AI and UX design

As a UX professional, it’s natural to wonder about the intersection of AI and UX design. Artificial intelligence is one of the most buzzy and fast-growing fields since the advent of the smartphone, with every major tech company entering into it in one way or another, and global leaders from across disciplines weighing in on the technology’s benefits and dangers. 

Anyone who has dabbled with publicly available AI programs has probably wondered how it might impact their daily lives, whether at home or professionally. UX design is no different. 

For many people, the emergence of technology like ChatGPT and Midjourney, which can credibly create writing, programming, artwork, and photography off of pretty much any prompt, can be a cause of anxiety. After all, if the tech looks this good now, how good will it be in a decade? 

At a global level, it raises fears of artificial general intelligence that could surpass human thinking, like a science-fiction story come to life. But on a more individual level, many people look at the work of generative AIs and wonder if their own skill sets might soon be irrelevant. 

Right now, it might be best to look at the available tools as interesting sandboxes to play in. Treat them collaboratively to streamline some work and make other tasks more interesting. 

How might you leverage existing AI tools and technologies to create better UX and UI designs? Let’s dive in and find out.  

AI history

Let’s start by setting some context. While discussions about AI have exploded in popularity since ChatGPT captured the public imagination in 2022, AI’s origins can be traced back to the mid-twentieth century. The term "artificial intelligence" was first coined in 1956 by John McCarthy, who is often referred to as the father of AI. McCarthy and a team of researchers from Dartmouth College proposed that AI could be achieved by programming computers to mimic human thought processes. 

One of the earliest examples of AI was a program called ELIZA, which was created in the mid-1960s by Joseph Weizenbaum, a computer scientist at MIT. ELIZA was a simple chatbot that could simulate a conversation with a user by using pattern-matching techniques to respond to the user’s input. While ELIZA was far from perfect, it showed the potential of AI to interact with humans in a more natural and conversational way.

Over the years, AI research continued, with researchers developing new algorithms and techniques for machine learning, natural language processing, and computer vision. (The term “algorithm” is used a lot these days, but in the context of AI it refers to a step-by-step set of instructions that a computer program follows to complete a task or solve a problem.) In the 1980s, expert systems became popular, which were AI programs that could simulate the decision-making abilities of a human expert in a particular domain. Expert systems were used in applications such as medical diagnosis, financial analysis, and engineering design.

In the 1990s, machine learning algorithms became more sophisticated, allowing AI systems to learn from large datasets and improve their performance over time. This led to the development of applications such as recommendation systems, which could suggest products or services based on a user's past behavior, and fraud detection systems, which could identify suspicious activity in financial transactions.

In recent years, breakthroughs in deep learning led to significant improvements in speech recognition, image classification, and natural language processing. Along with these developments, a new type of AI called generative AI has emerged. Generative AI, also known as creative AI, uses machine learning algorithms to generate original and creative content, such as music, art, and writing. It’s these technologies that have caused the recent spike in interest and conversation around the technology, but as we’ll see, it’s not the only type of AI that UX designers should be aware of. 

How to enhance UX and UI design with AI

AI has many applications in UX and UI design that can improve efficiency, effectiveness, and quality. 

Here are some ways UX and UI designers are using AI to enhance their work, and some ideas for how AI can help automate repetitive tasks and spur creativity. 

Chatbots

AI chatbots can help users navigate complex websites or apps, provide personalized assistance and support, and improve engagement and satisfaction. For example, H&M's chatbot on Facebook Messenger helps users find products, track orders, and get fashion advice.

Recommendation algorithms

Recommendation algorithms can analyze user data and provide personalized content and product recommendations that enhance user satisfaction and increase conversion rates. 

For example, Amazon’s recommendation engine analyzes user purchase and browsing history to provide personalized product recommendations to users. Streaming services (like Spotify and Netflix) similarly surface recommendations based on previous actions.

Analyze user behavior data

AI-powered tools like Hotjar and Google Analytics can analyze user behavior data to identify pain points, patterns, and insights that can inform design decisions. 

