AI in Product Design: Worth it?

If you are a product designer working today, you might be looking at the whirlwind of news about Artificial Intelligence and feeling a quiet sense of unease. You might ask yourself, “Am I missing something here, or is this mostly worthless?”. You see demonstrations of tools that spit out decent-looking screens, but they feel hollow. They are okayish flows that you need to spend a lot of time editing afterwards. If you feel this way, please know you are not alone, and you are not missing anything. Your assessment is, for the most part, the correct read on the state of things.

This moment of confusion and hype is not new. With every major technological shift—the internet, the smartphone, the cloud—we have passed through this same valley of noise. We are, as some observers have noted, deep in the “panic and excitement phase”.¹ AI is neither a magical solution that will solve all our problems, nor is it a passing fad that will amount to nothing. It is a powerful, immature technology that is slowly finding its place in the world, and our job is to navigate its arrival with wisdom and care.¹

A major part of the unease comes from a disconnect between what is promised and what is delivered. We see a flood of marketing from AI companies and breathless commentary from influencers, all promising a revolution in productivity. This message is often heard and amplified by leaders and executives who may not be deeply familiar with the craft of design. They see a potential tool that can produce results that seem “good enough” and, most importantly, might save the company money.¹

But for you, the practitioner, the guardian of quality, “good enough” is often not good enough at all. You see the flaws. You see the generic layouts, the accessibility failures, the components that don’t scale, and the user flows that lack a soul.¹ You understand that quality is not just about a plausible-looking screen; it is about deep user understanding, meticulous craft, and building systems that are robust, scalable, and maintainable. The tension you feel is born from this difference in perspective. The push for AI-driven speed can feel like a direct threat to the very quality that defines your professional life.

This is why your skepticism is not a weakness; it is a strength. It is a necessary and vital defense of your craft and your users against a rising tide of mediocrity. Your critical eye is the compass that will help your team, and your entire organization, navigate this new landscape. This journey is not about resisting change. It is about guiding it. It is about finding the real, tangible value that AI can offer while holding fast to the principles of quality and human-centeredness that give our work meaning. Together, let us walk this path, moving from the current state of confusion toward a future of clarity, where technology serves our craft, not the other way around.

The hesitation many seasoned professionals feel toward AI stems from a blend of practical frustration, professional pragmatism, and powerful ethical concerns. These are not the objections of Luddites, but the critical questions of craftspeople who are accountable for the quality and impact of their work.

There is the simple matter of practical frustration. Many designers have experimented with AI tools and found the experience underwhelming.⁴ Early iterations of generative AI were known for producing odd, impractical, or generic results.⁵ Even today’s more advanced models often generate flawed outputs that require more time to fix than they save.⁶ Designers on forums describe the output as “dystopian nightmare fuel” or complain that it misses the point entirely, producing work that is technically plausible but creatively and strategically bankrupt.⁸ For many teams, the biggest hurdle is even more basic: corporate IT departments, wary of security and data privacy, often block access to popular tools like ChatGPT, making widespread experimentation impossible in enterprise settings.¹⁰

There is a deep-seated professional pragmatism. In high-stakes environments where products serve millions of users and generate billions in revenue, “plausible-looking” is a dangerously low bar. AI models are known to “hallucinate”—to invent facts or produce information that is confidently wrong.¹¹ In the context of product design, this can manifest as an AI-generated image of a passenger drone that is physically unmanufacturable, with an uneven number of propellers and no doors for safety.¹² In software engineering, it can mean AI-generated code that looks correct but calls non-existent functions or violates a company’s internal architectural patterns, leading to system failures.¹³ The constant need for rigorous human oversight, validation, and post-production editing to ensure quality, legal compliance, and brand alignment makes the uncritical adoption of AI a major risk.⁵

Finally, the deepest form of skepticism is rooted in philosophical and ethical opposition. This perspective questions the very foundation of the technology and its societal impact. These concerns are layered. They include the shocking amounts of energy and water consumed by data centers to train large language models, which often conflicts with organizational sustainability goals.¹ There is also the unresolved ethical and legal issue of AI models being trained on vast troves of copyrighted material—the work of artists, authors, and journalists—without permission or compensation.¹

Perhaps most importantly, these skeptics worry about the second-order effects on society. AI systems, trained on data from the internet, can inherit and amplify existing human biases related to race, gender, and culture.⁵ There are powerful concerns that this technology will be used to power scams on an unimaginable scale, destabilize labor markets faster than society can adapt, and concentrate immense power and wealth in the hands of a few unaccountable companies.¹⁵ This is not a skepticism of the technology’s capabilities, but a deep and warranted skepticism about how those capabilities will be used and who will truly benefit.¹⁶

This widespread doubt among creative and technical professionals should not be dismissed as fear. It can be understood as a healthy and necessary immune response from a community that has witnessed previous technology cycles. The tech industry has a history of taking on interesting technical challenges, only to make moral and ethical trade-offs that end up shaping society for the worse.¹⁵ The promises of social media to connect the world and foster democracy, for example, were later shadowed by its role in spreading misinformation and disrupting democratic processes.¹⁷

From this perspective, the cautious approach of many designers and engineers is not a barrier to be overcome, but a vital part of the design process itself. The data shows that while experimentation is high, deep integration of AI into workflows remains low, suggesting a “wait and see” approach rather than outright rejection.¹⁰ This reflects a strategic posture, one that prioritizes asking critical questions such as, “Is AI even necessary to solve this problem?”.¹ This skepticism is a form of professional diligence—an ethical and quality check that ensures we build things responsibly, with a clear understanding of their potential consequences. It is not a sign of resisting the future, but of carefully and intentionally shaping it.

Part 1: Where Can AI Genuinely Help Me Today, Without the Hype?

In the midst of the grand promises of a complete design revolution, it is easy to miss the quiet ways AI is already making a real, practical difference. The most mature and reliable uses of AI today are not about replacing the core creative act of design. Instead, they are about taking on the role of an incredibly efficient assistant, one that can lift the burden of our most repetitive and time-consuming tasks, freeing our minds and schedules for the deep, focused work that only humans can do. Let’s set aside the hype and look at the areas where AI is already a trustworthy partner.

The answer to this question is a clear and resounding yes. Of all the areas in product design, the field of user research and research operations has seen the most immediate and transformative benefits from AI. This is because AI’s greatest strength lies in its ability to process and find patterns in vast amounts of data at a speed no human could ever match. It doesn’t replace the empathetic heart of the researcher, but it gives that heart a powerful new set of hands.

