Optimal Blog

Introduction

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The skills market has a familiar whiff to it. A decade ago, digital execs scratched their heads as great swathes of the delivery workforce decided to retrain as User Experience experts. Project Managers and Business Analysts decided to muscle-in on the creative process that designers insisted was their purview alone. Win for systemised thinking. Loss for magic dust and mystery.

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With UX, research and design roles being the first to hit the cutting room floor over the past 24 months, a lot of the responsibility to solve for those missing competencies in the product delivery cycle now resides with the T-shaped Product Managers, because their career origin story tends to embrace a broader foundation across delivery and design disciplines. And so, as UX course providers jostle for position in a distracted market, senior professionals are repackaging themselves as Product Managers.

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Another Talent Migration? We’ve Seen This Before.

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The skills market has a familiar whiff to it. A decade ago, Project Managers (PMs) and Business Analysts (BAs) pivoted into UX roles in their droves, chasing the north star of digital transformation and user-centric design. Now? The same opportunities to pivot are emerging again—this time into Product Management.

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And if history is anything to go by, we already know how this plays out.

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Between 2015 and 2019, UX job postings skyrocketed by 320%, fueled by digital-first strategies and a newfound corporate obsession with usability. PMs and BAs, sensing the shift, leaned into their adjacent skills—stakeholder management, process mapping, and research—and suddenly, UX wasn’t just for designers anymore. It was a business function.

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Fast-forward to 2025, and Product Management is in the same phase of maturation and despite some Covid-led contraction, bouncing back to 5.1% growth. The role has evolved from feature shipping to strategic value creation while traditional project management roles are trending towards full-stack product managers who handle multiple aspects of product development with fractional PMs for part-time or project-based roles.

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Why Is This Happening? The Data Tells the Story.

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📈 Job postings for product management roles grew by 41% between 2020 and 2025, compared to a 23% decline in traditional project management roles during the same period (Indeed Labor Market Analytics).

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📉 The demand for product managers has been growing, with roles increasing by 32% yearly in general terms, as mentioned in some reports.

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💰 Salary Shenanigans: Product Managers generally earn higher salaries than Business Analysts. In the U.S., PMs earn about 45% more than BAs on average ($124,000 vs. $85,400). In Australia, PMs earn about 4% to 30% more than BAs ($130,000 vs. $105,000 to $125,000) wave.

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Three Structural Forces Driving the Shift

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1. Agile and Product-Led Growth Have Blurred the Lines
   Project success is no longer measured in timelines and budgets—it’s about customer lifetime value (CLTV) and feature adoption rates. For instance, 86% of teams have adopted the Agile approach, and 63% of IT teams are also using Agile methodologies forcing PMs to move beyond execution into continuous iteration and outcome-based thinking.
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2. Data Is the New Currency, and BAs Are Cashing In
   89% of product decisions in 2025 rely on analytics (Gartner, 2024). That’s prime territory for BAs, whose SQL skills, A/B testing expertise, and KPI alignment instincts make them critical players in data-driven product strategy.
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3. Role Consolidation Is Inevitable
   The post-pandemic belt-tightening has left one role doing the job of three. Today’s product managers don’t just prioritise backlogs - they manage stakeholders, interpret data, and (sometimes poorly) sketch out UX wireframes. Product manager job descriptions now list "requirements gathering" and "stakeholder management"—once core PM/BA responsibilities.

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How This Mirrors the UX Migration of 2019

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Same pattern. Different discipline.

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The Challenges of Becoming a Product Manager (and Why Some Will Struggle)

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👀 Outputs vs. Outcomes – PMs think in deliverables. Transitioning PMs struggle to adjust to measuring success through customer impact instead of project completion.

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🛠️ Legacy Tech Debt – Outdated tech stacks can lead to decreased productivity, integration issues, and security concerns. This complexity can slow down operations and hinder the efficiency of teams, including product management.

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😰 Imposter Syndrome is Real – New product managers feel unqualified, mirroring the self-doubt UX migrants felt in 2019. Because let’s be honest—jumping into product strategy is a different beast from managing deliverables.

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What Comes Next? The Smartest Companies Are Already Preparing.

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🏆 Structured Reskilling – Programs like Google’s "PM Launchpad" reduce time-to-proficiency for new PMs. Enterprises that invest in structured career shifts will win the talent war.

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📊 Hybrid Role Recognition – Expect to see “Analytics-Driven PM” and “Technical Product Owner” job titles formalising this shift, much like “UX Strategist” emerged post-2019.

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🚀 AI Will Accelerate the Next Migration – As AI automates routine PM/BA tasks, expect even more professionals to pivot into strategic product roles. The difference? This time, the transition will be even faster.

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Conclusion: The Cycle Continues

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Tech talent moves in cycles. Product Management is simply the next career gold rush for systems thinkers with a skill for structure, process, and problem-solving. A structural response to the evolution of tech ecosystems.

