What’s next for design teams?

AI tools have changed so much for individual designers. The second-order effect is on teams. What is the role of a design team in a company, now that individuals from all backgrounds are able to bring their own designs to life?

I’ve been talking with folks on many types of teams, and have started to see some interesting patterns and opportunities for design teams more broadly.

At a high level, the biggest opportunities are around not just producing design, but multiplying it. Teams need to understand that everyone in an organization can ship design work now, and focus their efforts on helping that grow while simultaneously raising the design standard to a higher level.

1. Pay attention, as an organization

I wrote recently about how people dedicated to paying attention to design will remain an important part of successful product teams. The same thing is true at the organization level. Who is paying attention to company-level design issues?

• Is the brand evolving as desired, and are products evolving to meet it?
• How are leaders talking about and considering design in decisions?
• Are products connected to each other in thoughtful, elegant ways?
• Is anyone making sure the company has a vision for the future?

Promoting and maintaining attention to design at the company level takes education, consistent pressure, and deep knowledge of great design. Design teams should be the source of these efforts, and measured by their success.

2. Keep the bar high, systematically

It’s wonderful that people from every role and background can now visualize and contribute their ideas. But not all ideas, and not all designs, should be immediately merged and pushed live. Endless uncoordinated changes lead to disjointed, clumsy experiences that frustrate customers.

And yet no one wants to be the bad cop blocking their colleagues all day. Many designers I spoke to referenced how their role in maintaining design quality was exhausting as they were asked to review dozens of UI changes each day from all corners of the company.

One solution is redefining the “design system” one more time. An AI design system lives in code and is readable by agents. When that’s in place, designers can build agents, skills, and evals on top of it to be the first reviewer on proposed UI changes. Those agents can look at a well-documented design system and give rational, emotionally neutral feedback on known design system conflicts, reserving human review for higher-level discussions.

3. Be the inspiration

One of the most fun and effective practices my design teams have done at multiple companies is to bring regular, diverse, and thought-provoking inspiration to product and engineering teams. Often this takes the form of a weekly newsletter with snippets of work, things we’ve learned, and external products, ideas, and resources we’ve discovered.

Being the source of this inspiration is a powerful way to spark new ideas, conversations, and influence among the team. It also takes a lot of time to gather material from your team and everything they’re drawing on.

With AI doing the synthesis, this sort of practice is easier than ever. You can set up an agent to gather these resources for you, and simply choose which items to share out. Agents can look further and broader than you ever could, and bring you things that are truly inspirational.

For a design team, this might be an evolution of a content design practice, or a design ops one. In both cases the “customer” will often be an internal one.

4. Create tools and templates

In all of these cases, while doing the work yourself is good, it’s even better to make that work possible for others. You can sometimes just as easily build a tool for others to gather inspiration, prompt an exploration, or map a user journey. Stop thinking of your team as just designers of objects; think of them as designers of processes.

Design teams should have people dedicated to building design tools and templates for others, not just designing products directly.

5. Actually design “the experience”

The title “UX designer” has always been a bit of a reach. Sure, we talk about “designing the experience”, but most of our time has been spent moving pixels between rectangles.

With AI doing the pixel pushing, we’re free to consider the broader experience people have with our products, working at the level of customer journeys, emotional state, and jobs to be done. Blending those solutions with the detail and care for the UI is a powerful combination.

It also keeps us focused on the human with a need, not the display on their screen. Human designers will maintain better intuition about human emotions and desires than an LLM for the foreseeable future, and be a better judge of potential solutions.

Design teams can organize around and focus on customer problems rather than product features. Their impact should be seen in cross-team collaboration and elegant cross-product experiences.

6. Frame the problems

The old UX joke: “How many designers does it take to change a light bulb? Well… does it have to be a light bulb?”

Designers are infamous for reinterpreting and reframing product requests. It’s a habit we just can’t help, and it’s equally valuable and frustrating to our colleagues. In fact, when I trained my design agent on my own behaviors, I started getting frustrated with it too! It’s never easy or comfortable to be challenged on your own ideas.

Yet this is something unique to our role and perspective, and powerful. Designers are skilled at seeing problems through different lenses, comparing and contrasting them, and finding a framing that is elegant and clear.

Design teams should look across the problems they’re being asked to solve, and build a better way of thinking about them. That might mean reorganizing the team around new experience pillars, or proposing a design evolution that guides multiple efforts into one unified direction. A product strategy is also a design, of course.

