34 posts tagged with “product design

This is a really well-written piece that pulls the AI + design concepts neatly together. Sharang Sharma, writing in UX Collective:

As AI reshapes how we work, I’ve been asking myself, it’s not just how to stay relevant, but how to keep growing and finding joy in my craft.

In my learning, the new shift requires leveraging three areas
1. AI tools: Assembling an evolving AI design stack to ship fast
2. AI fluency: Learning how to design for probabilistic systems
3. Human-advantage: Strengthening moats like craft, agency and judgment to stay ahead of automation

Together with strategic thinking and human-centric skills, these pillars shape our path toward becoming an AI-native designer.

Sharma connects all the crumbs I’ve been dropping this week:

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AI tools + AI fluency + human advantage = AI-native designer

From tools to agency, is this what it would take to thrive as a product designer in the AI era?

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Copilots helped enterprises dip their toes into AI. But orchestration platforms and tools are where the real transformation begins — systems that can understand intent, break it down, distribute it, and deliver results with minimal hand-holding.

Think of orchestration as how “meta-agents” are conducting other agents.

The first iteration of AI in SaaS was copilots. They were like helpful interns eagerly awaiting your next command. Orchestration platforms are more like project managers. They break down big goals into smaller tasks, assign them to the right AI agents, and keep everything coordinated. This shift is changing how companies design software and user experiences, making things more seamless and less reliant on constant human input.

For designers and product teams, it means thinking about workflows that cross multiple tools, making sure users can trust and control what the AI is doing, and starting small with automation before scaling up.

Beyond Copilots: The Rise of the AI Agent Orchestration Platform

AI agent orchestration platforms are replacing simple copilots, enabling enterprises to coordinate autonomous agents for smarter, more scalable workflows.

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Let’s stay on the train of designing AI interfaces for a bit. Here’s a piece by Rob Chappell in UX Collective where he breaks down how to give users control—something I’ve been advocating—when working with AI.

AI systems are transforming the structure of digital interaction. Where traditional software waited for user input, modern AI tools infer, suggest, and act. This creates a fundamental shift in how control moves through a experience or product — and challenges many of the assumptions embedded in contemporary UX methods.

The question is no longer:
“What is the user trying to do?”

The more relevant question is:
“Who is in control at this moment, and how does that shift?”

Designers need better ways to track how control is initiated, shared, and handed back — focusing not just on what users see or do, but on how agency is negotiated between human and system in real time.

Most design frameworks still assume the user is in the driver’s seat. But AI is changing the rules. The challenge isn’t just mapping user flows or intent—it’s mapping who holds the reins, and how that shifts, moment by moment. Designers need new tools to visualize and shape these handoffs, or risk building systems that feel unpredictable or untrustworthy. The future of UX is about negotiating agency, not just guiding tasks.

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Beyond journey maps: designing for control in AI UX

When systems act on their own, experience design is about balancing agency — not just user flow

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Vitaly Friedman writes a good primer on the design possibilities for users to interact with AI features. As AI capabilities become more and more embedded in the products designers make, we have to become facile in manipulating AI as material.

Many products are obsessed with being AI-first. But you might be way better off by being AI-second instead. The difference is that we focus on user needs and sprinkle a bit of AI across customer journeys where it actually adds value.
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Design Patterns For AI Interfaces

Designing a new AI feature? Where do you even begin? From first steps to design flows and interactions, here’s a simple, systematic approach to building AI experiences that stick.

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Since its debut at Config back in May, Figma has steadily added practical features to Figma Make for product teams. Supabase integration now allows for authentication, data storage, and file uploads. Designers can import design system libraries, which helps maintain visual consistency. Real-time collaboration has improved, giving teams the ability to edit code and prototypes together. The tool now supports backend connections for managing state and storing secrets. Prototypes can be published to custom domains. These changes move Figma Make closer to bridging the gap between design concepts and advanced prototypes.

In my opinion, there’s a stronger relationship between Sites and Make than there is Make and Design. The Make-generated code may be slightly better than when Sites debuted, but it is still not semantic.

Anyhow, I think Make is great for prototyping and it’s convenient to have it built right into Figma. Julius Patto, writing in UX Collective:

Prompting well in Figma Make isn’t about being clever, it’s about being clear, intentional, and iterative. Think of it as a new literacy in the design toolkit: the better you get at it, the more you unlock AI’s potential without losing your creative control.
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How to prompt Figma Make’s AI better for product design

Learn how to use AI in Figma Make with UX intention, from smarter prompts to inclusive flows that reflect real user needs.

