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.
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:
Efficiency. Not only does AI expedite the problem solving process, it can also help efficiently identify problems.
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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The user research landscape has evolved significantly in recent years, but not all platforms have adapted at the same pace. UserTesting for example, despite being one of the largest players in the market, still operates on legacy infrastructure with outdated pricing models that no longer meet the evolving needs of mature UX, design and product teams. More and more we see enterprises choosing platforms like Optimal, because we represent the next generation of user research and insight platforms: ones that are purpose-built for modern teams that are prioritizing agility, insight quality, and value.
What are the biggest differences between Optimal and UserTesting?
Cost
Optimal has Transparent Pricing: Optimal offers flat-rate pricing without per-seat fees or session units, enabling teams to scale research sustainably. Our transparent pricing eliminates budget surprises and enables predictable research ops planning.
UserTesting is Expensive: In contrast, UserTesting has very high per user fees annually plus additional session-based fees, creating unpredictable costs that escalate the more research your team does. This means that teams often face budget surprises when conducting longer studies or more frequent research.
Return on Investment
The Best Value in the Market: Optimal's straightforward pricing and comprehensive feature set deliver measurable ROI. We offer 90% of the features that UserTesting provides at 10% of the price.
Justifying the Cost of UserTesting: UserTesting's high costs and complex pricing structure make it hard to prove the ROI, particularly for teams conducting frequent research or extended studies that trigger additional session fees.
Technology Evolution
Optimal is Purpose-Built for Modern Research: Optimal has invested heavily over the last few years in features for contemporary research needs, including AI-powered analysis and automation capabilities. Our new Interviews tool exemplifies this innovation, transforming hours of manual video analysis into automated, AI-powered insights that surface key themes, generate highlight reels, and produce timestamped transcripts in a fraction of the time.
UserTesting is Struggling to Modernize: UserTesting's platform shows signs of aging infrastructure, with slower performance and difficulty integrating modern research methodologies. Their technology advancement has lagged behind industry innovation.
Platform Integration
Built by Researchers for Researchers: Optimal has built from the ground up a single, cohesive platform without the complexity of merged acquisitions, ensuring consistent user experience and seamless workflow integration.
UserZoom Integration Challenges: UserTesting's acquisition of UserZoom has created platform challenges that continue to impact user experience. UserTesting customers report confusion navigating between legacy systems and inconsistent feature availability and quality.
Participant Panel Quality
Flexibility = Quality: Optimal prioritizes flexibility for our users, allowing our customers to bring their own participants for free or use our high-quality panels, with over 100+ million verified participants across 150+ countries who meet strict quality standards.
Poor Quality, In-House Panel: UserTesting's massive scale has led to participant quality issues, with researchers reporting difficulty finding high-quality participants for specialized research needs and inconsistent participant engagement.
Customer Support Experience
Agile, Personal Support: At Optimal we pride ourselves on our fast, human support with dedicated account management and direct access to product teams, ensuring fast and personalized support.
Impersonal, Enterprise Support: In contrast, users report that UserTesting's large organizational structure creates slower support cycles, outsourced customer service, and reduced responsiveness to individual customer needs.
The Future of User Research Platforms
The future of user research platforms is here, and smart teams are re-evaluating their platform needs to reflect that future state. What was once a fragmented landscape of basic testing tools and legacy systems has evolved into one where comprehensive user insight platforms are now the preferred solution. Today's UX, product and design teams need platforms that have evolved to include:
Advanced Analytics: AI-powered analysis that transforms data into actionable insights
Flexible Recruitment: Options for both BYO, panel and custom participant recruitment
Transparent Pricing: Predictable costs that scale with your needs
Responsive Development: Platforms that evolve based on user feedback and industry trends
Platforms Need to Evolve for Modern Research Needs
When selecting a vendor, teams need to choose a platform with the functionality that their teams need now but also one that will also grow with the needs of your team in the future. Scalable, adaptable platforms enable research teams to:
Scale Efficiently: Grow research activities without exponential cost increaeses
Embrace Innovation: Integrate new research methodologies and analysis techniques as well as emerging tools like AI
Maintain Standards: Ensure consistent participant, data and tool quality as the platform evolves
Stay Responsive: Adapt to changing business needs and market conditions
The key is choosing a platform that continues to evolve rather than one constrained by outdated infrastructure and complex, legacy pricing models.