Now that design and prototyping is more accessible than ever before, the role of a design team is shifting from producing design to multiplying it: focusing attention, setting the bar, building the tools, and framing the problems everyone else is now empowered to solve.

Maybe attention IS really all you need?

The famous paper that kickstarted the LLM revolution found that paying simultaneous attention to everything around a word helps computers understand its meaning far better than other approaches. Where its attention was really mattered.

However, while computers thrive at processing lots of things simultaneously, we humans are notoriously bad at it. Our brains immediately throw away most of the input they receive, in order to focus on what’s most important to them.

AI tools have dramatically expanded my reach as a designer. I now have advanced business strategy and technical capabilities at my fingertips, and it’s given me the ability to build lots of new things. However I find it interesting that I still spend most of my time–my attention–on the interface; the experience; the “design”.

That, I suspect, will continue to be the case for different roles working together on products. Yes, we all have the same access to ever-expanding knowledge and capabilities. And we should all take note of the opportunities and constraints in different areas. But with limited human time and attention, we’ll each end up spending it on the things we are most interested and capable in.

To make something truly great, you’ll still need people who pay most of their attention to the business; others who pay most attention to the technical capabilities; and yes, people who pay most attention to the design.

So maybe attention–in the right places–is all you need.

Product design and AI

AI is potentially the biggest change to computing since the internet. However, its broad potential scope makes it hard to predict specific impacts to our lives and work.

For product design in particular, AI will influence both the products we design and the way we design them. Through my own initial explorations I’ve started to refine what I think those changes will be.

What is design?

First, it’s helpful to clarify what design really is. My definition is that “design is decision-making with visual aids.”

The design process should start by identifying a decision that needs to be made–for instance, what problem to solve, how to solve it, or what to do next–and finish with a decision that all stakeholders understand. Sometimes it can help to initially define that decision in terms of “goals”, but you should still recognize that the end result of the work will be a decision from among options.

In between those two points is the process of creating and evaluating your options. This is the “messy middle”, the uncomfortable space of not knowing…and the reason most designers have jobs.

That’s because this is really hard for most people. Working through lots of ideas is admitting that you don’t know the answer, and you may not know when or even if you will.

Designers have set up lots of processes and structures to make this easier, but it remains a lot of work and deeply uncomfortable for many.

So there are three main parts of the design process:

1) Deciding what you want
2) Creating options
3) Choosing the one(s) you like best.

(This is, perhaps not surprisingly, the same general approach that AI models use to design themselves.)

How can AI be used for design?

The new LLM-powered AI models are fantastic at the second part, creating options. They can very quickly express in text, images, and sound a variety of different solutions for a problem. The constraints of discomfort and exhaustion that humans experience in this process are non-factors for AIs, and they are tens or hundreds of times faster at creation.

So for a typical design project, where you might normally create 2 or 3 options, now you could create 10-15 AI-assisted variations. AI can help with brainstorming differentiated designs, and even create them visually.

Whenever I’m asked how many designers I need for a project, I always answer “as many as we can get.” Every project benefits from more ideas, more perspectives, more diversity, and more options. AI promises to multiply your design options as much as you want.

AI also extends a designer’s capabilities further than before. One of the first powers unlocked by LLMs was writing code. A designer who previously would have stopped at a static Figma design can now make a working prototype for almost any platform. I expect that “design” will come to mean “design and prototyping” in short order.

Another extension is toward customer research. There’s no substitute for actually talking to customers (the effort is the point!), but AI is an efficient tool for researching markets, exploring customer personas, scheduling interviews, and summarizing interview transcripts. It can streamline all of the tasks that surround the actual conversation with a customer.

What new things can we design?

Just as with the metaverse, crypto, and other trends before it, every company today is fashioning itself an “AI” company. Some of these companies are truly centered around LLMs and machine learning; others are simply hopping aboard the hype train. It’s challenging to compare AI to previous changes, because it works in different ways.

At first I compared AI to the smartphone revolution. Smartphones allowed you to do the same things you did on a desktop, from anywhere in the world. But that’s an incomplete comparison, because there are new things you can do with AI that weren’t possible before.

A better analogy is with people, as AI simulates human intelligence and capabilities in particular. The opportunities it offers to the creative process are more like what an intelligent teammate might bring; the opportunities it offers to products are like having a hardworking employee on call for every customer interaction.