The most powerful impact is on the synthesis of qualitative data. Imagine you have just completed a survey with a thousand open-ended responses. In the past, a researcher would have to spend days, or even weeks, manually reading, tagging, and clustering this feedback to find meaningful themes. Today, AI tools can perform this task in minutes. Platforms like Hotjar AI can take a mountain of user survey responses and automatically generate a summary report that identifies the key findings and even suggests actionable recommendations. Similarly, tools like Miro Assist can look at a virtual whiteboard filled with digital sticky notes from a brainstorming session and instantly cluster them by keyword or user sentiment, bringing order to chaos.

This automation extends to the entire research workflow. Consider the process of conducting user interviews. A modern, AI-assisted workflow might look like this: you record the video call using a tool like Zoom, then upload the recording to a transcription service like Otter.ai, which uses AI to create a near-perfect text transcript in minutes. But the magic doesn’t stop there. You can then feed this transcript into a large language model like ChatGPT and begin a conversation with your own research. You can ask it to “pull all the direct quotes where the user mentioned frustration with the checkout process” or “summarize the user’s main pain points in five bullet points”. This transforms the painstaking task of scrubbing through recordings into a dynamic process of inquiry, allowing you to get to the core insights faster.

Beyond individual studies, AI is also accelerating the broader field of research operations. As you rightly suspected, this includes everything from sourcing candidates to analyzing the data. For example, platforms like Hotjar Engage give researchers access to a pre-screened pool of over 200,000 participants, making it easier to find and recruit diverse users for studies. Some of these platforms can even automatically transcribe interviews into dozens of languages, breaking down international barriers and capturing nuanced cultural insights that might have been missed before.

The return on these investments is not just theoretical. At the enterprise level, companies are already seeing powerful results. The research and operations leaders at Autodesk, for instance, have shared their experiences in creating and implementing a dedicated AI playbook to scale their UX research efforts and impact. In another case, a hospitality company was struggling because its guest feedback data was stored separately from its booking data. By using Natural Language Processing (NLP) to unify and analyze these two datasets, they were able to uncover a direct link between guest sentiment and behavior. This analysis revealed actionable patterns—such as discovering that sleep quality was a powerful driver of loyalty—which led to product changes that resulted in a 10% lift in both Net Promoter Score (NPS) and customer conversion.³

What we are witnessing is not the devaluing of the research profession, but its elevation. The work of a researcher has, for too long, been bogged down by logistical and almost clerical tasks: scheduling, transcribing, counting, and sorting. While necessary, these tasks are not where the true value of research lies. By handing off this grunt work to AI, we free the human researcher to focus on their most vital and irreplaceable skills: designing brilliant studies, asking insightful questions, exercising deep empathy during an interview, and, most importantly, weaving the data points surfaced by AI into a compelling strategic narrative that guides the entire product team. The AI can tell you what users are saying; only a human can truly understand why and determine so what.

Beyond the world of user research, AI is proving to be a capable assistant for a variety of other repetitive tasks that pepper a designer’s week. These may seem like small wins, but they add up, saving time and reducing the friction of turning an idea into a high-fidelity prototype. Many of these applications are already being used effectively in large design organizations, such as the ones for copywriting, translation, and content testing used at Delivery Hero.

AI-Assisted Copywriting

One of the most common bottlenecks in prototyping is the need for realistic text. Using “lorem ipsum” placeholder text can only get you so far; a design doesn’t truly come to life until it’s filled with meaningful copy. AI can now act as a first-draft copywriter, instantly populating your designs with text that matches your brand’s established tone of voice. This is more than just a time-saver; it allows for more realistic and persuasive prototypes that can be tested with users and presented to stakeholders.

This capability is rapidly being integrated directly into our primary design tools. The Figma plugin “UX Writing Assistant” by Frontitude, for example, acts as a “wordsmith teammate” right inside your design file. It can help you brainstorm copy ideas, rewrite existing text to be more concise or fit a different tone, and even check your work against your team’s established content guidelines, all without ever leaving Figma. This tight integration streamlines the workflow and empowers designers who may not be confident writers to produce higher-quality work. Other standalone tools like Jasper.ai and Copy.ai offer similar, powerful features for generating everything from button microcopy to full-length marketing articles.

Design Localization

For any company operating in a global market, localizing a product is a massive undertaking. In the past, creating versions of a design for different languages was a painstaking, manual process. AI has dramatically simplified this. It’s now possible to take a finished screen design and, with the help of AI, instantly duplicate it into multiple translated versions. This allows designers and developers to see how the UI will look with different languages and character sets early in the process.

This has evolved far beyond simple plugins. There are now entire AI-powered localization platforms like Crowdin, Smartcat, and Lokalise that offer end-to-end automation. These platforms can integrate directly with your design tools (like Figma), your code repositories (like GitHub), and your content management systems. They create a continuous localization pipeline, where new designs or copy changes are automatically sent for translation and then integrated back into the product, dramatically reducing manual effort and speeding up global releases.

Content Stress-Testing

Every experienced designer knows the pain of a perfectly crafted component breaking the moment it’s filled with real-world content. A user has a longer name than you anticipated, a marketing headline is shorter than expected, or an uploaded image has an unusual aspect ratio. AI provides a powerful new way to “pressure test” designs against this kind of variability. You can use AI to automatically populate your components with a wide range of content—very long text, very short text, text in different languages, images of different sizes—to quickly see where your layouts truncate, wrap awkwardly, or break entirely.

This idea connects to a more advanced practice: automated visual regression testing. When a component in your design system is updated, AI-powered tools can automatically compare a screenshot of the new version against an approved baseline image of the old one.⁴ What makes these tools “smart” is their ability to distinguish between meaningful changes and irrelevant noise. They can be trained to ignore tiny, pixel-level rendering differences but flag important regressions like a change in padding, a color contrast failure, or a typography error.⁴ Some tools can even simulate how different audience personas might perceive your creative content, giving you an early warning if a design might be misinterpreted.⁵ This acts as an automated quality assurance check, catching bugs and inconsistencies before they ever reach the user.

To make these applications more concrete, here is a summary of some of the most practical and reliable AI tools available to product designers today.

