Artificial intelligence has ceased to be a distant promise and has become a tangible reality in the daily lives of web designers and developers. From collaborative design platforms to AI-assisted code editors, the latest tools are radically transforming workflows. In this article, we explore the latest AI features officially integrated into Figma and FigJam (between 2024 and May 2025)—leaving aside third-party plugins—as well as the latest developments in Cursor, Anysphere’s AI-powered code editor. We’ll look at how these advancements bring strategic value to design and development teams, accelerating ideation, component creation, documentation, and bridging the gap between designers and developers. Finally, we’ll also review how AI is driving improvements in web testing and optimization.

Design: AI integrated into Figma and FigJam

The arrival of AI in design tools has been led by Figma, which since late 2023 and throughout 2024 has incorporated intelligent assistants into both its traditional design canvas and FigJam, its collaborative brainstorming space. Figma’s goal has been clear: to eliminate creative blocks and automate repetitive tasks, allowing designers to focus on solving problems and exploring ideas.

FigJam with “Jambot” and ideation assistants

At the end of 2023, Figma introduced new AI capabilities in FigJam, designed to facilitate brainstorming and planning sessions. It’s now possible to start from a blank page and, with just a natural language request, obtain a customized FigJam file in seconds. For example, we can ask “I need a board to plan a sprint with my team,” and FigJam will automatically generate an initial template with the appropriate structure. This assistant—initially dubbed Jambot in the experimental phase—can generate diagrams, timelines, or organizational charts based on common best practices, adapting to the context we need. Likewise, FigJam can summarize the content of a group brainstorming session by reading the sticky notes created by participants, extracting the key points of the discussion. There’s no longer any need to spend long minutes after a meeting synthesizing conclusions: AI can do it instantly, identifying recurring themes and team takeaways. Another very useful feature is the ability to automatically sort and group sticky notes by topic or category, detecting patterns in the proposed ideas.

This way, if dozens of notes are generated during an ideation session, the AI can organize them by affinity (e.g., “process improvements,” “technical issues,” “UX suggestions”), leaving the wall much cleaner and more understandable. These FigJam capabilities act as a facilitator in meetings and workshops: any team member, even without visual design skills, can contribute using simple language, and the AI is responsible for “bringing down to the canvas” those ideas in the form of visual templates, summaries, or organized lists. In the words of its creators, this “lowers the barrier to entry and raises the ceiling” for visual collaboration, making it easier for more people to participate while also enabling more advanced results. Ultimately, the AI-powered FigJam becomes a brainstorming companion that accelerates ideation and meeting documentation, ensuring that nothing important falls through the cracks and that there is always a visual starting point upon which the team can iterate.

Figma Design with productivity and generation wizards

Not far behind is Figma Design, the leading interface design tool, which took a leap forward in 2024 with the introduction of Figma AI—a set of intelligent features natively integrated into the app. These new capabilities range from resource discovery to content generation, prototyping, and more, all designed to streamline the designer’s workflow and reduce rote tasks.

