7 min read

When AI Meets UX: How to Navigate the Ethical Tightrope

As AI takes on a bigger role in product decision-making and user experience design, ethical concerns are becoming more pressing for product teams. From privacy risks to unintended biases and manipulation, AI raises important questions: How do we balance automation with human responsibility? When should AI make decisions, and when should humans stay in control?

These aren't just theoretical questions they have real consequences for users, businesses, and society. A chatbot that misunderstands cultural nuances, a recommendation engine that reinforces harmful stereotypes, or an AI assistant that collects too much personal data can all cause genuine harm while appearing to improve user experience.

The Ethical Challenges of AI

Privacy & Data Ethics

AI needs personal data to work effectively, which raises serious concerns about transparency, consent, and data stewardship:

  • Data Collection Boundaries – What information is reasonable to collect? Just because we can gather certain data doesn't mean we should.
  • Informed Consent – Do users really understand how their data powers AI experiences? Traditional privacy policies often don't do the job.
  • Data Longevity – How long should AI systems keep user data, and what rights should users have to control or delete this information?
  • Unexpected Insights – AI can draw sensitive conclusions about users that they never explicitly shared, creating privacy concerns beyond traditional data collection.

A 2023 study by the Baymard Institute found that 78% of users were uncomfortable with how much personal data was used for personalized experiences once they understood the full extent of the data collection. Yet only 12% felt adequately informed about these practices through standard disclosures.

Bias & Fairness

AI can amplify existing inequalities if it's not carefully designed and tested with diverse users:

  • Representation Gaps – AI trained on limited datasets often performs poorly for underrepresented groups.
  • Algorithmic Discrimination – Systems might unintentionally discriminate based on protected characteristics like race, gender, or disability status.
  • Performance Disparities – AI-powered interfaces may work well for some users while creating significant barriers for others.
  • Reinforcement of Stereotypes – Recommendation systems can reinforce harmful stereotypes or create echo chambers.

Recent research from Stanford's Human-Centered AI Institute revealed that AI-driven interfaces created 2.6 times more usability issues for older adults and 3.2 times more issues for users with disabilities compared to general populations, a gap that often goes undetected without specific testing for these groups.

User Autonomy & Agency

Over-reliance on AI-driven suggestions may limit user freedom and sense of control:

  • Choice Architecture – AI systems can nudge users toward certain decisions, raising questions about manipulation versus assistance.
  • Dependency Concerns – As users rely more on AI recommendations, they may lose skills or confidence in making independent judgments.
  • Transparency of Influence – Users often don't recognize when their choices are being shaped by algorithms.
  • Right to Human Interaction – In critical situations, users may prefer or need human support rather than AI assistance.

A longitudinal study by the University of Amsterdam found that users of AI-powered decision-making tools showed decreased confidence in their own judgment over time, especially in areas where they had limited expertise.

Accessibility & Digital Divide

AI-powered interfaces may create new barriers:

  • Technology Requirements – Advanced AI features often require newer devices or faster internet connections.
  • Learning Curves – Novel AI interfaces may be particularly challenging for certain user groups to learn.
  • Voice and Language Barriers – Voice-based AI often struggles with accents, dialects, and non-native speakers.
  • Cognitive Load – AI that behaves unpredictably can increase cognitive burden for users.

Accountability & Transparency

Who's responsible when AI makes mistakes or causes harm?

  • Explainability – Can users understand why an AI system made a particular recommendation or decision?
  • Appeal Mechanisms – Do users have recourse when AI systems make errors?
  • Responsibility Attribution – Is it the designer, developer, or organization that bears responsibility for AI outcomes?
  • Audit Trails – How can we verify that AI systems are functioning as intended?

How Product Owners Can Champion Ethical AI Through UX

At Optimal, we advocate for research-driven AI development that puts human needs and ethical considerations at the center of the design process. Here's how UX research can help:

User-Centered Testing for AI Systems

AI-powered experiences must be tested with real users to identify potential ethical issues:

  • Longitudinal Studies – Track how AI influences user behavior and autonomy over time.
  • Diverse Testing Scenarios – Test AI under various conditions to identify edge cases where ethical issues might emerge.
  • Multi-Method Approaches – Combine quantitative metrics with qualitative insights to understand the full impact of AI features.
  • Ethical Impact Assessment – Develop frameworks specifically designed to evaluate the ethical dimensions of AI experiences.