Tool Name	Specific Use Case	How It Helps	Pricing Model
Miro Assist	Clustering virtual sticky notes, summarizing board content, generating diagrams.	Instantly organizes messy brainstorming sessions and research synthesis boards, saving hours of manual sorting.	Freemium, Paid Tiers
Hotjar AI	Analyzing and summarizing open-ended survey responses.	Turns thousands of qualitative comments into actionable reports with key themes and insights.	Included in Paid Tiers
Otter.ai \+ ChatGPT	Transcribing user interviews and querying the text for insights.	Eliminates manual transcription and allows for rapid, conversational analysis of interview data.	Freemium, Paid Tiers
Frontitude	Generating and refining UX copy directly within Figma.	Acts as a “copilot” for UX writing, helping with ideation, rewrites, and enforcing style guidelines.	Freemium, Paid Tiers
Lokalise / Crowdin	Automating the entire design and content localization workflow.	Integrates with design tools and code to create a continuous pipeline for translating and deploying multilingual content.	Paid Tiers
Uizard	Generating wireframes and prototypes from sketches or text prompts.	Accelerates the earliest stages of ideation by turning rough ideas into interactive mockups.	Freemium, Paid Tiers
RehabAI Stress Tester	Simulating how different audience personas will react to creative content.	Provides early feedback on messaging and design, identifying potential misinterpretations before launch.	Custom 5

We now arrive at the heart of the designer’s quiet struggle: the place where the promise of AI clashes with the soul of our work—the design system. This is where the hype feels most disconnected from reality. You want AI to help you build faster, but not at the cost of quality or consistency. You want it to use your carefully crafted components, but the code it generates is often a mess. This is a journey into the two paths emerging for AI-generated UI: the tempting, easy route of public libraries, and the long, difficult road of teaching the machine your own language.

Think of tools like v0.dev by Vercel as a siren’s song for the busy designer. They promise a shortcut, a way to conjure a real, interactive webpage from a simple wish whispered as a text prompt.⁶ They are the first to deliver on this dream, turning a sketch or a few words into usable code, and it is natural to find them “working really cool”.⁶ They represent a tangible leap forward, and it is our duty to understand both their sweet melody and the rocky shores they might lead us to.

The strength of this approach is its breathtaking speed. For rapid prototyping, these tools are remarkable. You can describe a feature, and the AI will generate an entire interface section, like a dashboard header or a checkout form, complete with thoughtful layouts and interactions.⁷ It does this by using popular, open-source component libraries like shadcn/ui and styling them with modern frameworks.⁸ This allows a small team to go from a blank canvas to a functioning prototype in a fraction of the time, generating real code an engineer can use as a starting point.

But here, we must listen to the wiser voice of our craft. The speed is alluring, but it can be a hollow victory. The code is not production-ready. It often has bugs and errors that require major time to refactor.⁷ these tools only build the beautiful facade; they generate the visual layer but none of the underlying logic, database connections, or API functionality that make a product truly work.⁷ An engineer still has to build the entire engine and connect it to the pretty chassis the AI created.

This leads us to the deepest concern: the loss of our unique voice. As one design leader wisely noted, using a tool that relies on a “popular” public library is a choice that risks sacrificing differentiation. When everyone builds from the same set of blocks, it becomes harder to create something that feels special. It pushes us toward an “age of average,” a world of aesthetic sameness that we, as guardians of our brand’s soul, must consciously work against.

This is the essential, heartbreaking question. If AI can build from a public library, why is it so hard for it to use my company’s own, proprietary design system? You are not alone in this frustration. Design leaders at major companies are working on this very problem, and they describe the process as “bothersome” and time-consuming. The reason for this difficulty lies in a few deep and complex challenges.

The reason for this heartbreak is simple, yet powerful. The AI, for all its power, is a stranger in your home. It was trained on the vast, public world of the internet, primarily on open-source code found in public GitHub repositories. Your company’s codebase and design system, But are a private language, a unique dialect spoken only by your team. For the AI model, your system is an unknown tongue. It is “out of distribution” from the data it was trained on.¹⁰

The direct consequence of this is a phenomenon known as “hallucination”.¹⁰ When you ask the AI to generate code using your proprietary system, it tries its best to comply. But it is guessing. It produces code that looks plausible but is often subtly or catastrophically wrong. It might try to call an internal function that doesn’t exist or use properties on a component that are named incorrectly.¹⁰ This is precisely the experience of getting “okayish flows that you need to spend a lot of time editing afterwards”. The AI is not being lazy; it is speaking a language it does not truly understand.

This problem is magnified by the sheer scale and complexity of real-world enterprise applications. AI models today struggle with codebases that span millions of lines of code, lacking the context to understand the intricate web of dependencies and legacy decisions that define a mature product.¹⁰ they often confuse words that look alike for words that mean the same thing, a classic machine error that leads to buggy output.¹⁰

On top of these technical hurdles, there is a minefield of legal and security concerns. Teaching an AI your design system means feeding it your company’s most valuable intellectual property—its “crown jewels”—into a model, often one owned by a third-party vendor. This raises enormous concerns about data privacy, security, and ownership, making legal teams understandably cautious.

The path forward is not easy. Making an LLM truly “understand” a proprietary design system is a monumental effort. It requires creating exceptionally well-structured documentation, building highly modular components, and possibly adopting new standards designed to give AI models more context.¹² This is the frontier where major companies are investing today, but a simple solution is still a long way off. This reality creates a difficult strategic choice. Business leaders, focused on immediate results, will naturally be drawn to the faster, easier option of public libraries.¹ The challenge for the modern design leader is to articulate the trade-offs. The true competitive advantage lies not in using the same public tools as everyone else, but in the long-term, strategic investment in making your own unique design system AI-ready.

To help clarify this strategic choice, the following table compares the two paths side-by-side.

Evaluation Criteria	The Public Library Approach (e.g., v0.dev)	The Proprietary System Approach
Speed to Initial Output	Very Fast. Generates interactive prototypes in minutes or hours.	Very Slow. Requires months or years of dedicated R\&D effort.
Brand Fidelity & Customization	Low to Medium. Bound by the constraints of the public library, risking a generic look and feel.	High. The ultimate goal is to generate UI that is perfectly on-brand and uses custom components.
Integration with Legacy Code	Poor. Generates standalone code that must be manually integrated into existing, complex systems.	High. The model would be trained on the legacy system, allowing for seamless integration.
Code Quality & Scalability	Low. Often requires significant debugging and is not built for enterprise-scale maintainability.	High. Code would adhere to the company’s established quality and scalability standards.
Technical Feasibility & Cost	High Feasibility, Low Cost. Uses existing tools, often with affordable subscription models.	Low Feasibility, High Cost. A major technical and financial investment with significant legal and security risks.