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Ted Goas, writing in UX Collective:

I predict the early parts of projects, getting from nothing to something, will become shared across roles. For designers looking to branch out, code is a natural next step. I see a future where we’re fixing small bugs ourselves instead of begging an engineer, implementing that animation that didn’t make the sprint but you know would absolutely slap, and even building simple features when engineering resources are tight.

Our new reality is that anyone can make a rough draft.

But that doesn’t mean those drafts are good. That’s where our training and taste come in.

I think Goas is right and it echoes the AI natives post by Elena Verna. I wrote a little more extensively in my newsletter over the weekend.

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Designers: We’ll all be design engineers in a year

And that’s a good thing.

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Miquad Jaffer, a product leader at OpenAI shares his 4D method on how to build AI products that users want. In summary, it's…

  • Discover: Find and prioritize real user pain points and friction in daily workflows.
  • Design: Make AI features invisible and trustworthy, fitting naturally into users’ existing habits.
  • Develop: Build AI systematically, with robust evaluation and clear plans for failures or edge cases.
  • Deploy: Treat each first use like a product launch, ensuring instant value and building user trust quickly.
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OpenAI Product Leader: The 4D Method to Build AI Products That Users Actually Want

An OpenAI product leader's complete playbook to discover real user friction, design invisible AI, plan for failure cases, and go from "cool demo" to "daily habit"

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Sara Paul writing for NN/g:

The core principles of UX and product design remain unchanged, and AI amplifies their importance in many ways. To stay indispensable, designers must evolve: adapt to new workflows, deepen their judgment, and double down on the uniquely human skills that AI can’t replace.

They spoke with seven UX practitioners to get their take on AI and the design profession.

I think this is great advice and echoes what I’ve written about previously (here and here):

There is a growing misconception that AI tools can take over design, engineering, and strategy. However, designers offer more than interaction and visual-design skills. They offer judgment, built on expertise that AI cannot replicate.

Our panelists return to a consistent message: across every tech hype cycle, from responsive design to AI, the value of design hasn’t changed. Good design goes deeper than visuals; it requires critical thinking, empathy, and a deep understanding of user needs.
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The Future-Proof Designer

Top product experts share four strategies for remaining indispensable as AI changes UI design, accelerates feature production, and reshapes data analysis.

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Great reminder from Kai Wong about getting stuck on a solution too early:

Imagine this: the Product Manager has a vision of a design solution based on some requirements and voices it to the team. They say, “I want a table that allows us to check statuses of 100 devices at once.”

You don’t say anything, so that sets the anchor of a design solution as “a table with a bunch of devices and statuses.”
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Avoid premature solutions: how to respond when stakeholders ask for certain designs

How to avoid anchoring problems that result in stuck designers

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A futuristic scene with a glowing, tech-inspired background showing a UI design tool interface for AI, displaying a flight booking project with options for editing and previewing details. The screen promotes the tool with a “Start for free” button.

Beyond the Prompt: Finding the AI Design Tool That Actually Works for Designers

There has been an explosion of AI-powered prompt-to-code tools within the last year. The space began with full-on integrated development environments (IDEs) like Cursor and Windsurf. These enabled developers to use leverage AI assistants right inside their coding apps. Then came a tools like v0, Lovable, and Replit, where users could prompt screens into existence at first, and before long, entire applications.

A couple weeks ago, I decided to test out as many of these tools as I could. My aim was to find the app that would combine AI assistance, design capabilities, and the ability to use an organization’s coded design system.

While my previous essay was about the future of product design, this article will dive deep into a head-to-head between all eight apps that I tried. I recorded the screen as I did my testing, so I’ve put together a video as well, in case you didn’t want to read this.

Illustration of humanoid robots working at computer terminals in a futuristic control center, with floating digital screens and globes surrounding them in a virtual space.

Prompt. Generate. Deploy. The New Product Design Workflow

Product design is going to change profoundly within the next 24 months. If the AI 2027 report is any indication, the capabilities of the foundational models will grow exponentially, and with them—I believe—will the abilities of design tools.

A graph comparing AI Foundational Model Capabilities (orange line) versus AI Design Tools Capabilities (blue line) from 2026 to 2028. The orange line shows exponential growth through stages including Superhuman Coder, Superhuman AI Researcher, Superhuman Remote Worker, Superintelligent AI Researcher, and Artificial Superintelligence. The blue line shows more gradual growth through AI Designer using design systems, AI Design Agent, and Integration & Deployment Agents.

The AI foundational model capabilities will grow exponentially and AI-enabled design tools will benefit from the algorithmic advances. Sources: AI 2027 scenario & Roger Wong

The TL;DR of the report is this: companies like OpenAI have more advanced AI agent models that are building the next-generation models. Once those are built, the previous generation is tested for safety and released to the public. And the cycle continues. Currently, and for the next year or two, these companies are focusing their advanced models on creating superhuman coders. This compounds and will result in artificial general intelligence, or AGI, within the next five years. 

