S8E3 Sentient Design for Intelligent Interfaces

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Hello friends and welcome to season eight of Inside Techcom with Zahara Mutabana.
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This season features a collection of conversations that explore the ideas, challenges, and opportunities shaping technical communication today.
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From AI and content strategy to leadership, product thinking, and the future of our profession.
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Each episode offers a fresh perspective from people doing the work.
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Let's get started.
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Today's guests that I have are Josh Clark and Veronica Kindred.
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They are the co-authors of Sentient Design.
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And Josh is the founder of Big Medium and a longtime design leader known for helping organizations navigate major shifts in digital product design.
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Veronica is a product designer and design technologist whose work explores how AI is reshaping the way we design intelligent experiences.
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So we are going to dig into intelligent experiences and what does sanction design mean and just learn more about their world and how it impacts our field technical communication.
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So, Josh and Veronica, welcome to my show.
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Sora, so happy to be here.
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Thank you so much for having us.
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Yeah, thank you.
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Thank you so much for taking the time so early in the morning.
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So I want to, you know, kind of have both of you introduce yourselves.
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Terrific.
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Well, why don't I start?
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As you said, I lead a design agency called Big Medium.
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And a lot of what we do is helping companies design for what's next.
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And that changes from year to year.
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Most recently, you might have heard of this thing called AI.
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It's sort of a it's turning into a sort of a big deal.
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Everyone's obviously thinking about it.
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What is this thing?
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I mean, I think that's also that is a real question, you know?
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It's like, what is this?
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What does this mean for us?
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And so one of the things that we're doing as a product design strategy company and product design company as well, is helping companies figure out what does this mean for how it changes the experience of products?
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And so Veronica and I have written a book called Sentient Design, as you said, which kind of codifies the practice that we've developed around designing intelligent interfaces.
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And we'll talk about that some more.
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I will let Veronica introduce herself, but I will say that we are very different generations, that I've been doing this for 30 years, and Veronica is coming into this somewhat new in her career, and we can talk a little bit too about why that's been so important to have those two perspectives.
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But Veronica, over to you.
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Yeah, that's right.
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And um, for listeners who can't see us, I'll politely say that Josh has a few more gray hairs than I do.
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Hey, hey.
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All respect, all respect.
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But yeah, that's right.
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I was in my last year of school when ChatGPT came out when OpenAI dropped ChatGPT in the world, and LLMs really exploded onto the world.
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And I remember my data science professor walking into the classroom very somber one day and just being like, the world has changed.
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And we were all like, what?
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Because like no one had ever really heard of it.
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And then by of course, like the next week, the whole campus was like flooded with LLM-generated content.
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But yeah, that's right.
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So my whole career in design has really been kind of AI native.
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And I've spent the past couple of years working at Big Medium, and I've spent the past couple of years working at Big Medium and really exploring AI as a modality with design.
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Yeah.
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And you know, I mean, something that I'll say is Veronica is not only my colleague, but she is my daughter, and I'm very proud of her in a lot of different ways.
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Okay.
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So we are we are very literally two generations of design.
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Oh my god, that's so beautiful.
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I'm so glad that you shared that with us.
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So that's fair, Veronica.
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He's definitely many, many gray hairs ahead of me, if I may say so.
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She's allowed to say.
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She's allowed to say.
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All right, and Veronica and Josh, I mean, congratulations.
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This is such an amazing thing to have a father-daughter project.
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It's been a personal and professional highlight for me to I can imagine.
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As a parent, I know.
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It's been very fun.
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I think like writing a book is not always the most pleasant task and it is so solitary.
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I think it's really nice to be able to go through it with a close family friend.
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Absolutely.
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Absolutely.
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I think too, that's just sort of what we're saying is like, as you heard, Veronica doesn't yet have my experience.
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You know, I've been doing this and I've seen a lot of different transitions over the past 30 years, and Veronica's coming to this new.
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So she doesn't yet have my experience, but she's also not burdened by it, which I think is sort of an important thing right now, where what we're seeing is that the possibilities that AI opens for user experience means that we're able to make things that we weren't able to make before and experiences to provide to customers and users that we weren't able to do before.
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And that requires us to go back and say, are those best practices still best practices?
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Just because we've been doing it this way, should we continue doing it this way, or is there a better way?
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And I think having Veronica's fresh eyes has really changed the way that I have worked.
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And I think our practice around this wouldn't be the same.
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And the book certainly wouldn't have been the same without her.
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I couldn't have done this without Veronica.
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Stop it.
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Oh, well, so we are going to have two different perspectives.
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And this is almost like I feel like an inflection point in our field, and where we are not just pivoting, but kind of rethinking and challenging some of the old ways of doing things.
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So what a great way for you two to bring that perspective.
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And you said, what is AI?
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So to give that scope and in your words, describe AI and how it kind of lends itself to this new design material that, you know, rather than a simple tool.
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Yeah, it's a great and surprisingly complex question.
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I mean, we think of AI as this new thing, but it's been with us for gosh, over 75 years now, the better part of a century in one form or another, and it adapts.
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And the really interesting thing, and we see that dialogue happening right now, is at every new step, there's this question of like, is this artificial intelligence?
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Is this it?
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And the conversation now, is this AGI?
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Is it coming?
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And at every stage, kind of the goalposts keep changing.
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So when we first taught a computer how to play checkers, it's like, well, that's not real intelligence.
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That's just sort of rule following.
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Well, what about chess?
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Oh, no, that's not it.
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Well, what about Go?
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No, that's not it.
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And so the our expectations of what AI means is really different.
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If we had shown the people 75 years ago what large language models can do now, everyone's heads would have exploded.
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I mean, like Veronica was saying, on campus, people's heads did explode when it sort of came out.
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I mean, it's amazing how quickly we say, oh, you know what, that we become used to it.
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I mean, the the things that large language models enable now or the experiences we can have are truly incredibly different from what computing was just five years ago.
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So the way that we're thinking about it is what do we do with this latest era of large language models, things that can do some kinds of reasoning well, but also are incredibly stupid in other places.