When Bear Liu was teaching himself design, he struggled remembering concepts since English wasn’t his first language. To help, he started doodling. By drawing pictures that related to what he was learning, he found he could not only remember them better, he could understand and communicate more effectively too. Ever since, he’s used the power of drawings and pictures to relay information in ways people can use.
Bear gives examples of how visual communication can help design workplaces to relay information in a more memorable and usable way. It may only seem like a minor change, but the difference can be significant.
Bear’s background 🎤
Bear Liu is a Product Designer at Xero, an online accounting platform that’s used all over the world. He’s also a Design Mentor at Springboard and CareerFoundry, and an Apple Award-Winning podcast host at BearTalk.
His background is in science education. As a self-taught designer, Bear has helped a raft of large and small businesses with digital products over the last 16 years. His clients come from diverse backgrounds and industries across the globe. Bear's professional passions also carry over into his hobbies. Outside of work he enjoys reading, drawing, and producing videos & podcasts on tech and design.
Contact Details:
You can find Bear on LinkedIn, or listen to his podcast, BearTalk.
Unleash your visual superpower as a communication pro 🦸🏻
When it comes to addressing business challenges it is important to keep these three aspects in mind:
Understanding - break down complex problems and solutions so everyone can understand.
Memory - retaining information in your mind is difficult even with note taking.
Communication- People relate to words differently, and the meaning of something can easily get lost in translation. This issue is more prevalent with remote work.
Bear Liu strongly believes that visual communication helps people understand, remember and communicate information more effectively. Why?
It helps to focus. Pictures remove distractions and draw attention to where it’s desired.
It’s a token. A picture is universal - a house or a smiley face means the same thing to people that speak different languages.
Most people are visual thinkers. Studies have found humans are hard-wired to process visual information faster. We are better at storing information in images, rather than numbers and letters.
But what if I can’t draw? This is a common issue Bear finds when talking to people about this. It’s not about the quality of the drawing itself, it’s about what it means. By delivering a message through a picture, it becomes understandable. Many of Bear’s drawings only ever remain in draft form. Even simple doodles can have meanings that make concepts clear.
In his design work at Xero, Bear has used drawing and sketches to great effect in a range of instances:
The accessibility tree was a complex, abstract system, but by drawing it (on a literal tree), and adding a few notes alongside it, the terminology became much more understandable.
Sketching how customers work made it easier to describe how Xero could help them. It was much more memorable than writing it out in paragraphs.
Wrapping the year in product design. A written summary of a year’s work is long-winded. Instead, Bear drew a tree and pinned key words, quotes and achievements to communicate the highlights.
UX terminology explanations can be difficult for those outside the industry to comprehend. Bear challenged himself to share 1 minute videos that accompanied simple drawings to help colleagues understand them, and had rave reviews.
Sketching notes is a great alternative to writing notes at conferences or meetings. Presenters can draw to help audiences follow along, and people in the audience themselves can also sketch their own notes.
Why it matters 🔥
Bear has adapted visual thinking to his own product design process and has seen a noticeable improvement in communication as a result.
People are busy - their brains are packed with all sorts of information, and they’re easily distracted by other things they have on their minds. By delivering information in a way that helps them to focus on it, remember and understand it, designers can achieve their ultimate goals.
As Bear also notes, drawing is fun. It’s much more rewarding than using words, as well as much more effective.
Bear used the example of his talk at UX New Zealand 2023 as a great place to use a drawing. Rather than follow along with his message by scribbling notes the whole way through, those in the audience could capture the biggest lessons easily in one simple drawing.
First, Bear drew one stick figure to represent himself as a speaker. He drew three speech bubbles, where audience members could write the most notable points he said.
Then he drew another stick figure, which represented the audience member listening to him. They had three thought bubbles, which people could populate with their biggest takeaways from the speech.
That one simple drawing is a template that can be used in any speech or meeting to remember the key points.
