April 2, 2024
6 min

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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Meera Pankhania: From funding to delivery - Ensuring alignment from start to finish

It’s a chicken and egg situation when it comes to securing funding for a large transformation program in government. On one hand, you need to submit a business case and, as part of that, you need to make early decisions about how you might approach and deliver the program of work. On the other hand, you need to know enough about the problem you are going to solve to ensure you have sufficient funding to understand the problem better, hire the right people, design the right service, and build it the right way. 

Now imagine securing hundreds of millions of dollars to design and build a service, but not feeling confident about what the user needs are. What if you had the opportunity to change this common predicament and influence your leadership team to carry out alignment activities, all while successfully delivering within the committed time frames?

Meera Pankhania, Design Director and Co-founder of Propel Design, recently spoke at UX New Zealand, the leading UX and IA conference in New Zealand hosted by Optimal Workshop, on traceability and her learnings from delivering a $300 million Government program.

In her talk, Meera helps us understand how to use service traceability techniques in our work and apply them to any environment - ensuring we design and build the best service possible, no matter the funding model.

Background on Meera Pankhania

As a design leader, Meera is all about working on complex, purpose-driven challenges. She helps organizations take a human-centric approach to service transformation and helps deliver impactful, pragmatic outcomes while building capability and leading teams through growth and change.

Meera co-founded Propel Design, a strategic research, design, and delivery consultancy in late 2020. She has 15 years of experience in service design, inclusive design, and product management across the private, non-profit, and public sectors in both the UK and Australia. 

Meera is particularly interested in policy and social design. After a stint in the Australian Public Service, Meera was appointed as a senior policy adviser to the NSW Minister for Customer Service, Hon. Victor Dominello MP. In this role, she played a part in NSW’s response to the COVID pandemic, flexing her design leadership skills in a new, challenging, and important context.

Contact Details:

Email address: meera@propeldesign.com.au

Find Meera on LinkedIn  

From funding to delivery: ensuring alignment from start to finish 🏁🎉👏

Meera’s talk explores a fascinating case study within the Department of Employment Services (Australia) where a substantial funding investment of around $300 million set the stage for a transformative journey. This funding supported the delivery of a revamped Employment Services Model, which had the goal of delivering better services to job seekers and employers, and a better system for providers within this system. The project had a focus on aligning teams prior to delivery, which resulted in a huge amount of groundwork for Meera.

Her journey involved engaging various stakeholders within the department, including executives, to understand the program as a whole and what exactly needed to be delivered. “Traceability” became the watchword for this project, which is laid out in three phases.

  • Phase 1: Aligning key deliverables
  • Phase 2: Ensuring delivery readiness
  • Phase 3: Building sustainable work practices

Phase 1: Aligning key deliverables 🧮

Research and discovery (pre-delivery)

Meera’s work initially meant conducting extensive research and engagement with executives, product managers, researchers, designers, and policymakers. Through this process, a common theme was identified – the urgent (and perhaps misguided) need to start delivering! Often, organizations focus on obtaining funding without adequately understanding the complexities involved in delivering the right services to the right users, leading to half-baked delivery.

After this initial research, some general themes started to emerge:

  1. Assumptions were made that still needed validation
  2. Teams weren’t entirely sure that they understood the user’s needs
  3. A lack of holistic understanding of how much research and design was needed

The conclusion of this phase was that “what” needed to be delivered wasn’t clearly defined. The same was true for “how” it would be delivered.

Traceability

Meera’s journey heavily revolved around the concept of "traceability” and sought to ensure that every step taken within the department was aligned with the ultimate goal of improving employment services. Traceability meant having a clear origin and development path for every decision and action taken. This is particularly important when spending taxpayer dollars!

So, over the course of eight weeks (which turned out to be much longer), the team went through a process of combing through documents in an effort to bring everything together to make sense of the program as a whole. This involved some planning, user journey mapping, and testing and refinement. 

Documenting Key Artifacts

Numerous artifacts and documents played a crucial role in shaping decisions. Meera and her team gathered and organized these artifacts, including policy requirements, legislation, business cases, product and program roadmaps, service maps, and blueprints. The team also included prior research insights and vision documents which helped to shape a holistic view of the required output.

After an effort of combing through the program documents and laying everything out, it became clear that there were a lot of gaps and a LOT to do.

Prioritising tasks

As a result of these gaps, a process of task prioritization was necessary. Tasks were categorized based on a series of factors and then mapped out based on things like user touch points, pain points, features, business policy, and technical capabilities.

This then enabled Meera and the team to create Product Summary Tiles. These tiles meant that each product team had its own summary ahead of a series of planning sessions. It gave them as much context (provided by the traceability exercise) as possible to help with planning. Essentially, these tiles provided teams with a comprehensive overview of their projects i.e. what their user needs, what certain policies require them to deliver, etc.  

