#80 Part 2: UX Tools for an Era of AI
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Hello friends and welcome back to the Future of UX podcast. I am super excited because this is the second part of our series on AI and UX tools, the Future UX Toolkit. If you have and listened to the first part, do it first. This is my recommendation. You can find it in, I think this is the previous podcast episode.

And now we will dive into the second one. Of course, you will do a little recap. So last time we talked about the typical tools for UX designers, how AI is starting to change the game. We also talked about an example with Figma and some of the challenges we are currently facing. And today in this episode, we are diving a little bit deeper into the future, how tools will continue to evolve and yeah, what the designer's future toolkit might look like.

I'm also going to share a few tips and tricks and also, of course, some stories from like working with my own clients. So before we are diving into new content, let's quickly recap what we covered in the last podcast episode.

We talked about the typical tools for your ex-designers, which are, you know, like the bread and butter tools like Figma, but also Miro, FigJam, maybe even Adobe XD, After Effects, maybe 3D tools, Hotjar. There are a lot of tools that your ex-designers use, and those tools are fundamental in our design process. Although I'm seeing that even if you just know how to use Figma, that's awesome.

Then we talked about the changes that are currently happening. AI is beginning to influence how we design from generating suggestions to automating repetitive tasks to fundamentally altering our workflow. We explored how Figma is integrating AI features, making design adjustments easier and faster while hinting at the potential for even more personalized and efficient tools in the future. So Figma introduced

their AI assistant where you can basically enter a prompt and it creates an interface for you. Yes, it's not working properly at the moment, but this will change. So I'm pretty sure they're currently working on a lot of updates and in no time they will release the feature and then it's working great.

We also talked a little bit about the problems because we are also facing some challenges that AI presents, like the black box problems and the risk of over-reliance on AI, which could undermine the human creativity that is essential to an ex-designer. And in this podcast episode, we're going to shift our focus to the future.

So in this episode, we are going to explore how tools will change and what the designer's toolkit will look like in an AI-driven world. Yesterday, I shared a post on Instagram about the evolution of tools, which really sparked a lot of interesting discussions. Because the truth is, although it's great to learn a tool as a designer, just learning a tool or knowing how to use Figma won't make you a great designer.

Tools help us to visualize ideas and true design is really about the concept and the problem you're solving. So even if you are the best designer, the best Figma pro, you might not are a good designer. The same with AI. AI is also just another tool in the design toolbox. So what do you need to know? So AI is not a trend. It's here to stay. And AI will automate entire flows.

Soon, designing flows will be just a prompt. We're just designing things for a prompt, basically. AI will generate the visuals, the copy, and the content. And what is important for us is to learn how to use the tools and understand basically everything around it. So let's talk real quick about the human-machine relationship. This is an evolving relationship between the human and the machine.

And before AI, data transfer was a simple process, right? So a human would initiate something like clicking on a button and a machine would execute. But with AI, data has become the raw material for learned behavior.

So designing for AI isn't just about crafting interfaces. It's about creating a relationship between humans and computers that is constantly evolving. So when we talk about the future of design tools, we need to understand that AI will be everywhere. 99% of the tools will use AI or will be AI powered. And this means the way we design will fundamentally change.

Let's talk about an example: a record player versus Spotify. So the difference between the record player and Spotify. So a typical record player: the output is predictable, right? Like you place a platter, you drop the needle, and then the record plays the same way every time. The same song, the same time, the same structure, everything is same. It's very predictable.

In contrast, Spotify uses machine learning, right? Like if you have a Spotify app on your phone or on your computer, Spotify uses machine learning AI to recommend new songs, new playlists, or creates even custom playlists for you. So the output gets better over time as it learns from the user's behavior.

With non-AI machines, interactions remain static, right? Like it's always the same. It's very predictable, like the record player. But with AI-powered machines, interactions evolve over time. And in parallel, humans are also learning and perhaps even changing their behavior in response to those interactions. And this co-learning creates a super interesting dynamic feedback loop.

A constant exchange of information that drives both the AI and the user forward. And to facilitate this rich back-and-forth relationship between the human and the AI, data becomes central. And this two-way street of constant data exchange between the computer and the human creates a cycle of change.