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What we talk about with sentient design is it's the practice of creating intelligent interfaces, which are experiences that are aware of context and intent with the agency to adapt to the user in the moment.
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And that can take a whole range of changes.
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But essentially what we're talking about is what happens if we share design decisions in the moment with AI and we teach AI within our guardrails, give it a creative brief and the rules to decide, to make content or behavioral or structural or visual changes in the interface.
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Almost as if a designer was there being like, oh, I can create a custom interface for you in the moment.
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So this can be as wild as websites designing themselves, or small little interventions, or AI having some presence as just another user and a multiplayer experience driving its own cursor around your Figma or Canva board and making comments on your document, just like your colleagues would.
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That's a lot.
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But I mean, that's the idea is like, oh, we can make our experiences more intelligent.
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That's different than how do we use AI to produce stuff, to do the stuff we already do.
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It's about creating genuinely new experiences.
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And thank you for taking the time to kind of explain how you see AI.
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And then what I caught was the experiences that are aware and can adapt.
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Essentially, that's to me, that means that intelligence comes with that definition of sentient design.
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That's right.
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And I don't know, Veronica, it's like we hear a lot of sort of questions, sentient design, eh?
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That seems like an ambitious title.
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Yes, yeah.
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And what we'll say, we don't mean sentient in the in the literal sense, but by sentience, we do mean that awareness of context and intent.
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One of the things that has really taken me and that I've become increasingly interested in in the past year or two is really the combination of probabilistic and deterministic systems.
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So when we talk about things that are aware of context and intent, that's an LLM, right?
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Like you can say something and it will an LLM will understand what you mean.
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You don't have to have like an exact, you don't have to know SQL to be able to manipulate data anymore.
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You should still know data principles, but you know, that's a whole nother thing.
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But combining these algorithms that are probabilistic, like an LLM with deterministic systems, more of like old-fashioned algorithms that can be selectors, unlocks so many possibilities.
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And I think that's really what we're talking about when Josh is talking about kind of systems that can design themselves and systems that are like having a designer there for you in the moment, because you can combine prefab selections that a designer has approved and that makes sense for the customer experience and for the journey, and that's all been made and decided.
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But an LLM can come in and probabilistically choose the right one for the moment.
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So that's the combination of things that gets really interesting because there's no customer journey, there's no happy path, but you still have these reliable pieces, Lego pieces that you can like build together for an experience.
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I'm thinking doing something without a customer involved is a pretty bold thing to do, right?
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So to be clear, right, it has to begin with the customer's needs and of course the business needs too.
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And how do you marry those?
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And I think the the way that we've managed that traditionally is by defining some sort of course personas or segments and then designing static paths for those.
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It's the best we could do, right?
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It's like in truth, there's a million different scenarios that users bring to things.
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And we can't historically design a million scenarios.
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So maybe we design seven different dashboards for these different scenarios, or people individually create things.
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And then over time, it's like, oh, we've got a thousand dashboards that now people have to navigate.
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And so the idea here is not to say, oh, we don't care about what the user thinks, or that we're not designing around those user needs.
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But rather we're saying, what could we do if AI could help interpret that need in the moment and bring that experience to the user instead of make them climb through some complex navigation?
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So this can sound somewhat abstract, but really what we're doing, and this is what Veronica is talking about about sort of an LLM working within a deterministic system, is that we still, as designers, create the structure and the environment that both humans and AI will work within.
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So I don't know, as a very more concrete example, Veronica, do you want to talk about like Salesforce or Cisco?
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So this sounds like, wow, this is kind of a wild, fluid world.
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Who's doing this?
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And it turns out even in the very kind of conservative, buttoned-up world of enterprise software, we're seeing it.
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Yeah, that's right.
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The Salesforce example is something called Salesforce Generative Canvas.
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And it takes the context of a user.
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So whether that maybe it will look at a user's calendar and it sees that they have a meeting coming up with a client.
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This and we'll say this is a sales team member, and they see that they have a meeting coming up with the client, and they assemble their dashboard such that there are notes from the last time they had a meeting, the most up-to-date stats on that customer in the Salesforce database.
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And so it's assembling all of these components in the moment way, using context from the user's calendar.
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You can also ask it a direct question and it will and it will populate the canvas.
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But what's great here is that the data is not being LLM generated.
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The content of the components is not being made in the moment, but it's taking the data that already exists in the Salesforce system and bringing it to the user so that they don't have to navigate throughout.
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And you can definitely imagine this being really useful in like a client meeting where you're trying to stay focused, maintain good eye contact, even over Zoom, but you still have kind of the things you need coming to the service.
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So you can just quickly glance and keep talking.
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It's not like making up the design, right?
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That's got this little sort of scoped design system of sort of different widgets that are tuned to present certain kinds of information.
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And it's got certain sources of information.
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And it knows kind of like, oh, here's this sort of dashboard archetype.
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So there's rules involved, right?
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And AI is sort of saying, oh, here's the context, here's the kind of data that I should do.
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Let me fetch that.
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Oh, and here, because it's this kind of data, here are the right components to display.
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And so it's making sort of simple decisions within a fixed system, but that can give this a huge variety of results to the user.
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Now, to a large extent, it makes sense to me.
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I'm thinking right now, I'm trying to build a daily digest where I get to see what my day is going to be looking like.
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And maybe if an LLM can create a dashboard for me as the day changes, as my priorities change, that's a very positive user experience.
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Would that be something about it?
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Yeah, that's exactly it.
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And right, like you think about the different types of days that we have.
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Oh, this is a thinking day where I'm creating and making.
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What are the materials or context that I need for that?
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Or this is a meeting day.
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Like, remind me who these people are, what we decided the last time, what's next.
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Like those are very different kinds of experiences that you want to have different information.
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How can AI help to make that?
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Like we've been talking about adaptive interfaces and personalization for decades, but we finally have a technology that can actually help to do that on the fly at the presentation level, right?
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Like it's not about AI making up facts.
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It's about making, oh, really sort of simple decisions from available design material.
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And so as designers, we create that environment and then we teach AI to work within that environment.