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Companies that recognise and support this transition will outpace those still clinging to rigid org charts. Because one thing is clear—the talent migration isn’t coming. It’s already here.

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This article was researched with the help of Perplexity.ai

Introduction

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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.

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But in 2025, that assumption is being challenged.

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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.

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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?

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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.

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The Traditional UX Research Model: Why Change?

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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.

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This approach works well, but it has challenges:

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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.
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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.
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3. Small sample sizes create risk. Budget and timeline constraints often mean testing with small groups, leaving room for blind spots and bias.
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4. Long feedback loops slow decision-making. By the time research is completed, product teams may have already moved on, limiting its impact.

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

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Digital twins and synthetic users aim to change that.

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What Are Digital Twins and Synthetic Users?

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

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Digital Twins: Simulating Real-World Behavior

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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.

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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.

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Synthetic Users: AI-Generated Research Participants

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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.

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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.

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Think of it this way:

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• 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.

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

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Where Digital Twins and Synthetic Users Fit into UX Research

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

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• 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.

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These capabilities make digital twins particularly useful for:

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• 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.

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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.

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The Risks and Limitations of AI-Driven UX Research

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

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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.
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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.
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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.
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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.

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A Hybrid Future: AI + Human UX Research

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

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Where AI Can Lead:

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

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Where Humans Remain Essential:

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

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

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Final Thoughts: Proceeding With Caution and Curiosity

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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.

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

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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.

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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.

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

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2025 is already proving to be an exciting year for UX research, and we’re just getting started! With a range of new features and updates that empower teams to move from design to decisions faster, Optimal is bringing the best tools to the table for everyone and making research accessible across all teams. Let’s dive into what’s already here and what's coming next!
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Video Recording for Prototype Testing

We’re excited to announce that the closed beta for video recording for prototype testing has launched. By capturing screen, audio, and/or video, this feature takes research beyond metrics to give you a deeper understanding of user intent and pain points. Our Optimal Recruitment service also ensures you connect with the right participants for video recording, driving meaningful insights from the start.

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New Study Flow for Faster Study Creation

Say hello to the new Study Flow, an intuitive tab that helps to accelerate study creation, combining the Messages & instructions and Questionnaire tabs into one. Visualize every step of the participant’s journey, from the welcome screen to the final thank-you message with the Study Flow panel. Save time by quickly duplicating questions across Surveys and other study types. Navigate through studies with ease by collapsing and expanding sections as needed.

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What We’re Working On Next

Here’s a sneak peek at some exciting features in the works:

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AI-Powered Survey Question Simplification
We’re unleashing an AI-powered feature to help users simplify complex question wording and improve clarity. Users can quickly accept, reject, or regenerate more suggestions with a click. By improving clarity and simplifying your questions, you’ll gather more accurate, higher-quality insights that drive better results.

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This AI feature is available for surveys questions as well as screening, pre and post study questions for surveys, prototype tests, card sorts, tree tests and first-click tests. This is just the beginning of even more AI-driven improvements to come, all aimed at helping to accelerate your time to insights.

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Advanced Logic Capabilities
We’re working on bringing more advanced logic capabilities to Optimal - one of the most highly requested features for surveys. With display logic, the study changes dynamically - showing or hiding answer options or subsequence questions based on a participant’s previous responses. Apply display logic to Surveys, screening questions, and pre- and post-study questions. This is just the beginning. We’ll be exploring additional advanced logic capabilities in 2025. 
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Join Us on the Journey
‍Stay tuned for regular updates, and let us know how we can make your research experience even better. Have feedback or feature requests? We’d love to hear from you so we can continue to shape the future of Optimal.

Inspired by insights from Optimal users, we’ve reimagined study creation to bring you a beautifully streamlined experience with the new Study Flow tab.

With the new Study Flow, you’ll: 

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⚡ Enjoy faster study set up: Messages & instructions and Questionnaire tabs are combined in a single tab - called Study Flow - for faster editing and settings customization.

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✨ See it all at a glance: Easily visualize and understand the participant experience—from the welcome message to the final thank-you screen—every step of the way. 

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🎯 Duplicate questions: Save time and quickly replicate questions for surveys, screening questions, and pre- and post-study questions. 

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⭐ Experience enhanced UI: Enjoy a modern, clean design with intuitive updates that minimize scrolling and reduce mental load.

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🗂️ Collapse and expand sections: Easily navigate studies by collapsing and expanding sections, making it easier to build out specific parts of your study.

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This Study Flow tab is available across all Optimal tools, except for Qualitative Insights. 

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What’s next?

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We’re not stopping there. We have some significant improvements on the horizon designed to give you even greater flexibility and control.

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Advanced logic: Enhanced logic capabilities is one of our most highly requested features, and we’re thrilled to introduce new capabilities to help you build your ideal study experience – available for surveys and other tools. We will first introduce “display logic”, allowing for: 

• If answer is X for Question Y, then hide/show Question Z.
• If answer is X for Question Y, then hide/show specific answer options.