Karri Saarinen, writing for the Linear blog:

Unbounded AI, much like a river without banks, becomes powerful but directionless. Designers need to build the banks and bring shape to the direction of AI’s potential. But we face a fundamental tension in that AI sort of breaks our usual way of designing things, working back from function, and shaping the form.

I love the metaphor of AI being the a river and we designers are the banks. Feels very much in line with my notion that we need to become even better curators.

Saarinen continues, critiquing the generic chatbox being the primary form of interacting with AI:

One way I visualize this relationship between the form of traditional UI and the function of AI is through the metaphor of a ‘workbench’. Just as a carpenter's workbench is familiar and purpose-built, providing an organized environment for tools and materials, a well-designed interface can create productive context for AI interactions. Rather than being a singular tool, the workbench serves as an environment that enhances the utility of other tools – including the ‘magic’ AI tools.

Software like Linear serves as this workbench. It provides structure, context, and a specialized environment for specific workflows. AI doesn’t replace the workbench, it's a powerful new tool to place on top of it.

It’s interesting. I don’t know what Linear is telegraphing here, but if I had to guess, I wonder if it’s closer to being field-specific or workflow-specific, similar to Generative Fill in Photoshop. It’s a text field—not textarea—limited to a single workflow.

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Design for the AI age

For decades, interfaces have guided users along predefined roads. Think files and folders, buttons and menus, screens and flows. These familiar structures organize information and provide the comfort of knowing where you are and what's possible.

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Closeup of a man with glasses, with code being reflected in the glasses

From Craft to Curation: Design Leadership in the Age of AI

In a recent podcast with partners at startup incubator Y Combinator, Jared Friedman, citing statistics from a survey with their current batch of founders says, “[The] crazy thing is one quarter of the founders said that more than 95% of their code base was AI generated, which is like an insane statistic. And it’s not like we funded a bunch of non-technical founders. Like every one of these people is highly tactical, completely capable of building their own product from scratch a year ago…”

A comment they shared from founder Leo Paz reads, “I think the role of Software Engineer will transition to Product Engineer. Human taste is now more important than ever as codegen tools make everyone a 10x engineer.”

Still from a YouTube video that shows a quote from Leo Paz

While vibe coding—the new term coined by Andrej Karpathy about coding by directing AI—is about leveraging AI for programming, it’s a window into what will happen to the software development lifecycle as a whole and how all the disciplines, including product management and design will be affected.

The legacy of Swiss design and how On is writing a new page in its history

The legacy of Swiss design and how On is writing a new page in its history

Switzerland has a rich design heritage that has proved hugely influential. Here, we explore how designers today, including two working at the sportswear brand On, reinterpret this history within their work, and we consider why the principles underpinning “Swiss Style” have stood the test of time.

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A cut-up Sonos speaker against a backdrop of cassette tapes

When the Music Stopped: Inside the Sonos App Disaster

The fall of Sonos isn’t as simple as a botched app redesign. Instead, it is the cumulative result of poor strategy, hubris, and forgetting the company’s core value proposition. To recap, Sonos rolled out a new mobile app in May 2024, promising “an unprecedented streaming experience.” Instead, it was a severely handicapped app, missing core features and broke users’ systems. By January 2025, that failed launch wiped nearly $500 million from the company’s market value and cost CEO Patrick Spence his job.

What happened? Why did Sonos go backwards on accessibility? Why did the company remove features like sleep timers and queue management? Immediately after the rollout, the backlash began to snowball into a major crisis.

A collage of torn newspaper-style headlines from Bloomberg, Wired, and The Verge, all criticizing the new Sonos app. Bloomberg’s headline states, “The Volume of Sonos Complaints Is Deafening,” mentioning customer frustration and stock decline. Wired’s headline reads, “Many People Do Not Like the New Sonos App.” The Verge’s article, titled “The new Sonos app is missing a lot of features, and people aren’t happy,” highlights missing features despite increased speed and customization.

As a designer and longtime Sonos customer who was also affected by the terrible new app, a little piece of me died inside each time I read the word “redesign.” It was hard not to take it personally, knowing that my profession could have anything to do with how things turned out. Was it really Design’s fault?