So to summarize that, and it's something that is so important for us, especially as UX designers, we are used to design static flows. We create a flow in Figma for a certain scenario with an interface that is consistent. But with AI, it will be a little bit different because this flow might look very different for different users, for different situations, or for different times. So the context might change, the situation might change, which makes it difficult

more difficult and also very different to design these flows. So what does that mean for us as UX designers? This is always the big question, right? And I want to talk a little bit about that before I'm diving into like the toolkits. Or honestly, this is probably the toolkit in itself already. Because as designers, we are trained to create flows that are more or less static. The screens, the layouts and elements that don't really change. But in the AI-driven future,

content and interfaces will adapt dynamically. We are already seeing this with services like Spotify or Netflix, where content is personalized based on user data. I mean, we already talked about Spotify, but Netflix is another super interesting example of a platform using AI very strategically, using machine learning to recommend not only movies, but also the way how they present it or recommend it based on your watch behavior.

So for me, for example, I really like thriller, I like rom-com movies. So most of the preview images that I'm getting for images have something like romantic on it, like focused on, I don't know, like the relationship part between humans, something that looks a little bit more like romantic or funny, or even like a thriller that looks a little bit like mystic or interesting or, I don't know, exciting. And for other people who might watch action movies, for example,

might have very different preview images for the movies. So if you have a Netflix subscription, feel free to just compare your visuals that you are seeing with maybe your partner or your friends. It's super interesting. And this is not new. Netflix is doing this for, I don't know, like six, seven years now. So this is not new, right? Like they are already doing this.

So the content is personalized based on user data. This is what is happening at the moment, almost everywhere. This is pretty much normal at the moment. But the next step will be that interfaces will adapt as well. How they're structured and how it's displayed and also when and where.

So when designing for these kind of flows and interactions, we need to step back and consider the AI-human interaction more holistically, I would say. So it's about designing for day one and inevitably degradation, but also for the parameters of an evolving and ongoing relationship. Okay, that sounds a bit fuzzy and not so concrete, but I'm going to give you an example a bit later.

So if you're worried that AI is going to take over your job, like, you know, everyone's suddenly possible or can create screens with just a prompt and Figma, you don't need to be. Because creating screens and designing is just a very small part of your work. Even today, but in the future, even more. The bigger challenge will be understanding the user's mental models and setting the parameters for how things evolve.

I talked about this in a previous podcast episode, all about mental models, what it is, how to use it and how important it actually is when you design AI products. So make sure to check it out if you haven't yet. Mental models is super important to summarize it, basically understand the way how people think.

Don't think about interfaces, don't think about screens, don't think about any kind of elements. Think about what does the user wants to get done? What is the job a user wants to get done with a certain service? What is the mental model? Something that the user already knows or has learned. And if you understand how people think, you won't have any problems creating AI products because so many more things are possible.

So in summary, design without AI is static. With AI, it's fundamentally dynamic. So it used to be a one-way street. Humans push a button and something happens. But now software improves by capturing information about you and using that input to make decisions like Spotify, learning which songs you like.

So there's a lot of change happening and it will be less and less about tools and more about the things that you have in your head, the strategy, how you connect the dots, how you understand how to get the best insight out of user research, the questions to ask and prompting AI as well. And I want to talk a little bit about an example from let's think we need to design a fitness app, right?

Clients come to you and say, hey, Patricia, or enter your own name. I want to design a fitness app. It should be AI-based. So generally, when you think about aesthetic design, this would be basically a traditional fitness app, right? Like designed to track basic workout data such as steps, calories burned, and heart rate. And this app would offer maybe like a fixed user interface with predefined workouts, training plans, and static progress bars that shows users progress over time.

You know, like the step counting, you have calorie tracking, you have preset workout templates, you have static progress bars and charts to display calories burned or like distance covered, but not a lot of customization. You know, those are like when you have a look at most of the fitness apps, this is what they usually cover, right?

And what are the typical problems with these kind of apps? The app, of course, does not dynamically respond to the user's individual needs or changes in fitness levels. It doesn't really offer a lot of motivation or address the mental or emotional aspects. Of course, not a lot of super personalized recommendations or adjustments.