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And that sort of behavior design, and this is like what technical communicators are great at.
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Like it's like, oh, let me actually explain how you do this thing.
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That skill becomes incredibly important at this sentient design layer because you're teaching do's and don'ts, you're teaching behavior, you're kind of giving, in a way, a little a junior designer the brief for how to respond to the user and what to do, what happens when it's a confusing situation, what happens when the data is ambiguous?
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What do you do in these situations?
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And so, in a sense, technical communicators have a new audience, that it's not just the people on the company side and the people on the customer side.
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It's this enabling collaborative layer that AI provides at generating these experiences.
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Veronica, would you like to add something?
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Thanks, Josh.
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Yeah.
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Yeah, maybe I'll just talk about another example, if that's all right.
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I can go for it.
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Yeah.
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So we talked about the Salesforce example.
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The other example that Josh mentioned is the Cisco dashboard.
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And the Cisco dashboard is similar to the Salesforce canvas in that it's assembling things on the fly.
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But rather, if you can picture it visually, on the left hand side you have a kind of traditional LLM chat, and on the right hand side, you have like a big dashboard.
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And as you ask questions of the chat, it starts to populate the dashboard in in in response to your question.
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You can also ask specific questions about what's already on the dashboard, and it might show you a Viz in chat, which you can then drag onto the dashboard.
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So in this situation, you're really able to assemble dashboards.
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This has a heavier hand, right?
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You're assembling dashboards according to what you need and according to your questions rather than kind of imbued context.
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And then you could see how you can push this further.
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Like, oh, can I share this dashboard?
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Can I invite people onto my dashboard?
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Can I save it for later and come back to it?
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So there's all these possibilities when you start thinking of these dashboards that really bring content to you.
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This is just one of the examples.
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You know, I know we're being sort of very dashboard focused.
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It tends to be an example that's kind of easy to visualize.
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It's like, all right.
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It's like these elements that are being assembled on the fly.
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That's a pattern that we call bespoke UI, which is just like a custom interface that's being generated for users.
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But we have 14 other experiences, including, you know, how do you work with agents right now?
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Sort of this mainstream idea of agents, it's a very text-based, almost console-based experience.
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What happens when you have that, those actions and actors in the interface itself?
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So that they are in the same way, if you think about an agent as a multiplayer experience, like in other collaborative environments, how should they show up as collaborators in the experience?
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When you're editing a document, we know how one of our colleagues would edit in the comment and markup a document.
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What happens when AI shows up in that interaction?
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Or in a Canvas experience like Figma?
00:19:11.279 --> 00:19:16.640
What if an agent has its own cursor to do tasks for you or add comments?
00:19:16.880 --> 00:19:33.519
And so it's it's this thing of really about weaving intelligence into the interface so that the interface itself becomes a collaborator with the user or is responding in real time, as Veronica's mentioned in the Salesforce and and Cisco examples.
00:19:33.839 --> 00:19:35.119
And I'll say what they're not plugs.
00:19:35.359 --> 00:19:39.519
We're not no one's paying us to talk about them.
00:19:40.240 --> 00:19:42.559
Salesforce, Salesforce makes enough money.
00:19:42.799 --> 00:19:44.160
Yeah, no disclosures here.
00:19:49.839 --> 00:19:53.599
But what I want to ask you, Veronica, is you know where I'm looking from.
00:19:53.759 --> 00:19:58.240
I am a Gen Xer and I have kids who are probably just a little younger to you.
00:19:58.559 --> 00:20:04.319
I look at AI with I used to look at AI with fear, not as much anymore.
00:20:04.559 --> 00:20:06.640
But how are you looking at your future?
00:20:06.799 --> 00:20:09.599
Like I want to bring I want to understand your take on that.
00:20:10.079 --> 00:20:12.000
Yeah, thanks so much for that question.
00:20:12.079 --> 00:20:14.559
It's definitely something I've thought a lot about.
00:20:14.720 --> 00:20:19.359
And I think the fear that you used to feel, I think a lot of people still feel that.
00:20:19.599 --> 00:20:23.920
And there's like a lot of anxiety around AI and AI adoption.
00:20:24.240 --> 00:20:26.079
And it makes so much sense.
00:20:26.240 --> 00:20:37.519
Like AI is having a pretty tangible impact on the workforce, and people are worried about jobs or they've lost their jobs, partly in thanks to AI automation.
00:20:37.759 --> 00:20:38.799
And that's so scary.
00:20:38.880 --> 00:20:41.359
Like there's so many reasons to be fearful.
00:20:41.519 --> 00:20:44.240
And there's also like the other impacts of AI.
00:20:44.400 --> 00:20:54.799
Like, we don't know yet the social and economic impacts of this technology, and it's kind of being allowed to run rampant without great regulation or legislation surrounding it.
00:20:54.960 --> 00:20:59.119
So I think that's where a lot of my anxiety around it still comes in.
00:20:59.200 --> 00:21:04.640
It's like, oh wow, I really wish we were addressing this more head-on at an institutional level.
00:21:04.880 --> 00:21:21.279
That seems like a whole separate podcast, but I think what gives me hope is I think thus far the focus on AI has been on efficiency and has been on making things faster or cheaper.
00:21:21.519 --> 00:21:24.160
And that is just such a race to the bottom.
00:21:24.559 --> 00:21:27.759
Like, you know, who can make this the fastest and the cheapest?
00:21:27.920 --> 00:21:30.480
Has what about quality, you know?
00:21:30.640 --> 00:21:34.880
And so I think so much focus thus far has been on tooling.
00:21:35.039 --> 00:21:43.759
And even in like the design industry, a lot of our focus has been on like the tooling of AI, like, oh, how can I make my designs faster?
00:21:43.920 --> 00:21:46.160
Or how can I expedite this whole process?
00:21:46.319 --> 00:21:47.200
Like all of this.
00:21:47.440 --> 00:21:55.440
And what there's not been much focus on, and what Josh and I have tried really hard to focus on, is making new kinds of things.
00:21:55.599 --> 00:21:58.079
You know, what new products can I make?