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Customizable sections: Organize your questions into different sections to build a better study experience for your participants. For example, segment your questions into relevant groupings, such as demographics or product usage. With custom sections, you can add new sections, rename, reorder, duplicate, and move questions between different sections.* 

*Note: Questions cannot be moved to/from the screening questions section.

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These upcoming features will empower you to create dynamic, tailored study experiences for different audiences with ease for more valuable insights. 

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Start exploring the new Study Flow now.

Introduction

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

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

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

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

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1. Time Allocation: Invest in What Matters Most

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

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

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

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

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

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

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

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3. Collaboration: Make Research a Team Sport

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

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

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

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4. Prioritization: Focus on the Right Questions

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

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

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

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

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

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

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

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6. Anticipation: Stay Ahead of Research Needs

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

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

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

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7. Justification of Methodology: Explain Why Your Approach Works

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

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

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

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8. Individual Engagement: Tailor Research Communication to Your Audience

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

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

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

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9. Business Actions: Ensure Research Leads to Real Change

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

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

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

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Final Thoughts: Research That Moves the Needle

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

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

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

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Watch the full webinar here

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Investing in prototype testing and user-focused design isn't just about creating better products—it's a proven strategy to save costs, accelerate timelines, and drive customer loyalty. According to Forrester Research, companies that incorporate prototype testing in their design process can reduce development costs by 33% and cut collaboration time by 25%. UX-focused companies also see products hit the market 50% faster and loyalty rise by 240% (Forrester Research, Nielsen Norman Group)!

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Whether you're refining user flows, testing new concepts, or optimizing your onboarding experience or conversion flows, prototype testing helps ensure your designs hit the mark—before you invest too heavily in the build.

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With those benefits in mind, let's dive into how prototype testing can help you deliver user-centered designs efficiently and effectively. 

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Common Use Cases for Prototype Testing
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1. Test Onboarding and Sign-Up Flows
   How intuitive is your onboarding process? Prototype testing can help identify friction points, ensuring users can navigate and complete sign-ups seamlessly. For example, you can simulate different scenarios to determine whether users can easily register, set up accounts, or retrieve forgotten passwords.
   
   
2. A/B Test Email Designs
   Test different layouts, calls-to-action (CTAs), or visual elements in your email prototypes to discover what resonates best with your audience. Measure metrics like click-through rates or time spent engaging with content to refine your design.
   
   
3. Evaluate User Flows and Wireframes
   Whether you're testing a new feature, redesigning a user journey, or validating a wireframe, prototype testing gives you real-world insights. Observe how users interact with your design and identify areas for improvement before you move to development.
   
   
4. Test Concepts
   Before launching a new idea, validate it through prototype testing. Let users interact with your concept to gauge feasibility and potential impact. This can save time and resources by helping you focus on ideas that resonate.
   
   
5. Evaluate Conversion Flows
   Are users completing purchases or achieving desired outcomes? Use prototype testing to analyze conversion flows and pinpoint where users drop off. From landing pages to payment processes, you can optimize every step for success.
   
   
6. Test User Interfaces (UI)
   Ensure your UI elements—buttons, navigation menus, or forms—are intuitive and accessible. Prototype testing can help you identify design inconsistencies or usability challenges early in the process.
   
   
7. Conduct Usability Tests
   Have a new feature in development? Prototype testing lets you see how users interact with it, revealing insights that can guide refinements and improve overall satisfaction.

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Real-Life Prototype Testing Scenarios

Airline
Imagine your flight has been canceled. Ask how your customers self-service on the airline website to find new flight options. 

Bank
Have a prospect or customer interact with a prototype to open a business account online. Uncover usability issues and streamline the process.

Insurance
Imagine you’re interested in switching car insurance. Explore how intuitive it is for customers to view coverage details in an app, helping insurers improve navigation and accessibility.

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Prototype Testing Analysis & Insights

Optimal’s prototype testing gives you a variety of analysis options to help you to evaluate the effectiveness and usability of your prototypes. Use these to see exactly how users navigate, where they face challenges, and what areas are proving to be successful.
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• Task-based scenarios: Observe how users complete tasks like purchasing a product or updating account settings and set correction paths and destinations.
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• Clickmaps: See how users navigate and locate information. See hits and misses on designated clickable areas, average task completion times, and heatmaps showing where users believed the next steps to be.
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• Task results: Gather insights into how long it took to take a task (time taken), misclicks, directness score (considering backtracks or incorrect pathways), and success score.
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• Participant paths: The Paths tab provides a powerful visualization, including thumbnails, to understand and identify common navigation patterns and potential obstacles participants encounter while completing tasks.
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• Video, audio, and/or screen recording: See how your users interact with and respond to your prototype. Listen to their thought process and pick up on nonverbal cues, like hesitation, frustration, or confusion to pinpoint areas for improvement or exploration.  

Ready to use prototype testing to help your team reduce development costs and get a faster time to market? Get started in your account by creating a new prototype test.

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