And this will change. All the possibilities will change if you integrate AI, especially in the next year. So let's think a little bit into the future. What is possible? I know that because I am working with a lot of innovation companies at the moment, working on AI products. A lot of those products are very futuristic, are already looking into the future and things that are possible. So let's think about what is possible, how you tackle a problem when you think

You want to design a fitness app, but it should be AI-powered. So first of all, a modern dynamic fitness product could be an AI product-powered fitness platform. Not just only tracks workout data, but also adapt to the individual needs, the current mental models and emotional state of the user.

And this platform uses AI to deliver maybe personalized workout plans, motivation strategy and adjustments based on real-time data on context and on situations in the user's mental state. Could be personalized workout, mental support, adaptive training, social community features, continuously learning, right? And to do that, you would first need to do research. Understand why is the user...

in need of this fitness app? Who is your target group? Do the same research as you would do for a static product. Really understand the deepest needs and desires and then think about how could I solve that? How could I help with this? And how do you do that? You have a look at the mental models, right? So this platform is based on users' mental models, recognizing when they need support or a challenge.

And you focus on a problem-oriented solutions. You don't start to think about screens. The tools are not important at that point. What is them? You can do pen and paper to come up with these kind of strategies. But what is important that you think, you basically go one step above of designing in Figma. But you really think about like, what is the overall goal? And through this very dynamic AI-powered platform,

the fitness experience becomes not only more efficient, but also more motivating and supportive by catering to individual needs and emotional states.

That would be a long process, not something that you design in Figma and then it's done, but something where you would invite the data scientists team, the developers, to go see what kind of data do we have, what kind of data could we track, what is public data, what is private data, right? And then see how you can maybe even collect more data, where it's stored, how it's stored, if it's safe. What are the ethical considerations that you need to come up with? What kind of content can change based on what kind of behavior?

So you come up with the parameters behind it. The way of working is very different and you need to know and to understand AI tools for this kind of working because you need to create a lot of content. If it's like creating super fast flows that you want to test on the go, creating images, content like visual content or maybe also text copy, right? So the way of working will become much, much faster.

you will be able to test faster and try things out and experiment. Quantitative and qualitative testing and user research are super important, especially for AI products.

And for those who want to dive deeper into AI, who really understand the importance of AI, I can promise you now is the time to really do a deep dive into AI to secure your future. The next cohort of AI for designers is starting in just a few weeks. So this is the perfect time to jump in and start leveraging this knowledge.

You can sign up to the waiting list in the description box. You will get the early bird price and some other bony. The AFO Designer is a six-week intensive program that gets a huge, huge, huge upgrade for the next cohort, which is cohort number three already. I can't believe it. I already had two amazing cohorts this year with wonderful, beautiful people who loved the course. I got so much feedback.

People who are using AI now in their work, who got AI consulting positions, AI design positions afterwards, make me so, so, so happy and super, super proud of my students and hope that you are one of the lucky designers who will join Cohort 3. Because Cohort 3 will be very special. There will be a huge upgrade because we are also going to dive into how to design for AI products.

So the course will be about how to design with AI and how to design for AI. So that will be a completely new part. A lot of amazing content waiting for you. Going to create your own case study that you can put in your portfolio. You will get a certificate and also make connections for life. Really use this inspiration, this kickstart to get ready for the future. And as I mentioned, if you want to learn more, you can find all the information in the description box.

Would love to have you in the cohort. If you have questions, you can of course always reach out and say hi. And yes, a little summary of the episode. I think this is a good reminder for us that tools are important, that we still should play around with tools, experiment, try things out. But tools won't be the future. Your brain is the future, understanding, trying things out, having a lot of

knowledge and understanding the human, the mental models above a certain tool. Of course, it's absolutely necessary to know how to use Figma, how to use TGPT, any other large language models, how to create visuals for your content. This is essential. This is the basic. This is the foundation. But besides that, there are a lot of other skills that we should dive deeper in. And

As always, thank you so much for joining me today for this podcast episode. I hope you had a lot of interesting takeaways, some inspiration for yourselves. If you have thoughts or questions, feel free to reach out on social media at ux.patricia or leave a comment. Yeah, stay curious and I will see you in the next episode.