April 2, 2024
—
6 min

Clara Kliman-Silver: AI & design: imagining the future of UX

Header graphic for the article 'Clara Kliman-Silver: AI & design: imagining the future of UX'

In the last few years, the influence of AI has steadily been expanding into various aspects of design. In early 2023, that expansion exploded. AI tools and features are now everywhere, and there are two ways designers commonly react to it:

‍

  • With enthusiasm for how they can use it to make their jobs easier
  • With skepticism over how reliable it is, or even fear that it could replace their jobs

‍

Google UX researcher Clara Kliman-Silver is at the forefront of researching and understanding the potential impact of AI on design into the future. This is a hot topic that’s on the radar of many designers as they grapple with what the new normal is, and how it will change things in the coming years.

‍

Clara’s background 

‍

Clara Kliman-Silver spends her time studying design teams and systems, UX tools and designer-developer collaboration. She’s a specialist in participatory design and uses generative methods to investigate workflows, understand designer-developer experiences, and imagine ways to create UIs. In this work, Clara looks at how technology can be leveraged to help people make things, and do it more efficiently than they currently are.

‍

In today’s context, that puts generative AI and machine learning right in her line of sight. The way this technology has boomed in recent times has many people scrambling to catch up - to identify the biggest opportunities and to understand the risks that come with it. Clara is a leader in assessing the implications of AI. She analyzes both the technology itself and the way people feel about it to forecast what it will mean into the future.

‍

Contact Details:

You can find Clara in LinkedIn or on Twitter @cklimansilver

‍

What role should artificial intelligence play in UX design process? 🤔

‍

Clara’s expertise in understanding the role of AI in design comes from significant research and analysis of how the technology is being used currently and how industry experts feel about it. AI is everywhere in today’s world, from home devices to tech platforms and specific tools for various industries. In many cases, AI automation is used for productivity, where it can speed up processes with subtle, easy to use applications.

‍

As mentioned above, the transformational capabilities of AI are met with equal parts of enthusiasm and skepticism. The way people use AI, and how they feel about it is important, because users need to be comfortable implementing the technology in order for it to make a difference. The question of what value AI brings to the design process is ongoing. On one hand, AI can help increase efficiency for systems and processes. On the other hand, it can exacerbate problems if the user's intentions are misunderstood.

‍

Access for all 🦾

‍

There’s no doubt that AI tools enable novices to perform tasks that, in years gone by, required a high level of expertise. For example, film editing was previously a manual task, where people would literally cut rolls of film and splice them together on a reel. It was something only a trained editor could do. Now, anyone with a smartphone has access to iMovie or a similar app, and they can edit film in seconds.

‍

For film experts, digital technology allows them to speed up tedious tasks and focus on more sophisticated aspects of their work. Clara hypothesizes that AI is particularly valuable when it automates mundane tasks. AI enables more individuals to leverage digital technologies without requiring specialist training. Thus, AI has shifted the landscape of what it means to be an “expert” in a field. Expertise is about more than being able to simply do something - it includes having the knowledge and experience to do it for an informed reason. 

‍

Research and testing 🔬

‍

Clara performs a lot of concept testing, which involves recognizing the perceived value of an approach or method. Concept testing helps in scenarios where a solution may not address a problem or where the real problem is difficult to identify. In a recent survey, Clara describes two predominant benefits designers experienced from AI:

‍

  1. Efficiency. Not only does AI expedite the problem solving process, it can also help efficiently identify problems. 
  2. Innovation. Generative AI can innovate on its own, developing ideas that designers themselves may not have thought of.

‍

The design partnership 🤝🏽

‍

Overall, Clara says UX designers tend to see AI as a creative partner. However, most users don’t yet trust AI enough to give it complete agency over the work it’s used for. The level of trust designers have exists on a continuum, where it depends on the nature of the work and the context of what they’re aiming to accomplish. Other factors such as where the tech comes from, who curated it and who’s training the model also influences trust. For now, AI is largely seen as a valued tool, and there is cautious optimism and tentative acceptance for its application. 

‍

Why it matters 💡

‍

AI presents as potentially one of the biggest game-changers to how people work in our generation. Although AI has widespread applications across sectors and systems, there are still many questions about it. In the design world, systems like DALL-E allow people to create AI-generated imagery, and auto layout in various tools allows designers to iterate more quickly and efficiently.