Imagine if every product was a custom product, where the customer could have a completely unique offering. Or that your new smartphone had its own hands to interact with any household appliance that didn’t yet have an app. AI unlocks new experiences in a much more flexible way than ever before.

I’m particularly excited about the new interface potential. For the first time, truly non-deterministic interfaces are possible. People won’t have to choose from limited options, or constrain their requests to a particular structure. Anything you can say, you can ask for; and a computer can respond with whatever answer format is best.

When I worked on Google Glass, we had to carefully constrain the voice UI to the particular hotwords we’d trained. “Ok Glass” was chosen for the way its syllables were unique and uncommon in everyday speech, to improve the detection performance, and we designed a branching UI for speech commands that guided you down specific pathways to supported functions. Now voice devices can be much more open to different and natural ways of speaking, and provide unique results better suited to your query.

Or imagine a touch interface that customizes itself for every interaction–new buttons, different layouts, custom graphics. Or one that seamlessly connects speech, touch, and keyboard input with audio, visual, and haptic output. Because the new AI techniques can understand both language and visual input, these interfaces are now possible.

What can’t AIs do?

Despite all these capabilities, AIs by definition can’t tell you what you want, or what you like best–the first and third steps of the design process. And that’s why current AI tools still require careful “prompting”, and have to wait for your response to know if what they created was useful.

So it’s likely that even with future AI tools, the design process will require careful human crafting of the goals and requirements of a desired solution. This is the task of AI “alignment”, a field that extends from basic AI tools up to potential superintelligent AGI and ASI systems.

In traditional product development, this is often the role of the “PRD”–a product requirements document. A well-written PRD can be a helpful resource, while a poorly-written one is a problematic distraction. In the same way, an AI prompt that is carefully considered, crafted, and staged will result in much better outcomes than a brief, vague one. Making the most of multimodal inputs (text, images, speech) can also improve the results. AI can help you draft all of these things…but it can’t tell you what you want.

AIs will also never know whether what they’ve created meets human expectations without checking with the humans again. I expect that remote, asynchronous customer testing platforms like UserTesting and Maze will someday connect with AIs to automate individual tests, but when all that feedback is in (and processed), someone will still have to make the decision about which option is best for the product. Data can guide, but shouldn’t decide–especially while alignment is an unsolved problem.

What should designers do?

Most of my design colleagues feel a mixture of excited and terrified by the advances in AI. I’m the same.

In one potential future, AI assists us in crafting more beautiful and effective designs than we ever could have before. We could solve seemingly unsolvable problems and bring incredible experiences to the world.

In another future, hard-working designers are replaced by tireless AIs, just as elevator operators were by automated lifts. The AIs expand beyond their call-and-response behavior to initiate projects and evaluate solutions.

I think both are possible. Given the exponential progress in AI technology, I would expect AIs to become ever more capable, and eventually surpass human ability in most design skills. I can see enough similarities to machine learning in my own design experience (identifying patterns, connecting stories, “trusting my gut”) that I believe AIs will eventually be able to replicate most of my traditional design process.

At the same time, people who learn to work well alongside AIs will be able to accomplish amazing things. They will guide AIs toward solutions that benefit themselves and the world, and fully express their own creative desires.

In an AI-infused world, a “designer” will continue to be someone who makes decisions about what they want and what they like. People who learn to clearly express their desires and their opinions will thrive. And the more they understand and communicate the needs of others, the more popular their creations will be.

Product designers should focus on the most human, most emotional, most opinionated parts of the design process to succeed in a world with AI. They should learn to deeply empathize with customers, to express their desires in creative and beautiful ways, and to develop their unique sense of style and taste.

Steve Jobs used to highlight how having poets and musicians on the Macintosh team made that product better. Those same talents will be valuable in the next generation of design careers. Sure, AIs can write songs and poems–but they don’t know what to write them about, and they don’t know if they’re any good.

AIs can create products, but humans will need to tell them what they want. That’s still design.

Other people’s problems

I’ve struggled to express why I don’t get too into dramatic books, movies, or (especially) video games despite admiring the craft, but this from a rare Bill Watterson interview is a pretty close approximation:

I’m shockingly ill-read. I’m not proud of this, but when faced with a work of fiction–a book, a movie, or anything–I tend to think, “You know, I’ve got problems of my own.”

Somehow as my own life and work got more complicated, my tolerance for drama in my free time has diminished. Still a sucker for comedy though.

