February 20, 2024
3 min

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

Bear Liu: How visual thinking can improve communications in design workplaces

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: 

  1. Understanding - break down complex problems and solutions so everyone can understand. 
  2. Memory - retaining information in your mind is difficult even with note taking.
  3. 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.

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The Evolution of UX Research: Digital Twins and the Future of User Insight

Introduction

User Experience (UX) research has always been about people. How they think, how they behave, what they need, and—just as importantly—what they don’t yet realise they need. Traditional UX methodologies have long relied on direct human input: interviews, usability testing, surveys, and behavioral observation. The assumption was clear—if you want to understand people, you have to engage with real humans.

But in 2025, that assumption is being challenged.

The emergence of digital twins and synthetic users—AI-powered simulations of human behavior—is changing how researchers approach user insights. These technologies claim to solve persistent UX research problems: slow participant recruitment, small sample sizes, high costs, and research timelines that struggle to keep pace with product development. The promise is enticing: instantly accessible, infinitely scalable users who can test, interact, and generate feedback without the logistical headaches of working with real participants.

Yet, as with any new technology, there are trade-offs. While digital twins may unlock efficiencies, they also raise important questions: Can they truly replicate human complexity? Where do they fit within existing research practices? What risks do they introduce?

This article explores the evolving role of digital twins in UX research—where they excel, where they fall short, and what their rise means for the future of human-centered design.

The Traditional UX Research Model: Why Change?

For decades, UX research has been grounded in methodologies that involve direct human participation. The core methods—usability testing, user interviews, ethnographic research, and behavioral analytics—have been refined to account for the unpredictability of human nature.

This approach works well, but it has challenges:

  1. Participant recruitment is time-consuming. Finding the right users—especially niche audiences—can be a logistical hurdle, often requiring specialised panels, incentives, and scheduling gymnastics.
  2. Research is expensive. Incentives, moderation, analysis, and recruitment all add to the cost. A single usability study can run into tens of thousands of dollars.
  3. Small sample sizes create risk. Budget and timeline constraints often mean testing with small groups, leaving room for blind spots and bias.
  4. Long feedback loops slow decision-making. By the time research is completed, product teams may have already moved on, limiting its impact.

In short: traditional UX research provides depth and authenticity, but it’s not always fast or scalable.

Digital twins and synthetic users aim to change that.

What Are Digital Twins and Synthetic Users?

While the terms digital twins and synthetic users are sometimes used interchangeably, they are distinct concepts.

Digital Twins: Simulating Real-World Behavior

A digital twin is a data-driven virtual representation of a real-world entity. Originally developed for industrial applications, digital twins replicate machines, environments, and human behavior in a digital space. They can be updated in real time using live data, allowing organisations to analyse scenarios, predict outcomes, and optimise performance.

In UX research, human digital twins attempt to replicate real users' behavioral patterns, decision-making processes, and interactions. They draw on existing datasets to mirror real-world users dynamically, adapting based on real-time inputs.

Synthetic Users: AI-Generated Research Participants

While a digital twin is a mirror of a real entity, a synthetic user is a fabricated research participant—a simulation that mimics human decision-making, behaviors, and responses. These AI-generated personas can be used in research scenarios to interact with products, answer questions, and simulate user journeys.

Unlike traditional user personas (which are static profiles based on aggregated research), synthetic users are interactive and capable of generating dynamic feedback. They aren’t modeled after a specific real-world person, but rather a combination of user behaviors drawn from large datasets.

Think of it this way:

  • A digital twin is a highly detailed, data-driven clone of a specific person, customer segment, or process.
  • A synthetic user is a fictional but realistic simulation of a potential user, generated based on behavioral patterns and demographic characteristics.

Both approaches are still evolving, but their potential applications in UX research are already taking shape.

Where Digital Twins and Synthetic Users Fit into UX Research

The appeal of AI-generated users is undeniable. They can:

  • Scale instantly – Test designs with thousands of simulated users, rather than just a handful of real participants.
  • Eliminate recruitment bottlenecks – No need to chase down participants or schedule interviews.
  • Reduce costs – No incentives, no travel, no last-minute no-shows.
  • Enable rapid iteration – Get user insights in real time and adjust designs on the fly.
  • Generate insights on sensitive topics – Synthetic users can explore scenarios that real participants might find too personal or intrusive.

