large front-end staff, which is a huge budget saving. A single developer or a small group can manage both the AI logic and the UI through the generative system, which streamlines communication and eliminates back-and-forth between separate teams. In effect, you can achieve the results of a big development team with a fraction of the people. These productivity gains (more output per developer) and cost savings (lower payroll and opportunity cost) flow straight into a better ROI for the project.

Better UX = Better ROI
An AI solution is only as valuable as the interface that lets users interact with it. Generative UI inherently produces better user experiences because it creates a real-time adaptive UI tailored to each user and context. Instead of a rigid, one-size-fits-all screen, the interface can change moment to moment, presenting exactly the information or options the user needs. This leads to higher user satisfaction and engagement. Users feel like the software “understands” their intent, because the UI adjusts to what they’re trying to accomplish.

For example, if a user is working with an AI agent and asks for a comparison, a generative UI might automatically display a comparison chart or interactive elements to explore that data, rather than just a text answer. If another user is new and struggling, the UI could simplify itself or offer guided steps, generated by the AI on the fly. This level of personal relevance in the interface makes users more likely to trust and regularly use the AI product. Early adopters of generative interfaces have indeed observed greater uptake of their AI features and faster iteration based on user feedback (What is Generative UI). When the interface can evolve based on how people actually use the tool, it allows the product team to refine the experience quickly, creating a positive feedback loop. All of this translates to ROI because an AI tool that users love and use frequently will generate far more value than one that sits on the shelf due to a poor UI. By bridging the gap between what the AI can do and what the user experiences, GenUI ensures that AI investments pay off in practical, day-to-day usage.

Use Cases and Scenarios
Generative UI can sound abstract, so it helps to consider concrete scenarios. For example, a customer support chatbot with GenUI could do more than just chat - if it needs information, it could pop up a form for the user to fill, or if it’s walking someone through troubleshooting, it could generate a step-by-step interactive checklist. The user gets a richer, more helpful interaction without the company’s developers having to pre-design those screens. Similarly, an analytics co-pilot in a business app could generate charts or data tables in response to a user’s query, instead of only providing a written summary. Ask it about quarterly sales figures, and the UI might instantly display a graph and some filters to drill down further. In both cases, the AI agent isn’t limited to a static UI; it can present information in the most suitable format on the fly. These kinds of dynamic experiences are difficult and costly to achieve with traditional development, but GenUI makes them feasible and easy, leading to solutions that are both more powerful for the user and faster/cheaper for the team to deliver.

The Role of C1 by Thesys
To make generative UI practical, developers can use platforms like C1 by Thesys. C1 by Thesys is a Generative UI API that plugs into your AI application and allows your LLM to turn its outputs into live interface elements. In simple terms, C1 by Thesys acts as an AI dashboard builder or interface engine: you send your model’s response through C1 by Thesys, and it returns instructions to render UI components (buttons, forms, charts, etc.) on the user’s screen. Thesys provides a front-end SDK (for React and other frameworks) that takes those instructions and creates the interactive UI in real time. This means you don’t have to manually code the frontend for every possible thing your AI might do; the AI and C1 by Thesys handle it together. C1 by Thesys is designed to integrate into existing stacks without a complete rewrite, so teams can adopt generative UI incrementally. It uses your design system and components, ensuring that even though the UI is generated dynamically, it stays on-brand and consistent.

For development teams, C1 by Thesys streamlines the AI-to-UI workflow dramatically. Instead of weeks of building and tweaking interface code, you spend perhaps hours defining how the AI should present certain results, and let C1 by Thesys generate the rest. Product managers and designers can iterate on the user experience by adjusting prompts or interface guidelines in C1 by Thesys, rather than going back into a dev cycle (What is Generative UI). And because C1 by Thesys has guardrails (it knows what types of components and layouts are allowed), it ensures the AI’s creativity results in a usable UI, not a chaotic one. The impact on velocity and cost has been significant: Thesys reports that teams using C1 by Thesys have accelerated their product launch timelines and reduced development costs by relying on GenUI instead of manual UI coding (Thesys Introduces C1 by Thesys to Launch the Era of Generative UI). In other words, C1 by Thesys helps deliver the ROI of generative UI in practice - faster development, lower overhead, and adaptive interfaces that can keep up with any AI advancements.

Conclusion: Embracing Generative UI for Faster AI Delivery
Generative UI represents a fundamental shift in how software interfaces are built. For enterprise tech teams, developers, and startup founders, it offers a way to deliver AI products faster and with less front-end overhead, all while providing a superior user experience. By letting AI systems generate their own interfaces, you remove the UI as a traditional bottleneck and turn it into a competitive advantage. In the push to maximize ROI on AI initiatives, generative UI is a game-changer: it ensures that the time and money spent on developing AI actually translate into user-facing value quickly and efficiently.

Thesys, a company building AI frontend infrastructure, is at the forefront of this shift. Its flagship product, C1 by Thesys, is a Generative UI API that allows AI tools to generate live, interactive UIs directly from LLM outputs. Using C1 by Thesys, teams can essentially let the AI handle the frontend assembly, freeing developers to focus on core logic and AI improvements. The result is faster iteration and delivery of AI features and a more adaptive, engaging user interface. To learn more about how Thesys is enabling this new way of building software, visit Thesys and explore the documentation. Generative UI is unlocking new levels of team velocity and creativity. With the right tools, it can turn your AI vision into a tangible product experience faster than ever, delivering real ROI in the process.