00:21:58.240 --> 00:22:01.039
What new experiences the This enable?
00:22:01.279 --> 00:22:08.880
And I think that has yet to hit mainstream or to hit other industries, not just our book specifically, though you know, I hope it does.
00:22:09.200 --> 00:22:14.640
But just that idea of like, okay, this technology does mean we can do things faster.
00:22:14.880 --> 00:22:18.799
It does mean we don't have to write long emails anymore.
00:22:19.039 --> 00:22:26.319
And that's where a lot of people stop thinking about it is like, oh, how can this help me do what I already did with lesser effort?
00:22:26.559 --> 00:22:33.440
And there's not been a ton of innovation or imagination around what new types of things it can do.
00:22:33.599 --> 00:22:36.640
So I think that's where I get very hopeful and excited.
00:22:36.960 --> 00:22:39.359
You and I are generational peers, Zora.
00:22:39.440 --> 00:22:47.839
And so there's like a lot of this thing that is, oh man, all right, this does this threaten my decades of experience and hard-won knowledge?
00:22:48.000 --> 00:22:49.599
Is this just gonna sort of take over?
00:22:49.920 --> 00:23:01.119
I will say that I have never been so excited as a designer than I am right now in this career for exactly the reasons that Veronica is mentioning about sort of the upsides of this.
00:23:01.359 --> 00:23:11.680
If we shift our thinking from how do we save money, how do we cut, how do we do what we have always done, but faster, that's fine.
00:23:11.839 --> 00:23:14.160
There's like there's value to that for sure.
00:23:14.319 --> 00:23:16.559
I like productivity, I like efficiency.
00:23:16.720 --> 00:23:19.839
But what's missing is how do we create new value?
00:23:20.000 --> 00:23:31.599
How do we create something that we couldn't do before that can create a tremendously valuable and meaningful experience for both customer and business?
00:23:31.759 --> 00:23:33.279
Like that's exciting.
00:23:33.519 --> 00:23:36.160
We have new material to work with.
00:23:36.319 --> 00:23:47.599
And that's the shift that Veronica is talking about is going from a tool for production to a material for product.
00:23:47.839 --> 00:23:50.799
And product ultimately is what we're all here for.
00:23:50.960 --> 00:23:52.880
Production is just a means to an end.
00:23:53.039 --> 00:23:59.039
What the only thing that matters is what we put in front of the customer and what happens next.
00:23:59.279 --> 00:24:04.000
And we have the opportunity to invent meaningful new ways to do that.
00:24:04.160 --> 00:24:16.400
And when you think about that, right, my job isn't just about the production, it's about imagining what's possible and now working with this new to the world material to make those things happen.
00:24:16.559 --> 00:24:18.160
That is exciting.
00:24:18.240 --> 00:24:20.799
And that is design and content work.
00:24:20.960 --> 00:24:26.559
You know, there's the the old saying in product management of good, fast, cheap, pick two.
00:24:26.960 --> 00:24:33.440
If fast and cheap are just available to everybody, then the only thing that matters is good, right?
00:24:33.599 --> 00:24:35.279
Is what shall we make?
00:24:35.519 --> 00:24:37.039
What shall we imagine?
00:24:37.440 --> 00:24:41.200
And that we have a whole new world to invent right now.
00:24:41.359 --> 00:24:55.039
With sentient design, we're trying to establish what are the foundations that are emerging now, what are sort of some of the principles that we can use to invent these new kinds of experiences, but there's still so much invention ahead.
00:24:55.200 --> 00:25:05.359
And I think that's gonna take a generation of work to figure out in the same way that the web itself and mobile took generations to figure out.
00:25:05.519 --> 00:25:08.400
And that's the work, Veronica, for you ahead.
00:25:08.960 --> 00:25:15.039
What is the one thing that you can say if you're kind of worried, looking through the lens of where you are sitting?
00:25:15.359 --> 00:25:21.759
I mean, jobs are changing, and some of the tasks that we associate with our role will go away.
00:25:21.920 --> 00:25:24.960
And some jobs may go away too, some roles may go away.
00:25:25.200 --> 00:25:30.640
And you know, I mean, I don't think it's anything super new to say that production is at risk, right?
00:25:30.799 --> 00:25:46.559
The stuff that it used to take, you know, that especially around code, some aspects of writing, although that really depends on the context, are at risk that AI can just crank this stuff out or at least make an incredibly credible first draft.
00:25:46.720 --> 00:26:00.160
And so the shift has to be around we are more than just generators of code or generators of word, that we think about what's important, why are we creating this thing?
00:26:00.319 --> 00:26:16.640
And so, in some ways, while a lot is changing in terms of the production of our work, all of those kinds of efficiency things that Veronica was talking about, or what we can just ask Claude or ChatGPT or Jim and I or co-pilot to just please just do this thing and it happens.
00:26:16.880 --> 00:26:18.960
That is at risk for sure.
00:26:19.119 --> 00:26:20.880
And that work is changing.
00:26:21.200 --> 00:26:29.119
But I think it also lets us use these things as material to create new things, like we were talking about.
00:26:29.279 --> 00:26:41.039
And I think that that's especially important at the at the product level of you know, what can we get show to customers, not just how do we get to it faster, but what can we show to them that's new?
00:26:41.279 --> 00:26:44.640
But also like the foundations of our work don't change.
00:26:44.799 --> 00:26:55.920
So, especially for Zora, for folks like you and I who have been at this for a while, I want to give some reassurance that it's still about the fundamental questions of what problems are we trying to solve?
00:26:56.079 --> 00:26:58.079
What frictions can we overcome?
00:26:58.240 --> 00:27:02.799
And now we have a new material to work with to do that.
00:27:02.960 --> 00:27:07.039
Like we've just like when the web came around, it's like, oh, this is new.
00:27:07.200 --> 00:27:12.799
This is a new vocabulary to learn as a creator and as a consumer.
00:27:13.119 --> 00:27:19.920
Same with mobile, you know, and so every 10 or 15 years we get one of these things that are both crisis and opportunity.