These capabilities make digital twins particularly useful for:

  • Early-stage concept validation – Rapidly test ideas before committing to development.
  • Edge case identification – Run simulations to explore rare but critical user scenarios.
  • Pre-testing before live usability sessions – Identify glaring issues before investing in human research.

However, digital twins and synthetic users are not a replacement for human research. Their effectiveness is limited in areas where emotional, cultural, and contextual factors play a major role.

The Risks and Limitations of AI-Driven UX Research

For all their promise, digital twins and synthetic users introduce new challenges.

  1. They lack genuine emotional responses.
    AI can analyse sentiment, but it doesn’t feel frustration, delight, or confusion the way a human does. UX is often about unexpected moments—the frustrations, workarounds, and “aha” realisations that define real-world use.
  2. Bias is a real problem.
    AI models are trained on existing datasets, meaning they inherit and amplify biases in those datasets. If synthetic users are based on an incomplete or non-diverse dataset, the research insights they generate will be skewed.
  3. They struggle with novelty.
    Humans are unpredictable. They find unexpected uses for products, misunderstand instructions, and behave irrationally. AI models, no matter how advanced, can only predict behavior based on past patterns—not the unexpected ways real users might engage with a product.
  4. They require careful validation.
    How do we know that insights from digital twins align with real-world user behavior? Without rigorous validation against human data, there’s a risk of over-reliance on synthetic feedback that doesn’t reflect reality.

A Hybrid Future: AI + Human UX Research

Rather than viewing digital twins as a replacement for human research, the best UX teams will integrate them as a complementary tool.

Where AI Can Lead:

  • Large-scale pattern identification
  • Early-stage usability evaluations
  • Speeding up research cycles
  • Automating repetitive testing

Where Humans Remain Essential:

  • Understanding emotion, frustration, and delight
  • Detecting unexpected behaviors
  • Validating insights with real-world context
  • Ethical considerations and cultural nuance

The future of UX research is not about choosing between AI and human research—it’s about blending the strengths of both.

Final Thoughts: Proceeding With Caution and Curiosity

Digital twins and synthetic users are exciting, but they are not a magic bullet. They cannot fully replace human users, and relying on them exclusively could lead to false confidence in flawed insights.

Instead, UX researchers should view these technologies as powerful, but imperfect tools—best used in combination with traditional research methods.

As with any new technology, thoughtful implementation is key. The real opportunity lies in designing research methodologies that harness the speed and scale of AI without losing the depth, nuance, and humanity that make UX research truly valuable.

The challenge ahead isn’t about choosing between human or synthetic research. It’s about finding the right balance—one that keeps user experience truly human-centered, even in an AI-driven world.

This article was researched with the help of Perplexity.ai. 

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Tips for recruiting quality research participants

If there’s one universal truth in user research, it’s that at some point you’re going to need to find people to actually take part in your studies. Be it a large number of participants for quantitative research or a select number for in-depth, in-person user interviews. Finding the right people (and number) of people can be a hurdle.

With the right strategy, you can source exactly the right participants for your next research project.

We share a practical step-by-step guide on how to find participants for user experience research.

The difficulties/challenges of user research recruiting 🏋️

It has to be acknowledged that there are challenges when recruiting research participants. You may recognize some of these:

  • There are so many channels and methods you can use to find participants, different channels will work better for different projects.
  • Repeatedly using the same channels and methods will result in diminishing returns (i.e. burning out participants).
  • It’s a lengthy and complex process, and some projects don’t have the luxury of time.
  • Offering the right incentives and distributing them is time-consuming.
  • It’s hard to manage participants during long-term or recurring studies, such as customer research projects.

We’ll simplify the process, talk about who the right participants are, and unpack some of the best ways to find them. Removing these blocks can be the easiest way to move forward.

