Photo by Zura Modebadze: https://www.pexels.com/photo/person-in-black-jacket-standing-on-brown-field-4400664/

AI won't replace you as a designer, but it will undoubtedly change which design skills are valuable to employers.

This was partially inspired by a tweet by Kent Beck, who said 90% of his skills were now worth $0, while the remaining 10% were now worth 1000x the value.

AI will not be as devastating to design as his skillset (as a copywriter or editor). However, after using ChatGPT and Midjourney AI for the past few months, there is a significant push (along with Product Design overtaking UX Design) toward employers valuing different Design skills that you may be used to.

While I can't predict all the design skills valuable in the future, I can at least tell you one: Can you, with your design work, explain the "Why?"

Understanding that, and the Golden Circle Model, may help you distinguish yourself if you're seeking UX or Design jobs right now.

Why-How-What: The Golden Circle Model of design communication

The Golden Circle Model is developed by Simon Sinek, a world-famous business consultant, and it will be essential moving forward.

The Golden Circle model consists of 3 concentric circles, which ask one of three questions:

1. What did you do?
2. How did you do that?
3. Why did you do that?

https://www.smartinsights.com/digital-marketing-strategy/online-value-proposition/start-with-why-creating-a-value-proposition-with-the-golden-circle-model/

Sinek says that most people operate on the first two layers in an organization, with only a few visionaries or deep thinkers able to get to the core message of "Why," which is how the organization is guided (i.e., through the CEO's vision and whatnot).

However, the Golden Circle Model also aligns well with design communication, as these are the three levels we touch on through our design process. In that case, the questions are altered a little bit to this:

1. What did you do?
2. How did you come to that conclusion/design?
3. Why are you designing something?

Understanding these questions will become increasingly important as AI continues to develop. Because while AI (to some degree) can address the first question, it cannot address the 2nd or 3rd question.

So if you address those questions in your portfolio, it's easier for your work to stand by itself.

Talking about the process (and addressing the why) will increasingly become important

Melody Koh, a Senior Product Designer, and Hiring manager, went through 138 UX portfolios and found that many aspiring UX professionals fail to show a well-thought-out UX process.

There were three common reasons she failed UX portfolios:

1. UI work was shown without showing the process

2. The process was shown without UI work

3. Shallow UX work (i.e., not showing critical thinking or relevant application of skills)

In other words, these are the people that weren't talking at all about the "Why" (or even the "How") of the process. Instead, they were either expecting that the visuals (or the right combination of keywords) by themselves were enough to get them the job.

These are mistakes that Junior Designers cannot afford to make in the age of AI. It's not enough when anyone can log into ChatGPT and spit out the right keywords highlighting the design process or log into Midjourney AI and get something that looks halfway decent.

While AI can't do everything, people believe in it enough to think of it as a replacement for good UX work (including the ridiculous notion of AI-based user-less research, which thankfully seems to have been debunked).

This is why it's not enough to talk about "What you did," even if it looks nice. Instead, it's more important to consider the "Why" of your process because no AI can answer that for you.

Crucially, the "Why" helps do something that no AI can replicate: considering feedback and iterating on designs.

Understanding the Why is more crucial than ever.

I know the "Why" will matter more than ever because of one fundamental truth about designs: you never nail it 100% on your first design iteration.

This is a fundamental truth of our field, and that will not change: there will always be a need to iterate, test, gather user feedback, and more to understand what does and doesn't work.

On the other hand, ChatGPT (and Midjourney AI) are called Generative AI for a reason: they're quicker than humans at generating ideas, essays, images, and more from prompts. However (and this is important), they're not great at fine-tuning something further.

In other words, consider these AIs as the next upgrade to your sketching tools. Instead of pen and Post-It notes, you can generate something much higher quality in a concise amount of time. However, you don't build your sketches; you're likely to refine, test, and iterate on them.

This is where the "Why" comes in and what will be valuable to this iterative process. Here are some of the "Whys" and "Hows" you should consider:

• Why did you choose one design idea over another?
• Why did you use this design pattern for this feature?
• Why are you recommending a specific design to your team?
• What did users say, and why keep some features and change others?
• How do you iterate on user feedback?
• Which design recommendations do you prioritize if you can't fix them all?

These are judgment calls that no AI will ever be able to make because they're not good at it (and few companies will try and fix this). This was discovered in the 1960s by Paul Fitts (of Fitts' Law fame), and few things have changed about machines since then.

Source: https://link.springer.com/article/10.1007/s10111-011-0188-1

No matter how beautiful the first design iteration looks, it will change based on user feedback, organizational constraints, and more. This means fine-tuning the designs rather than starting from scratch again, which AIs aren't well-equipped to do.

These judgment calls are what humans will (almost) always be better at for a straightforward reason: it's hard to use Data alone to drive Design decisions. If you (or your business) think you can A/B test your way to the best design iteration, I encourage you to read my book about Data-Informed UX Design.

My intuition tells me that design's bulk of value might shift away from the 1st design iteration (i.e., sketching and generating designs) towards 2nd or 3rd iterations.

Because these tools will make it easier than ever to paint in broad strokes, the ability to quickly sketch something up is going to become less valuable (if it wasn't already).

Yet the ability to fine-tune based on user feedback, make the right judgment calls, and more on 2nd iterations will be more valuable than ever. Businesses don't want to publish sketches, even if they seem well-polished, so understanding what works and doesn't work will be a crucial skill Designers can master to stand out in the crowd.

This means understanding the "Why" behind each action you do (or don't) take will be a valuable skill to master.

AI will change which design skills are valuable to employers

Your visual design skills will still be essential as a designer, but the distribution of what brings value to organizations will shift with AI.

But this has happened before, whether you realize it or not. For example, pre-pandemic, there was a period where businesses valued a designer's skill to run workshops.

However, it's a safe bet that understanding the "Whys" will bring value to organizations (and get you hired) even with AI's possibilities.

This means a few different skills might become increasingly valuable:

1. Help Product (and yourself) to understand precisely what the problem is through problem framing
2. Understanding how to create Generative AI prompts (i.e., "Prompt Engineering") and incorporating them into your Divergent Thinking process
3. Understanding and analyzing user feedback (or other analytics data) to understand what works (or doesn't work)
4. Tweaking the design and understanding it well enough to facilitate design-developer handoff and answer other questions
5. Understanding the value and being tied more to Product-based ideas (Strategy, Business models, user motivations for building communities, etc.)

AI can also help you do tasks that you might otherwise find tedious. For example, ChatGPT is an excellent source for first drafts of design documentation, especially when creating tables of color palettes or others.

However, understanding what users like (or don't like) and tweaking designs will likely become one of the core skills that will fit into every job description in the future.

So if you haven't begun already, learn to ask questions and ask yourself "Why" whenever you choose to take specific design actions. Whether through user psychology, general best practices, or previous user feedback, understanding which design choices to make and why will be at the center of any design work that you'll do moving forward.

Designers that understand the "Why," in turn, are the ones that have less to fear from AI, as they have skills that will make them valuable (and hireable).

Kai Wong is a Senior Product Designer, Data-Informed Design Author, and Data and Design newsletter author. His new free book, The Resilient UX Professional, provides real-world advice to get your first UX job and advance your UX career.