00:27:20.079 --> 00:27:35.839
And so I think that the way to think about it and to sort of future-proof our jobs or adapt our roles is how do I work with this new material, not as a tool, but as a something that can deliver outcomes in new ways.
00:27:36.400 --> 00:27:57.119
And I think an exciting and challenging thing is that this does change the process of our work and the kind of collaboration that we can have, that this idea of working together to help AI make good decisions really calls for more collaboration.
00:27:57.279 --> 00:28:03.440
And the cool thing is that the work surface and workspace can be the same.
00:28:03.599 --> 00:28:08.000
And by the way, this is like stuff that technical communicators are can be really great at.
00:28:08.160 --> 00:28:27.119
That if we think of, you know, really what we're talking about is giving systems the context and instructions that they need to make good decisions, whether they're writing technical documentation or we're teaching them the behavior of creating one of these bespoke interfaces that Veronica was talking about.
00:28:27.279 --> 00:28:33.359
This takes many disciplines: product, technical communicators, designers, developers.
00:28:33.519 --> 00:28:37.039
And traditionally, that's been a waterfall step, right?
00:28:37.200 --> 00:28:40.799
You know, despite all the talk about Agile, we're not working together on this stuff.
00:28:41.039 --> 00:28:42.079
But now we can.
00:28:42.240 --> 00:28:55.519
We can, through all of the phases of a product development, we can all contribute our discipline to this common workspace, this common system prompt, the context that goes in it together.
00:28:55.680 --> 00:29:03.920
And one of the things that Veronica and I are experiencing a lot is that all of our teams are working together in the same common repo.
00:29:04.319 --> 00:29:13.759
And even if we aren't coding, we are contributing the documentation that AI needs to make good decisions in the moment.
00:29:13.920 --> 00:29:15.440
And it's been really powerful.
00:29:15.599 --> 00:29:22.000
The risk is that we do things, is that AI makes me think, oh, I can do all of this on my own without my colleagues.
00:29:22.160 --> 00:29:24.240
I'm a designer, but now I can deliver code.
00:29:24.319 --> 00:29:25.200
I don't need developers.
00:29:25.279 --> 00:29:28.000
I'm a developer, but now I AI can do the design.
00:29:28.079 --> 00:29:29.359
I don't need designers.
00:29:29.519 --> 00:29:33.039
That's a risk because then everyone runs in different directions.
00:29:33.279 --> 00:29:40.079
The real opportunity, and this is another area to think about for career, is how do we collaborate better?
00:29:40.240 --> 00:29:51.279
How do we bring people together to make decisions together quickly and iteratively in the same workspace that is product focused, customer focused?
00:29:51.440 --> 00:29:52.720
Those are new skills.
00:29:52.880 --> 00:29:59.119
I think there's a lot of talk about, oh, you can be a 10x designer, you can be a 10x technical communicator.
00:29:59.519 --> 00:30:00.880
What about your team?
00:30:01.119 --> 00:30:03.599
Like this is, you know, how do we make our team awesome?
00:30:03.680 --> 00:30:06.400
And more to the point, how do we make our customer awesome?
00:30:06.720 --> 00:30:14.640
Well, I'd love to kind of pick up on something Josh was talking about, which is just this idea of like a shared workspace within a team.
00:30:14.799 --> 00:30:28.319
I think with the I think with AI coming into the mix, there's been this kind of flattening of skills where, like Josh was saying, like now when I'm designing, most of the time I'm designing in cloud code.
00:30:28.480 --> 00:30:37.359
Like I use Figma probably 80% less than I did last year, which is just a crazy difference in process for one year.
00:30:37.599 --> 00:30:46.480
And I think what's really interesting is now when I'm in Figma, more often I'm in a fig jam board rather than a Figma design file.
00:30:46.559 --> 00:30:52.000
And more often I'm in that Fig Jam board specifically to collaborate with people from different disciplines.
00:30:52.160 --> 00:30:59.680
So having like a product person and a developer all in the fig jam board working on the same thing and all trying to get our ideas out.
00:30:59.920 --> 00:31:02.799
So there's less technical barriers.
00:31:02.880 --> 00:31:12.799
And so just to kind of pull a thread that Josh was picking out as well, it becomes even more important to really have your discipline's perspective.
00:31:12.960 --> 00:31:24.000
Like I still need to, even though I'm working on code technically, what you know, and you know, that gets sent to the developer who's like making sure it's healthy, just to catch any claudisms or anything.
00:31:24.240 --> 00:31:33.119
But it becomes so important for me to have that design perspective and to really hone down on like the main thing a designer is there for, which is like, how am I looking out for the customer?
00:31:33.279 --> 00:31:36.400
Like, how am I making sure the customer is getting what they need from this experience?
00:31:36.559 --> 00:31:41.920
And and the techniques, which you know, last year was like, how well can you use Figma?
00:31:42.079 --> 00:31:49.599
And don't get me wrong, like I can use Figma, but it's becoming less important as the perspective that you're bringing and the principles that you're bringing to it.
00:31:49.680 --> 00:31:51.359
So I think that's been really exciting.
00:31:51.519 --> 00:31:54.079
It's like a strange distillation of the craft.
00:31:54.400 --> 00:32:00.319
I do think there's sort of an evolution of how we adopt new technology that that I think Veronica is calling out here.
00:32:00.400 --> 00:32:02.799
Is first we start with, oh, we have these tools.
00:32:02.880 --> 00:32:05.440
How do we fit this into our existing process?
00:32:05.599 --> 00:32:08.559
How do we do what we already do faster?
00:32:08.720 --> 00:32:10.559
And then that has some knock-on effects.
00:32:10.640 --> 00:32:12.640
It's like, oh, wait, I'm I'm working differently.
00:32:12.799 --> 00:32:14.720
Now we as a team have to work differently.
00:32:14.880 --> 00:32:18.240
How do we change process and maybe organization?
00:32:18.480 --> 00:32:21.599
Those things can be hard, especially in large organizations.
00:32:22.000 --> 00:32:23.440
That's not nothing, right?