This Clamshell Keyboard Case turns your iPhone into an AI-Powered Laptop

This Clamshell Keyboard Case turns your iPhone into an AI-Powered Laptop - Yanko Design

Details on the Amber case are scarce, but it comes from an AI startup looking to revolutionize how writers use AI. The startup responsible for the case is Amber.Page, an AI-powered writing assistant that works to analyze writing styles and replicate them using powerful online as well as offline AI. The service is available for

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Zuckerberg believes Apple “[hasn’t] really invented anything great in a while…”

Appearing on Joe Rogan’s podcast, this week, Meta CEO Mark Zuckerberg said that Apple “[hasn’t] really invented anything great in a while. Steve Jobs invented the iPhone and now they’re just kind of sitting on it 20 years later."

Let's take a look at some hard metrics, shall we?

I did a search of the USPTO site for patents filed by Apple and Meta since 2007. In that time period, Apple filed for 44,699 patents. Meta, nee Facebook, filed for 4,839, or about 10% of Apple’s inventions.

Side-by-side screenshots of patent searches from the USPTO database showing results for Apple Inc. and Meta Platforms. The Apple search (left) returned 44,699 results since 2007, while the Meta search (right) returned 4,839 results.
A stylized digital illustration of a person reclining in an Eames lounge chair and ottoman, rendered in a neon-noir style with deep blues and bright coral red accents. The person is shown in profile, wearing glasses and holding what appears to be a device or notebook. The scene includes abstract geometric lines cutting across the composition and a potted plant in the background. The lighting creates dramatic shadows and highlights, giving the illustration a modern, cyberpunk aesthetic.

Design’s Purpose Remains Constant

Fabricio Teixeira and Caio Braga, in their annual The State of UX report:

Despite all the transformations we’re seeing, one thing we know for sure: Design (the craft, the discipline, the science) is not going anywhere. While Design only became a more official profession in the 19th century, the study of how craft can be applied to improve business dates back to the early 1800s. Since then, only one thing has remained constant: how Design is done is completely different decade after decade. The change we’re discussing here is not a revolution, just an evolution. It’s simply a change in how many roles will be needed and what they will entail. “Digital systems, not people, will do much of the craft of (screen-level) interaction design.”

Scary words for the UX design profession as it stares down the coming onslaught of AI. Our industry isn’t the first one to face this—copywriters, illustrators, and stock photographers have already been facing the disruption of their respective crafts. All of these creatives have had to pivot quickly. And so will we.

Teixeira and Braga remind us that “Design is not going anywhere,” and that “how Design is done is completely different decade after decade.”

A close-up photograph of a newspaper's personal advertisements section, with one listing circled in red ink. The circled ad is titled "DESIGN NOMAD" and cleverly frames a designer's job search as a personal ad, comparing agency work to casual dating and seeking an in-house position as a long-term relationship. The surrounding text shows other personal ads in small, dense print arranged in multiple columns.

Breadth vs. Depth: Lessons from Agencies and In-House Design

I recently read a post on Threads in which Stephen Beck wonders why the New York Times needs an external advertising agency when it already has an award-winning agency in-house. You can read the back-and-forth in the thread itself, but I think Nina Alter’s reply sums it up best:

Creatives need to be free to bring new perspectives. Drink other kool-aid. That’s much of the value in agencies.

This all got me thinking about the differences between working in-house and at an agency. As a designer who began my career bouncing from agency to agency before settling in-house, I’ve seen both sides of this debate firsthand. Many of my designer friends have had similar paths. So, I’ll speak from that perspective. It’s biased and probably a little outdated since I haven’t worked at an agency since 2020, and that was one that I owned.

I think the best path for a young designer is to work for agencies at the beginning of their careers. It’s sort of like casually dating when you first start dating. You quickly experience a bunch of different types of people. You figure out what your preferences are. You make mistakes. You learn a lot about your own strengths and weaknesses. And most importantly, you grow. This is all training for eventually settling down and investing in a long-term relationship with a partner.

Griffin AI logo

How I Built and Launched an AI-Powered App

I’ve always been a maker at heart—someone who loves to bring ideas to life. When AI exploded, I saw a chance to create something new and meaningful for solo designers. But making Griffin AI was only half the battle…

Birth of an Idea

About a year ago, a few months after GPT-4 was released and took the world by storm, I worked on several AI features at Convex. One was a straightforward email drafting feature but with a twist. We incorporated details we knew about the sender—such as their role and offering—and the email recipient, as well as their role plus info about their company’s industry. To accomplish this, I combined some prompt engineering and data from our data providers, shaping the responses we got from GPT-4.

Playing with this new technology was incredibly fun and eye-opening. And that gave me an idea. Foundational large language models (LLMs) aren’t great yet for factual data retrieval and analysis. But they’re pretty decent at creativity. No, GPT, Claude, or Gemini couldn’t write an Oscar-winning screenplay or win the Pulitzer Prize for poetry, but it’s not bad for starter ideas that are good enough for specific use cases. Hold that thought.