Persona creation

AI-powered tools like Crystal can analyze user data from social media profiles, email, and other sources to create accurate and detailed personas. This allows you to create more personalized and effective user experiences without spending hours conducting user research. 

Additionally, while fleshing out some of the creative aspects of a user persona, conversation-based AI tools can be turned to for suggestions. Over the course of a conversation with a role-playing AI, you can develop real characters and test their way of thinking. 

Sourcing fresh perspectives

Give an AI the rough outline of a problem you’re facing and ask for 20 suggestions or new ways of looking at it. You could have the AI ask you questions you may not be expecting about the problem. 

It’s a bit like Brian Eno’s famous Oblique Strategies, although you can tailor it to your specific situation.

Placeholder copy

AI-powered tools like Articoolo and Copy.ai can generate suggestions for headlines, body text, and other design elements based on your objectives and preferences. If your team doesn’t have a UX writer or copywriter on-hand just yet, AI copy is a little more colorful than basic lorum ipsum. (But you’ll still want a professional writer to rework it later!)

Kickstart creativity

AI can generate suggestions for color palettes, font styles, and other design elements, freeing you to focus on more critical aspects of the design process.

Best practices for AI and UX design

If you’re a UX or UI designer, AI offers many opportunities to streamline your work and create more effective and personalized user experiences. New technologies will continue to emerge given the field’s explosive growth in recent years, so it’s worth covering some best practices for employing AI tools. 

Treat AI as a collaborator, not a replacement for critical thinking

‍While AI can automate tasks and provide unexpected insights, you should double check what it provides. AI often gets facts incorrect, so anything it tells you should be fact-checked and taken with a grain of salt. 

Consider ethical concerns when working with AI algorithms

‍AI can perpetuate biases and discrimination if not appropriately programmed and tested. For example, an AI-powered hiring system may perpetuate bias against women or people of color if the training data used to develop the algorithm includes historical hiring patterns that favored white men. 

Be transparent about the use of AI

‍One significant issue with AI algorithms is the lack of transparency. Many AI algorithms are opaque and difficult to interpret, making it challenging to identify and correct potential biases or errors. These are new technologies, so it’s best to remain transparent about your own usage of them. This is part of why AI should be used collaboratively rather than as a replacement for human labor. 

AI tools for UX and UI designers

So, now that we’ve covered some background on the emergence of AI, how you can use it to improve efficiencies, and gone over some best practices, let’s get our hands dirty. 

Here’s a quick rundown of some popular AI tools that are widely used in the UX/UI design industry.

ChatGPT

ChatGPT (and GPT-4) is an AI-powered chatbot that can help you generate ideas and create common-sense narratives. GPT-4 is the newest version of OpenAI's language model systems.

Adobe Sensei

Adobe Sensei is a suite of AI-powered tools that can help you with tasks such as automated image tagging, content-aware fill, and personalized recommendations.

Figma

Figma is a collaborative design tool that offers AI-powered features such as auto-layout and smart selection. These features help to create designs faster and more efficiently.

Sketch2Code

Sketch2Code is a tool from Microsoft that uses AI to convert hand-drawn sketches into working HTML code. This tool can save you significant time in the development process.

Chatfuel

Chatfuel is a no-code platform that enables you to create chatbots quickly and easily. It offers AI-powered features such as natural language processing and sentiment analysis to make chatbots more effective and intuitive.

TensorFlow

TensorFlow is an open-source platform for building and deploying machine learning models. It can be used to build AI-powered features, such as image recognition, speech recognition, and natural language processing.

Lobe

Lobe is an easy-to-use platform that enables you to create custom machine learning models with no coding. It can be used to build models for tasks such as image classification and object detection.

These are just a few of the many AI tools available to UX and UI designers. As new technologies emerge, treat them as platforms to play around with, playfully but with a healthy skepticism.

At Lyssna, we provide various tools to help UX and UI designers create better user experiences. To learn more, sign up for a free Lyssna plan and start exploring our tools today.

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