This question touches the deepest fear in any creative heart. The very purpose of our work is to create differentiation, to give a product a unique soul and a memorable presence in the world. Because AI models are trained on vast datasets of existing human-created work, their outputs are inherently derivative. They excel at recognizing and replicating patterns, which often results in designs that feel generic or like an average of everything the model has seen before.⁵ If multiple companies rely on the same popular AI tools, their products and marketing materials risk converging toward a homogenous look and feel, erasing the brand differentiation that design is meant to create.³⁴

So, when we see the rise of AI tools trained on the same massive datasets, it is natural to fear that they will push us all toward a great, homogenous middle. The concern that we are entering an “age of average” is not just valid; it is a critical danger we must actively work to avoid.

The fear is that our vibrant, diverse digital world will fade to a uniform gray, a chorus where every voice sings the same predictable tune. This is the very definition of homogenization, and it is the enemy of authentic branding. We are already seeing a “perceived quality” bump where AI makes it easy to add slick animations and polished effects, but this can come at the cost of genuine originality.¹² Some consumers are even developing an eye for this aesthetic and are turned off by products that have the sterile, uncanny look of AI-generated art.¹³

This leads to a deeper issue: the absence of taste and craft. AI can follow rules and execute instructions with precision, but it cannot replicate human “taste”—the nuanced, intuitive judgment that separates good from great.³⁵ It also lacks “craft,” which is not just about aesthetics but about the thousands of small, thoughtful decisions born from experience, empathy, and iteration.³⁵ A former OpenAI executive noted that “taste is going to become a distinguishing factor in the age of AI because there’s going to be so much drivel that is generated”.³⁵ This sentiment is echoed by founders and engineers who observe that as AI makes execution easier, human taste becomes more important than ever.³⁵

But to see AI only as a force for homogenization is to miss its true potential. The solution is not to abandon the tool, but to change how we use it. The “Age of Average” is not an inevitable outcome of the technology itself; it is the result of a failure of creative strategy. Companies that use AI simply as a cheap and fast generator of final assets will likely end up with generic results. But the companies that win will be those that use AI as a powerful listener at the beginning of the creative process to uncover unique strategic insights.

Think of it this way: a generic prompt given to an image generator will produce a generic image. The quality of the output is limited by the quality of the input. The same is true for product strategy. Instead of asking AI to “design a landing page for a bank,” a more strategic approach would be to use AI for the deep, analytical work that precedes design. For example, AI can be used for:

• Hyper-Personalization at Scale: This is a form of differentiation that was simply not possible before. AI can analyze vast amounts of user data—browsing history, purchase behavior, support interactions—to understand individual preferences. It can then tailor every aspect of the brand experience, from the marketing messages a user sees to the specific UI they interact with, creating a feeling of deep, personal resonance. This is how a company like Netflix moves beyond generic recommendations to guide its entire product and content strategy, creating a highly differentiated experience for each user.
• Uncovering Unique Market Insights: A brand’s position in the market is defined by how it differs from its competitors. AI can sift through immense quantities of market data, social media conversations, customer reviews, and competitor strategies to identify “hidden patterns” and “white space”—unique opportunities that a brand can own. It can tell you not just what your customers say they want, but also what they truly value, based on their behavior.
• Deeply Aligning with Brand Voice: A strong brand has a consistent and authentic voice. AI can be trained on your company’s existing content, brand guidelines, and mission statement. It can then act as a guardian of that voice, analyzing new content to ensure its emotional tone aligns with your values and resonates with your target audience.

In this strategic model, the AI is not replacing the designer; it is empowering them with a brief of unprecedented clarity and depth. The human designer remains the indispensable core of the creative process. It is the human who brings the cultural context, the emotional depth, the empathy, and the imaginative spark that can transform a strategic insight into a beautiful and meaningful product experience. Over-reliance on AI without this human oversight and creative translation is what leads to the dilution of the brand’s soul.

The threat of an “Age of Average” is real, but it is a choice, not a destiny. It is the path taken by those who see AI as a shortcut to bypass creativity. The path to differentiation lies in using AI as a tool for deeper understanding—to map the unique contours of your customers’ needs and your brand’s soul. AI can draw the map, but only a human designer can lead the journey.

The arrival of a technology that can perform tasks once thought to be uniquely human inevitably leads to a period of soul-searching. If an AI can generate a design, write copy, and analyze user feedback, what is left for the designer to do? This is perhaps the most personal and pressing question of all. The fear of being replaced or devalued is real, and it is felt by professionals in many fields, not just our own. The answer, But is not one of despair, but one of evolution. Our purpose is not disappearing; it is becoming more focused, more strategic, and more human.

From Creator to Curator

The ground is shifting beneath our feet, and we must find our new footing. Our role is not vanishing, but deepening, evolving from being primarily “creators” of tangible assets to becoming “curators” of intelligent systems.¹⁴ Other terms for this evolved role include “design arbiter” or “interface strategist”.¹⁵ But what do these new titles really mean in our hearts?

A creator’s primary job is to make things from scratch. A curator’s job is to select, guide, and give context. The designer as curator becomes a gardener. We don’t just accept what the machine grows; we tend the soil, prune the branches, and nurture the ecosystem, ensuring that what blossoms is not just functional, but beautiful and true.¹⁵ We provide the guiding frameworks, the constraints, and the goals within which the AI is allowed to operate, trusting the system to handle the details but never ceding final creative control.¹⁷

This new role places a much higher premium on the virtues that AI cannot replicate. Our value will no longer be measured by our speed in Figma, but by our wisdom and judgment. The most critical callings for the designer of the future will be:

• The Wisdom to Ask “Why?”: The most important question a designer can ask is, “Is AI even the right tool to solve this problem?” In a world where AI is often inserted into products in search of a problem, the designer must be the voice of reason, advocating for the simplest, most elegant solution, whether it involves AI or not.¹⁸
• The Courage to Be the Conscience: Large language models are trained on the vast, messy, and biased content of the internet. They can and do produce outputs that are wrong, misleading, or offensive. The designer must act as the product’s conscience, actively designing against bias, anticipating unintended consequences, and fighting to preserve a human touch in every interaction.¹⁸
• The Spark of True Innovation: At its core, a generative AI is a massive statistical model. It is designed to produce the most likely, or average, response based on its training data. It can reveal patterns we can’t see, but it cannot make the surprising cognitive leaps that lead to genuine, groundbreaking innovation. The designer’s purpose is to push beyond the probable and imagine the possible, ensuring our products don’t simply regress to the mean.¹⁸
• The Heart of Collaboration: As the lines between design and engineering blur, the design process becomes more fluid. The designer’s role is less about handing off a perfect, static mockup and more about inspiring the entire team with user stories and clear design principles. It’s about sharing problems with your engineering counterparts and collaborating to find the best path forward, not just dictating a solution.¹⁷

How Do We Preserve the Human Touch?

