Why “Designerly” Skills Are the Human Edge AI Can’t Code
Every wave of automation brings the same question: will this replace me? The current surge of Artificial Intelligence tools aimed at writing, image generation, and UI design has put that question to creatives with particular force. It’s reasonable to worry. But it rests on a false premise — that AI and humans are interchangeable. They are not.
AI is technology trained on massive datasets to mimic human intelligence and perform specific tasks. By some estimates, the training data for systems like GPT-3 is equivalent to a quarter of the Library of Congress. That allows AI to process and analyze vast amounts of information quickly, accurately, and consistently. It can even *create* output. But it cannot be *creative* in the way humans are.
Designers, in particular, possess a set of behaviors that the late design researcher Nigel Cross labeled *designerly* — underlying patterns of how designers think and act. These are the non-technical skills that make design work impactful: curiosity, observation, empathy, advocacy, visual communication, and collaboration. They depend on consciousness, intuition, cultural context, and the ability to change one’s mind based on lived experience. They cannot be programmed, no matter how sentient a machine seems.
During the pandemic, I led the redesign of a tablet app for sales representatives at a major food and beverage company. I had never been a sales rep. So I followed the first rule of design: know thy user. Lockdown pushed us to video interviews, but when restrictions lifted, I joined two reps at a local Walmart. Masked and socially distant, I walked through the dairy, pet food, and freezer aisles with them. That single visit revealed what remote research could not: the juggling of devices and printouts, the struggle to use tech in a low-lit walk-in freezer, the need to work without blocking busy shoppers. The reps did this 20 to 30 times a day, five days a week.
Those insights, shared with a global team, allowed everyone to empathize. We built experiments, refined concepts based on rep feedback, and shipped a redesign that earned praise from both users and stakeholders. None of that would have happened without curiosity, empathy, and collaboration — behaviors that are invisible in a requirements doc.
AI will get better at mimicking outputs. It cannot get better at being human. The edge belongs to people who deliberately develop the skills AI lacks.
Thinking Like a Designer
The *head* dimension of designerly practice involves the habits that shape a designer’s mindset: curiosity and observation.
Curiosity Beyond the Answer
Curiosity is the desire to know — the pleasure of asking, exploring, experimenting, and discovering. Children have it relentlessly. Many adults lose it, partly because schooling teaches us to hunt for a single answer rather than to explore questions.
That focus on the correct answer is precisely what AI is designed to do. AI is also bounded by its training data and its limited understanding of the world. It cannot wonder about the things we touch, taste, hear, or smell. It cannot be curious in the physical world because it has no senses.
The psychological model from the 1950s is still useful. Daniel Berlyne distinguished between *perceptual* curiosity (driven by stimulation) and *epistemic* curiosity (a genuine desire for knowledge), and between *diversive* exploration (seeking novel stimulation) and *specific* exploration (seeking new information). The most fertile ground for designers is the intersection of diversive and epistemic curiosity — exploring broadly in order to learn. TED Talks are a prime example of knowledge exploration.
You can train this. Set aside 10–20 minutes a day to learn something new. Watch a talk, read a book summary, or start a small skill. Reading several summaries on the same topic helps you decide which book deserves a full read. The goal is to make exploration a habit.
Curious exploration broadens the mind to new ideas, perspectives, and approaches, lays the foundation for the cross-pollination of ideas, and leads to creative and innovative solutions.
AI cannot experience this. It is locked to its training data, and it cannot grasp the emotional content of the situations it analyzes. People, by contrast, can observe, interpret, and feel — and that changes the questions they ask.
Advantage: People
Noticing and Observing
Noticing is seeing something for the first time. Observing is paying close attention. Creative design often begins with noticing what others skip, then observing it deliberately.
Traditional ethnography requires immersive, years-long study of a culture. Design ethnography is condensed: designers spend days or weeks watching and listening to users to inform design decisions. You do not need a formal research project to practice. Make observation a daily habit.
Pick any public place — a coffee shop, a checkout line. Put down the phone, take out the headphones, and look.
- What devices are people using, and how?
- Are they consuming passively or interacting actively?
- What emotions are visible?
You will notice things you have walked past for years. Practice makes it second nature, and it feeds directly into better ideas and more useful solutions.
AI cannot do this. It is limited to its dataset, and even with more data, it lacks the emotions and context to understand what it is "seeing." Observation for a designer is as much about meaning as it is about detail.
Advantage: People
Head, Heart, and Hands: Why People Still Win
The technical skills of design are the easiest to replicate with AI. The designerly behaviors that produce meaningful work are far harder to fake. Beyond curiosity and observation, meaningful design practice requires a genuine orientation toward others — an ethic that shows up in how designers think, feel, and act. Breaking those dispositions into head, heart, and hands clarifies where humans still have the advantage over machines.
Empathy Is a Doing Word
Psychologists Daniel Goleman and Paul Ekman group empathy into three types: cognitive, emotional, and compassionate. Cognitive empathy is understanding another person’s perspective; emotional empathy is feeling what they feel; compassionate empathy goes one step further and drives action to help. For designers, compassionate empathy is the kind that matters most. It moves past lip service into the territory of genuinely improving people’s lives.