Who are the right participants for different types of research? 🤔

1. The first step to a successful participant recruitment strategy is clarifying the goals of your user research and which methods you intend to use. Ask yourself:

  • What is the purpose of our research?
  • How do we plan to understand that?

2. Define who your ideal research participant is. Who is going to have the answers to your questions?

3. Work out your research recruitment strategy. That starts by understanding the differences between recruiting for qualitative and quantitative research.

Recruiting for qualitative vs. quantitative research 🙋🏻

Quantitative research recruiting is a numbers game. For your data analysis to be meaningful and statistically significant, you need a lot of data. This means you need to do a lot of research with a lot of people. When recruiting for quantitative research, you first have to define the population (the entire group you want to study). From there, you choose a sampling method that allows you to create a sample—a randomly selected subset of the population who will participate in your study.

Qualitative recruiting involves far fewer participants, but you do need to find a selection of ‘perfect’ participants. Those that fit neatly into your specific demographic, geographic, psychographic, and behavioral criteria relevant to your study. Recruiting quality participants for qualitative studies involves non-random sampling, screening, and plenty of communication.

How many participants do you need? 👱🏻👩👩🏻👧🏽👧🏾

How many participants to include in a qualitative research study is one of the most heavily discussed topics in user research circles. In most cases, you can get away with 5 people – that’s the short answer. With 5 people, you’ll uncover most of the main issues with the thing you’re testing. Depending on your research project there could be as many as 50 participants, but with each additional person, there is an additional cost (money and time).

Quantitative research is obviously quite different. With studies like card sorts and tree tests, you need higher participant numbers to get statistically meaningful results. Anywhere from 20 - 500 participants, again coming back to the purpose of your test and your research budget. These are usually easier and quicker to implement therefore the additional cost is lower.

User research recruitment - step by step 👟

Let’s get into your research recruitment strategy to find the best participants for your research project. There are 5 clear steps to get you through to the research stage:

1. Identify your ideal participants

Who are they? What do they do? How old are they? Do they already use your product? Where do they live? These are all great questions to get you thinking about who exactly you need to answer your research questions. The demographic and geographic detail of your participants are important to the quality of your research results.

2. Screen participants

Screening participants will weed out those that may not be suitable for your specific project. This can be as simple as asking if the participants have used a product similar to yours. Or coming back to your key identified demographic requirements and removing anyone that doesn’t fit these criteria.

3. Find prospective participants

This is important and can be time-consuming. For qualitative research projects, you can look within your organization or ask over social media for willing participants. Or if you’re short on time look at a participant recruitment service, which takes your requirements and has a catalog of available persons to call on. There’s a cost involved, but the time saving can negate this. For qualitative surveys, a great option can be a live intercept on your website or app that interrupts users and asks them to complete a short questionnaire.

4. Research incentives

In some cases you will need to provide incentives. This could be offering a prize or discount for those who complete online qualitative surveys. Or a fixed sum for those that take part in longer format quantitative studies.

5. Scheduling with participants

Once you have waded through the emails, options, and communication from your inquiries make a list of appropriate participants. Schedule time to do the research, either in person or remotely. Be clear about expectations and how long it will take. And what the incentive to take part is.

Tips to avoid participant burnout 📛

You’ve got your participants sorted and have a great pool of people to call on. If you keep hitting the same group of people time and time again, you will experience the law of diminishing returns. Constantly returning to the same pool of participants will eventually lead to fatigue. And this will impact the quality of your research because it’s based on interviewing the same people with the same views.

There are 2 ways to avoid this problem:

  1. Use a huge database of potential participant targets.
  2. Use a mixture of different recruitment strategies and channels.

Of course, it might be unavoidable to hit the same audience repeatedly when you’re testing your product development among your customer base.

Wrap up 🌯

Understanding your UX research recruitment strategy is crucial to recruiting quality participants. A clear idea of your purpose, who your ideal participants are, and how to find them takes time and experience. 

And to make life easier you can always leave your participant recruitment with us. With a huge catalog of quality participants all at your fingertips on our app, we can recruit the right people quickly.

Check out more here.

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