Not sure how that exactly squares with my last post though. See, complicated!

Hope is hard

This is a wonderful way to explain why being hopeful and trying to change the world is hard, from climate scientist Kate Marvel:

Hope is not comfortable. It demands things, drains you, makes you sad and anxious. Hope is the knowledge that we can prevent bad things, and the realization that we might choose not to.

If something is guaranteed to happen, you don’t need hope. That’s faith:

Now faith is the assurance of things hoped for, the conviction of things not seen. – Hebrews 11:1

Hope is for when you’re gonna have to work for it.

Working less, deliberately

Interesting research on how we might be more productive working [4 days a week](https://www.theguardian.com/money/2019/feb/19/four-day-week-trial-study-finds-lower-stress-but-no-cut-in-output), and/or [4 hours a day](https://theweek.com/articles/696644/why-should-work-4-hours-day-according-science).

The basic premise is that humans are severely limited in our cognitive capacity, and working more than that amount of time actually causes us to do worse:

> The [productivity] curve rose steeply at first and peaked at between 10 to 20 hours per week. The curve then turned downward. Scientists who spent 25 hours in the workplace were no more productive than those who spent five. Scientists working 35 hours a week were half as productive as their 20-hours-a-week colleagues.

I recently finished [Why We Sleep](https://smile.amazon.com/dp/B06ZZ1YGJ5/), which makes a persuasive argument that while studying and exercising are important, we only learn and grow when we sleep. Perhaps our cognitive capacity is capped not by the amount we work or study, but by how much we can then solidify through rest?

The 4-hours-a-day article closes with a similar thought:
> This is how we’ve come to believe that world-class performance comes after 10,000 hours of practice. But that’s wrong. It comes after 10,000 hours of deliberate practice, 12,500 hours of deliberate rest, and 30,000 hours of sleep.

Be careful little eyes what you see

The past few years have taught the human race a few surprising things about itself, and they’re not very flattering.

First, we are not the rational creatures we think we are; our decisions are largely driven by emotions, biases, and even unrelated activities. For instance, simply [using hand sanitizer can temporarily change your political beliefs](https://www.washingtonpost.com/news/inspired-life/wp/2017/11/22/at-yale-we-conducted-an-experiment-to-turn-conservatives-into-liberals-the-results-say-a-lot-about-our-political-divisions/).

Second, the new way to exert power in the world is not physical but digital. [Online social networks have immense mindshare and impact on our lives](https://www.simplilearn.com/real-impact-social-media-article).

And third, [dangerous, powerful professionals are using these digital tools to manipulate us](https://www.vox.com/2018/10/19/17990946/twitter-russian-trolls-bots-election-tampering).

Renee DiResta has written [an in-depth article looking at how state-sponsored professional attackers use misinformation to divide and influence society](https://www.ribbonfarm.com/2018/11/28/the-digital-maginot-line/). Increasingly, their strategy is to directly target individual citizens, through the media and social networks, feeding them misinformation to steer their minds in specific directions.

In a warm information war, the human mind is the territory. If you aren’t a combatant, you are the territory. And once a combatant wins over a sufficient number of minds, they have the power to influence culture and society, policy and politics…

Combatants are now focusing on infiltration rather than automation: leveraging real, ideologically-aligned people to inadvertently spread real, ideologically-aligned content instead.

What’s especially dangerous about this kind of polarization is that it’s often good business. Digital influence is cheap, as online advertising platforms love to remind us, and [angry or scared viewers are especially profitable](https://www.nbcnews.com/news/world/fake-news-how-partying-macedonian-teen-earns-thousands-publishing-lies-n692451).

Combatants evolve with remarkable speed, because digital munitions are very close to free. In fact, because of the digital advertising ecosystem, information warfare may even turn a profit.

If you’ve ever felt that a news show, reshared Facebook post, or blog post was designed to rile you up and make you angry…well, it probably was. And this misinformation will only get more extreme and convincing over time, [as technologies like deepfaked videos move into politics](https://www.buzzfeednews.com/article/davidmack/obama-fake-news-jordan-peele-psa-video-buzzfeed#.el7Eqkeo7A).

So what can we do against such attacks? DiResta’s analogy of the [Maginot Line](https://en.wikipedia.org/wiki/Maginot_Line) suggests that our current understanding of how to fight this war is outdated, and she lists several alternative defenses that will require the world to work together against the attackers. Much responsibility lies with the tech platforms to develop and enforce stronger policies and filters, but DiResta also argues:

The government has the ability to create meaningful deterrence, to make it an unquestionably bad idea to interfere in American democracy and manipulate American citizens.