AI-powered Visual and Component Search: With Visual Search, Figma lets you find existing designs based on an image or text description. For example, you can select a part of a design or upload a screenshot, and Figma will instantly show you similar designs in your team’s other files. This is invaluable for repurposing previous work or gathering inspiration without having to manually search through dozens of files. In the future, Figma plans to expand this search to the entire community, including results from public files with attribution. Along with this, asset search in the component library has been enhanced with semantic AI. This means that Figma understands the intent behind our search terms and isn’t limited to the exact name of the component. For example, when searching for “primary button,” it will suggest the correct component even if the library names it as btn_large or other internal code. The tool learns from the context and typical usage of elements to offer more accurate results, like a collaborating designer who “knows” what we’re referring to even if we don’t use the exact term. In short, finding that specific component or design in a broader design system is now as easy as describing it with our own words or images.
Content editing and generation (text and images): Another important block of Figma AI are the tools for manipulating and generating content within the design. On the one hand, we have assisted text utilities: it’s now possible to select text in a design and ask the AI to rewrite, summarize, lengthen, or translate it with a single click. This is ideal for iterating on UX writing (microcopy) without leaving Figma, achieving more concise or tonal variants in seconds. “The new text rewriting feature allows me to automate heavy lifting and explore high-fidelity copy ideas faster”, comments a beta tester. Similarly, you can adjust the length of a text to fit perfectly in a designated space or translate content for global products without resorting to other tools. On the other hand, Figma has incorporated realistic visual content generation. Traditionally, designers filled their mockups with lorem ipsum and filler images, but now AI can suggest more contextually relevant text and obtain authentic images, giving presentations much more realism. For example, instead of a simple gray block indicating “image here,” we can request a photo of “a user using a mobile app in a cafe” and instantly get a corresponding image. Or, if we have a recipe design, replace the placeholder with an attractive photo of the dish mentioned. This improves the quality of prototypes and helps better communicate design intent, as the content is closer to what it would be in production. Additionally, a handy built-in background removal feature has been added: with one click, we can remove the background from a photo (for example, of a product) without leaving Figma, isolating the element and enabling cleaner scenes. All of these capabilities, converted into quick actions within the editor, mean that designers can iterate on true-to-life content without relying on external editors, gaining speed and maintaining focus on the design.
Prototyping and UI Generation: One strategic area where Figma’s AI shines is in bridging the gap between static design and an interactive experience. The new “Make Prototype” feature allows, with a single command, a series of static screens to be transformed into a navigable prototype with interactive connections. The AI infers possible flows (for example, linking the “Home” button to the login screen, etc.) to quickly bring the design to life. We can then preview that prototype directly on the design canvas itself, which greatly speeds up revisions and adjustments. While designers could previously create prototypes manually, this automation speeds up the creation of a functional prototype for demos or initial usability testing with the click of a button. Another much-discussed new feature is the design generation assistant based on natural language prompts, known in beta as “Make Designs”. Located in the action panel, this wizard lets us describe what we want—for example, “a user profile page with a photo, name, details, and an edit option”—and the AI will generate a first draft of that UI: a layout with layers and components aligned with the description. Essentially, it creates high-fidelity sketches from text, tackling head-on the “blank page syndrome” that sometimes hinders project launches. While it doesn’t aim to deliver a perfect final design, it does provide a foundation on which to iterate, saving time on the initial setup of grids, panels, buttons, and so on. An interesting detail is that Figma plans for this generator to leverage familiar design systems: it can currently use standard components (e.g., Google’s Material Design 3), and later even incorporate an organization’s own design systems to generate interfaces that are already aligned with the company’s brand and default components. Imagine asking for a “corporate-style login screen” and having the AI directly use the colors, fonts, and patterns from our internal design library—that’s the direction they’ve charted. While features like automatic prototyping and UI generation are groundbreaking and will continue to improve, Figma envisions them as a “new creative starting line,” rather than a replacement for the designer. That is, the AI quickly takes you to a working draft, but then the designer’s expert eye will polish and take that design to the next level. The important thing is that the time saved on assembling screens or connecting initial flows can be invested in thinking up better solutions, testing variations, or refining details that really matter.
Other smart aids in Figma: A special mention goes to those small, routine tasks that AI now handles in a snap. One example is automatic layer renaming. Who hasn’t ended up with a file full of layers called Rectangle 123 or Frame 45? Now, with a single click, Figma analyzes the content/function of each layer and assigns it a descriptive and coherent name. A button might be renamed “Button/Primary” or a text field to “Input/Email,” following conventions that the team understands. This “triviality” actually saves hours of tedious work on large projects and leaves files clean and ready for development, something crucial for handoffs with developers. Along the same lines, there are also commands to, for example, automatically adjust the distribution of elements or clean vectors, supported by AI but transparent to the user. Finally, it’s worth noting that all of these Figma AI assistants are in free beta until 2024 while details are being fine-tuned. Figma has emphasized its responsible approach: the AI models it uses are pre-trained third-party models (they haven’t been trained with users’ private files), and they have established controls so that companies decide whether their data can be used for future training. The priority is to provide practical value while maintaining trust, so that design and development teams adopt these aids as a natural part of their workflow, knowing that their privacy is respected.