00:32:23.599 --> 00:32:30.319
And I think that most companies are there right now in the between those first and two stages of changing our tooling.
00:32:30.400 --> 00:32:32.240
Now, how do we change our process?
00:32:32.480 --> 00:32:35.599
And then that third bit is like, how do we change our product?
00:32:35.759 --> 00:32:48.880
And that's the bit that that Veronica and I are especially fascinated with and that we help our clients work with is like, what is the new thing that instead of just making the old thing faster, how can we make the new thing?
00:32:48.960 --> 00:32:50.400
And what are the opportunities?
00:32:50.480 --> 00:32:51.599
Does that open?
00:32:51.759 --> 00:32:58.880
And I think that's the most exciting thing for the next generation of product design and product organizations.
00:32:59.200 --> 00:33:04.559
Because from there, what happens is sometimes those things open up entirely new lines of business.
00:33:04.799 --> 00:33:09.920
And in some small cases, might even disrupt a whole industry and sort of reshape an industry.
00:33:10.160 --> 00:33:17.839
Most companies aren't in the disruption business, you know, that we're sort of like looking at, you know, how do we do, how do we provide our services now better?
00:33:18.000 --> 00:33:23.839
And I think that is like an especially exciting thing about that product invention phase.
00:33:24.079 --> 00:33:24.720
So it's okay.
00:33:24.799 --> 00:33:27.440
It's okay if you recognize it's like, oh, you know what, we're still doing tooling.
00:33:27.519 --> 00:33:29.119
We're not saying you're doing the wrong thing.
00:33:29.279 --> 00:33:30.720
That is where things begin.
00:33:30.880 --> 00:33:36.319
And learning to use those tools often say, oh, what if we did that in our experience, in our product?
00:33:36.400 --> 00:33:38.640
How did or what new product would we create?
00:33:38.960 --> 00:33:44.400
I think we need to step away and kind of really think about where can I, what can I do differently?
00:33:44.720 --> 00:33:44.960
Yeah.
00:33:45.119 --> 00:33:57.599
I mean, I think one of the things that's interesting too, like Veronica was saying, there's so many reasons to be skeptical or even fearful of this change and what it means for us individually and as a field.
00:33:57.759 --> 00:33:59.920
There are plenty of reasons for that.
00:34:00.160 --> 00:34:14.800
I guess I would say that skepticism and fear should be more reason to engage with this, not less, and not least because the technology changes so quickly, that the capability changes.
00:34:14.960 --> 00:34:18.320
And so thus, so it's the possibility and creative material.
00:34:18.480 --> 00:34:38.079
And so I think one thing is if, for example, you're someone who hasn't tried to use AI to do a task in say six months, because it was sort of meh then, you may be very surprised at how well it can do that thing now, either in a customer-facing way or how it supports your own work.
00:34:38.320 --> 00:34:49.920
And so I think one of these things is if you think of AI as your enemy, well, then know your enemy because soon you might be able to like understand it better, make it a friend.
00:34:50.079 --> 00:35:03.679
I don't want to sort of like over-personalize uh what AI is here, but I do think there is this thing of like understanding the material will help you use it in ways that serve not only yourself, but the customer and the business.
00:35:04.000 --> 00:35:06.159
Veronica, would you like to add something?
00:35:06.559 --> 00:35:15.920
Yeah, I not to be a downer, but I just want to acknowledge, I think there are some barriers that lead to people not adopting new tools.
00:35:16.000 --> 00:35:26.719
You know, if you are in a huge enterprise, you know, there's very real like firewalls and you can access this and that type of thing, as well as yeah, that that intimidation factor.
00:35:26.960 --> 00:35:44.000
So for anyone who maybe feels like they face those kinds of obstacles, I would just want to give a message of encouragement to just try and even even for a month, you know, just to really like push it as far as you can and use it in in different ways, not just for work.
00:35:44.320 --> 00:35:48.639
Zora, you're talking about, oh, like maybe I can assemble this dashboard to help me get through my day.
00:35:48.800 --> 00:35:50.000
And it's like, yes, totally.
00:35:50.159 --> 00:35:54.079
And Josh, you were talking about, you know, is it a thinking day or is it a meeting day?
00:35:54.159 --> 00:35:58.800
And I think it's also like, well, are you responsible for picking kids up from school?
00:35:58.960 --> 00:36:00.559
Do you have to go grocery shopping later?
00:36:00.719 --> 00:36:03.519
Can it give you the recipe when you need the recipe and all these things?
00:36:03.599 --> 00:36:11.679
Like, because I think using it in non-work circumstances is really been a place where like my imagination around it grows.
00:36:11.760 --> 00:36:19.360
And I'm like, or I discover new tooling, or I'm like, I didn't know Claude could do this because I'm like doing it out of a context that I like think that I already know.
00:36:19.599 --> 00:36:24.000
I mean, that's getting at that perspective shift from tool to material.
00:36:24.159 --> 00:36:41.760
Like a lot of things, if you can shift your thinking as a technical communicator, as a designer, as a product leader, from how do I just use AI over here in this specific role of making the thing that I need to make to how do I incorporate it into the thing that I make?
00:36:42.000 --> 00:36:50.639
That is a big unlock, is what we've discovered of creative possibility for the individual and product for the end user.
00:36:50.960 --> 00:36:55.119
This is definitely, I think, stepping outside of what you're doing, Veronica.
00:36:55.199 --> 00:36:57.519
I want to go back to your thought, what that you shared.
00:36:57.760 --> 00:37:02.000
Stepping away from what you do day to day and thinking creatively can actually be fun.
00:37:02.079 --> 00:37:06.320
So interact with that's that's such a fun way of thinking.
00:37:07.280 --> 00:37:08.719
There is this thing too, right?
00:37:08.800 --> 00:37:19.679
It's like in projects now, we reserve some time for what we call mad science to experiment and play around the subject instead of rushing to solution.
00:37:19.840 --> 00:37:26.320
Because right now there is opportunity to discover new ways to accomplish something or to present something.