References:

Firestorm Consulting. “Stop Patching, Start Building: Tech’s Future Runs on LLMs.” Firestorm Consulting, 14 June 2025.

Krill, Paul. “Thesys Introduces Generative UI API for Building AI Apps.” InfoWorld, 25 Apr. 2025.

Thesys. Thesys Introduces C1 to Launch the Era of Generative UI. Press Release, Business Wire, 18 Apr. 2025.

Deshmukh, Parikshit. “Bridging the Gap Between AI and UI: The Case for Generative Frontends.” Thesys Blog, 10 June 2025.

Deshmukh, Parikshit. “Glue Code Is Killing Your AI Velocity: How Generative UI Frees Teams to Build Faster.” Thesys Blog, 11 June 2025.

Firestorm Consulting. “Rise of AI Agents.” Firestorm Consulting, 14 June 2025.

FAQ:

What is Generative UI and how does it work?
Generative UI (GenUI) is a new approach to building user interfaces where the UI is generated by an AI (often an LLM) in real time instead of being pre-designed by a developer. The AI outputs UI components (like forms, buttons, charts, etc.) on the fly based on the context and user request. Essentially, the AI drives the frontend. For example, if a user asks a question, the system might not only return an answer in text but also create a relevant chart or interactive element via the generative UI engine. Frameworks like C1 by Thesys enable this by letting developers send an AI’s response to an API, which then returns instructions to render those LLM UI components live in the application. The result is an interface that adapts instantly to each user’s needs without manual coding for every scenario.
How does Generative UI improve ROI for development teams?
Generative UI improves ROI by making development faster and more efficient while also enhancing the product’s effectiveness. Teams save a lot of time and engineering effort because much of the UI is created automatically; this frontend automation means developers write far less code. Faster development and fewer bugs (since there’s less custom code) reduce costs. It also allows small teams to produce complex, AI-native software without a huge budget, maximizing the output per dollar spent. On the flip side, generative UIs provide a better user experience, which means customers or internal users are more likely to adopt the AI solution. When an AI product is delivered sooner, costs less to build, and gets utilized more by users, the return on investment goes up significantly.
What are LLM UI components in a generative interface?
LLM UI components are the building blocks of a generative UI that an AI model can command into existence. These include familiar UI elements like text inputs, dropdown menus, buttons, tables, graphs, and more - all the pieces a typical UI might have, but here they are instantiated by the AI. In a LLM-driven product interface, the AI’s output might include instructions like “show a bar chart of sales by region” or “display a form with two input fields.” The generative UI system (such as C1 by Thesys with its React-based SDK) takes those instructions and renders the actual interactive chart or form on the screen for the user. Essentially, LLM UI components let the AI communicate in visuals and interaction elements, not just words. This allows the software to be much more dynamic, since the AI isn’t limited to a pre-set static UI - it can pull in whatever component is needed to best serve the user’s request.
Can generative UI be added to existing applications without a full rewrite?
Yes. Generative UI can be integrated into existing applications by using APIs and libraries, so you don’t need to rebuild your whole app from scratch. For example, an AI frontend API like C1 by Thesys is designed to plug into your current stack. You can call the C1 by Thesys API from your backend (or wherever you handle AI model calls) instead of the standard OpenAI/LLM endpoint, and it will return a response formatted with UI instructions. On the frontend side, you include the C1 by Thesys SDK which knows how to translate those instructions into UI elements within your existing web app or mobile app. This means you can start using GenUI for specific features - say, your AI chatbot or a new analytics module - inside a larger traditional application. You don’t have to convert everything at once. Many teams start small, enabling generative UI in one part of the app, and expand as they see the benefits. The ability to layer GenUI onto what you already have makes it a practical enhancement rather than requiring a risky “big bang” rebuild.
What is C1 by Thesys and how does it relate to Generative UI?
C1 by Thesys is a platform that makes implementing Generative UI easier. It’s essentially a generative UI API and toolkit. C1 by Thesys works with LLMs to let them output not just text, but UI components. When you use C1, you send your model’s response to the C1 by Thesys API (instead of directly to the user) and C1 returns structured data describing the UI to display. The C1 frontend library then renders that UI live in your application. In this way, C1 serves as an AI dashboard builder or UI engine powered by your model. It handles the heavy lifting of rendering and ensures consistency (so the AI doesn’t create anything outside the bounds of your design system). For developers, C1 by Thesys provides a set of pre-built components and integration points, so you don’t have to write a whole custom generative UI framework from scratch. It’s directly related to GenUI because it’s one of the first products to offer a ready-made solution to implement GenUI. By using C1 by Thesys, teams can quickly turn their LLM into a source of dynamic UI, gaining the benefits of generative interfaces - rapid development, adaptability, improved UX - with minimal setup.

The ROI of Generative UI: Unlocking Team Velocity and Faster AI Product Delivery

The ROI of Generative UI: Unlocking Team Velocity and Faster AI Product Delivery

Related articles

How to design AI-Native Conversational Interfaces : From Templates to Generative UI

GPT 5 vs. GPT 4.1

Architecting AI-first interfaces: Going beyond chat with context-aware UI

Implementing Generative Analytics with Thesys and MCP

Evolution of Analytics: From Static Dashboards to Generative UI

Why Generating Code for Generative UI is a bad idea

Building the First Generative UI API: Technical Architecture and Design Decisions Behind C1

How we evaluate LLMs for Generative UI

Generative UI vs Prompt to UI vs Prompt to Design

What is Generative UI?