How Figma Make Is Changing the Way Designers, Marketers and UX Teams Work

Introduction

For digital professionals working in design, marketing, UX or web production, the arrival of new tools can shift how teams collabourate, test and deliver products. One such tool is Figma Make - the AI-powered "prompt-to-app" capability from Figma. In this article we explore what Figma Make offers, how it is transforming workflows for designers, marketers and UX teams and what implications it carries for training, collaboration and professional practice.

What is Figma Make?

Figma Make is described by Figma as a way to "turn a design into a fully functional prototype or web app" using prompting and style context, rather than beginning with hand-coded assets. It enables designers and product teams - even those with limited coding experience - to create interactive prototypes and apps by drawing on existing design libraries, attaching frames or providing prompts to the AI chat interface.

By blending design and functional prototype generation with minimal coding overhead, Figma Make can speed up workflows, reduce hand-offs and broaden who can participate in early prototyping.

Key capabilities

• The ability to import an existing Figma library (colours, typography, styling) into the Make file to ensure visual consistency.
• Integration with a backend (for example Supabase) to build prototypes with real-data support, authentication and database connectivity.
• A chat interface to guide the generation of your app and then the ability to fine-tune and edit the generated result (copy, images, layout) before or after export.

How Figma Make is changing workflows for designers and UX teams

From mock-ups to interactive prototypes

Traditionally, designers would create static mock-ups or hi-fi designs, pass them to prototyping tools or engineers and only much later see working code or flows. With Make, the gap between design and functional prototype narrows. Designers can provide a prompt such as "create a dashboard UI with login, data table, filter on the left, quick-action buttons on top" and get a working interactive prototype more quickly.

One of the early tips from Figma's own team emphasises: "The more detail you include about your design in the initial prompt, the fewer follow-up exchanges you'll need". This is meaningful for creative professionals who often iterate rapidly, explore multiple design directions or test variations for stakeholders. By shortening the loop between idea and working prototype, teams can move faster from concept to user-testing, feedback or alignment with marketing or development.

Collaboration and consistency across teams

For marketing professionals, UX teams and content creators, the ability to produce an interactive prototype reduces dependency on dedicated engineering resources at early stages. This means:

• Marketers can model landing-page flows, dynamic content transitions or interactive features more confidently.
• UX teams can test flows with stakeholders, embed real-data back-ends and validate early without full engineering investment.
• Design teams can iterate on layout, responsiveness and behaviour faster and hand off a more refined artefact.

Figma Make also supports importing a design library (for example, styles, typography or colour usage). That is significant for organisations that rely on consistent branding, component libraries and design systems because by anchoring generated prototypes to an existing design system, the risk of disparate visual styles or re-work is reduced.

A useful case study comes from Workday, a corporation with over 18,000 employees and a global presence. It explained how Figma helped them scale design and development collaboration, with designers and cross-functional partners working together, reducing silos and creating smoother hand-offs between design and development.

What this means for marketers, UX teams and digital creatives

For marketers They can prototype interactive campaigns, micro-web apps or landing-page flows with more autonomy, freeing up developer capacity for production-ready work. When collabourating with design teams, the speed of prototyping can enable earlier feedback loops, internal testing and user validation. Because Figma Make supports back-end connections, prototypes can behave more like live tools - for example, authentic form submissions or data display - which enhances marketing proofs of concept.

For UX teams They can test usability flows, complex interactions or dynamic states without waiting for full engineering builds. They can iterate with stakeholders, show interactive prototypes rather than static frames and improve clarity of communication. The fact that Make is anchored in Figma means the prototype and the design system can remain within a unified environment.

For designers and visual creatives Those who previously created visuals and then passed them to prototypers or engineers can now take a more end-to-end role by using Make to produce interactive versions themselves. The learning curve to produce functional prototypes is lower because the prompt-to-app model requires less coding knowledge. However, design discipline still matters: good prompts, well-structured libraries and appropriate editing of the generated output remain key for quality results.