Phase 2: Ensuring delivery readiness 🙌🏻

Meera wanted every team to feel confident that we weren’t doing too much or too little in order to design and build the right service, the right way.

Standard design and research check-ins were well adopted, which was a great start, but Meera and the team also built a Delivery Readiness Tool. It was used to assess a team's readiness to move forward with a project. This tool includes questions related to the development phase, user research, alignment with the business case, consideration of policy requirements, and more. Ultimately, it ensures that teams have considered all necessary factors before progressing further. 

Phase 3: Building sustainable work practices 🍃

As the program progressed, several sustainable work practices emerged which Government executives were keen to retain going forward.

Some of these included:

  • ResearchOps Practice: The team established a research operations practice, streamlining research efforts and ensuring that ongoing research was conducted efficiently and effectively.
  • Consistent Design Artifacts: Templates and consistent design artifacts were created, reducing friction and ensuring that teams going forward started from a common baseline.
  • Design Authority and Ways of Working: A design authority was established to elevate and share best practices across the program.
  • Centralized and Decentralized Team Models: The program showcased the effectiveness of a combination of centralized and decentralized team models. A central design team provided guidance and support, while service design leads within specific service lines ensured alignment and consistency.

Why it matters 🔥

Meera's journey serves as a valuable resource for those working on complex design programs, emphasizing the significance of aligning diverse stakeholders and maintaining traceability. Alignment and traceability are critical to ensuring that programs never lose sight of the problem they’re trying to solve, both from the user and organization’s perspective. They’re also critical to delivering on time and within budget!

Traceability key takeaways 🥡

  • Early Alignment Matters: While early alignment is ideal, it's never too late to embark on a traceability journey. It can uncover gaps, increase confidence in decision-making, and ensure that the right services are delivered.
  • Identify and audit: You never know what artifacts will shape your journey. Identify everything early, and don’t be afraid to get clarity on things you’re not sure about.
  • Conducting traceability is always worthwhile: Even if you don’t find many gaps in your program, you will at least gain a high level of confidence that your delivery is focused on the right things.

Delivery readiness key takeaways 🥡

  • Skills Mix is Vital: Assess and adapt team member roles to match their skills and experiences, ensuring they are positioned optimally.
  • Not Everyone Shares the Same Passion: Recognize that not everyone will share the same level of passion for design and research. Make the relevance of these practices clear to all team members.

Sustainability key takeaways 🥡

  • One Size Doesn't Fit All: Tailor methodologies, templates, and practices to the specific needs of your organization.
  • Collaboration is Key: Foster a sense of community and collective responsibility within teams, encouraging shared ownership of project outcomes.

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1 min read

Unlocking UX excellence: Practical use cases for Optimal's research platform

In today's digital landscape, delivering exceptional user experiences is no longer optional—it's essential for success. At Optimal, we're committed to empowering UX professionals and organizations with the best-in-class tools and methodologies to create outstanding digital products and experiences. 

In this blog post, we'll explore practical use cases that demonstrate how Optimal's research platform can drive meaningful improvements across various UX scenarios.

Use case 1: Make Collaborative Design Decisions or A/B Test a Design

Whether you're refining an existing product, launching a new website, or rebranding, Optimal's user research and insights platform empowers your team to make informed, collaborative design decisions. Here's how to leverage our tools for impactful results:

1. Qualitative Insights: Establish organizational priorities

  • Use our Qualitative Research tool to develop a comprehensive list of top tasks or goals from your organization's perspective.
  • Engage stakeholders across departments to ensure alignment on key objectives.

2. Surveys: Validate user priorities and pain points

  • Deploy a targeted survey using our Survey tool to confirm users' top tasks and identify existing issues.
  • Gather quantitative data to support or challenge organizational assumptions about user needs.

3. First-click Testing: Conduct preference testing

  • Use our First-Click Testing tool to evaluate the effectiveness of different design options.
  • This method provides valuable insights for A/B testing decisions, ensuring designs resonate with your target audience.

4. Qualitative Insights: Deep dive into user preferences

  • Conduct follow-up interviews or focus groups using our Qualitative Research tool to gain a deeper understanding of user preferences and experiences with different design options.
  • Explore the 'why' behind user choices to inform more nuanced design decisions.

By systematically applying these research methods, your team can collaboratively create designs that not only look great but also deliver exceptional user experiences. Optimal's platform empowers you to make data-driven design decisions, fostering innovation while minimizing the risk of costly missteps.

Remember, the key to successful collaborative design is continuous iteration and testing. Use Optimal's tools throughout your design process to validate decisions, gather feedback, and refine your approach for optimal results.

