On this week's episode of Inside Outside Innovation, we sit down with Wayne Li, author of the new book, Design Empathy and Contextual Awareness. Wayne and I talk about the changing landscape of design and some of the important concepts needed to make better products and services. Let's get started.

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Welcome to another episode of Inside Outside Innovation. I'm your host, Brian Ardinger, and as always, we have another amazing guest. Today we have Wayne Li. He's the author of a new book called Design Empathy and Contextual Awareness: Frames of Reference for the 21st Century Creative. Welcome, Wayne.

Wayne Li: Hi, Brian. Great to be here.

[00:00:59] Brian Ardinger: Wayne, it's great to have you back. Why don't you give a little bit of refresher of who you are and where you've been? 

[00:01:04] Wayne Li: Happy to do so. It's been a wonderful journey up to this point. My background is degrees in fine arts and in engineering and product design. Wasn't always a professor, right?

I started, besides my collegiate career, working at design firm such as like IDEO product development and Design Edge in Austin, Texas. And then wound up working at Ford Motor Company and Volkswagen as a car designer. So started out as a vehicle engineer doing chassis and body systems. Helping to actually design the infrastructure of a car and then moved into the studio where you draw and sculpt cars.

So, for listeners who are interested in that, in that side of the business, you're thinking about the aesthetic of the vehicle, the psychology of the vehicle, its placement in society, and how people use it. People in today's parlance is user experience, right? The user experience of a car. I was at Ford for five years.

At Volkswagen I was in the electronic research unit and then I wound up graduate school on the west coast at Stanford University. Was doing what they call interaction design back then, which is human machine interface. What is the interior of the car, how do the controls work, how does the interior look?

And so they have an electronics research lab in Western California right in the Northern Bay area that couples with their advanced studio in Semi Valley in Southern California helping design advanced concept cars and things like that. Stanford's work was creating this philosophy, you now know it's called design thinking.

Or human-centered design. So that was the very first initial work into how to understand how your creative process incorporates different aspects of your mind. In design thinking, they look at triple Venn diagram, so they look at technology, which is like the engineering side. They would call that say like feasibility. And then they have like the business circle, right? Which is, can we make this, can we produce it? Can we sell it for a profit? Now it would be like it's viability. And then there's the desirability or usability side, which is the art or the, the human element. Do people like it? Is it beautiful to look at? Is it easy to use? All those kind of psychological principles. So, Stanford helped to kind of create that philosophy.

Then after that really got into the academia space. I did dabble after graduate school for five years, working at Williams Sonoma as part of the creative staff at Pottery Barn. Home decor products, furniture, all those types of things where we're part of that creative process.

Wound up teaching. Taught at Stanford for seven years, and now I'm at Georgia Tech. They've kind of pulled me from the west coast to the east coast. So I've set up a design thinking center here called the Design Block Innovation Design Collaborative. Stanford's where we said that we were the founding class of, was the Hasso Plattner Institute of Design, which now people know as the D School. Helped kind of set up a similar center here at Georgia Tech, and I've been teaching here for almost 15 years now. It has been a wonderful journey to shepherd students and creatives and young professionals, and I do consulting on the side, so I do work with Fortune 500 companies to figure out their business processes and things like that, their product mixes, that type of thing. Design strategy, product management, all that good stuff. 

[00:03:54] Brian Ardinger: You cover so much. Again, you've been in the trenches. You helped teach. One of the reasons I wanted to have you back on is the fact that I think you can give a lot of insights into the real world stuff. Not just, you know, what's happening today and student perspective and that.

But I wanted to dive into your new book. There's a lot of design books out there. Obviously, design has gotten hotter over the years and it's always been a topic that's, I think, super important for early-stage innovation, to really understand what problem you're solving and then the practical steps of trying to solve that particular problem.

So, the book's called Design, Empathy, and Contextual Awareness. Talk about what that actually means. Contextual awareness. I think a lot of people don't think of design as the context around that. 

[00:04:35] Wayne Li: There's two parts of this, right? So, one is if you think about the customer themselves and the infrastructure around them. So, design empathy is really leveraging all aspects of interest empathy.

We talk about in the book that there are different types of empathy. There's compassionate, there's cognitive, there's emotional, and there's compassionate concern. Utilizing all three in tandem to actually inform your ideas is one way of thinking about design empathy. And then what are those techniques?