‍

Like many other industries, designers are wondering where AI might go in the future and what it might look like. The answer to these questions has very real implications for the future of design jobs and whether they will exist. In practice, Clara describes the current mood towards AI as existing on a continuum between adherence and innovation:

‍

  • Adherence is about how AI helps designers follow best practice
  • Innovation is at the other end of the spectrum, and involves using AI to figure out what’s possible

‍

The current environment is extremely subjective, and there’s no agreed best practice. This makes it difficult to recommend a certain approach to adopting AI and creating permanent systems around it. Both the technology and the sentiment around it will evolve through time, and it’s something designers, like all people, will need to maintain good awareness of.

‍

‍

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We're thrilled to announce the re-launch of our Qualitative Insights tool, formerly known as Reframer. This powerful upgrade brings new features designed to revolutionize your qualitative data analysis process, making it faster, easier, and more insightful than ever before.

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Introducing the new Qualitative Insights 🔍

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Qualitative Insights has always been your go-to tool to help you plan and organize interviews, take notes, tag, and analyze rich, unstructured data. Now, we've taken it to the next level with two game-changing additions:

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Discover insights effortlessly 💡 

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The new Insights feature transforms how you work with qualitative data:

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  • Centralized hub: All your analytical discoveries in one place.
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  • Collaboration tools: Share and discuss findings with your team.

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How it works 🛠️ 

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Manual insight creation

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  1. Filter your data using keywords, tags, affinity map groupings, tasks, segments, and sessions.
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AI-Powered Insight Generation (Optional)

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  1. Click "Generate" to activate our AI assistant.
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AI: Your analysis assistant 🤖

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Our AI capabilities are designed to enhance your abilities, not replace them. Use AI to:

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  • Speed up insight discovery
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Remember, your expertise is crucial. Always review and refine AI-generated insights to ensure accuracy and capture nuances that only human understanding can provide.

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Your data, your choice 🔒 

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Get started today 🌟 

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Ready to experience the power of the new Qualitative Insights? Learn more and dive in. Upgrade your qualitative analysis workflow and uncover deeper insights faster than ever before with Qualitative Insights!

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Best UX Research Methods for Every Phase of Product Development

What is UX research?

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User experience (UX) research, or user research as it’s commonly referred to, is an important part of the product design process. Primarily, UX research involves using different research methods to gather information about how your users interact with your product. It is an essential part of developing, building and launching a product that truly meets the requirements of your users. 

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UX research is essential at all stages of a products' life cycle:

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  1. Planning
  2. Building
  3. Introduction
  4. Growth & Maturity

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While there is no one single time to conduct UX research it is best-practice to continuously gather information throughout the lifetime of your product. The good news is many of the UX research methods do not fit just one phase either, and can (and should) be used repeatedly. After all, there are always new pieces of functionality to test and new insights to discover. We introduce you to best-practice UX research methods for each lifecycle phase of your product.

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1. Product planning phase

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While the planning phase it is about creating a product that fits your organization, your organization’s needs and meeting a gap in the market it’s also about meeting the needs, desires and requirements of your users. Through UX research you’ll learn which features are necessary to be aligned with your users. And of course, user research lets you test your UX design before you build, saving you time and money.

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Qualitative Research Methods

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Usability Testing - Observational

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One of the best ways to learn about your users and how they interact with your product is to observe them in their own environment. Watch how they accomplish tasks, the order they do things, what frustrates them, and what makes the task easier and/or more enjoyable for your subject. The data can be collated to inform the usability of your product, improving intuitive design, and what resonates with users.

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Competitive Analysis

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Reviewing products already in the market can be a great start to the planning process. Why are your competitors’ products successful and how well do they behave for users. Learn from their successes, and even better build on where they may not be performing the best and find your niche in the market.

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Quantitative Research Methods

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Surveys and Questionnaires

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Surveys are useful for collecting feedback or understanding attitudes. You can use the learnings from your survey of a subset of users to draw conclusions about a larger population of users.

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There are two types of survey questions:
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Closed questions are designed to capture quantitative information. Instead of asking users to write out answers, these questions often use multi-choice answers.

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Open questions are designed to capture qualitative information such as motivations and context.  Typically, these questions require users to write out an answer in a text field.

