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.

Share this article
Author
Optimal
Workshop

Related articles

View all blog articles
Header graphic for the article 'Efficient Research: Maximizing the ROI of Understanding Your Customers'
Learn more
1 min read

Efficient Research: Maximizing the ROI of Understanding Your Customers

Introduction

User research is invaluable, but in fast-paced environments, researchers often struggle with tight deadlines, limited resources, and the need to prove their impact. In our recent UX Insider webinar, Weidan Li, Senior UX Researcher at Seek, shared insights on Efficient Research—an approach that optimizes Speed, Quality, and Impact to maximize the return on investment (ROI) of understanding customers.

At the heart of this approach is the Efficient Research Framework, which balances these three critical factors:

  • Speed – Conducting research quickly without sacrificing key insights.
  • Quality – Ensuring rigor and reliability in findings.
  • Impact – Making sure research leads to meaningful business and product changes.

Within this framework, Weidan outlined nine tactics that help UX researchers work more effectively. Let’s dive in.

1. Time Allocation: Invest in What Matters Most

Not all research requires the same level of depth. Efficient researchers prioritize their time by categorizing projects based on urgency and impact:

  • High-stakes decisions (e.g., launching a new product) require deep research.
  • Routine optimizations (e.g., tweaking UI elements) can rely on quick testing methods.
  • Low-impact changes may not need research at all.

By allocating time wisely, researchers can avoid spending weeks on minor issues while ensuring critical decisions are well-informed.

2. Assistance of AI: Let Technology Handle the Heavy Lifting

AI is transforming UX research, enabling faster and more scalable insights. Weidan suggests using AI to:

  • Automate data analysis – AI can quickly analyze survey responses, transcripts, and usability test results.
  • Generate research summaries – Tools like ChatGPT can help synthesize findings into digestible insights.
  • Speed up recruitment – AI-powered platforms can help find and screen participants efficiently.

While AI can’t replace human judgment, it can free up researchers to focus on higher-value tasks like interpreting results and influencing strategy.

3. Collaboration: Make Research a Team Sport

Research has a greater impact when it’s embedded into the product development process. Weidan emphasizes:

  • Co-creating research plans with designers, PMs, and engineers to align on priorities.
  • Involving stakeholders in synthesis sessions so insights don’t sit in a report.
  • Encouraging non-researchers to run lightweight studies, such as A/B tests or quick usability checks.

When research is shared and collaborative, it leads to faster adoption of insights and stronger decision-making.

4. Prioritization: Focus on the Right Questions

With limited resources, researchers must choose their battles wisely. Weidan recommends using a prioritization framework to assess:

  • Business impact – Will this research influence a high-stakes decision?
  • User impact – Does it address a major pain point?
  • Feasibility – Can we conduct this research quickly and effectively?

By filtering out low-priority projects, researchers can avoid research for research’s sake and focus on what truly drives change.

5. Depth of Understanding: Go Beyond Surface-Level Insights

Speed is important, but efficient research isn’t about cutting corners. Weidan stresses that even quick studies should provide a deep understanding of users by:

  • Asking why, not just what – Observing behavior is useful, but uncovering motivations is key.
  • Using triangulation – Combining methods (e.g., usability tests + surveys) to validate findings.
  • Revisiting past research – Leveraging existing insights instead of starting from scratch.

Balancing speed with depth ensures research is not just fast, but meaningful.

6. Anticipation: Stay Ahead of Research Needs

Proactive researchers don’t wait for stakeholders to request studies—they anticipate needs and set up research ahead of time. This means:

  • Building a research roadmap that aligns with upcoming product decisions.
  • Running continuous discovery research so teams have a backlog of insights to pull from.
  • Creating self-serve research repositories where teams can find relevant past studies.

By anticipating research needs, UX teams can reduce last-minute requests and deliver insights exactly when they’re needed.

7. Justification of Methodology: Explain Why Your Approach Works

Stakeholders may question research methods, especially when they seem time-consuming or expensive. Weidan highlights the importance of educating teams on why specific methods are used:

  • Clearly explain why qualitative research is needed when stakeholders push for just numbers.
  • Show real-world examples of how past research has led to business success.
  • Provide a trade-off analysis (e.g., “This method is faster but provides less depth”) to help teams make informed choices.