Strategic value (designers ⇄ developers)

AI capabilities in Figma and FigJam not only streamline the work of individual designers, but also improve collaboration within the product team. For one thing, tools like layer renaming, realistic content insertion, and smart component search make design files more understandable for developers. A tidy file with clear names and realistic text and images reduces friction when a developer enters Dev Mode to inspect elements. In addition, functions such as prototyping or the future conversion of designs into code (Figma Make) aim to close the gap communication between design and development. In fact, in May 2025, Figma announced an improved Dev Mode and “Figma Make”, a prompt-to-code tool that takes designs or natural language descriptions and automatically turns them into working prototypes or even application code. This allows anyone—designer or developer—to quickly experiment with an idea and produce a tangible result, greatly shortening the “design to production” cycle. Furthermore, in the early conceptualization phase, AI-powered FigJam more closely engages stakeholders (product, marketing, customers, etc.) as anyone can contribute text, and the tool transforms it into something visual. By summarizing meetings or automatically generating minutes, it eliminates misunderstandings and accelerates decision documentation. In short, Figma’s “design assistants” don’t isolate designers in a futuristic bubble, but rather serve as a bridge and a catalyst: designers can iterate more and show work sooner, and developers receive more complete and consistent assets. The result is teams with more fluid communication and projects that move forward at greater speed, with AI taking on the role of “tireless teammate” that handles the tedious work so humans can focus on creativity and high-level problem-solving.

Development: Next-generation code assistants

In the field of web development, AI is also revolutionizing tools, especially with the emergence of increasingly sophisticated code assistants. Until recently, GitHub Copilot dominated the conversation as the emblematic example of AI writing code alongside the programmer. However, alternatives and developments have emerged that expand what a “code copilot” can do. One of the most notable offerings is Cursor, a full-featured code editor built with AI in mind, which promises to be an always-available “co-programmer.” Let’s look at what’s new in this area and how these tools compare.

GitHub Copilot and its evolution

For context, Copilot (commercially released in 2021-2022) laid the groundwork for AI-based code assistance: real-time line or block suggestions as the developer types, powered by language models trained on large amounts of source code. Copilot is primarily integrated as an extension into popular editors (VS Code, JetBrains, etc.) and has been shown to boost productivity by auto-completing entire functions from comments or clues in the code. However, Copilot originally focused on inline suggestions that were quite contextual to the open file. In 2023-2024, GitHub introduced Copilot X, a series of improvements including an in-editor chat to ask the AI more complex questions (e.g., “explain what this code does” or “fix this bug and optimize this feature”) and features like Copilot for Pull Requests (which helps generate PR descriptions or even spot potential issues). They also added integrations with the terminal: for example, Copilot CLI allows you to describe an action in natural language (“create a new React component called X”) and get the command or steps to achieve it.Some support for multiple file editing has even been introduced via special commands (Copilot “Edits”), where one can request something like “rename this variable project-wide” or “extract this logic into a reusable module,” and Copilot will attempt to apply the changes to the relevant files. However, these Copilot extensions have been incremental and somewhat limited. For example, the multiple file editing feature still requires manually defining the set of files to edit and, according to reports from early users, can be slow or fail to function in some complex cases. In short, Copilot has evolved from a simple autocomplete to an assistive ecosystem (chat, CLI, contextual suggestions, etc.), but it remains somewhat dependent on how the user invokes it in each case and the limits of the environment in which it is installed.

Cursor: A natively integrated AI-powered IDE

In contrast to Copilot’s incremental approach, Cursor is a development environment (IDE) built from the ground up to take full advantage of AI in every corner. Developed by the startup Anysphere, Cursor is based on the open-source editor VS Code (in fact, it’s compatible with its extensions, themes, and shortcuts, so its look and feel are familiar to developers). Building on this familiar foundation, it integrates a set of advanced features that take the “Copilot++” idea to the next level. Key features include:

Intelligent multi-line autocompletion (“Tab Tab Tab”): Cursor excels at proactive code prediction. As we type, it not only suggests the next line, but can anticipate entire blocks of code that we might need, often chaining together multiple logical lines of a function. In fact, users comment that it sometimes seems to “read minds” because it hits exactly the implementation they were thinking of, to the point where they can code “at the speed of thought” by accepting suggestions with Tab . This power comes from the fact that Cursor analyzes the entire project for its suggestions, not just the current file. For example, if we reference an unimported function in a file, Cursor not only suggests it, but automatically adds the necessary import to the beginning of the file. It also detects the project context (dependencies, framework, code style) to adjust its suggestions. The result is a very fluid writing flow: the developer types one part and Cursor literally types the rest, including library integrations, with minimal human intervention.
Natural Language Edits and Refactorings: Beyond completing new code, Cursor allows you to modify existing code using natural language prompts. This feature, similar to having a helper to whom you say, “Hey, make this function asynchronous and add error handling,” takes the form of an integrated chat panel or specific commands. Thanks to its deep understanding of the codebase, Cursor can automatically rewrite and improve sections of code based on user instructions. For example, if you have a somewhat messy block of code, you can ask, “Optimize this code using best practices,” and the AI will refactor it, correcting common or stylistic errors. Even if the code was written “hastily” with minor errors, Cursor can polish it to improve its quality and readability. Important: Before applying changes, the developer can always review the proposed diff, maintaining control over the final result.
Complete code understanding (full context): A key advantage of Cursor is its ability to analyze the entire project, building an internal map of classes, functions, and dependencies. This allows it to do something very powerful: answer questions about the codebase or navigate it intelligently. For example, we can ask it in the integrated chat, “Where is the processPayment function defined and how is it used?” and the AI will provide the answer with file references. Or we could say, “Open the User model,” and it will immediately take us to the correct file. This awareness of the entire code also enables it to make consistent changes across multiple files when asked. Unlike Copilot, which primarily sees what’s in the editor at the time, Cursor maintains the entire structure in context. In fact, it supports multi-file editing: if we ask it to rename a component or change a protocol, it understands which files need to be changed together and does so, taking dependencies into account so as not to break anything. The developers describe this capability as having an AI-powered “smart search engine” and “global refactor”, streamlining maintenance tasks that would normally involve manual searches and error-prone replacements.
Contextual Chat and File Attachment: Cursor includes an AI chat panel (accessible with a shortcut like ⌘+L) similar in spirit to Copilot Chat, but with some interesting twists. The chat is always aware of the file you have open and the latest changes made to it, so your questions receive answers with immediate context. You can ask for explanations of a feature, suggestions for improving a certain block, or help fixing a bug, and the response will take into account the current state of the code in question. Additionally, if you need to provide more information, you can drag and drop files or even entire folders into the chat window for the AI to consider. This is useful if, for example, you want to discuss a part of the system but also provide a relevant configuration file: the model will incorporate both. In short, Cursor’s chat acts as a virtual pair programming partner, whom you can interrogate about your own project and receive answers with contextual knowledge—something that, until now, only a human colleague familiar with the repo could do.
Entire Project Generation (Composer) and Automation: Cursor takes assisted generation beyond the individual file with a feature called Composer. This tool is capable of creating components or even entire applications from a description. For example, we could say “Create a task-based CRUD app with React and Node.js” and Composer will generate the project structure, with its frontend, backend, configuration files, and so on, writing coherent code that fits together. It does all of this with our open project in mind (in case it needs to be integrated) and general good practices. It’s like an AI-guided smart template. But perhaps most impressive is the Cursor Agent: a sort of autonomous mode that is activated within Composer (⌘+.). Cursor Agent can take a high-level task and break it down into subtasks, execute terminal commands, create files, and coordinate changes with almost no human intervention. It basically acts as a junior developer who follows orders and performs repetitive work: for example, if we ask it to “install library X, configure the test database, and run migrations,” Cursor Agent will do all of that sequentially. This approach is reminiscent of some experimental autonomous AI agents, but integrated in a controlled way into the IDE. Although it sounds futuristic, in practice it saves time setting up environments or executing mechanical tasks. It is worth mentioning that Cursor also offers assistance in the conventional terminal (invoked with ⌘+K): if we don’t remember a command, we can describe what we want (“initialize a new npm module”) and the AI suggests/executes the appropriate command. This kind of cross-integration (editor + chat + terminal) turns Cursor into a unified development environment with omnipresent AI.
Privacy and Performance: One advantage of Cursor for many companies is that it offers advanced privacy and security options. We can activate a “Privacy Mode” in which our code is not sent to remote servers for AI processing, but rather certain models are kept locally or with techniques that safeguard sensitive information. Additionally, Anysphere (the company behind it) has a SOC 2 certification, indicating a level of maturity in enterprise security. This contrasts with Copilot, which, while it encrypts and carefully manages code, does not offer a completely private mode unless very specific self-hosted solutions are installed. On the performance side, users have noted that Cursor is very agile in its suggestions, using state-of-the-art “frontier” models (such as GPT-4 or similar) combined with optimized proprietary models. In short, we perceive an engineering approach very oriented towards maximizing real utility for the developer, reducing friction (importing VSCode extensions easily, immediate cross-platform support, etc.) and incorporating AI where it really contributes (not just autocomplete, but search, chat, terminal, refactoring…).