You're designing a baby stroller. How do you get into the mindset of a haired parent or how do you go and method act yourself at age two? Part of that is like there are method acting techniques, there are interviewing techniques, there are field observation. The way I like to look at this book is kind of think of it in thirds, right?

I know that when I wrote the proposal to write the manuscript, I was like, there'll be some things that are kind of anecdotal. Anecdotes of me being a designer. And then learning things that I think other students of design or creative professionals, so whether you're a third-year university student in industrial design or architecture, or you're a first-year creative professional. Like a music producer or just, or first-time software creator, right. The making developer.

This book has something for you because it's got a little bit of that Malcolm Gladwell anecdote faith. You're like, what? I did not know that about the field of design. So that's one part of it, and that includes interviews and things like that.

We've got an interview with the co-founder of IDEO, David Kelly. We got an interview with the head of design products at Royal College of Art, Christina Choi. So, there'll be like anecdotes about, you know, things like that. Then, then there's a third business case studies. So you're like, why did this product win and this product fail? Like why did the Nintendo, we work so well with underdeveloped hardware, right? 

And beat the pants off the PS 2 and the Xbox 360. Like why did that happen? So, there are business cases and like that to show how well do you know what your customer really wants and how well do you assume you know the customer.

A lot of times when we're in business, people just assume they know what customer wants best, and then they just create something. Part of that is, again, if you're a young entrepreneur, a young creative professional, there are ways in which you can really tap into knowing what the customer wants and feeling what the customer feels more than just learning rote what a requirement might be.

The reverse out of that, not customer is producer. The contextual awareness is how well do I know how much this person uses, this thing, where they use it, how they use it, and then my ability to actually build the thing they want at the scale of which they want. This. Now is the context of do I have the supply chain for it? How would this thing get made? Is the society ready for it? How so? Have there been others who have tried knowing the context of the use and of the production and your role? Either as a singular entrepreneur or as a member of a team, how well your company can address that issue. That's contextual awareness.

[00:07:32] Brian Ardinger: That context plays such an important point, I think back to like product, like a GoPro. Yeah. You know, when it first came out, it, it wasn't the best camera around or had the best visuals, et cetera, but the context was it could take pictures when it was strapped to your helmet and going down a ski slope.

Okay. And the context changed the value proposition of the whole product based on that particular use case scenario and understanding how a person will use that. Again, you can take two similar types of quotes, quote unquote feature sets or products, and yet the context changes their desirability, the viability, the feasibility around it.

[00:08:07] Wayne Li: I couldn't agree with you more, Brian. I mean like that is why contextual awareness is so important. I was just at an executive consulting training. I said there's always more than one way to juice. There's always like, you know, you can have a hand mixologist cocktail with a handmade strainer, or you can have like this giant like orange juicer Cuisinart device.

Like they devise different contexts. One's at a bar as a performance with a single customer. One is, I'm making smoothie for like Orange Juice Barn. Right. So that's different context, even though they're both juicers. The idea of yes, the GoPro made choices. They didn't need the best image sensor because if you wrecked a 40-megapixel image sensor, you'd be out of luck cause your retail cost would be too high. 

And consider the context. You're going down a ski slope, it's got to be waterproof. You're surfing or you're skateboarding. This thing has to be rough and tumble. Yeah. So you don't want to destroy a hugely expensive piece of glass. You have to use a polycarbonate lens because that's the context of use.

But what did that do? The empathy was I have a wonderful ex sports athlete. Let me show you my point of view. By providing that human, that ability to communicate, they've always had that requirement to show you their skill, to show you their performance, to show you their trade, and to to promote themselves.

Because now all of a sudden you see that video like, wow, I want to do that. I always say it's not inventing demand; it's identifying implicit demand that has always been there, that has never been capitalized upon. Your empathy skill unlocks that. Your contextual awareness allows you to leverage it. I want you to identify it. Then you need to leverage it. So, like that's these one, two tandems, right? We're really talking about early-stage product ideation, product development. 

[00:09:54] Brian Ardinger: How hard is it to teach these skills or this thought process? You know, I you think a lot of times new founders, developers, et cetera, you know, they want to build product and they're good at doing that, but they oftentimes jump over the step of really understanding the customer, or they think they know the customer better than the customer themselves. How hard is it to teach these particular skill sets or, or mindsets or tool sets around it? 