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2. Product building phase

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Once you've completed your product planning research, you’re ready to begin the build phase for your product. User research studies undertaken during the build phase enable you to validate the UX team’s deliverables before investing in the technical development.

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Qualitative Research Methods

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Focus groups

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Generally involve 5-10 participants and include demographically similar individuals. The study is set up so that members of the group can interact with one another and can be carried out in person or remotely.
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Besides learning about the participants’ impressions and perceptions of your product, focus group findings also include what users believe to be a product’s most important features, problems they might encounter while using the product, as well as their experiences with other products, both good and bad.

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Quantitative Research Methods

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Card sorting gives insight into how users think. Tools like card sorting reveal where your users expect to find certain information or complete specific tasks. This is especially useful for products with complex or multiple navigations and contributes to the creation of an intuitive information architecture and user experience.

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Tree testing gives insight into where users expect to find things and where they’re getting lost within your product. Tools like tree testing help you test your information architecture.
Card sorting and tree testing are often used together. Depending on the purpose of your research and where you are at with your product, they can provide a fully rounded view of your information architecture.

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3. Product introduction phase

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You’ve launched your product, wahoo! And you’re ready for your first real life, real time users. Now it’s time to optimize your product experience. To do this, you’ll need to understand how your new users actually use your product.

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Qualitative Research Methods

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Usability testing involves testing a product with users. Typically it involves observing users as they try to follow and complete a series of tasks. As a result you can evaluate if the design is intuitive and if there are any usability problems.

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User Interviews - A user interview is designed to get a deeper understanding of a particular topic. Unlike a usability test, where you’re more likely to be focused on how people use your product, a user interview is a guided conversation aimed at better understanding your users. This means you’ll be capturing details like their background, pain points, goals and motivations.

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Quantitative Research Methods

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A/B Testing is a way to compare two versions of a design in order to work out which is more effective. It’s typically used to test two versions of the same webpage, for example, using a different headline, image or call to action to see which one converts more effectively. This method offers a way to validate smaller design choices where you might not have the data to make an informed decision, like the color of a button or the layout of a particular image.

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Flick-click testing shows you where people click first when trying to complete a task on a website. In most cases, first-click testing is performed on a very simple wireframe of a website, but it can also be carried out on a live website using a tool like first-time clicking.

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4. Growth and maturity phase

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If you’ve reached the growth stage, fantastic news! You’ve built a great product that’s been embraced by your users. Next on your to-do list is growing your product by increasing your user base and then eventually reaching maturity and making a profit on your hard work.

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Growing your product involves building new or advanced features to satisfy specific customer segments. As you plan and build these enhancements, go through the same research and testing process you used to create the first release. The same holds true for enhancements as well as a new product build — user research ensures you’re building the right thing in the best way for your customers.

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Qualitative research methods

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User interviews will focus on how your product is working or if it’s missing any features, enriching your knowledge about your product and users.

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It allows you to test your current features, discover new possibilities for additional features and think about discarding  existing ones. If your customers aren’t using certain features, it might be time to stop supporting them to reduce costs and help you grow your profits during the maturity stage.

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Quantitative research methods

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Surveys and questionnaires can help gather information around which features will work best for your product, enhancing and improving the user experience. 

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A/B testing during growth and maturity occurs within your sales and onboarding processes. Making sure you have a smooth onboarding process increases your conversion rate and reduces wasted spend — improving your bottom line.

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Final Thoughts: Why Continuous UX Research Matters

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UX research testing throughout the lifecycle of your product helps you continuously evolve and develop a product that responds to what really matters - your users.

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Talking to, testing, and knowing your users will allow you to push your product in ways that make sense with the data to back up decisions. Go forth and create the product that meets your organizations needs by delivering the very best user experience for your users.

Header graphic for the article 'Clara Kliman-Silver: AI & design: imagining the future of UX'
Learn more
1 min read

Clara Kliman-Silver: AI & design: imagining the future of UX

In the last few years, the influence of AI has steadily been expanding into various aspects of design. In early 2023, that expansion exploded. AI tools and features are now everywhere, and there are two ways designers commonly react to it:

‍

  • With enthusiasm for how they can use it to make their jobs easier
  • With skepticism over how reliable it is, or even fear that it could replace their jobs

‍

Google UX researcher Clara Kliman-Silver is at the forefront of researching and understanding the potential impact of AI on design into the future. This is a hot topic that’s on the radar of many designers as they grapple with what the new normal is, and how it will change things in the coming years.