Embracing this new identity requires more than a personal mindset shift; it requires a deliberate evolution in how we structure our teams and our processes. For design leaders, the challenge is to create an environment where this new, more strategic form of design can flourish. This involves several key actions:

• Cultivate a Garden of Curiosity: The AI landscape is changing at a dizzying pace. Leaders must give their teams the time and psychological safety to experiment with new tools. This could involve creating dedicated channels for sharing successes and failures or holding regular team meetings to discuss new techniques.²⁰
• Start with Focused Pilot Projects: It is tempting to try and apply AI to everything at once, but this is a recipe for chaos. A much wiser approach is to identify a few high-value, relatively low-risk pilot projects. This allows the team to learn and adapt in a controlled environment.²⁰
• Build Fences of Trust and Safety: With power comes responsibility. Leaders must work with legal and security teams to institute clear policies around the use of AI. This includes rules about data security and intellectual property. The team must also define its own standards for quality control and take responsibility for the final output.²⁰
• Deliberately Add Human-Centered Friction: In a rush for efficiency, it’s easy to automate too much. A wise leader will look at their design process and ask, “Where should we deliberately slow down? Where must a human eye and a human heart be involved?” This might mean requiring a human review of all AI-generated copy or mandating a manual ethics check before a new feature is launched. Preserving the human touch sometimes means intentionally adding friction back into the process.¹⁸
• Mentor the Next Generation: If AI can generate a competent first draft of a UI, how will junior designers learn the fundamentals of layout, typography, and interaction design? Leaders must think critically about how they will mentor and grow their junior talent, ensuring they develop the core skills of curiosity, critical thinking, and collaboration that will define the next generation of great designers.¹⁸

The greatest danger we face is not that AI will make designers obsolete, but that we, as a profession, will fail to redefine and articulate our new, more powerful value. For decades, the tangible assets we produced—the wireframes, the mockups, the prototypes—were the primary measure of our contribution. If we continue to define ourselves by them, our function will inevitably be seen as a commodity to be optimized. The most urgent work for designers today is to change this definition. We must proactively evangelize our new role as strategic partners, ethical guardians, and human-centered curators.

Table: The Human-AI Collaboration Framework

To make this shift practical, teams need a clear model for how to divide labor between human intelligence and artificial intelligence. The following framework breaks down the product design process, assigning tasks to either AI or humans based on their respective strengths. This turns the abstract fear of replacement into a concrete plan for collaboration.

Design Phase	Best for AI (The “What” and “How”)	Best for Humans (The “Why” and “Should We”)
User Research	Transcribing interviews, summarizing long documents, identifying keywords and patterns in large datasets, sentiment analysis. 22	Defining the research goals, crafting empathetic interview questions, interpreting nuance and unspoken needs, building rapport with participants. 24
Ideation	Generating hundreds of layout variations, exploring diverse visual styles, suggesting unconventional color palettes. 20	Defining the core problem to be solved, setting the strategic vision and creative direction, identifying which ideas are truly innovative vs. derivative. 1
Prototyping & UI	Creating high-fidelity mockups from text, building responsive web pages, generating component variations within a design system. 10	Ensuring the user flow is intuitive and emotionally resonant, sweating the details of micro-interactions and spacing, making final decisions on typography and hierarchy (craft). 35
Content	Generating UX copy variations, summarizing articles, translating content into different languages. 4	Defining the brand’s tone of voice, ensuring copy is empathetic and contextually appropriate, writing for strategic impact and emotional connection. 36
Testing & QA	Automating accessibility checks (e.g., color contrast), running automated UI tests, identifying performance bottlenecks in code. 42	Conducting qualitative usability testing, interpreting user frustration and delight, making the final judgment on whether a design is ethically sound and brand-aligned. 5

Part 5: Finding Our Way Forward

Navigating the landscape of AI in product design requires more than just technical skill; it demands wisdom, foresight, and a deep commitment to human values. The path forward is not about blindly adopting every new tool, nor is it about resisting change. It is about finding a thoughtful, intentional way to weave this new technology into the fabric of our work. The best teams are already showing us how.

How Are the Best Teams Actually Using AI Today?

A close look at how leading technology companies are integrating AI reveals a consistent pattern: they are using it to augment human capabilities and solve specific, friction-filled problems, not to replace human judgment or creativity.

• Case Study: Spotify – The Art of Personalization. Spotify’s genius lies in its use of AI to understand user taste with incredible depth. Features like the AI DJ and the iconic Discover Weekly playlist are not about AI writing music; they are about removing the friction of music discovery.⁴⁸ In the past, finding new music that you truly loved was a labor-intensive process of sifting through blogs, record stores, and radio stations. Spotify uses AI to automate this personal music expert. Its machine learning models analyze trillions of data points—what you listen to, what you skip, what you add to playlists—to do the heavy lifting of finding relevant songs.⁴⁹ This allows the user to experience the pure joy of discovery without the work. The core design principle, as articulated by Spotify’s own design team, is to “identify friction and automate it away”.⁴⁸
• Case Study: Airbnb – Synthesizing Insights at Scale. For a platform as vast as Airbnb, making sense of its data is a monumental challenge. The company uses AI not to design its app’s interface, but to make the entire platform smarter and more efficient. For its customer support, machine learning models analyze the transcribed speech of callers to predict their “contact reason” with high accuracy, routing them to the right help articles or agents much faster.⁵⁰ On the host side, AI-powered computer vision analyzes uploaded photos, automatically tagging them with labels like “kitchen,” “pool,” or “ocean view”.⁵¹ This saves hosts manual effort and dramatically improves the search experience for guests, who can filter for specific amenities. In both cases, AI is used to synthesize information at a scale no human team could manage, creating a better foundation upon which the product and design teams can build.⁵²
• Case Study: Microsoft – AI for Accessibility. Perhaps the most profoundly human-centered application of AI can be seen in Microsoft’s AI for Accessibility initiative. This program focuses on using AI to solve deep human needs and break down barriers for people with disabilities. The Seeing AI app, for example, uses a smartphone’s camera and computer vision to narrate the visual world for people who are blind or have low vision, describing people, text, and objects in their surroundings.⁵³ Within Microsoft Teams, real-time captioning and transcription powered by AI make meetings more accessible for individuals with hearing impairments.⁵³ The crucial lesson from this work, as Microsoft emphasizes, is the importance of “designing with, not for,” the disability community, ensuring that the technology is developed in close partnership with the people it is intended to serve.⁵³

What Should We Do Next?