[00:10:17] Wayne Li: Yes. I remember back in grad school; I wrote my thesis topic about are we human centered designers or ego centered designers? And like, you know, again, there's a balance, right? If you believe you are the world's gift to high fashion like I'm Versace or you know, something like that, right?

Like I am God's gift to, then you probably will think you know the most, the best about what is high fashion. You are the artist that will drive the trend. Yeah, that's ego centered design. The reverse side of that is this human-centered design. This need-based centered design of unlocking that implicit need that is there.

It's always been there. Now, whether or not you're prescient, some ego centered designers just have their thumb on the market. They just are prescient in some kind of way. I just knew that about people. Okay. That's one side of it, but you can teach it to be more consistent, because if you just guess, that's not gonna be great.

There are techniques in which you could do so. Part of that, of that is that humility to be able to say, even though I'm the creator of a new technology, a new fashion, a new trend, how well do I actually understand the people I'm targeting, the people I care about. How well do I know their lives, their problems, their issues, and that the thing I'm building addresses those issues.

And those issues are implicit. They're underneath the surface. So on the one hand, if you have a very egotistical designer coming in, you kind of have to break them on that a little bit. And usually, the ones that are most egotistical are the ones that, like you said, think they know everything about the customer they already know.

Now, the way to disrupt that is to actually. Make them prove it.

[00:11:48] Brian Ardinger: Show me the evidence.

[00:11:49] Wayne Li: So you say, okay, you say, well, you say you know everything about teenage skateboarders. You're making some new fangled boost four, you know, motorized, e scooter, skateboard. Great. Tell me what you know. They'll say they like speed or they like aggressive da da, da.

And then you, okay, great. Now let me do, use my techniques and spend the day or two with those people. And let's compare notes at the end of the day. And you know, and if the notes are very simpler, then you probably do know a lot. If I could find something that you had never heard of such that, like for example, young skateboarders like to show tricks off to each other and they show tricks off each other, not only to compel each other to outperform each other, but to bond with each other.

If you did not know that, and it was all about the speed of the motor or how fast you can go on the skateboard, then you really, truly didn't understand your customer at all. The other, my thing being like they show their personality through their board. Not the wheels, only the stickers. Did you know that about your company?

The only way you would've known that let's say, would be as if you had spent time with them to see how they display their personality through the equipment they use. And so that becomes very key, right? Your ability to do field observation, your ability to interview. And again, for young entrepreneurs who do know how to do this, do this intrinsically. That's great. There are techniques in which they can unlock even more. And so there are methods in the book that describe how you might unlock more. 

[00:13:13] Brian Ardinger: Do you think corporate innovation oftentimes stumbles on this problem? Because they, again, have existing customers, so they have a natural bias. We know what the customer wants, we're just going to continue to go down a particular path. And so there, well, I guess you also have potentially the fear of the fact that what if we're wrong? Yeah. And corporates don't like being wrong. 

[00:13:30] Wayne Li: Yeah. In my dealings with corporate. With different, with corporate customers. Yeah. I mean obviously not like they don't spend money on customer discovery, right? Yeah. On their side. It's interesting because what I look for in Fortune 500 companies is how much they balanced their quantitative analysis with their qualitative. They're going to say something like this. Yes. We did a marketing study seven years ago. We did a marketing study on 17-year-olds, males for this target demographic.

We think we know that customer base. I would say, how long ago would you do that study? Seven years ago. Well, those 17-year-olds are now 24. Right. So, they're, they're no longer in the market. They're in the 25 region. So, 17 to 24. That's too old. You have to do a new study cause there's a new generation of that.

And now how did you word that quantitative marketing study. How good is that data? So, what I look into, like what was it that you were looking at? Qualitatively, you might have missed something that was never in something you could have asked on a numerical survey. Again, if you did not spend time with that skateboarder, you didn't know they would not use customization on their wheels, only the stickers that are on their skateboard. Your marketing study would've showed. How much would you like to customize on a scale of one to 10 your skateboard? You would not know with finality what to customize. You just know that customization is important on scaled one to 10.

So, part of this is coupling qualitative analysis of customers in the field talking to them, doing the processes and running through the activities that they do with the quantitative, the big data sets, the things that you could already generate through the numerical customer databases you already have.