Use case 2: Developing effective content strategies

Developing a robust content strategy is crucial for intranets, help documents, websites, and product copy. Optimal's user research and insights platform empowers you to create content that resonates with your audience and drives engagement. Here's how to leverage our tools for effective content strategy development:

1. Card Sorting: Organize content intuitively

  • Use our Card Sorting tool to understand how users naturally categorize and group your content.
  • Gain insights into users' mental models to inform your content hierarchy and organization.
  • Apply findings to create a content structure that aligns with user expectations, enhancing findability and engagement.

2. Tree Testing: Validate information architecture

  • Employ our Tree Testing tool to confirm whether information placed within your proposed hierarchy is findable and understandable.
  • Identify areas where users struggle to locate content, enabling you to refine your structure for optimal user experience.
  • Iterate on your information architecture based on concrete user data, ensuring your content is easily accessible.
  • Test different content structures and then compare them with each other using the task comparison tool available in Optimal to understand which structure is most likely to drive users to perform the targeted actions.

3. Qualitative Insights: Analyze language perceptions

  • Leverage our Qualitative Research tool to conduct in-depth interviews or focus groups.
  • Explore user perceptions of terminology, language style, and content tone.
  • Gather rich insights to inform your content voice and style guide, ensuring your messaging resonates with your target audience.

4. Additional Applications of Qualitative Insights

   Expand your content strategy research by using our Qualitative Research tool to:

  • Review internal tools and processes to streamline content creation workflows.
  • Compare content experiences across desktop and mobile devices for consistency.
  • Gather event feedback to inform content for future marketing materials.
  • Analyze customer service and support interactions to identify common issues and FAQs.
  • Conduct usability testing on existing content to identify areas for improvement.

   Key questions to explore:

  • What's working well in your current content?
  • What's not resonating with users?
  • What are users' first impressions of your content?
  • How do users typically interact with your content?
  • How well does your content foster empathy and connection with your audience?

By systematically applying these research methods, you'll develop a content strategy that not only meets your organizational goals but also deeply resonates with your audience. Optimal's platform empowers you to create content that informs, engages, and converts, driving meaningful results for your business.

Remember, content strategy is an ongoing process. Regularly use Optimal's tools to assess the effectiveness of your content, gather user feedback, and iteratively improve your approach for continued success.

Use case 3: Increase website traffic

Empower your team to boost conversion rates by leveraging Optimal's best-in-class user research and insights platform. Here's how you can unlock meaningful improvements:

1. Qualitative Insights & Surveys: Uncover user motivations

  • Conduct in-depth interviews or targeted surveys to gather rich, qualitative feedback about user experiences, motivations, and pain points on your site.
  • Add an intercept snippet to your existing website to survey users as they come to your website to get a clear understanding of user motivations in context.
  • Analyze responses to identify key themes and opportunities for optimization.

2. Tree Testing: Optimize navigation structure

  • Use our Tree Testing tool to evaluate the effectiveness of your site's navigation structure.
  • Identify areas where users struggle to find information, enabling you to streamline pathways to conversion.

3. Card Sorting: Enhance information architecture

  • Leverage our Card Sorting tool to understand how users naturally categorize your site's information.
  • Apply insights to refine the layout of product features or benefits on your landing pages, aligning with user expectations.

4. Prototype Testing: Validate Design Changes

  • Develop prototypes of new landing pages or key conversion elements (like CTAs) using our Prototype Testing tool.
  • Conduct first-click tests to ensure your design changes resonate with users and drive desired actions.

5. Follow-up Qualitative Insights: Iterate and improve

  • After implementing changes, conduct follow-up interviews or surveys to gauge the impact of your optimizations.
  • Gather feedback on the improved user experience and identify any remaining pain points.

By systematically applying these research methods, you'll gain the actionable insights needed to create a more intuitive, engaging, and conversion-friendly website. Optimal empowers you to make data-driven decisions that not only boost conversions but also enhance overall user satisfaction.

Embracing mixed methods research

To truly unlock the power of user research, we recommend a mixed methods approach. By combining quantitative data from surveys and usability tests with qualitative insights from interviews and open-ended responses, you can gain a comprehensive understanding of your users' needs and behaviors.

For more information on mixed methods research and how it can enhance your UX strategy, check out our detailed guide: What is mixed methods research?

And that’s a wrap

Optimal's user research and insights platform provides the tools and methodologies you need to deliver exceptional digital experiences. By leveraging these use cases and adopting a mixed methods approach, you can make data-driven decisions that resonate with your users and drive business success.

Remember, great UX is an ongoing journey. Regularly employ these research methods to stay attuned to your users' evolving needs and preferences. With Optimal as your partner, you're equipped to create digital products and experiences that truly stand out in today's competitive landscape.

Ready to elevate your UX research? Explore Optimal's platform and start unlocking actionable insights today!

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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