The journey into AI-driven design is unique for every team. There is no one-size-fits-all roadmap. But there are guiding principles that can help any team begin this journey with intention and care. This is not a mandate, but a set of starting points for your own conversation.

1. Foster Curiosity, Not Fear. The most important first step is cultural. The noise and hype around AI can create anxiety and a sense of threat. Leaders must create psychological safety for their teams to experiment, to ask difficult questions, and to express concerns without judgment.² The conversation around AI should be framed as an exploration of a new tool that can help the team, not as a precursor to reducing staff.⁵⁵ Involve employees early by asking them where they see opportunities for AI to improve their own work and the business as a whole.
2. Invest in Uniquely Human Skills. As AI begins to automate routine execution, the most durable and valuable skills will be those that machines cannot replicate. Organizations should actively invest in training and development for their teams in these areas 2:
3. Systems Thinking: The ability to see the big picture and understand how all the parts of a product, a business, and a user’s life connect.
4. Strategic Foresight: The skill of identifying opportunities and defining a vision before a formal brief is even written. This shifts design from a reactive to a proactive function.⁵⁶
5. Deep Empathy & Ethical Judgment: The capacity to understand human emotion and make responsible decisions that prioritize well-being over simple metrics.
6. The Craft of Curation: Developing the expert “taste” and critical eye to distinguish great work from the sea of “good enough” AI-generated content.³⁵
7. Start Small and Solve Real Pain Points. Resist the urge to launch a massive, top-down “AI transformation” initiative. This approach is often too abstract and disconnected from the daily realities of your team. Instead, start by identifying one or two specific, nagging points of friction in your current workflow.¹⁴ Is user research synthesis taking weeks? Pilot an AI transcription and analysis tool. Are your designers spending too much time creating repetitive marketing assets? Experiment with a generative image tool for first drafts. By starting with a clear, contained, and valuable use case, you can demonstrate the benefits of AI, build confidence, and learn valuable lessons before scaling your efforts.⁴⁶

To close, let us return to the idea of craft. Perhaps the best way to think about AI is not as the new craftsperson, but as a new material. Like a new type of wood, a new metal, or a new polymer, it has unique properties. It is incredibly malleable, capable of generating form at an unprecedented speed. It is also brittle in places, prone to flaws, and lacks the inherent warmth of materials we know well. Its ultimate value is not located within the material itself. Its value will be realized through the skill, the wisdom, and the intention of the human designer who chooses to pick it up, to understand its nature, and to shape it into something meaningful, useful, and beautiful.³ The future of design is not about what the machine can do, but about what we, as thoughtful creators, choose to do with it.

Works cited

Works Cited (Click To Show)