‍

Clara’s background 

‍

Clara Kliman-Silver spends her time studying design teams and systems, UX tools and designer-developer collaboration. She’s a specialist in participatory design and uses generative methods to investigate workflows, understand designer-developer experiences, and imagine ways to create UIs. In this work, Clara looks at how technology can be leveraged to help people make things, and do it more efficiently than they currently are.

‍

In today’s context, that puts generative AI and machine learning right in her line of sight. The way this technology has boomed in recent times has many people scrambling to catch up - to identify the biggest opportunities and to understand the risks that come with it. Clara is a leader in assessing the implications of AI. She analyzes both the technology itself and the way people feel about it to forecast what it will mean into the future.

‍

Contact Details:

You can find Clara in LinkedIn or on Twitter @cklimansilver

‍

What role should artificial intelligence play in UX design process? 🤔

‍

Clara’s expertise in understanding the role of AI in design comes from significant research and analysis of how the technology is being used currently and how industry experts feel about it. AI is everywhere in today’s world, from home devices to tech platforms and specific tools for various industries. In many cases, AI automation is used for productivity, where it can speed up processes with subtle, easy to use applications.

‍

As mentioned above, the transformational capabilities of AI are met with equal parts of enthusiasm and skepticism. The way people use AI, and how they feel about it is important, because users need to be comfortable implementing the technology in order for it to make a difference. The question of what value AI brings to the design process is ongoing. On one hand, AI can help increase efficiency for systems and processes. On the other hand, it can exacerbate problems if the user's intentions are misunderstood.

‍

Access for all 🦾

‍

There’s no doubt that AI tools enable novices to perform tasks that, in years gone by, required a high level of expertise. For example, film editing was previously a manual task, where people would literally cut rolls of film and splice them together on a reel. It was something only a trained editor could do. Now, anyone with a smartphone has access to iMovie or a similar app, and they can edit film in seconds.

‍

For film experts, digital technology allows them to speed up tedious tasks and focus on more sophisticated aspects of their work. Clara hypothesizes that AI is particularly valuable when it automates mundane tasks. AI enables more individuals to leverage digital technologies without requiring specialist training. Thus, AI has shifted the landscape of what it means to be an “expert” in a field. Expertise is about more than being able to simply do something - it includes having the knowledge and experience to do it for an informed reason. 

‍

Research and testing 🔬

‍

Clara performs a lot of concept testing, which involves recognizing the perceived value of an approach or method. Concept testing helps in scenarios where a solution may not address a problem or where the real problem is difficult to identify. In a recent survey, Clara describes two predominant benefits designers experienced from AI:

‍

  1. Efficiency. Not only does AI expedite the problem solving process, it can also help efficiently identify problems. 
  2. Innovation. Generative AI can innovate on its own, developing ideas that designers themselves may not have thought of.

‍

The design partnership 🤝🏽

‍

Overall, Clara says UX designers tend to see AI as a creative partner. However, most users don’t yet trust AI enough to give it complete agency over the work it’s used for. The level of trust designers have exists on a continuum, where it depends on the nature of the work and the context of what they’re aiming to accomplish. Other factors such as where the tech comes from, who curated it and who’s training the model also influences trust. For now, AI is largely seen as a valued tool, and there is cautious optimism and tentative acceptance for its application. 

‍

Why it matters 💡

‍

AI presents as potentially one of the biggest game-changers to how people work in our generation. Although AI has widespread applications across sectors and systems, there are still many questions about it. In the design world, systems like DALL-E allow people to create AI-generated imagery, and auto layout in various tools allows designers to iterate more quickly and efficiently.

‍

Like many other industries, designers are wondering where AI might go in the future and what it might look like. The answer to these questions has very real implications for the future of design jobs and whether they will exist. In practice, Clara describes the current mood towards AI as existing on a continuum between adherence and innovation:

‍

  • Adherence is about how AI helps designers follow best practice
  • Innovation is at the other end of the spectrum, and involves using AI to figure out what’s possible

‍

The current environment is extremely subjective, and there’s no agreed best practice. This makes it difficult to recommend a certain approach to adopting AI and creating permanent systems around it. Both the technology and the sentiment around it will evolve through time, and it’s something designers, like all people, will need to maintain good awareness of.

‍

‍

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