A well-justified approach ensures research is respected and acted upon.

8. Individual Engagement: Tailor Research Communication to Your Audience

Not all stakeholders consume research the same way. Weidan recommends adapting insights to fit different audiences:

  • Executives – Focus on high-level impact and key takeaways.
  • Product teams – Provide actionable recommendations tied to specific features.
  • Designers & Engineers – Share usability findings with video clips or screenshots.

By delivering insights in the right format, researchers increase the likelihood of stakeholder buy-in and action.

9. Business Actions: Ensure Research Leads to Real Change

The ultimate goal of research is not just understanding users—but driving business decisions. To ensure research leads to action:

  • Follow up on implementation – Track whether teams apply the insights.
  • Tie findings to key metrics – Show how research affects conversion rates, retention, or engagement.
  • Advocate for iterative research – Encourage teams to re-test and refine based on new data.

Research is most valuable when it translates into real business outcomes.

Final Thoughts: Research That Moves the Needle

Efficient research is not just about doing more, faster—it’s about balancing speed, quality, and impact to maximize its influence. Weidan’s nine tactics help UX researchers work smarter by:


✔️  Prioritizing high-impact work
✔️  Leveraging AI and collaboration
✔️  Communicating research in a way that drives action

By adopting these strategies, UX teams can ensure their research is not just insightful, but transformational.

Watch the full webinar here

Header graphic for the article 'Event Recap: Measuring the Value of UX Research at UXDX'
Learn more
1 min read

Event Recap: Measuring the Value of UX Research at UXDX

Last week Optimal Workshop was delighted to sponsor UXDX USA 2024 in New York. The User Experience event brings together Product, Design, UX, CX, and Engineering professionals and our team had an amazing time meeting with customers, industry experts, and colleagues throughout the conference. This year, we also had the privilege of sharing some of our industry expertise by running an interactive forum on “Measuring the Value of UX Research” - a topic very close to our hearts.

Our forum, hosted by Optimal Workshop CEO Alex Burke and Product Lead Ella Fielding, was focused on exploring the value of User Experience Research (UXR) from both an industry-wide perspective and within the diverse ecosystem of individual companies and teams conducting this type of research today.

The session brought together a global mix of UX professionals for a rich discussion on measuring and demonstrating the effectiveness of and the challenges facing organizations who are trying to tie UXR to tangible business value today.

The main topics for the discuss were: 

  • Metrics that Matter: How do you measure UXR's impact on sales, customer satisfaction, and design influence?
  • Challenges & Strategies: What are the roadblocks to measuring UXR impact, and how can we overcome them?
  • Beyond ROI:  UXR's value beyond just financial metrics

Some of the key takeaways from our discussions during the session were: 

  1. The current state of UX maturity and value
    • Many UX teams don’t measure the impact of UXR on core business metrics and there were more attendees who are not measuring the impact of their work than those that are measuring it. 
    • Alex & Ella discussed with the attendees the current state of UX research maturity and the ability to prove value across different organizations represented in the room. Most organizations were still early in their UX research maturity with only 5% considering themselves advanced in having research culturally embedded.
  1. Defining and proving the value of UX research
    • The industry doesn’t have clear alignment or understanding of what good measurement looks like. Many teams don’t know how to accurately measure UXR impact or don’t have the tools or platforms to measure it, which serve as core roadblocks for measuring UXRs’ impact. 
    • Alex and Ella discussed challenges in defining and proving the value of UX research, with common values being getting closer to customers, innovating faster, de-risking product decisions, and saving time and money. However, the value of research is hard to quantify compared to other product metrics like lines of code or features shipped.
  1. Measuring and advocating for UX research
    • When teams are measuring UXR today there is a strong bias for customer feedback, but little ability or understanding about how to measure impact on business metrics like revenue. 
    • The most commonly used metrics for measuring UXR are quantitative and qualitative feedback from customers as opposed to internal metrics like stakeholder involvement or tieing UXR to business performance metrics (including financial performance). 
    • Attendees felt that in organizations where research is more embedded, researchers spend significant time advocating for research and proving its value to stakeholders rather than just conducting studies. This included tactics like research repositories and pointing to past study impacts as well as ongoing battles to shape decision making processes. 
    • One of our attendees highlighted that engaging stakeholders in the process of defining key research metrics prior to running research was a key for them in proving value internally. 
  1. Relating user research to financial impact
    • Alex and Ella asked the audience if anyone had examples of demonstrating financial impact of research to justify investment in the team and we got some excellent examples from the audience proving that there are tangible ways to tie research outcomes to core business metrics including:
    • Calculating time savings for employees from internal tools as a financial impact metric. 
    • Measuring a reduction in calls to service desks as a way to quantify financial savings from research.
  1. Most attendees recognise the value in embedding UXR more deeply in all levels of their organization - but feel like they’re not succeeding at this today. 
    • Most attendees feel that UXR is not fully embedded in their orgnaization or culture, but that if it was - they would be more successful in proving its overall value.
    • Stakeholder buy-in and engagement with UXR, particularly from senior leadership varied enormously across organizations, and wasn’t regularly measured as an indicator of UXR value 
    • In organizations where research was more successfully embedded, researchers had to spend significant time and effort building relationships with internal stakeholders before and after running studies. This took time and effort away from actual research, but ended up making the research more valuable to the business in the long run. 