As individuals, meanwhile, we can be far more critical in what we read and believe. Understanding that malevolent forces are constantly trying to manipulate us is a good first step.

We can also be more careful in what we repeat and share with others, checking multiple trusted sources and fact-checkers (like [PolitiFact](https://www.politifact.com/) and [Snopes](https://snopes.com)) before resharing an article with friends or online. The best way to influence Americans, after all, is to get another American they trust to do it for you.

World War III is a guerrilla information war with no division between military and civilian participation. – [Marshall McLuhan](https://www.ribbonfarm.com/2018/11/28/the-digital-maginot-line/)

And there’s never been a better time to [support a professional, free, and independent press](https://www.aclu.org/issues/free-speech/freedom-press). One good way to tell if a news outlet is worth trusting and supporting is, of course, how they cover the news about digital manipulation and misinformation. People and sources that deny manipulation is happening are likely not worth trusting about other things either.

Be careful, little eyes, what you see.

The Automation Charade

> The phrase “robots are taking our jobs” gives technology agency it doesn’t (yet?) possess, whereas “capitalists are making targeted investments in robots designed to weaken and replace human workers so they can get even richer” is less catchy but more accurate. – [The Automation Charade](https://logicmag.io/05-the-automation-charade)

Design by inquiry: Can this be a question?

I recently started a new job with new brilliant, experienced colleagues, and it’s been difficult to make helpful contributions while I’m still learning about the problems we’re working on. Often when I propose a solution it turns out to be already considered and rejected, hopelessly naïve, or entirely misguided. And when asked for my opinion on the ideas of others, I sometimes freeze up and stammer out something noncommittal.

I’ve found that the most useful technique is to constantly ask myself, “Can this be a question?” Specifically, I take whatever proposal I was about to make, and turn it into a question for others.

For instance, if I think we should change a design element from blue to green, I might ask “How did we decide on blue for this?” or “How well is blue working here?” If that doesn’t lead anywhere, I could continue by asking “What are the goals of the color choices?” followed by “Would any other colors do that even better?”. Even if we don’t end up making it green, we’re likely to end up with some improvement, and I’m certain to learn something along the way.

Asking questions like this–something I call “design by inquiry”–has several benefits:

1. It encourages others to share their thoughts and ideas, and puts them in a creative mode rather than a critical one. Often when people hear a strongly-presented idea they feel responsible for pointing out its flaws rather than building constructively on it. And design always benefits from more diverse perspectives.

2. It gives the people with the most context–they’re asking the question, after all–the opportunity to answer it themselves. If an engineer comes to me with a problem, they’ve already started thinking about it. I’d like to hear what they’ve considered already, and what they feel might be best now.

3. It can open up an overly-constrained problem to new opportunities. More often than not, the difficulty in design comes from solving the wrong problem, and restating the question gives everyone a chance to reframe the problem and make sure you’re still looking in the right direction.

In several ways, this is similar to the Socratic method, which is often employed in order to discredit a hypothesis or proposal, and sometimes characterized as “acting dumb”. However, design by inquiry comes from a place of open creativity and “actually being dumb”–as designers always are when starting a new project.

One of my mantras is “be the dumbest person in the room”–to make sure I’m always learning–and that means asking a lot of questions!

No more rock stars

[This is a wonderful takedown of “rock stars” and stopping abuse](https://hypatia.ca/2016/06/21/no-more-rock-stars/)–in the tech industry, but applicable to any field–by Leigh Honeywell. Some of my favorite points:

* Have explicit rules for conduct and enforce them for everyone (of course, you say! But you still have to do it)
* Insist on building a “deep bench” of talent at every level of your organization
* Flatten the organizational hierarchy as much as possible
* Avoid organizations becoming too central to people’s lives (when the job is all they have, people get crazy)

“Rockstar” in tech has become synonymous with narcissist. I avoid any contact with companies or teams who are looking for them.

> A leader is best when people barely know that he exists, not so good when people obey and acclaim him, worst when they despise him. Fail to honor people, They fail to honor you. But of a good leader, who talks little, when his work is done, his aims fulfilled, they will all say, “We did this ourselves.” – [Laozi, _Tao Te Ching_](https://en.wikiquote.org/wiki/Laozi)