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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Sachi Taulelei: Odd one out - embracing diversity in design and technology

It’s no secret - New Zealand has a diversity problem in design and technology. 

Throughout her career, Sachi often felt like the odd one out - the only woman, the only Pasifika person, the one who laughed too loud, the one who looked different and sounded different. But as a leader, Sachi has been able to create change.

Sachi Taulelei, Head of Design, ANZ, recently spoke at UX New Zealand, the leading UX and IA conference in New Zealand hosted by Optimal Workshop, on how she is building a diverse team of designers at New Zealand’s largest bank.

In her talk, Sachi shares the challenges she’s faced as a Pasifika woman in design and technology; and how this has shaped her approach to leadership and her drive to create inclusive environments where individuals and teams thrive.

Background on Sachi Taulelei

Sachi is a creative strategist, a design leader, and a recovering people pleaser. She has worked in digital and design for over 25 years, spending most of her career creating and designing digital experiences centered on people.

As a proud Pasifika woman, she has a particular interest in diversity, equity, and inclusion. She has spoken out about the need for more diversity within design and technology and the impact it can have on the technology we create.

Sachi is passionate about giving back - when she's not running after her two kids, you'll find her mentoring Pasifika youth, cheering on young leaders through the Young Enterprise Scheme, judging awards for Women in AI, or volunteering at the local hospice.

Contact Details:

Email: sachi.taulelei@anz.com

LinkedIn: https://www.linkedin.com/in/sachi-taulelei/

Odd one out: embracing diversity in design and technology ✨

Looking and sounding different from her peers, Sachi always felt like she was trying to find her place in the office. She always felt like she didn’t belong. 

Sachi has experienced all forms of racism and discrimination as a result of her heritage. These experiences aren’t spoken about and often go unnoticed by the majority. She has held equivalent jobs to male counterparts but received lower pay, and was advised to change her name from Sachi to Sacha on her job applications to improve her chances.  

Sachi’s response was to work hard and become great at what she does, which was recognized over time. Slowly, she began to rise through the ranks. However, having reached leadership roles, she struggled to be heard and participate, without knowing why. The advice was given freely by managers to “stick at it”, to “grow thicker skin”, and to grow through the “school of hard knocks”. Although this advice worked at face value and she flourished, Sachi began to feel like a fraud and constantly second-guessing herself. She began to “edit” herself to fit into an acceptable mold and, in doing so, felt like she lost part of who she was.

What is success? 🏆🎯💎

Success often comes in the form of our leaders who have already climbed the mountains of achievement. When you see success in this way, as someone who doesn’t fit the mold, there is pressure to conform to get ahead. Using the same tools and advice given to these leaders, she realized, would actually hold her back. 

Realizing true value through our uniqueness 🪐🦋

Sachi recounts the treatment of Japanese-American citizens in the U.S. in the years following Pearl Harbour, where Japanese-American citizens were moved to concentration camps. This happened despite an official report finding conclusively that there was no threat from this population. Even though Germany and Italy were also at war with the U.S., for example, citizens with Italian and German heritage were not treated this way. This caused immeasurable pain, shame, and fear for the victims, and fostered a head-down, work-hard mentality in order to try and forget the treatment they received. This attitude, Sachi believes, was passed down to her from her ancestors who experienced that reality. Sachi explains that while there are many things that can hold someone back in life, creating meaningful change starts with introspection. Often, that requires us to work through fear and shame.

Reflecting on her heritage, which is part Samoan and part Japanese, Sachi started to embrace her unique traits. In her case, she embraced the deep empathy and human compassion from her Japanese side and the deep sense of community and connection from her Samoan side. Her uniqueness is something to celebrate, not to hide behind. 

Becoming a leader and realizing this, Sachi wanted to create a team culture based on equity, openness, and a sense of belonging – all things that Sachi wished for herself on her journey.

Why it matters 💫

Once she understood herself and what she wanted for her team, Sachi set to work on building a new team culture. Sachi breaks down key learnings from how she turned this vision into reality.

Define

Define what diversity means for your team. You need to clearly understand what it is you want to achieve before you can achieve it. For Sachi’s team, they knew that they wanted to create a team that was representative of New Zealand. Sachi knew, for example, that she had a lack of Māori and Pacific representation within the team. Māori and Pasifika represent 25% of the population. So, an effort was made to increase ranks by hiring talent from these cultures. 

