Does UX still matter — or is AI making us obsolete?

AI generates layouts in seconds. Writes microcopy on demand. Analyzes user data faster than any research team.

So why do we still need UX designers?

Here's what AI already does better than you:
→ Generate 5 layout variants in 30 seconds
→ Write 10 error message alternatives on command
→ Summarize 15 interview transcripts in minutes
→ Spot patterns across thousands of data points

Impressive. But here's what AI can't do:
Empathy isn't computable. AI does sentiment analysis. It can't sense why a 63-year-old goes silent during a usability test — not from confusion, but from shame.

Context has no data model. AI doesn't know your CEO said "mobile-first" because he got a new iPhone. It doesn't know your engineering team is burned out and every design change is political.

Ethics require conviction. AI optimizes for whatever goal you set. If that goal is "more clicks," it will suggest dark patterns without blinking. A designer asks: Should we?

Saying no is a skill. AI always delivers an answer. Even when the right answer is: We're solving the wrong problem.

The designers who thrive aren't the ones pushing pixels. They're the ones asking questions before designing answers.

AI makes you faster. It doesn't make you smarter, more empathetic, or braver. That's still on you.

I've packed everything I've learned in 24 years of design into one interactive platform — methods, tools, Nielsen's 10 heuristics, a glossary, and a section on how to actually use AI in your UX workflow.
→ https://lnkd.in/gHT-zJ44
#UX #AI #ProductDesign #UserExperience #DesignLeadership

I've been sharing a lot about AI-powered product design workflows — what I try, what I actually use in my own work, and what sticks.

Every time I post, someone asks for tutorials or a deeper walkthrough. So I finally put it all in one place.

The guide covers everything I've tested so far - design workflow across 7 modules:
→ UX Research & Synthesis → UX Strategy & Methodology → Visual & UI Design → Content & Copy → Handoff & Documentation → Design Systems (coming soon) → Claude Skills

If you've been curious about how to bring AI into your design practice in a way that's actually useful, this is for you.

Link in the comments 👇

There’s a lot of noise around AI in design. Some say it will replace designers. Others dismiss it entirely. After three months of deliberately integrating AI tools into my daily workflow, my view is more nuanced—and more interesting than either extreme.
AI did not make me less of a designer. But it permanently changed how I design.

AI tools won't replace UX Designers.

But designers who ignore AI will be replaced by those who don't.

Here's how I'm using AI in my UX process right now (without losing the human touch):

🔍 Research synthesis Dump interview notes into AI → get themes in minutes, not hours. I still validate every insight but I save hours of grunt work.

✍️ First-draft microcopy AI gives me 10 variations of a button label or error message. I pick, refine, and make it actually sound human.

🗺️ User journey mapping I use AI to spot gaps in a journey I've been staring at too long. Fresh (artificial) eyes catch what tired human ones miss.

🧪 Usability test prep AI helps me draft screener questions and discussion guides 3x faster. More time for actual conversations with users.

The goal isn't to hand over your design thinking. It's to free up more time for your design thinking.

What's one AI tool that's genuinely changed how you work as a designer? 👇

#uxdesign #aiindesign #ai #userexperience #productdesign #designtools

How I actually use AI to build enterprise software in 2026 — and what models do what.
I've been designing enterprise dashboards and CRM systems for years. The complexity never changes: dense data, stakeholder chaos, and users who need answers in seconds.

What has changed is my stack. Here's exactly how I use AI today — not buzzwords, but the real workflow.

🧠 LLMs for reports & stakeholder communication
I use large language models (Claude, GPT-4o) to compress 40-page research reports into executive summaries. After user interviews, I feed transcripts in and get structured insight briefs — mapped to business goals. The LLM doesn't do the thinking. It does the scaffolding, so I can focus on the strategic layer. My synthesis still comes from me.

🔍 NLP for CRM intelligence
In enterprise CRM design, I use Natural Language Processing models to run sentiment analysis on support tickets, chat logs, and survey responses. Tools like AWS Comprehend and Azure Language Studio let me surface emotional friction at scale — before it shows up in churn data. Designing the CRM interface is one thing. Understanding what users are actually feeling when they use it is another.

📊 NLU/NLM for dashboard design
When designing enterprise dashboards, I use Natural Language Understanding models to map how users actually describe the data they need — versus what developers built. That gap is where bad UX lives. I run semantic clustering across user interviews with tools like Marvin AI, then use the patterns to redesign information hierarchies. The model finds the signal. I design the solution.

⚡ My actual research workflow in 2026:
→ Discovery: I use ChatGPT or Claude to rapidly orient myself in new domains (cloud architecture, logistics ops, fintech compliance) before writing a single research question. It's scaffolding, not truth.
→ Screeners & discussion guides: I use Copilot or Claude to turn assumption lists into first-draft discussion guides. Then I edit. Hard.
→ Thematic analysis: After interviews, I use Marvin AI for early thematic clustering. LLMs accelerate the first pass — but I own the actual interpretation.
→ Synthesis: I don't outsource this. AI can't read the room, notice hesitation, or catch the moment a user says "it's fine" while clearly frustrated. That's still mine.
→ Stakeholder reporting: I use LLMs to tailor the same insight for three different audiences — dev teams get friction maps, C-suite gets revenue implications.

Still figuring this out in public. If you're building enterprise products with AI in your workflow, drop a comment — I want to know what's actually working for your team.

#UXDesign #EnterpriseUX #ProductDesign #AITools #LLM #NLP #CRMDesign #DashboardDesign #UXResearch #HumanCenteredDesign

Let’s be honest... the sheer volume of noise surrounding Artificial Intelligence right now is exhausting and all over the place. Every tool has a “generate” button, and social media (LinkedIn especially) is full of people proclaiming the end of traditional design jobs.

As a product designer, I initially approached this wave with skepticism. I enjoy designing. I like solving complex problems. I was not looking for a machine to do my job for me.

However, almost two years has passed since those early iterations and ignoring AI is no longer an option. Instead of viewing it as a replacement, I have spent my recent time treating AI as a very fast and somewhat literal-minded supporter. The goal is not to let it design the final product. The goal is to clear the path so I have more time for actual critical thinking.

Here is a look at how I have started integrating these tools into my daily workflow:

AI didn’t change what I design. It changed how I design.

Over the past several months, my design process hasn’t just evolved — it’s been fully restructured. My AI tool stack isn’t something I use occasionally; it is an operational layer embedded into my day-to-day and across discovery, ideation, prototyping, validation, and delivery.

This article outlines how I structure projects using AI models — primarily Claude and ChatGPT — alongside specialized tools such as Figma Make and Amplitude.