Inclusive Research Practices

Ensuring diverse user participation helps prevent bias and ensures AI works for everyone:

  • Representation in Research Panels – Include participants from various demographic groups, ability levels, and socioeconomic backgrounds.
  • Contextual Research – Study how AI interfaces perform in real-world environments, not just controlled settings.
  • Cultural Sensitivity – Test AI across different cultural contexts to identify potential misalignments.
  • Intersectional Analysis – Consider how various aspects of identity might interact to create unique challenges for certain users.

Transparency in AI Decision-Making

UX teams should investigate how users perceive AI-driven recommendations:

  • Mental Model Testing – Do users understand how and why AI is making certain recommendations?
  • Disclosure Design – Develop and test effective ways to communicate how AI is using data and making decisions.
  • Trust Research – Investigate what factors influence user trust in AI systems and how this affects experience.
  • Control Mechanisms – Design and test interfaces that give users appropriate control over AI behavior.

The Path Forward: Responsible Innovation

As AI becomes more sophisticated and pervasive in UX design, the ethical stakes will only increase. However, this doesn't mean we should abandon AI-powered innovations. Instead, we need to embrace responsible innovation that considers ethical implications from the start rather than as an afterthought.

AI should enhance human decision-making, not replace it. Through continuous UX research focused not just on usability but on broader human impact, we can ensure AI-driven experiences remain ethical, inclusive, user-friendly, and truly beneficial.

The most successful AI implementations will be those that augment human capabilities while respecting human autonomy, providing assistance without creating dependency, offering personalization without compromising privacy, and enhancing experiences without reinforcing biases.

A Product Owner's Responsibility: Leading the Charge for Ethical AI

As UX professionals, we have both the opportunity and responsibility to shape how AI is integrated into the products people use daily. This requires us to:

  • Advocate for ethical considerations in product requirements and design processes
  • Develop new research methods specifically designed to evaluate AI ethics
  • Collaborate across disciplines with data scientists, ethicists, and domain experts
  • Educate stakeholders about the importance of ethical AI design
  • Amplify diverse perspectives in all stages of AI development

By embracing these responsibilities, we can help ensure that AI serves as a force for positive change in user experience enhancing human capabilities while respecting human values, autonomy, and diversity.

The future of AI in UX isn't just about what's technologically possible; it's about what's ethically responsible. Through thoughtful research, inclusive design practices, and a commitment to human-centered values, we can navigate this complex landscape and create AI experiences that truly benefit everyone.

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When Personalization Gets Personal: Balancing AI with Human-Centered Design

AI-driven personalization is redefining digital experiences, allowing companies to tailor content, recommendations, and interfaces to individual users at an unprecedented scale. From e-commerce product suggestions to content feeds, streaming recommendations, and even customized user interfaces, personalization has become a cornerstone of modern digital strategy. The appeal is clear: research shows that effective personalization can increase engagement by 72%, boost conversion rates by up to 30%, and drive revenue growth of 10-15%.

However, the reality often falls short of these impressive statistics. Personalization can easily backfire, frustrating users instead of engaging them, creating experiences that feel invasive rather than helpful, and sometimes actively driving users away from the very content or products they might genuinely enjoy. Many organizations invest heavily in AI technology while underinvesting in understanding how these personalized experiences actually impact their users.

The Widening Gap Between Capability and Quality

The technical capability to personalize digital experiences has advanced rapidly, but the quality of these experiences hasn't always kept pace. According to a 2023 survey by Baymard Institute, 68% of users reported encountering personalization that felt "off-putting" or "frustrating" in the previous month, while only 34% could recall a personalized experience that genuinely improved their interaction with a digital product.

This disconnect stems from a fundamental misalignment: while AI excels at pattern recognition and prediction based on historical data, it often lacks the contextual understanding and nuance that make personalization truly valuable. The result? Technically sophisticated personalization regularly misses the mark on actual user needs and preferences.

The Pitfalls of AI-Driven Personalization

Many companies struggle with personalization due to several common pitfalls that undermine even the most sophisticated AI implementations:

Over-Personalization: When Helpful Becomes Restrictive

AI that assumes too much can make users feel restricted or trapped in a "filter bubble" of limited options. This phenomenon, often called "over-personalization," occurs when algorithms become too confident in their understanding of user preferences.