Additionally, Sachi focused on creating new role levels - from intern right through to graduates, juniors, and intermediate-level positions. This helped to acknowledge age differences within her team and also helped to manage career progression opportunities.

Effort 

It can be difficult to achieve diversity and inclusion and it requires a lot of work. For example, Sachi learned that posting an ad on job boards and expecting to receive hundreds of Māori and Pasifika applicants wasn’t realistic. Instead, partnerships were built with local design schools, and networking events were consistently attended. Job referrals from within the team were also leveraged, as well as establishing a strong direction for recruitment specialists within the organization.

Sachi also recognized that, as a leader, she needed to be more visible and more vocal about sharing her views of the world and what she was trying to achieve. It was important to be clear about the type of culture she was building within her team so that she could promote it.

In less than a year her team grew (from 11 to 40!) which meant a focus on building an inclusive team culture was required. The central theme throughout this time was, “You have to connect to yourself and your strengths first and foremost, before you can connect with others and as a team”. This meant that the team used tools like the Clifton Strength Finder, in order to learn about themselves and each other. Each designer was then encouraged to delve into their own natural working styles and were taught how to amplify their own strengths through various workshops. This approach also becomes handy when recruiting and strengthening potential weak spots.

Integrity

It’s important to have leaders who care - you can’t do it on your own. There can be pain points on the journey to creating diversity and inclusion, so it’s necessary to have leaders who listen, support, and work through some of the challenges that can arise.

Benefits of diversity and inclusion in design teams 👩🏼🤝👨🏿

Why push for diversity and inclusion? Sachi argues that the benefits are evident in the way that her team designs. 

For example, her team:

  • Insist that research is done with diverse customer groups
  • Advocates for accessibility when no one else will
  • Understand problems from different perspectives before diving into a project

Most importantly, the benefits show up in the way that each other is treated, and the relationships that are built with key stakeholders. Diversity and inclusion are wins for everyone - the team, the organization, and the customer.

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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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1 min read

5 key areas for effective ResearchOPs

Simply put, ResearchOps is about making sure your research operations are robust, thought through and managed. 

Having systems and processes around your UX research and your team keep everyone (and everything) organized. Making user research projects quicker to get started and more streamlined to run. And robust sharing, socializing, and knowledge storage means that everyone can understand the research insights and findings and put these to use - across the organization. And even better, find these when they need them. 

Using the same tools across the team allows the research team to learn from each other, and previous research projects and be able to compare apples with apples, with everyone included. Bringing the team together across tools, research and results.

We go into more detail in our ebook ResearchOps Checklist about exactly what you can do to make sure your research team is running at its best. Let’s take a quick look at 5 way to ensure you have the grounding for a successful ResearchOps team.

1. Knowledge management 📚

What do you do with all of the insights and findings of a user research project? How do you store them, how do you manage the insights, and how do you share and socialize?

Having processes in place that manage this knowledge is important to the longevity of your research. From filing to sharing across platforms, it all needs to be standardized so everyone can search, find and share.

2. Guidelines and process templates 📝

Providing a framework for how to run research projects is are important. Building on the knowledge base from previous research can improve research efficiencies and cut down on groundwork and administration. Making research projects quicker and more streamlined to get underway.

3. Governance 🏛

User research is all about people, real people. It is incredibly important that any research be legal, safe, and ethical. Having effective governance covered is vital.

4. Tool stack 🛠

Every research team needs a ‘toolbox’ that they can use whenever they need to run card sorts, tree tests, usability tests, user interviews, and more. But which software and tools to use?

Making sure that the team is using the same tools also helps with future research projects, learning from previous projects, and ensuring that the information is owned and run by the organization (rather than whichever individuals prefer). Reduce logins and password shares, and improve security with organization-wide tools and platforms. 

5. Recruitment 👱🏻👩👩🏻👧🏽👧🏾

Key to great UX research is the ability to recruit quality participants - fast! Having strong processes in place for screening, scheduling, sampling, incentivizing, and managing participants needs to be top of the list when organizing the team.

Wrap Up 💥

Each of these ResearchOps processes are not independent of the other. And neither do they flow from one to the other. They are part of a total wrap around for the research team, creating processes, systems and tools that are built to serve the team. Allowing them to focus on the job of doing great research and generating insights and findings that develop the very best user experience. 

Afterall, we are creating user experiences that keep our users engaged and coming back. Why not look at the teams user experience and make the most of that. Freeing time and space to socialize and share the findings with the organization. 

Seeing is believing

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