So, I always look for that balance. How current is their qualitative knowledge with the demographic and then the quantitative. How much are you relying only on the quantitative? And again, you have spent money doing that survey for customer analysis. They totally understand if you crowdsource something, you spend money on it. But you're only as good as the survey instrument you took it with.

So, if you write, ask the wrong question, you're going to get the wrong data. So that's the other thing, just to remember, you're only as good as the curated taste of the questionnaire that you chose to give out. 

[00:15:41] Brian Ardinger: The last main topic I kind of want to talk about is when people think of design, a lot of times they think about website design or, or mobile design or things like that. We have this new quote unquote technology called AI. Yeah. That seems to be changing the way people interact with things and that, right. So, I'd love to get your take on how AI is impacting the design industry. 

[00:16:01] Wayne Li: This fall, I'm teaching a class in AI and design for school. Listen, AI is a wonderful technology. It is a tool. It's a tool to help designers. But here's the thing, it's not going to replace designers. It will improve upon your process. It may illuminate and give you information or areas of exploration that you perhaps you didn't think of. But at the end of the day, AI is not replicating taste. You're only as good as the database that it is built off of.

Let me give you an example what I mean by that. My understanding of large language models would be something like IBM Watson, they gestated all the world's data, every single encyclopedia, all the New York Times, everything that's ever been printed on the internet. So, if you ask it a factual question, what large language model does is it looks for occurrence and proximity.

So, if you say, George Washington is blank, blank president, it'll find out and go, okay, well, or who's the first president? It will go out and search everything that's ever been written about. Then say and go, well, there's a high proximity of the words George Washington next to first president. So it's going to be accurate pretty almost all the time, because every source it finds from the Encyclopedia Britannica to Wikipedia to the New York Times, if that sentence has been uttered, it's got it correct.

Now that's devoid of context. From what I remember, I think reading in the, in some of the public papers, obviously winning in Jeopardy, that's easy because they're all factual ideas. Now we'll take a different context. Prescribing the right medicine for people. If your data set comes in and someone's like, okay, I'm analyzing this group, this person, and this person is overweight and has headaches, so this person needs insulin and Tylenol.

Okay, so the prescription writes those two in proximity. The database has that. If an algorithm like IBM Watson, now all of a sudden, the next person comes in is like, I've got a headache. Then they go, well, you know what you need? You need insulin and Tylenol. The proximity in the database, like there's no context of that person had a sprained ankle and a headache.

Or on the reverse side, if there are more prone to a specific ailment like sickle cell anemia, now all of a sudden, if you're hospital, if you take all the prescriptions of a certain geographic area and then everyone gets insulin with their Tylenol, well that's a bad prescription. So, part of this is understanding, the context with when those prescriptions are written, who they're written for, how they're based by race or genetic proclivity, those types of things. Is the AI smart enough to know that difference? Not yet because the databases aren't that smart. 

[00:18:39] Brian Ardinger: What are your thoughts on from the interaction side itself? So right now, you know, most people interact with AI through some type of chat bot type of environment. It's less visual, it's a different design aesthetic, I suppose. Do you see that changing or, or how do you think that interaction with the technology might influence design or, 

[00:18:59] Wayne Li: I would say that we're more visual creatures than verbal. I mean, they've gotten to the point now with those, like with chat bots and things like that, right? GBT, they have this whole thing now called prompt engineering. How do I get the right prompt to get the right actual answer that is actually useful for me based on how the database was programmed, right? So now there's a whole, how do I human suss it out? How the humans program the database.

Yeah, that's really tricky. It depends on how the machine learning program ingested that information and how it's using the information ingested. From a prompt engineering standpoint. Yeah. I find that rather cumbersome, right? It would probably be best such that you have a more natural language idea of what it is you use or what I instructed my students is know how to utilize the algorithm for what it is for.

So if you know that the database is useful for understanding the common bell curve, you know this data is about frequency of occurrence and proximity of those words being together. It's not on the extreme user case. It's not having a specialist talk about a special topic. It's about what is the common topic most of the time.