Cursor vs Copilot in practice

What are the notable differences between using Cursor as your primary environment or adding Copilot to your usual editor? First, the depth of integration: Copilot acts as an intelligent plugin within VS Code or other IDEs, while Cursor is the editor, molded around AI. This means that with Copilot, one tends to work file by file, asking for local suggestions or using the chat for specific questions; however, with Cursor, the AI is more globally present, facilitating operations that involve the entire project (multiple files, configurations, etc.) with fewer manual steps. For example, if we wanted to refactor a component used in 5 modules, with Copilot Chat we could do iterating file by file, while Cursor could address all 5 in a single instruction, understanding the relationships between them. Another difference is proactivity: Copilot waits longer to be invoked (by writing code or with a comment), whereas Cursor sometimes anticipates the developer’s action, such as when it guesses where you will edit after finishing the current line.

One practical detail mentioned by developers is that Cursor offers checkpointing and easy undo of bulk actions, giving peace of mind when testing its aggressive suggestions (for example, if you applied changes to 10 files, it’s easy to review them one by one and revert if something isn’t convincing). Copilot, because it operates primarily inline, lacks this notion of a consolidated “change package”. In terms of the quality of its suggestions, many consider that Cursor tends to produce code more tailored to the project context thanks to its larger context window and the fact that it may have been specifically trained/tuned for full-blown code editing tasks. That said, Copilot is no slouch, and with GPT-4 in its chat mode, it also produces very advanced responses; perhaps the difference lies in the fluidity of the workflow: with Cursor, you interact more naturally with the AI as part of the IDE (without as much copy/pasting or manual scope selection), which effectively doubles the development speed compared to using Copilot alone, according to user testimonials.

Finally, there’s the issue of ecosystem and cost: Copilot is a subscription service (approximately $10/month) integrated into Github, while Cursor, at the time of writing, offers freemium and paid modalities but allows the use of VS Code extensions, which reduces adoption barriers (you don’t have to give up your favorite linters, debuggers, etc.). In short, Copilot remains an excellent timely assistance tool within your editor of choice, but Cursor represents a more holistic vision, turning the entire editor into a copilot that not only suggests code but also understands and acts on your project as a whole. For a web development team, this can translate into fewer overlooked changes, more up-to-date documentation (because AI can help generate it on the fly), and, above all, less time between idea and implementation, which is pure gold in tight deadlines.

Other options in code wizards

It’s worth mentioning that the landscape of AI assistants in development is broad and constantly evolving. In addition to Copilot and Cursor, there are alternatives such as Codeium, Amazon CodeWhisperer, Tabnine, Replit Ghostwriter, and even specialized assistants for some technologies (for example, Google’s Studio Bot for Android). Each has its pros and cons—some focus on being free, others on support for certain stacks or auto-completion in different IDEs—but they all share the mission of speeding up mechanical coding work. Even OpenAI has recently launched ChatGPT plugins and modes geared toward developers, where you can paste blocks of code or logs and request analysis or corrections. And as we’ve seen, even design tools like Figma are venturing into code generation (with Figma Make). We are therefore entering an era where collaboration between designers and developers is enhanced by AI in both directions: the designer delivers clearer specifications (thanks to automatic documentation/generation), and the developer can use AI to more quickly translate those specifications into functional code. The strategic value is clear: more synchronized hybrid teams, faster iterations, and a quality final product achieved in less time.

Testing: AI to ensure quality at greater speed

The field of testing and quality assurance (QA) is also being transformed by artificial intelligence. Traditionally, testing—whether interface, functional, or unit testing—involves a lot of repetitive work and constant maintenance in the face of application changes. Current tools are incorporating AI to automatically generate test cases, detect errors or anomalies, and even self-heal broken tests after changes to the code or interface.