Signs of over-personalization include:

  • Content feeds that become increasingly homogeneous over time
  • Disappearing options that might interest users but don't match their history
  • User frustration at being unable to discover new content or products
  • Decreased engagement as experiences become predictable and stale

A study by researchers at University of Minnesota found that highly personalized news feeds led to a 23% reduction in content diversity over time, even when users actively sought varied content. This "filter bubble" effect not only limits discovery but can leave users feeling manipulated or constrained.

Incorrect Assumptions: When Data Tells the Wrong Story

AI recommendations based on incomplete or misinterpreted data can lead to irrelevant, inappropriate, or even offensive suggestions. These incorrect assumptions often stem from:

  • Limited data points that don't capture the full context of user behavior
  • Misinterpreting casual interest as strong preference
  • Failing to distinguish between the user's behavior and actions taken on behalf of others
  • Not recognizing temporary or situational needs versus ongoing preferences

These misinterpretations can range from merely annoying (continuously recommending products similar to a one-time purchase) to deeply problematic (showing weight loss ads to users with eating disorders based on their browsing history).

A particularly striking example occurred when a major retailer's algorithm began sending pregnancy-related offers to a teenage girl before her family knew she was pregnant. While technically accurate in its prediction, this incident highlights how even "correct" personalization can fail to consider the broader human context and implications.

Lack of Transparency: The Black Box Problem

Users increasingly want to understand why they're being shown specific content or recommendations. When personalization happens behind a "black box" without explanation, it can create:

  • Distrust in the system and the brand behind it
  • Confusion about how to influence or improve recommendations
  • Feelings of being manipulated rather than assisted
  • Concerns about what personal data is being used and how

Research from the Pew Research Center shows that 74% of users consider it important to know why they are seeing certain recommendations, yet only 22% of personalization systems provide clear explanations for their suggestions.

Inconsistent Experiences Across Channels

Many organizations struggle to maintain consistent personalization across different touchpoints, creating disjointed experiences:

  • Product recommendations that vary wildly between web and mobile
  • Personalization that doesn't account for previous customer service interactions
  • Different personalization strategies across email, website, and app experiences
  • Recommendations that don't adapt to the user's current context or device

This inconsistency can make personalization feel random or arbitrary rather than thoughtfully tailored to the user's needs.

Neglecting Privacy Concerns and Control

As personalization becomes more sophisticated, user concerns about privacy intensify. Key issues include:

  • Collecting more data than necessary for effective personalization
  • Lack of user control over what information influences their experience
  • Unclear opt-out mechanisms for personalization features
  • Personalization that reveals sensitive information to others

A recent study found that 79% of users want control over what personal data influences their recommendations, but only 31% felt they had adequate control in their most-used digital products.

How Product Managers Can Leverage UX Insight for Better AI Personalization

To create a personalized experience that feels natural and helpful rather than creepy or restrictive, UX teams need to validate AI-driven decisions through systematic research with real users. Rather than treating personalization as a purely technical challenge, successful organizations recognize it as a human-centered design problem that requires continuous testing and refinement.

Understanding User Mental Models Through Card Sorting & Tree Testing

Card sorting and tree testing help structure content in a way that aligns with users' expectations and mental models, creating a foundation for personalization that feels intuitive rather than imposed:

  • Open and Closed Card Sorting – Helps understand how different user segments naturally categorize content, products, or features, providing a baseline for personalization strategies
  • Tree Testing – Validates whether personalized navigation structures work for different user types and contexts
  • Hybrid Approaches – Combining card sorting with interviews to understand not just how users categorize items, but why they do so

Case Study: A financial services company used card sorting with different customer segments to discover distinct mental models for organizing financial products. Rather than creating a one-size-fits-all personalization system, they developed segment-specific personalization frameworks that aligned with these different mental models, resulting in a 28% increase in product discovery and application rates.

Validating Interaction Patterns Through First-Click Testing

First-click testing ensures users interact with personalized experiences as intended across different contexts and scenarios:

  • Testing how users respond to personalized elements vs. standard content
  • Evaluating whether personalization cues (like "Recommended for you") influence click behavior
  • Comparing how different user segments respond to the same personalization approaches
  • Identifying potential confusion points in personalized interfaces

Research by the Nielsen Norman Group found that getting the first click right increases the overall task success rate by 87%. For personalized experiences, this is even more critical, as users may abandon a site entirely if early personalized recommendations seem irrelevant or confusing.