Then you could use it to generate things. You would prompt it in a way that makes sense. You know, this is the most common knowledge, the most commonly spoken. So you would say something like, give me the top 10 areas that I think toys would be used in. That would be a general answer. and can give you a good top 10 different things. But it wouldn't necessarily be something specific such that you might say something like this. For someone who is neurodivergent 2-year-old female, to truly understand and empathize with a toy teddy bear.

Now we're looking at child development. A very specific thing. That one, I would interview a child development specialist in that subject. I wouldn't ask that of chat GPT because it's doubtful they've actually scanned that. I would know how to curate that use. Now, I think over time, the avatars that represent these different things, Watson was represented with a kind of swirling circle of colored discs.

Sooner later, they're going to have an avatar to it. So now you'll be like, oh, you're Co-pilot, or you're IBM Watson, or you know you're Chat GPT. You'd probably be interacting with a digital avatar that actually resembles what type of knowledge you have. Are you looking for generalist knowledge? Are you looking for specific behavioral development knowledge? And now you'll have different avatars that switch between the different data sets that are useful for whatever that purpose is.

[00:21:30] Brian Ardinger: It'll be, you know, it'll be interesting to see, will there be like a design layer on top of the traditional audio or word input that you get? Or how fast can the technology evolve to read visual cues off a camera that you have in your eyeglasses or something along those lines. So it, the contact is more being fed to the LLM on a consistent basis. 

[00:21:52] Wayne Li: No, I agree with you. I mean, people who have read Kurzweil, that building on what you just said, people who read Kurzweil while he, he also mentions that same thing, right? It's like you have digital avatars, like this kind of digital layer that then represent the truthfulness of the database that you are running against, right? So then if you have this intermediary like, oh, that's Bob, or if you say that's Watson and then Watson's visually will represent, or through the user interface itself will represent authentically how they will be able to serve you off the right information. And that will help drive you into the right personality type of what AI you want to deal with. 

[00:22:29] Brian Ardinger: So last question is, what are you looking forward to most? What are the biggest challenge or opportunities you see in your field and in endeavors that you're working on?

[00:22:38] Wayne Li: On the one hand, definitely we are, at least at Georgia Tech, we are looking at, you know, the use of AIs, how we're doing it. I have a lab besides the kind of Design Block and the books that I'm writing, I do research in the automotive and mobility space. And so like I can see a lot of AI being utilized for autonomous driving, facial recognition.

We have an algorithm in our lab that already does real-time visual facial recognition. You know if there's a camera facing into the driver's situation, but just by reading their face. Can that AI go, yep, this person's starting to get tense. And if that informs then the driving experience. Right. I see a wonderful area of research. How might visual facial recognition, visual cognition, that help pave the way along with sensors and mobility to the fully, truly level five autonomous vehicle. I can see that being used. I can see robotics and how that may help through healthcare, warehouse, logistical, supply chain, all of those types of things. How might the use of AI robotics help that situation as well?

So there, there's a ton in healthcare infrastructure mobility where I'm really, really excited to see where AI can help empower people. It doesn't replace people, it empowers them to perform better. 

[00:23:54] Brian Ardinger: We are unfortunately out of time, but I would like to give you an opportunity to let people know what's the best way to reach you or learn about the book.

[00:24:02] Wayne Li: If you're interested in the book, you can look my name up, Wayne Li, last name spelled LI, Design Empathy and Contextual Awareness. It's on Amazon now listed. Now we can easily just cross search that. My email, if people are really interested reaching out to me directly is my name. You can look at gatech.edu, GATECH.edu, and type up Wayne Li in a Google search and email, WLI the number 78 @gatech.edu. So, feel free to reach out. Always happy to rap about design, rap up about AI.

If you are a company thinking about management consulting, I do offer consulting services, executive education, those types of things too. So always happy to talk about that through our channels here at Georgia Tech or globally in that situation.

[00:24:47] Brian Ardinger: Excellent. Well, thank you Wayne for coming on Inside Outside Innovation. Looking forward to diving into the book and the next series that you come out with as well. So, appreciate you being on the podcast. 

[00:24:55] Wayne Li: Likewise, Brian, wonderful pleasure talking with you. Thanks a lot. 

[00:24:59] Brian Ardinger: Thank you. That's it for another episode of Inside Outside Innovation. If you want to learn more about our team, our content, our services, check out InsideOutside.io or follow us on Twitter @theIOpodcast or @Ardinger. Until next time, go out and innovate.

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