With the large range of UX maturity and the democratization of research across teams, we know there’s a lot of opportunity for our customers to improve their ability to tie their user research to tangible business outcomes and embed UX more deeply in all levels of their organizations. To help fill this gap, Optimal Workshop is currently running a large research project on Measuring the Value of UX which will be released in a few weeks.

Keep up to date with the latest news and events by following us on LinkedIn.

Header graphic for the article 'The future of UX research: AI’s role in analysis...'
Learn more
1 min read

The future of UX research: AI's role in analysis and synthesis

As artificial intelligence (AI) continues to advance and permeate various industries, the field of user experience (UX) research is no exception. 

At Optimal Workshop, our recent Value of UX report revealed that 68% of UX professionals believe AI will have the greatest impact on analysis and synthesis in the research project lifecycle. In this article, we'll explore the current and potential applications of AI in UXR, its limitations, and how the role of UX researchers may evolve alongside these technological advancements.

How researchers are already using AI

AI is already making inroads in UX research, primarily in tasks that involve processing large amounts of data, such as

  • Automated transcription: AI-powered tools can quickly transcribe user interviews and focus group sessions, saving researchers significant time.

  • Sentiment analysis: Machine learning algorithms can analyze text data from surveys or social media to gauge overall user sentiment towards a product or feature.

  • Pattern recognition: AI can help identify recurring themes or issues in large datasets, potentially surfacing insights that might be missed by human researchers.

  • Data visualization: AI-driven tools can create interactive visualizations of complex data sets, making it easier for researchers to communicate findings to stakeholders.

As AI technology continues to evolve, its role in UX research is poised to expand, offering even more sophisticated tools and capabilities. While AI will undoubtedly enhance efficiency and uncover deeper insights, it's important to recognize that human expertise remains crucial in interpreting context, understanding nuanced user needs, and making strategic decisions. 

The future of UX research lies in the synergy between AI's analytical power and human creativity and empathy, promising a new era of user-centered design that is both data-driven and deeply insightful.

The potential for AI to accelerate UXR processes

As AI capabilities advance, the potential to accelerate UX research processes grows exponentially. We anticipate AI revolutionizing UXR by enabling rapid synthesis of qualitative data, offering predictive analysis to guide research focus, automating initial reporting, and providing real-time insights during user testing sessions. 

These advancements could dramatically enhance the efficiency and depth of UX research, allowing researchers to process larger datasets, uncover hidden patterns, and generate insights faster than ever before. As we continue to develop our platform, we're exploring ways to harness these AI capabilities, aiming to empower UX professionals with tools that amplify their expertise and drive more impactful, data-driven design decisions.

AI’s good, but it’s not perfect

While AI shows great promise in accelerating certain aspects of UX research, it's important to recognize its limitations, particularly when it comes to understanding the nuances of human experience. AI may struggle to grasp the full context of user responses, missing subtle cues or cultural nuances that human researchers would pick up on. Moreover, the ability to truly empathize with users and understand their emotional responses is a uniquely human trait that AI cannot fully replicate. These limitations underscore the continued importance of human expertise in UX research, especially when dealing with complex, emotionally-charged user experiences.