00:37:26.480 --> 00:37:32.239
And so I think it's really important that kind of play and experimentation that both of you are talking about.
00:37:32.480 --> 00:37:38.480
Yeah, I love the phrase on that side because oh, that that totally vibes with me, I think.
00:37:38.639 --> 00:37:45.039
Not that I'm a scientist, but uh, feel like one, yeah.
00:37:45.199 --> 00:37:47.440
Yeah, I know, or why not feel like one, right?
00:37:47.519 --> 00:37:49.679
Uh and I think AI allows me to think that.
00:37:49.920 --> 00:37:50.480
So why not?
00:37:50.880 --> 00:37:51.280
Absolutely.
00:37:51.440 --> 00:37:52.400
You're like, I can code.
00:37:52.559 --> 00:37:54.480
I'm like super I'm so STEM.
00:37:55.360 --> 00:37:58.639
You introduced the ideal of defensive design.
00:37:58.880 --> 00:38:00.320
What do you mean by that?
00:38:00.639 --> 00:38:04.480
We don't want to kind of come off as boosters and that AI can do everything.
00:38:04.719 --> 00:38:16.960
All of us have had the experience working with AI where it's like that's not right, or it's just made that up, or terrible assumptions, or we've all had experiences where things go sideways.
00:38:17.199 --> 00:38:30.880
And so, you know, I think Veronica, if you want to talk a little bit about this, we've had this kind of strategy around defensive design, both as a mindset and specific patterns that you can use to sort of promote safety and trust.
00:38:31.199 --> 00:38:32.159
Yeah, absolutely.
00:38:32.320 --> 00:38:38.159
Yeah, defensive design is is really the practice of designing in guardrails.
00:38:38.400 --> 00:38:46.880
So rather than just right now, you know, you're using an LLM and there's like a bit of legalese below a chat bot, it's like answers might not be right.
00:38:47.119 --> 00:38:48.000
Double check.
00:38:48.320 --> 00:38:54.400
Or there's, you know, you see a new feature on a website and there are some purple sparkles on it.
00:38:54.559 --> 00:38:57.679
And those purple sparkles are symbols of AI.
00:38:57.760 --> 00:39:01.760
So you know you're using AI, like we're all very well adjusted to that symbol now.
00:39:02.000 --> 00:39:10.880
But it's kind of giving you the message of like this might not work, like this is for fun, this isn't you know central to your task, or it can't exactly be relied upon.
00:39:11.199 --> 00:39:12.239
Do not trust.
00:39:12.639 --> 00:39:14.000
Do not trust, yeah.
00:39:14.239 --> 00:39:23.199
So rather than designing in guardrails, we're getting used to these kinds of like caution or warning signs.
00:39:23.519 --> 00:39:28.880
So, which really kind of go to undermine the product because if it doesn't work, why are you putting it on your website?
00:39:29.199 --> 00:39:34.000
That said, of course, AI hallucinates, it can't be trusted 100% of the time.
00:39:34.159 --> 00:39:41.760
It does a remarkably good job of giving you things that look like answers, and in a very miraculous way, it's often right.
00:39:41.840 --> 00:39:43.599
Like it is often the correct answer.
00:39:43.920 --> 00:39:49.519
But there is, you know, that 10 or 5 or 1% of the time where it's wrong.
00:39:49.840 --> 00:39:54.239
And in a creative context, that can be fine, fun, whatever.
00:39:54.400 --> 00:39:58.480
That picture now has six fingers on somebody's hand, like that's funny.
00:39:58.800 --> 00:40:01.280
But in other situations, that's a real problem.
00:40:01.440 --> 00:40:10.639
If you're in healthcare or airlines or any kind of safety regulation that is super, super high stakes, you can't be wrong like that.
00:40:10.880 --> 00:40:19.519
So, how can you build in signals that encourage productive humility within the system?
00:40:19.760 --> 00:40:28.079
So the system is not being too arrogant about its own abilities, as well as some healthy skepticism on the part of the user without just like adding those sparkles in.
00:40:28.239 --> 00:40:30.960
And there's a number of different ways to do this.
00:40:31.119 --> 00:40:35.840
You know, there are things to be said for the way something is worded.
00:40:36.000 --> 00:40:49.760
There was an interesting experiment done that said if you present percentages to people, like if you're like this is 90% likely to be an elephant and it's a picture of a giraffe or something like that, but that doesn't mean much to people.
00:40:50.079 --> 00:40:57.039
There's a similar thing, maybe this is a better example on on Netflix where it like has a movie and it's like you are 74% likely to like this movie.
00:40:57.199 --> 00:40:58.800
And it's like, what does that mean?
00:40:59.039 --> 00:41:02.400
Like, what does 74% likely mean?
00:41:02.639 --> 00:41:13.440
But if you're wording something and saying, like, um, you're like probably gonna like this movie, there's a lot more information being given, also in my tone, right?
00:41:13.519 --> 00:41:20.800
Like, you're probably gonna like, you know, it's you know, we're able to understand that it's like, oh, I don't know, I might not, you know.
00:41:21.039 --> 00:41:21.199
Right.
00:41:21.440 --> 00:41:26.480
But saying it's saying I'm 10% confident is different from I really have no idea.
00:41:26.719 --> 00:41:31.920
So I'm making this up a little bit, but here's a guess based on very slim data.
00:41:32.079 --> 00:41:33.360
Like that's really different.
00:41:33.440 --> 00:41:40.639
And it's just like, oh, I'm listening to you now and I'm understanding so so there's a presentation aspect, not just a clinical.
00:41:41.199 --> 00:41:42.639
Yes, the presentation aspect.
00:41:42.800 --> 00:41:58.960
So in the wording and also in the tone, I'm trying to like encourage some healthy skepticism on your part, and I'm being a little bit humble about what I know, even if I'm not saying, you know what, I don't actually know, I'm still giving you an answer, but it's giving you an answer that is also like encouraging, like you might want to look a little further into this.
00:41:59.119 --> 00:42:02.559
It doesn't really work right, you know, like lawyers don't like that.
00:42:02.639 --> 00:42:04.559
They're gonna lie, that type of thing.