In front-end and user experience testing, platforms such as Testim (now part of Tricentis) and Mabl stand out, employing machine learning algorithms to facilitate the automation of graphical interface tests. For example, Testim uses AI to create and maintain E2E (end-to-end) tests: when recording a user interaction, the tool intelligently identifies UI elements (beyond static selectors) and generates a robust test case. If the application later changes—say, a button changes its text or position—Testim’s AI can still recognize the element and automatically adjust the test in many cases, avoiding false errors due to minor changes. This is known as test auto-healing and drastically reduces the time spent maintaining test suites. Additionally, these tools can group similar tests, optimize execution flows, and prioritize the most critical ones thanks to intelligent analysis of failure history. Simply put, AI-powered testing systems streamline test creation and make them more resilient to change, which fits well with agile methodologies where the product is continuously evolving.

On the unit and code testing side, programming assistants also play a role. GitHub Copilot, for example, can suggest unit test cases when writing a function specification—even including alternative scenarios and edge values—with just a comment like “// Tests:”. Specialized tools like Diffblue Cover (for Java) generate complete unit tests using AI to infer expected behaviors from existing code. Similarly, language models (GPT-4, etc.) integrated into development environments can analyze a piece of code and point out potential bugs or logical inconsistencies that the developer might miss. Microsoft, for example, has explored integrating AI-powered analysis into Visual Studio to detect typical bugs or suggest performance improvements at compile time, combining traditional static techniques with machine learning.

Performance and security testing shouldn’t be forgotten. Here, too, AI helps identify bottlenecks or vulnerabilities by analyzing patterns. A service like Amazon CodeGuru uses ML to review code for inefficient queries, resource leaks, or insecure practices, providing feedback on developers’ pull requests. Similarly, Snyk Code (formerly DeepCode) applies models trained on millions of commits to point out potential null pointer errors, insufficient data sanitization, and so on, with a very low false positive rate. These “smart” suggestions appear almost in real time while writing code or during review, acting as an extra pair of eyes that never tires.

In practice, AI in testing means we can launch our applications with more confidence and in less time. One possible flow today is: the developer implements a new feature assisted by Copilot/Cursor, then asks the AI to generate basic unit test cases; in parallel, tools like Testim will have already updated the end-to-end tests to cover this new UI flow; finally, before merging, an AI analysis by CodeGuru/Snyk can warn us “hey, in this method you could have an overflow if X condition occurs.” All of this happens automatically or semi-automatically, leaving QA engineers and developers with the more elevated task of deciding what to test (strategy) and verifying the important results, instead of wasting time writing or updating trivial test scripts. In short, AI is turning testing into a smarter and preemptive process, where bugs are caught earlier and more stable versions are released without delaying the development cycle.

Optimization: AI-Powered Performance and SEO

The optimization phase of a web project—whether to improve speed, scalability, or search engine visibility—is also leveraging AI to achieve superior results compared to traditional methods. Two areas where this is particularly noticeable are asset/performance optimization and SEO (positioning) optimization.

Performance and asset optimization

A fast and efficient website depends on a multitude of technical factors: clean code, good resource loading practices, lightweight images, and more. Modern tools incorporate AI to analyze and optimize these aspects more intelligently. For example, the DeepCode platform (now part of Snyk) we mentioned not only finds bugs, but also suggests refactorings that can make the code more efficient. When it identifies an inefficient pattern, it proposes an improved snippet by learning from solutions in thousands of projects. In parallel, services like Cloudinary employ AI to automatically optimize media (images, videos). Cloudinary can generate image variants adapted to different resolutions and devices, choose the optimal format (WebP, AVIF, etc.) in real time based on the user’s browser, and even use computer vision to focus on the important part of the image and crop it without losing essence. All of this reduces page weight and improves loading, without manual intervention by the developer on each asset. Another example: Cloudflare, the well-known CDN service, has integrated ML algorithms into its “Polish” engine to compress images as much as possible without appreciable loss, and into “Early Hints” to predict which resources to preload based on global traffic. We can see then that AI acts as an automated fine-tuner, examining usage and content patterns to squeeze performance where manual use would be impossible given the volume of combinations.