Practical strategies for using Figma Make effectively

Writing effective prompts and integrating with Figma workflows

Although Make can generate a functional app from a short sentence, Figma's documentation recommends structuring prompts carefully. A clear prompt might include:

• Purpose: For example, "Build a customer-feedback dashboard with filters and charts."
• Audience: Such as, "Designed for internal marketing teams."
• Layout hints: Eg. "Include a left navigation, main content area and top header."
• Data requirements: For example, "Use example data from an attached CSV or Supabase table."

This structured approach mirrors good practice in prompt-writing across AI tools. The clearer the constraints and goals, the more aligned the prototype.

Rather than replacing the design process, Figma Make can complement it. Teams can start in Figma Design to create assets, then export or reference those in Make for interaction. They can use Make to generate early-stage prototypes for stakeholder buy-in or rapid testing, then return to Design to refine the visual layer once structure and flow are validated. This cyclical approach helps maintain a balance between creative freedom and systematic design governance.

Maintaining design-system integrity

A recurring challenge in AI-generated design is the drift from established brand guidelines. Make's integration with Design Systems and Libraries helps counter this by enabling projects to stay linked to predefined styles. Teams should ensure that:

• The organisation's design tokens (colour, spacing, type styles) are imported before generating prototypes.
• Generated components are reviewed against existing patterns before they enter production.

As a result, design consistency is maintained even when AI assists in generation.

Implications for collaboration and workflow

How Make is reshaping collaboration

Figma Make promotes a shared understanding across design, marketing and development teams. Because prototypes are interactive, non-technical stakeholders can provide feedback without needing engineering builds. This speeds up decision-making and minimises misinterpretation of design intent.

While Make can create functional prototypes, it doesn't remove the developer's role. The code it produces is intended for demonstration and testing, not direct deployment. However, it offers developers a clearer reference of desired interactions, reducing guesswork and improving implementation speed.

By giving non-engineers the ability to realise interactive ideas, Make supports a more inclusive approach to design iteration. Product and marketing teams can explore ideas collaboratively, not sequentially.

Challenges and limitations

Dependency on good prompts

The quality of the outcome is directly tied to the quality of the prompt. Poorly written prompts can lead to awkward layouts or missing functionality, so teams still need skilled practitioners who understand design systems, UX principles and the logic of user flows.

Early-stage tool maturity

Although now generally available, Figma Make remains a young product. Some users report limited control over complex behaviours, variable fidelity when integrating data and occasional constraints on exporting generated code. These issues are expected to evolve as the platform matures.

Ethical and intellectual-property considerations

Make relies on large-scale language and generative models. Teams using it for commercial projects must understand how data is handled, what is stored and how intellectual property is preserved - particularly when prompts or design assets include client material.

What this means for professional training

The growing need for hands-on ai training

Designers, UX practitioners and marketers who learn to use Make effectively will find they can accelerate concept development, strengthen collaboration and bring ideas closer to production-ready form. Yet success depends on understanding both AI prompting and the underlying logic of interactive design.

Training therefore needs to cover:

• Writing effective prompts aligned to user and business goals.
• Managing design systems and libraries within Make.
• Testing and refining prototypes using real or simulated data.
• Communicating results clearly to developers and stakeholders.

As Figma integrates more AI capabilities - from Make to FigJam AI and beyond - digital professionals must continually update their skills. What today seems innovative soon becomes expected competence. Understanding how to blend design sensibility with AI capability will increasingly define professional advantage.

The future of AI-assisted design and prototyping

Emerging research suggests a growing convergence between design, development and data-driven iteration. Tools such as Figma Make act as early indicators of this convergence - bridging creative thinking and functional delivery.

In practice, this means:

• Fewer barriers between concept and prototype.
• Greater experimentation during early design stages.
• Increased expectation that marketing and UX teams understand both design logic and AI-assisted generation.

Conclusion

Figma Make represents more than just another AI feature within Figma; it marks a shift in how design and marketing teams approach early-stage creation. By combining prompting, design-system intelligence and prototype generation, it reduces the technical gap that once separated creative and engineering workflows.

However, effective use of Make still relies on human skill - from prompt precision and design-system management to clear stakeholder communication. As with all AI-driven tools, its success depends on the expertise of those who use it. For organisations aiming to stay competitive, investing in continuous professional learning and hands-on training will be essential to harnessing these new capabilities effectively.