1. AI’s transformative role in brand design – 2023 – Articles, accessed July 20, 2025, https://www.transformmagazine.net/articles/2023/ais-transformative-role-in-brand-design/
2. 6 Common AI Model Training Challenges – Oracle, accessed July 20, 2025, https://www.oracle.com/artificial-intelligence/ai-model-training-challenges/
3. The AI writing assistant for design teams | UX Writing Assistant | Frontitude, accessed July 20, 2025, https://write.frontitude.com/
4. The Rise of AI in Branding: How Artificial Intelligence is Changing Marketing – BBdirector, accessed July 20, 2025, https://bbdirector.com/brand-id-and-development/ai-in-branding/
5. AI-First Design | UX Magazine, accessed July 20, 2025, https://uxmag.com/topics/ai-first-design
6. Is V0 by Vercel Worth It in 2025 – Momen, accessed July 20, 2025, https://momen.app/blogs/v0-vercel-worth-it-2025/
7. Copyright and Artificial Intelligence, Part 3: Generative AI Training Pre-Publication Version, accessed July 20, 2025, https://www.copyright.gov/ai/Copyright-and-Artificial-Intelligence-Part-3-Generative-AI-Training-Report-Pre-Publication-Version.pdf
8. Generative AI and Design Systems: My thoughts, accessed July 20, 2025, https://designsystemdiaries.com/p/generative-ai-and-design-systems-my-thoughts
9. Generative AI for Enterprises | Deloitte US, accessed July 20, 2025, https://www.deloitte.com/us/en/what-we-do/capabilities/applied-artificial-intelligence/articles/generative-ai-for-enterprises.html
10. Case Studies | AI UX Navigator, accessed July 20, 2025, https://www.aiuxnavigator.com/case-studies
11. Unveiling the Complexity of Designers’ Intention to Use Generative AI in Corporate Product Design: A Grounded Theory and fsQCA – MDPI, accessed July 20, 2025, https://www.mdpi.com/2079-8954/13/4/275
12. AI in Product Development: Netflix, BMW, and PepsiCo by Virtasant, accessed July 20, 2025, https://www.virtasant.com/ai-today/ai-in-product-development-netflix-bmw
13. Critical questions for design leaders working with artificial intelligence, New York 2025, accessed July 20, 2025, https://leadingdesign.com/new-york-2025-ai
14. Crowdin: AI-Powered Localization Software for Agile Teams, accessed July 20, 2025, https://crowdin.com/
15. Is AI killing creativity, or just exposing lazy design? : r/graphic_design – Reddit, accessed July 20, 2025, https://www.reddit.com/r/graphic_design/comments/1iktj1q/is_ai_killing_creativity_or_just_exposing_lazy/
16. Vercel v0 Review 2025: What Most Developers Get Wrong About It, accessed July 20, 2025, https://content.trickle.so/blog/vercel-v0-review
17. Using AI for Product Design: The Complete Guide – CareerFoundry, accessed July 20, 2025, https://careerfoundry.com/en/blog/product-design/ai-product-design/
18. My experience developing a full stack app with v0 – Vercel Community, accessed July 20, 2025, https://community.vercel.com/t/my-experience-developing-a-full-stack-app-with-v0/6863
19. The AI platform for global content | All-in-one solution, accessed July 20, 2025, https://www.smartcat.com/
20. Automated UI Testing for Design Systems: Validate Components with AI – ProCreator, accessed July 20, 2025, https://procreator.design/blog/automated-ui-testing-design-systems-ai/
21. Designing great AI products — Effective collaboration for designers …, accessed July 20, 2025, https://uxplanet.org/designing-great-ai-products-effective-collaboration-for-designers-1faebcafa395
22. Critical questions for design leaders working with AI | Clearleft, accessed July 18, 2025, https://clearleft.com/thinking/critical-questions-for-design-leaders-working-with-ai
23. Leading Through the AI Design Revolution: How Design Leaders Navigate Human-AI Collaboration | by Jamie Rothwell | Beyond the Pixels: A guide to UX Management | Jun, 2025 | Medium, accessed July 18, 2025, https://medium.com/ux-management/leading-through-the-ai-design-revolution-how-design-leaders-navigate-human-ai-collaboration-e9fb8c6dbeec
24. Preserving Craft in the Era of AI | Balancing Tech & Creativity – DevRev, accessed July 18, 2025, https://devrev.ai/blog/era-of-ai
25. Using AI for product design! : r/UXDesign – Reddit, accessed July 18, 2025, https://www.reddit.com/r/UXDesign/comments/1ipeack/using_ai_for_product_design/
26. Generative AI: The Next Evolution In Product Design And Marketing – Forbes, accessed July 18, 2025, https://www.forbes.com/councils/forbesbusinesscouncil/2025/03/17/generative-ai-the-next-evolution-in-product-design-and-marketing/
27. Best AI tool for product design in 2025? : r/UXDesign – Reddit, accessed July 18, 2025, https://www.reddit.com/r/UXDesign/comments/1l0hami/best_ai_tool_for_product_design_in_2025/
28. My honest review of AI Product Designer backed by Y-Combinator (v0 Users Need to See This) – YouTube, accessed July 18, 2025, https://www.youtube.com/watch?v=150EzogAp_c&pp=0gcJCfwAo7VqN5tD
29. Using Ai for product design? : r/IndustrialDesign – Reddit, accessed July 18, 2025, https://www.reddit.com/r/IndustrialDesign/comments/1jogrmh/using_ai_for_product_design/
30. Does GitHub Copilot Improve Code Quality? Here’s How We Lie With Statistics, accessed July 18, 2025, https://jadarma.github.io/blog/posts/2024/11/does-github-copilot-improve-code-quality-heres-how-we-lie-with-statistics/
31. The State of AI in UX and Product Design: Insights & Expertise, accessed July 18, 2025, https://designlab.com/blog/the-state-of-ai-in-ux-and-product-design-insights-expertise
32. AI Challenges and How You Can Overcome Them: How to Design for Trust | IxDF, accessed July 18, 2025, https://www.interaction-design.org/literature/article/ai-challenges-and-how-you-can-overcome-them-how-to-design-for-trust
33. How to use generative AI in product design | McKinsey, accessed July 18, 2025, https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/generative-ai-fuels-creative-physical-product-design-but-is-no-magic-wand
34. Can AI really code? Study maps the roadblocks to autonomous …, accessed July 18, 2025, https://news.mit.edu/2025/can-ai-really-code-study-maps-roadblocks-to-autonomous-software-engineering-0716
35. AI Product Design: A Realistic Technology Overview – Trinetix, accessed July 18, 2025, https://www.trinetix.com/insights/ai-product-design
36. I’m an AI skeptic. I’m probably wrong. This article makes me feel kinda wrong. B… | Hacker News, accessed July 18, 2025, https://news.ycombinator.com/item?id=44166096
37. The phony comforts of AI skepticism – Platformer, accessed July 18, 2025, https://www.platformer.news/ai-skeptics-gary-marcus-curve-conference/
38. The Future of Human-Computer Interaction | Irene Au | TEDxYouth@TheNuevaSchool, accessed July 18, 2025, https://www.youtube.com/watch?v=t_ZzhadA3DY
39. Current State, Potentials and Challenges for the Use of Artificial Intelligence in the early Phase of Product Development: A Survey – ResearchGate, accessed July 18, 2025, https://www.researchgate.net/publication/388696634_Current_State_Potentials_and_Challenges_for_the_Use_of_Artificial_Intelligence_in_the_early_Phase_of_Product_Development_A_Survey
40. Product, Design and AI – Silicon Valley Product Group – SVPG, accessed July 18, 2025, https://www.svpg.com/product-design-and-ai/