We also find AI in experience optimization. For example, some A/B testing and personalization tools (Adobe Target, Optimizely) have incorporated intelligent recommendation engines that automatically choose the best variation of a page for each audience segment, learning from the results of previous experiments. So, instead of running manual A/B tests one by one, the system tests multiple combinations of designs or content and skews traffic toward the statistically significant winner, or even dynamically personalizes components for different users. This leads to faster and more continuous improvements in conversion or engagement than traditional optimization.

SEO and content optimization

On the web positioning front, AI has become an essential ally for SEO specialists and content marketers. Tools like WordLift apply artificial intelligence to automatically improve a site’s semantic structure and metadata, increasing its visibility in search engines. WordLift, for example, analyzes a page’s content and generates schema.org markup, internal links, and rich structured data without the webmaster having to code them individually. By doing so, search engines better understand what the site is about and can display rich snippets, resulting in more qualified traffic. Similarly, solutions like Surfer SEO or Frase use NLP models to analyze our competitors’ content for certain keywords and suggest ways to improve our texts: what topics are missing, what is the optimal length, the frequency of certain terms is relevant, etc. Basically, they reverse-engineer what Google considers quality content for a given search and present it in the form of actionable recommendations. Another interesting application is assisted content generation: tools like Copy.ai or Jasper are capable of writing SEO-optimized paragraphs on a given topic, including suggestions for attractive titles, meta descriptions with the ideal length, and even a selection of suggested images. This speeds up the creation of marketing pages or blogs with a high probability of ranking well, although always with human supervision to ensure veracity and the appropriate tone. Finally, AI also helps in traffic and behavior analysis: analytics platforms with ML can detect patterns in user data and find, for example, which steps of the conversion funnel have the most friction or which user segments could be better exploited. From there, they can recommend changes to the UX or content to optimize results (for example, “mobile users coming from campaign X have low conversion, maybe create a lighter version of the landing page for them”).

In conclusion, AI-powered web optimization is a more continuous and automated process than ever before. Instead of performing manual tuning once and crossing our fingers, today we have systems that monitor, learn, and adjust on the fly. Developers and designers benefit because their sites achieve faster performance and better SEO rankings without requiring specialized expertise in each area—the intelligent tool guides them on what to do or does it for them. Of course, it’s essential to continue validating the results (AI can suggest changes, but the final decision and overall strategy remain in the hands of the human team). Used correctly, these tools help unleash the potential of our websites: fast, visible, and adapted to the audience, without wasting resources on endless testing.

Conclusion

The AI tool landscape for web design and development has matured significantly between 2024 and 2025. We’ve gone from experimenting with generative AI to having features officially integrated into our everyday work platforms: Figma offers a creative assistant that accelerates sketching, prototyping, and documentation; code editors like Cursor take assisted programming to a level of complete project understanding; automated testing systems hunt for bugs and adapt to change almost like a team member; and optimization solutions fine-tune every aspect of the product (performance, SEO, UX) with intelligent analysis and adjustments.

For designers and developers, this represents an unprecedented opportunity to focus on what adds true value: creativity, complex problem-solving, and strategic decisions. Heavy, monotonous, or error-prone tasks can be delegated to these “digital copilots,” knowing that we’ll always be in control to validate and correct the course. AI acts as an effort multiplier: where previously a single person could only do so many things in a day, now with proper use of the tools they can do double or triple that, or collaborate seamlessly with other functions (design ↔ development) without the bottlenecks of the past.

Of course, adopting these technologies entails learning new work dynamics and maintaining a critical eye. AI is not infallible and requires guidance: the best solutions will emerge from the symbiosis between human and artificial intelligence. At Doowebs, as a web design agency with many years of experience, we believe that incorporating these advances thoughtfully and uniquely into workflows is key to remaining competitive and offering high-quality products. At the end of the day, tools are just that—tools—and the differentiating touch will continue to come from trained professionals, now empowered with superpowers of automation and prediction.

In short, AI has moved from being the province of futurologists to becoming an everyday ally of web designers and developers. The recent tools we’ve explored demonstrate that work can be more efficient without sacrificing quality or creativity. Below, we offer a comparative table of all these AI tools and assistants mentioned, with their main features and scope of application, as a quick reference guide.