Gathering Qualitative Insights Through User Interviews & Usability Testing

Direct observation and conversation with users provides critical context for personalization strategies:

  • Moderated Usability Testing – Reveals how users react to personalized elements in real-time
  • Think-Aloud Protocols – Help understand users' expectations and reactions to personalization
  • Longitudinal Studies – Track how perceptions of personalization change over time and repeated use
  • Contextual Inquiry – Observes how personalization fits into users' broader goals and environments

These qualitative approaches help answer critical questions like:

  • When does personalization feel helpful versus intrusive?
  • What level of explanation do users want for recommendations?
  • How do different user segments react to similar personalization strategies?
  • What control do users expect over their personalized experience?

Measuring Sentiment Through Surveys & User Feedback

Systematic feedback collection helps gauge users' comfort levels with AI-driven recommendations:

  • Targeted Microsurveys – Quick pulse checks after personalized interactions
  • Preference Centers – Direct input mechanisms for refining personalization
  • Satisfaction Tracking – Monitoring how personalization affects overall satisfaction metrics
  • Feature-Specific Feedback – Gathering input on specific personalization features

A streaming service discovered through targeted surveys that users were significantly more satisfied with content recommendations when they could see a clear explanation of why items were suggested (e.g., "Because you watched X"). Implementing these explanations increased content exploration by 34% and reduced account cancellations by 8%.

A/B Testing Personalization Approaches

Experimental validation ensures personalization actually improves key metrics:

  • Testing different levels of personalization intensity
  • Comparing explicit versus implicit personalization methods
  • Evaluating various approaches to explaining recommendations
  • Measuring the impact of personalization on both short and long-term engagement

Importantly, A/B testing should look beyond immediate conversion metrics to consider longer-term impacts on user satisfaction, trust, and retention.

Building a User-Centered Personalization Strategy That Works

To implement personalization that truly enhances user experience, organizations should follow these research-backed principles:

1. Start with User Needs, Not Technical Capabilities

The most effective personalization addresses genuine user needs rather than showcasing algorithmic sophistication:

  • Identify specific pain points that personalization could solve
  • Understand which aspects of your product would benefit most from personalization
  • Determine where users already expect or desire personalized experiences
  • Recognize which elements should remain consistent for all users

2. Implement Transparent Personalization

Users increasingly expect to understand and control how their experiences are personalized:

  • Clearly communicate what aspects of the experience are personalized
  • Explain the primary factors influencing recommendations
  • Provide simple mechanisms for users to adjust or reset their personalization
  • Consider making personalization opt-in for sensitive domains

3. Design for Serendipity and Discovery

Effective personalization balances predictability with discovery:

  • Deliberately introduce variety into recommendations
  • Include "exploration" categories alongside highly targeted suggestions
  • Monitor and prevent increasing homogeneity in personalized feeds over time
  • Allow users to easily branch out beyond their established patterns

4. Apply Progressive Personalization

Rather than immediately implementing highly tailored experiences, consider a gradual approach:

  • Begin with light personalization based on explicit user choices
  • Gradually introduce more sophisticated personalization as users engage
  • Calibrate personalization depth based on relationship strength and context
  • Adjust personalization based on user feedback and behavior

5. Establish Continuous Feedback Loops

Personalization should never be "set and forget":

  • Implement regular evaluation cycles for personalization effectiveness
  • Create easy feedback mechanisms for users to rate recommendations
  • Monitor for signs of over-personalization or filter bubbles
  • Regularly test personalization assumptions with diverse user groups

The Future of Personalization: Human-Centered AI

As AI capabilities continue to advance, the companies that will succeed with personalization won't necessarily be those with the most sophisticated algorithms, but those who best integrate human understanding into their approach. The future of personalization lies in creating systems that:

  • Learn from qualitative human feedback, not just behavioral data
  • Respect the nuance and complexity of human preferences
  • Maintain transparency in how personalization works
  • Empower users with appropriate control
  • Balance algorithm-driven efficiency with human-centered design principles

AI should learn from real people, not just data. UX research ensures that personalization enhances, rather than alienates, users by bringing human insight to algorithmic decisions.

By combining the pattern-recognition power of AI with the contextual understanding provided by UX research, organizations can create personalized experiences that feel less like surveillance and more like genuine understanding: experiences that don't just predict what users might click, but truly respond to what they need and value.