41. Top 13 AI Product Design Tools Every Team Should Know – ProCreator, accessed July 18, 2025, https://procreator.design/blog/top-ai-product-design-tools-teams-must-know/
42. Innovative AI Product Design: Revolutionizing The Industry – NoTriangle Studio, accessed July 18, 2025, https://notrianglestudio.com/ai-product-design/
43. 25 AI Tools for UX Research: A Comprehensive List – Dovetail, accessed July 18, 2025, https://dovetail.com/ux/ai-tools-for-ux-research/
44. AI UX Research: Practical Tips for Every Stage of the Process – Eleken, accessed July 18, 2025, https://www.eleken.co/blog-posts/ai-in-ux-research
45. Exploring AI for User Research – Where Does It Actually Help? : r/UXResearch – Reddit, accessed July 18, 2025, https://www.reddit.com/r/UXResearch/comments/1iy1jny/exploring_ai_for_user_research_where_does_it/
46. How AI is Transforming UX Research in 2025 (+10 Powerful AI Tools) | Looppanel, accessed July 18, 2025, https://www.looppanel.com/blog/ai-uxresearch-10-powerful-tools
47. AI Powered Code Quality: The future of software development – Infosys Blogs, accessed July 18, 2025, https://blogs.infosys.com/digital-experience/emerging-technologies/ai-powered-code-quality.html
48. How AI has started to impact our work as designers | by Fabricio Teixeira – UX Collective, accessed July 18, 2025, https://uxdesign.cc/how-ai-will-impact-your-routine-as-a-designer-2773a4b1728c
49. Is AI Bloating Your Technical Debt? What You Need to Know – Virtasant, accessed July 18, 2025, https://www.virtasant.com/ai-today/is-ai-bloating-your-technical-debt-what-you-need-to-know
50. GitHub Copilot Research Finds “Downward Pressure on Code Quality” – Jesse Warden, accessed July 18, 2025, https://jessewarden.com/2024/01/github-copilot-research-finds-downward-pressure-on-code-quality.html
51. How AI generated code compounds technical debt – LeadDev, accessed July 18, 2025, https://leaddev.com/software-quality/how-ai-generated-code-accelerates-technical-debt
52. AI Makes Tech Debt More Expensive – Gauge – Solving the monolith/microservices dilemma, accessed July 18, 2025, https://www.gauge.sh/blog/ai-makes-tech-debt-more-expensive
53. Maintaining Code Quality in the Age of Generative AI: 7 Essential …, accessed July 18, 2025, https://medium.com/@conneyk8/maintaining-code-quality-in-the-age-of-generative-ai-7-essential-strategies-b526532432e4
54. AI and Design Systems | Brad Frost, accessed July 18, 2025, https://bradfrost.com/blog/post/ai-and-design-systems/
55. Human-AI Collaboration Can Unlock New Frontiers in Creativity, accessed July 18, 2025, https://csd.cmu.edu/news/humanai-collaboration-can-unlock-new-frontiers-in-creativity
56. Why Tech Leaders Are Doubling Down on Craft in an AI-Powered World | Designlab, accessed July 18, 2025, https://designlab.com/blog/design-craft-in-an-ai-powered-world
57. When AI Joined My Design Team (And Didn’t Steal My Job) | by Liz LeCompte – Medium, accessed July 18, 2025, https://medium.com/@LizLeCompte/when-ai-joined-my-design-team-and-didnt-steal-my-job-1eb9a95d148c
58. AI product design: Identifying skills gaps and how to close them | by …, accessed July 18, 2025, https://uxdesign.cc/ai-product-design-identifying-skills-gaps-and-how-to-close-them-5342b22ab54e
59. The Future of Product Design: From Creators to Curators in an AI-First World – UX Magazine, accessed July 18, 2025, https://uxmag.com/articles/the-future-of-product-design-from-creators-to-curators-in-an-ai-first-world
60. The Future of Creative Intelligence: Human-Machine Collaboration | Connect The Dots | Ep. 5 – YouTube, accessed July 18, 2025, https://www.youtube.com/watch?v=wEMHASrHzuY
61. Using AI tools as a Product Designer | by And Kanashiro – Medium, accessed July 18, 2025, https://medium.com/@and.kanashiro/using-ai-tools-as-a-product-designer-c0a5d2f171a3
62. AI isn’t just for coders: 7 emerging non-tech career paths in artificial intelligence, accessed July 18, 2025, https://timesofindia.indiatimes.com/education/careers/ai-isnt-just-for-coders-7-emerging-non-tech-career-paths-in-artificial-intelligence/articleshow/122722679.cms
63. Web Accessibility Audits – WCAG Testing | Lumar Website Intelligence, accessed July 18, 2025, https://www.lumar.io/use-case/accessibility-audits/
64. How AI is Transforming Accessibility Testing: Smarter, Faster, and More Inclusive, accessed July 18, 2025, https://www.testingtools.ai/blog/how-ai-is-transforming-accessibility-testing-smarter-faster-and-more-inclusive/
65. Research: Quantifying GitHub Copilot’s impact in the enterprise with Accenture, accessed July 18, 2025, https://github.blog/news-insights/research/research-quantifying-github-copilots-impact-in-the-enterprise-with-accenture/
66. Best practices for using GitHub Copilot, accessed July 18, 2025, https://docs.github.com/en/copilot/get-started/best-practices-for-using-github-copilot
67. Integrating AI into Legacy Systems: A Step-by-Step Guide – Taazaa, accessed July 18, 2025, https://www.taazaa.com/integrating-ai-into-legacy-systems-a-step-by-step-guide/
68. How AI Integration is Transforming Legacy Systems – TestingXperts, accessed July 18, 2025, https://www.testingxperts.com/blog/ai-integration-transforming-legacy-systems/
69. Three Principles for Designing ML-Powered Products | Spotify Design, accessed July 18, 2025, https://spotify.design/article/three-principles-for-designing-ml-powered-products
70. How Spotify Uses AI (And What You Can Learn from It) – Marketing AI Institute, accessed July 18, 2025, https://www.marketingaiinstitute.com/blog/spotify-artificial-intelligence
71. Listening, Learning, and Helping at Scale: How Machine Learning Transforms Airbnb’s Voice Support Experience | by Yuanpei Cao – Medium, accessed July 18, 2025, https://medium.com/airbnb-engineering/listening-learning-and-helping-at-scale-how-machine-learning-transforms-airbnbs-voice-support-b71f912d4760
72. 5 ways AirBnB is using AI [Case Study] [2025] – DigitalDefynd, accessed July 18, 2025, https://digitaldefynd.com/IQ/airbnb-using-ai-case-study/
73. How Airbnb is Using Artificial Intelligence (AI) to Transform the Travel Experience in 2025, accessed July 18, 2025, https://everitesolutions.com/how-airbnb-is-using-artificial-intelligence-ai-to-transform-the-travel-experience-in-2025/
74. Case Study: AI for Accessibility (Microsoft) – Responsible Use of AI | Toolkit – Founderz, accessed July 18, 2025, https://responsibleai.founderz.com/toolkit/case_study_ai_for_accessibility_microsoft
75. Boost teamwork with AI in Microsoft Teams, accessed July 18, 2025, https://www.microsoft.com/en-us/microsoft-teams/teams-ai
76. Play the Long Game With Human-AI Collaboration – Gallup.com, accessed July 18, 2025, https://www.gallup.com/workplace/660572/play-long-game-human-ai-collaboration.aspx
77. The new mandate: design leadership in an AI-native world | by Rachel Kobetz | Jun, 2025, accessed July 18, 2025, https://uxdesign.cc/the-new-mandate-design-leadership-in-an-ai-native-world-19819be29e4e
78. Design After AI: Why Craft Still Matters | by Peter Barber | Medium, accessed July 18, 2025, https://peter-barber.medium.com/design-after-ai-why-craft-still-matters-c765ab936451