00:42:04.719 --> 00:42:12.719
But I think it's giving a type of personality to these systems, personality in the sense of presentation rather than personas.
00:42:13.039 --> 00:42:23.760
Like we're not trying to pass them off as people or as human, but a certain personality of deference or humbleness that is is missing a lot right now.
00:42:24.400 --> 00:42:31.599
Critical thinking, in your opinion, is that going to go away or is that something that will get sharpened?
00:42:32.159 --> 00:42:38.639
Yeah, I mean, I think a lot of that is up to us as product designers of how we present this stuff.
00:42:38.800 --> 00:42:45.440
You know, our friend Chris Nossel wrote a wonderful book called Designing Assistive Technologies that is about exactly this.
00:42:45.679 --> 00:42:53.679
How do we create assistance that amplify judgment and agency instead of replace it?
00:42:53.840 --> 00:42:58.400
You know, and I think we've all had experiences where we have become dumber by using technology.
00:42:58.480 --> 00:43:02.400
I cannot read a map anymore unless it has a little glowing blue dot on it.
00:43:02.639 --> 00:43:08.880
There are definitely things where we're like, oh, I have outsourced some critical thought here and lost some skill before.
00:43:08.960 --> 00:43:10.639
There is de-skilling as a risk.
00:43:10.880 --> 00:43:22.000
So I think one of the real challenges are what are the things that we actually is a skill that we legitimately don't care about or need that is appropriate for technology to do?
00:43:22.159 --> 00:43:30.159
And what are the things that we want to reinforce as we think about introducing intelligent interfaces, as we think about productivity?
00:43:30.320 --> 00:43:32.880
What are the things we don't want to automate away?
00:43:33.119 --> 00:43:55.519
Either because they are special to us, prized by us, things that we want to, as human beings, continue to do as part of our craft, or things that just require at this point our discernment and judgment, and that we should have people involved in this in a collaborative process.
00:43:55.760 --> 00:44:01.519
So I think that there are a lot of things of like, where do we want to pause and make people think?
00:44:01.679 --> 00:44:06.000
And there are moments of where do we want to put intentional friction into this?
00:44:06.239 --> 00:44:13.679
Just to give a super fast example, you know, kind of of like what Veronica was talking about, about sort of different kinds of presentation.
00:44:13.840 --> 00:44:16.880
We see this in the familiar things of Google search.
00:44:17.039 --> 00:44:23.360
If you ask, is a lizard a good pet, you'll get different results from if you ask, is a lizard a bad pet.
00:44:23.440 --> 00:44:28.079
You're asking the same information, but the way you've asked biases the algorithm.
00:44:28.239 --> 00:44:30.480
And so Google tries a lot of things around this.
00:44:30.559 --> 00:44:31.519
They will detect that.
00:44:31.599 --> 00:44:34.239
And when you ask, is our lizards good pets?
00:44:34.400 --> 00:44:37.840
it will say, also showing results for our lizards' bad pets.
00:44:38.000 --> 00:44:44.639
It has recognized that these things are similar but different queries, and it will ask both and give you the results.
00:44:44.880 --> 00:44:47.199
Or people also ask, right?
00:44:48.000 --> 00:44:50.800
Those things of like, you can ask it this way or that way.
00:44:50.960 --> 00:44:54.880
And what it's doing is showing adjacent answers or models.
00:44:55.039 --> 00:44:56.880
There's more than one way to get this.
00:44:57.039 --> 00:45:09.039
And here's a few nearby paths that suggest A, the system is only showing one corner of this area and helping the user to show how to explore more.
00:45:09.280 --> 00:45:16.079
So it's becomes it reveals its bias and it helps the user overcome it and show through it.
00:45:16.159 --> 00:45:17.440
And it's like, oh, that's subtle.
00:45:17.519 --> 00:45:19.039
There's a lot of nuance there.
00:45:19.199 --> 00:45:27.440
How do we think about that of like showing, revealing the shortcomings of the system and helping the user answer those?
00:45:27.760 --> 00:45:28.159
Great.
00:45:28.320 --> 00:45:29.599
Veronica, what's your take?
00:45:29.840 --> 00:45:34.719
Yeah, I will wrap up by kind of referencing our generational difference again.
00:45:34.880 --> 00:45:42.480
And I've thought about this in terms of literacy, in terms of media literacy and as well as data literacy.
00:45:42.719 --> 00:45:53.679
We've really seen Gen Z grow up in the age of social media and kind of develop this skepticism and cynicism towards social media, towards advertisements.
00:45:53.840 --> 00:46:03.199
I think Gen Z is much more easily able to tell when something is an ad, you know, just because like we're so exposed to it and inundated with it.
00:46:03.280 --> 00:46:07.360
And it's not always a problem, but it does become a problem when it's not disclosed.
00:46:07.519 --> 00:46:16.559
And so I do wonder if Gen Z and Gen Alpha and ensuing generations will develop a similar kind of literacy towards AI.
00:46:17.039 --> 00:46:24.400
And on that note, thank you so much, both of you, for you know joining me this morning and educating me.
00:46:24.639 --> 00:46:26.480
I hope you all had fun as well as I did.
00:46:26.800 --> 00:46:27.920
You really did, Zora.
00:46:28.239 --> 00:46:28.800
I definitely did.
00:46:28.880 --> 00:46:29.360
Thank you so much.
00:46:29.440 --> 00:46:31.840
And we have a discount code for your listeners.
00:46:32.159 --> 00:46:32.719
Yes, please.
00:46:32.800 --> 00:46:34.880
And I will be adding that in the show notes.
00:46:35.039 --> 00:46:36.719
So thank you for reminding me about it.
00:46:36.880 --> 00:46:38.800
Thank you and have a lovely day.
00:46:39.199 --> 00:46:41.679
Thank you for listening to InsightTechcom.
00:46:42.079 --> 00:46:46.000
If there's a guest you would love to hear on the show, please let me know.
00:46:46.159 --> 00:46:51.280
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00:46:51.519 --> 00:46:52.639
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