Today, everyone loves to talk about empathy as if it’s a magical tool: “Understand your users, and success will follow!” But if you’ve actually built a product, you know that behind this “magic” lies hard, sometimes messy work in design thinking. And with the rise of AI (Artificial Intelligence), this process has changed dramatically.
1. Empathy — not just a soft skill
In design, empathy is not sympathy, humanitarianism, or pretty textbook quotes. It’s the ability to see the world through the eyes of your user and understand their needs and frustrations. Without empathy, all your interfaces are for yourself, not for real people. Before AI, this process was slow and emotional. Now, it’s fast and digital — and that’s not always a good thing.
2. Before AI: Human Empathy
Before the AI era, UX and product design teams did everything manually:
Real-life process:
1. User interviews — a researcher sits with the user and listens to how they use existing tools (banking apps, Excel spreadsheets, etc.).
2. Field observation — the designer observes how the user actually makes purchases, records expenses, or gets frustrated by complex tasks.
3. Empathy maps — visualizing the user’s emotions, thoughts, actions, and words.
4. Contextual immersion — designers themselves use the product as the user would.
Tools: Miro, Lookback, Otter.ai, Dovetail
Result: deep understanding of emotions and hidden needs; insights not found in analytics; real connection with users.
Challenges: slow, expensive, and some interviews may be useless if questions are wrong.
3. After AI: Digital Empathy
Now, teams use AI to speed up and scale empathy:
New workflow:
1. Upload interviews — ChatGPT or Grain analyzes transcripts and finds patterns.
2. Generate personas — AI creates synthetic users with behavioral patterns.
3. Pattern analysis — Useberry or Reframer identifies recurring pains and triggers.
4. User journey simulation — testing interfaces on “virtual” users.
Tools: ChatGPT, Grain, Useberry, Reframer, Notably, Fathom
Pros: fast, scalable, cost-effective.
Cons: lacks real emotional connection; synthetic users do not show subtle nuances; risk of “beautiful but false insights”.
4. Before vs After — The Real Contrast
| Aspect | Before AI | After AI |
|---|---|---|
| Contact | Personal, emotional | Data-driven, simulated |
| Speed | Weeks | Hours |
| Depth | High | Medium (without validation) |
| Scale | Local | Global |
| Tools | Miro, Lookback, Dovetail | ChatGPT, Useberry, Reframer, Grain |
| Errors | Subjectivity | Algorithm bias |
5. Practical Example — Designing a Budget App
Let’s imagine a company creating a mobile app for personal budgeting.
Step 1 — Define hypothesis: “Young professionals avoid budgeting because it’s stressful and boring.”
Step 2 — Field research: User interviews observing how the user tracks expenses in Excel or manually. Emotions recorded: frustration, fatigue, anxiety.
Step 3 — Empathy map:
| Category | User Insight |
|---|---|
| Says | “I hate counting expenses.” |
| Thinks | “I’ll never get it right.” |
| Does | postpones budgeting |
| Feels | stressed / anxious |
Step 4 — AI-assisted synthesis: Grain/Reframer analyze interviews, showing repeated emotions and barriers in 80% of users.
Step 5 — Design solution: App doesn’t show all numbers upfront; uses progress visualization. Motivational nudges, gamified experience: small rewards for achievements. Goal: user feels supported, not stressed.
Outcome: solves a real pain; user emotions are central; AI accelerated analysis, but human feelings remained the core.
6. Hybrid Empathy — The Right Way
Formula:
1. Talk to real users (5–10 people)
2. Use AI for pattern recognition
3. Validate insights again with humans
4. Build solutions based on real pain, not assumptions
Empathy is a continuous loop, not a one-time step.
Truth About Empathy Today
AI didn’t kill empathy — it changed its form. True products emerge at the intersection of human insight and machine intelligence. Speed and scale are nice, but human nuance is irreplaceable.
“Good designers listen. Great designers listen AND scale that insight through technology.”
UX/product people: How do you combine live empathy and AI-assisted insights in your workflow? Do you use synthetic users, or prefer real interviews? Share your stack of tools in the comments!
Must-read on the topic:
If you want to understand how generative interfaces affect user perception, check out the article “Generative UI and the Spoiled User Effect”. It explains in detail how AI can create interfaces that, on one hand, make life easier, but on the other, make users less tolerant of complex or non-standard interactions. This phenomenon, called the “spoiled user effect”, is crucial to consider in design to maintain a balance between innovation and real user needs.