Furthermore, the creative problem-solving aspect of UX research remains firmly in the human domain. While AI can identify patterns and trends with remarkable efficiency, the creative leap from insight to innovative solution still requires human ingenuity. UX research often deals with ambiguous or conflicting user feedback, and human researchers are better equipped to navigate these complexities and make nuanced judgment calls. As we move forward, the most effective UX research strategies will likely involve a symbiotic relationship between AI and human researchers, leveraging the strengths of both to create more comprehensive, nuanced, and actionable insights.

Ethical considerations and data privacy concerns‍

As AI becomes more integrated into UX research processes, several ethical considerations come to the forefront. Data security emerges as a paramount concern, with our report highlighting it as a significant factor when adopting new UX research tools. Ensuring the privacy and protection of user data becomes even more critical as AI systems process increasingly sensitive information. Additionally, we must remain vigilant about potential biases in AI algorithms that could skew research results or perpetuate existing inequalities, potentially leading to flawed design decisions that could negatively impact user experiences.

Transparency and informed consent also take on new dimensions in the age of AI-driven UX research. It's crucial to maintain clarity about which insights are derived from AI analysis versus human interpretation, ensuring that stakeholders understand the origins and potential limitations of research findings. As AI capabilities expand, we may need to revisit and refine informed consent processes, ensuring that users fully comprehend how their data might be analyzed by AI systems. These ethical considerations underscore the need for ongoing dialogue and evolving best practices in the UX research community as we navigate the integration of AI into our workflows.

The evolving role of researchers in the age of AI

As AI technologies advance, the role of UX researchers is not being replaced but rather evolving and expanding in crucial ways. Our Value of UX report reveals that while 35% of organizations consider their UXR practice to be "strategic" or "leading," there's significant room for growth. This evolution presents an opportunity for researchers to focus on higher-level strategic thinking and problem-solving, as AI takes on more of the data processing and initial analysis tasks.

The future of UX research lies in a symbiotic relationship between human expertise and AI capabilities. Researchers will need to develop skills in AI collaboration, guiding and interpreting AI-driven analyses to extract meaningful insights. Moreover, they will play a vital role in ensuring the ethical use of AI in research processes and critically evaluating AI-generated insights. As AI becomes more prevalent, UX researchers will be instrumental in bridging the gap between technological capabilities and genuine human needs and experiences.

Democratizing UXR through AI

The integration of AI into UX research processes holds immense potential for democratizing the field, making advanced research techniques more accessible to a broader range of organizations and professionals. Our report indicates that while 68% believe AI will impact analysis and synthesis, only 18% think it will affect co-presenting findings, highlighting the enduring value of human interpretation and communication of insights.

At Optimal Workshop, we're excited about the possibilities AI brings to UX research. We envision a future where AI-powered tools can lower the barriers to entry for conducting comprehensive UX research, allowing smaller teams and organizations to gain deeper insights into their users' needs and behaviors. This democratization could lead to more user-centered products and services across various industries, ultimately benefiting end-users.

However, as we embrace these technological advancements, it's crucial to remember that the core of UX research remains fundamentally human. The unique skills of empathy, contextual understanding, and creative problem-solving that human researchers bring to the table will continue to be invaluable. As we move forward, UX researchers must stay informed about AI advancements, critically evaluate their application in research processes, and continue to advocate for the human-centered approach that is at the heart of our field.

By leveraging AI to handle time-consuming tasks and uncover patterns in large datasets, researchers can focus more on strategic interpretation, ethical considerations, and translating insights into impactful design decisions. This shift not only enhances the value of UX research within organizations but also opens up new possibilities for innovation and user-centric design.

As we continue to develop our platform at Optimal Workshop, we're committed to exploring how AI can complement and amplify human expertise in UX research, always with the goal of creating better user experiences.

The future of UX research is bright, with AI serving as a powerful tool to enhance our capabilities, democratize our practices, and ultimately create more intuitive, efficient, and delightful user experiences for people around the world.

Seeing is believing

Explore our tools and see how Optimal makes gathering insights simple, powerful, and impactful.