Empathy is the art of stepping imaginatively into the shoes of another person, understanding their feelings and perspectives, and using that understating to guide your actions.
— Roman Krznaric
Human-centered design depends on exactly this. Successful designers start by observing people in their own environments and absorbing the realities of their context. That depth of understanding shapes products and experiences that actually work. AI can measure facial expressions and be trained to mimic emotional responses, but it doesn’t have consciousness, personal history, or shared experience. It cannot feel or intuit. It can only pattern-match.
Empathy is a skill, not a fixed trait. It can be practiced in everyday interactions:
- Suspend judgment. If you are mentally grading the person you are speaking with, your empathy is already lost. Voicing that judgment ends the conversation entirely.
- Listen attentively with your eyes and ears. Engage more than one sense so you can respond deeply. Pay attention to what the other person is saying, not how you plan to reply. Put aside distractions and be fully present.
Advantage: People
The Designer as Advocate
Curiosity, observation, and empathy build a deep understanding of users. Advocacy is what turns that understanding into action. A user advocate represents the interests of users in an ocean of competing interests — giving the user a voice, bringing them to life, and making the impersonal personal.
AI cannot advocate. It lacks the lived experience to feel responsibility and the creativity to imagine responses to human needs. It can be programmed with rules intended to protect people, and ethical AI is an increasingly important field, but the results so far are mixed. AI cannot make ethical judgment calls on its own. People can — and they should use that ability in design:
- Do no harm. Your designs can affect the minds, behavior, and lives of users and others around them. Guard against misusing that influence. Ask yourself: would you be comfortable with someone else using your design on you, your parents, or your child?
- Be aware of your responsibility to your intended users, unintended users, and society at large. Accept responsibility for the outcomes of your design. When answering “How might we…?”, always follow up with “At what cost?”
You are not the user. Represent the user when they are absent, and advocate for them.
Advantage: People
Visual Communication Bridges the Gap
Storytelling paints a vivid picture, but each listener imagines something different. The moment you add an image, a sketch, or a diagram, everyone is literally on the same page. That is the power of visual thinking and communication: it makes ideas tangible quickly and gets teams to the right idea faster.
You do not have to be artistic to think or communicate visually. A rough sketch on a whiteboard or notepad often communicates faster than words. Boxes and arrows describe a process; a rough layout with arrows can show how someone moves between screens; a quick screen recording can demonstrate a flow. Simple images work too. A non-designer boss of mine communicated visually using only PowerPoint.
AI cannot do any of this on its own. It lacks physical senses, emotions, contextual understanding, and the creativity to generate ideas outside its dataset. But AI can be a tool for visual communication — text-to-image generators are a clear example. The human still has to direct it.
Show. Don’t only tell.
Advantage: People
Collaboration, Not Solo Genius
The days of the lone designer are gone. No single person or discipline has the answer to all problems; multi-disciplinary teams bring different perspectives to idea generation, feedback, and validation. Collaboration depends on understanding and navigating social dynamics, the ability to negotiate and compromise, and adapting to live inputs and feedback. Traditional AI has only limited capability there, beyond what it was programmed to do.
AI can support collaboration when treated as an assistant. It reduces manual effort (e.g., transcribing), makes people more efficient (e.g., text-based video editing), provides machine learning-based insight (e.g., attention prediction), and augments human effort (e.g., AI evaluation). It also gets things plainly wrong with confidence. Next time you are working on a project, get others involved — different departments, specialties, backgrounds, and, where appropriate, even customers. Maybe even AI.
Advantage: People
A Chef’s Analogy and a Human Future
Think of the difference between a chef and a cook. Chefs can cook meals by themselves but are more effective when they focus on strategic work: planning the menu, overseeing the cooks who follow recipes tactically, improvising now and then, and applying finishing touches. Robots have already replaced parts of what cooks do, and technology may suggest recipes — but it still needs the chef to make the final decisions.
Designers can use AI to support ideation, analyze data, generate variations, and predict behavior based on patterns. That frees us to focus on the strategic aspects of design — the designerly skills that are impossible to duplicate. We can use AI to be more efficient, to understand users, stakeholders, and real-world constraints, then collaborate to design successful solutions. What we do won’t change as much as how we do it, with AI augmenting rather than replacing us.
Advantage: People
Resources
Links
- “What Is ChatGPT Doing … and Why Does It Work?,” Stephen Wolfram
- “Why Chatbots Sometimes Act Weird and Spout Nonsense,” Cade Metz
- “What Has Feelings?,” Kristin Andrews and Jonathan Birch
- “Achieving Transformative Sustainability Learning: Engaging Head, Hands And Heart,” Yona Sipos, Bryce Battisti, Kurt Grimm
- “The Ethics Of Persuasion,” Lyndon Cerejo
- “The Future Of Design: Human-Powered Or AI-Driven?,” Keima Kai
- “How AI Technology Will Transform Design,” Nick Babich & Gleb Kuznetsov
Books
- Designerly Ways Of Knowing, Nigel Cross
- Curious: The Desire To Know and Why Your Future Depends On It, Ian Leslie
- Empathy: Why It Matters, And How to Get It, Roman Krznaric
