AI in Product Design: Worth it?

Turning designs into Production Code

I was skeptical for a long time, but as of right now, I am fully on the hype train. AI is going to cook mid-level engineers before it cooks designers.

What many people don’t realize is that computers — as in, the microchips — don’t understand programming languages. They only understand voltage: on/off, or zero/one. Everything we put on top of that, as programming languages, are for us, as people, to more easily communicate with computers.

It started with a woman in 1947, who invented assembler code. So now instead of figuring out what zeros and ones equate into a command, you could just write simple, more human readable commands. The assembler code became an abstraction layer that hid the complexity of the zeros and ones, and made it easier for humans to issue commands to machines.

Every subsequent programming language added additional abstraction layers, making assembly code invisible and the commands we issue to computers easier to read and define for humans.

These AI coding tools are the conclusion to this trend: They will turn programming languages into an invisible abstraction layer, just as those languages once turned turned assembler code into an invisible layer. This is happening already, and fast.

I’ve spoken with folks about this thesis, and it’s fascinating to see how defensive they get. “You can’t just vibe code, this is complex stuff” they say. “You can’t just wave a magic wand and have it be done”, conveniently ignoring that I never said it needs to be done in one shot, magically.

But the trend is clear: What you can do with these coding tools in terms of speed and productivity increases, is miraculous. And the people who will benefit from it the most are not the people who can write code fast, but the people who can think clearly, communicate clearly, and understand, conceptually, how different technologies interact with each other.

Let me give you an example: In my spare time, I am building the Halo App. It took me about 8 weeks to build and ship the MVP, and it was grueling. Then, I discovered AI Coding tools. I let them roll over the code, understand it, and shipped, in just one week:

• Multi-language support
• Integrated full children’s bible
• Custom audio bible (that I built with some clever conceptual engineering work that I would have never been able to code myself)
• Ability to purchase single stories in addition to a subscription model

In one week! While having a newborn at home! This would not have been possible for me to do at this pace a year ago.

Yes you still need to iterate and debug. Yes you need to pay close attention to what the AI is doing. But the trajectory is clear. This tech is coming, and it’s coming fast.

I have been working in this field long enough to be very careful about crazy hot take statements. But I will issue one: I think that if your company doesn’t work in this AI-native way, with these AI Coding tools, your company needs to change. It’s easier to completely rebuild a platform with these tools than it is to debug what you have.

UX Research

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. 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.

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

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.

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