Tabla comparativa reciente de herramientas IA para diseñadores

Tool	Area	Highlighted AI Features
Figma (Figma AI)	UI/UX Design	Visual and semantic component search; realistic text and image generation within designs; text translation and rewriting; automatic screen prototyping; intelligent layer renaming; design/variant generation from descriptions (prompts). Free beta until 2024.
FigJam (Integrated AI)	Collaboration/Ideation	FigJam Assistant for facilitating brainstorming sessions and workshops: creation of boards and diagrams from natural language prompts; generation of meeting templates (syncs, retrospectives, etc.); automatic summary of a session’s sticky notes; thematic grouping of ideas; help you start working from a blank page in seconds.
Figma Make	Design ⇄ Code	New (Config 2025) prompt-to-code tool: Turn high-level descriptions or Figma designs into working web prototypes. Generate frontend/backend code from a selected design or text instructions. Allows editing of the generated code and publishing as a responsive web page within Figma. Helps designers and devs quickly explore ideas without leaving the Figma ecosystem.
Cursor (Anysphere)	Development (IDE)	Native AI-powered code editor (based on VS Code): advanced multi-line auto-completion and contextual auto-imports; natural language command-based editing/refactoring (e.g., “optimize this feature”); built-in code-aware chat for Q&A and contextual explanations; project-wide understanding to apply consistent changes across multiple files; “Composer” mode to generate components or entire applications from descriptions; Cursor Agent capable of automatically executing tasks (creating files, running commands); Terminal integration (suggests commands); support for VSCode extensions and data privacy mode.
GitHub Copilot	Development (VS Code, etc.)	GitHub AI Extension: Instantly suggests code as you type (usually one or a few lines) based on local context; completes features from comments; has Copilot Chat for further queries and explanations; Copilot CLI for suggesting terminal commands from natural language; ability to apply batch edits using the Multifile/Edits feature (though with scope and somewhat limited performance); GitHub integration to suggest descriptions for Pull Requests and detect potential issues in reviews. It has been shown to significantly speed up coding, although it mostly suggests boilerplate solutions.
Testim (Tricentis)	Testing UI	AI-powered test automation platform: records user interactions and generates robust automated tests; uses machine learning to identify UI elements (avoiding reliance on fragile selectors); self-maintenance of tests: if the UI changes (text, position, etc.), it automatically updates test cases in many cases; AI-powered test suite grouping and optimization; visual interface for creating complex test flows without extensive scripting. Focused on web application QA with rapid release cycles.
Cloudinary (AI optim.)	Performance Optimization	Media management service (images/videos) with AI capabilities: automatic image optimization (selecting the best format and compression for each client); smart resizing and cropping (detecting image focus and cropping without losing key content); dynamic generation of responsive versions of images; for video, it allows for selecting the best thumbnails using computer vision. These optimizations reduce weight and increase loading speed without manual intervention. Cloudinary applies AI models to optimally balance visual quality and performance.
DeepCode (Snyk Code)	Code Optimization/QA	AI/ML-powered static analysis tool: Scans code repositories for bugs, vulnerabilities, and inefficient patterns; learns from a large open-source database to detect common errors (null pointers, leaks, bad practices) with high accuracy and few false positives; suggests performance improvements and refactorings based on how other developers have solved similar problems; integrates with IDEs and CI to provide continuous feedback to developers. Helps maintain a high-quality, high-performance codebase.
WordLift	SEO Optimization	AI-powered SEO assistant focused on structured data and content: analyzes web pages and automatically generates metadata and semantic markup (schema.org) to improve search engine understanding; suggests contextual internal links; builds a site knowledge graph (entities, topics) to enhance content relevance; and can be integrated into a CMS. Essentially, it acts as an intelligent SEO tool that optimizes site structure and increases visibility in search results without requiring manual developer intervention on every detail.

Each of these tools addresses a specific point in the web design/development workflow, but together they create an ecosystem where AI is present from start to finish: from the first sketch in a brainstorming to the production launch and continuous optimization of a site. Adopting them with a strategic vision will allow teams to take advantage of the benefits of automation while maintaining creative control, achieving more robust, innovative, and time-saving final products. The real beneficiaries will be both professionals—who will dedicate their talents to higher-impact tasks—and end users, who will receive higher-quality digital experiences thanks to this new generation of intelligent tools.