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1 min read

My journey running a design sprint

Recently, everyone in the design industry has been talking about design sprints. So, naturally, the team at Optimal Workshop wanted to see what all the fuss was about. I picked up a copy of The Sprint Book and suggested to the team that we try out the technique.

In order to keep momentum, we identified a current problem and decided to run the sprint only two weeks later. The short notice was a bit of a challenge, but in the end we made it work. Here’s a run down of how things went, what worked, what didn’t, and lessons learned.

A sprint is an intensive focused period of time to get a product or feature designed and tested with the goal of knowing whether or not the team should keep investing in the development of the idea. The idea needs to be either validated or not validated by the end of the sprint. In turn, this saves time and resource further down the track by being able to pivot early if the idea doesn’t float.

If you’re following The Sprint Book you might have a structured 5 day plan that looks likes this:

  • Day 1 - Understand: Discover the business opportunity, the audience, the competition, the value proposition and define metrics of success.
  • Day 2 - Diverge: Explore, develop and iterate creative ways of solving the problem, regardless of feasibility.
  • Day 3 - Converge: Identify ideas that fit the next product cycle and explore them in further detail through storyboarding.
  • Day 4 - Prototype: Design and prepare prototype(s) that can be tested with people.
  • Day 5 - Test: User testing with the product's primary target audience.
Design sprint cycle
 With a Design Sprint, a product doesn't need to go full cycle to learn about the opportunities and gather feedback.

When you’re running a design sprint, it’s important that you have the right people in the room. It’s all about focus and working fast; you need the right people around in order to do this and not have any blocks down the path. Team, stakeholder and expert buy-in is key — this is not a task just for a design team!After getting buy in and picking out the people who should be involved (developers, designers, product owner, customer success rep, marketing rep, user researcher), these were my next steps:

Pre-sprint

  1. Read the book
  2. Panic
  3. Send out invites
  4. Write the agenda
  5. Book a meeting room
  6. Organize food and coffee
  7. Get supplies (Post-its, paper, Sharpies, laptops, chargers, cameras)

Some fresh smoothies for the sprinters made by our juice technician
 Some fresh smoothies for the sprinters made by our juice technician

The sprint

Due to scheduling issues we had to split the sprint over the end of the week and weekend. Sprint guidelines suggest you hold it over Monday to Friday — this is a nice block of time but we had to do Thursday to Thursday, with the weekend off in between, which in turn worked really well. We are all self confessed introverts and, to be honest, the thought of spending five solid days workshopping was daunting. At about two days in, we were exhausted and went away for the weekend and came back on Monday feeling sociable and recharged again and ready to examine the work we’d done in the first two days with fresh eyes.

Design sprint activities

During our sprint we completed a range of different activities but here’s a list of some that worked well for us. You can find out more information about how to run most of these over at The Sprint Book website or checkout some great resources over at Design Sprint Kit.

Lightning talks

We kicked off our sprint by having each person give a quick 5-minute talk on one of these topics in the list below. This gave us all an overview of the whole project and since we each had to present, we in turn became the expert in that area and engaged with the topic (rather than just listening to one person deliver all the information).

Our lightning talk topics included:

  • Product history - where have we come from so the whole group has an understanding of who we are and why we’ve made the things we’ve made.
  • Vision and business goals - (from the product owner or CEO) a look ahead not just of the tools we provide but where we want the business to go in the future.
  • User feedback - what have users been saying so far about the idea we’ve chosen for our sprint. This information is collected by our User Research and Customer Success teams.
  • Technical review - an overview of our tech and anything we should be aware of (or a look at possible available tech). This is a good chance to get an engineering lead in to share technical opportunities.
  • Comparative research - what else is out there, how have other teams or products addressed this problem space?

Empathy exercise

I asked the sprinters to participate in an exercise so that we could gain empathy for those who are using our tools. The task was to pretend we were one of our customers who had to present a dendrogram to some of our team members who are not involved in product development or user research. In this frame of mind, we had to talk through how we might start to draw conclusions from the data presented to the stakeholders. We all gained more empathy for what it’s like to be a researcher trying to use the graphs in our tools to gain insights.

How Might We

In the beginning, it’s important to be open to all ideas. One way we did this was to phrase questions in the format: “How might we…” At this stage (day two) we weren’t trying to come up with solutions — we were trying to work out what problems there were to solve. ‘We’ is a reminder that this is a team effort, and ‘might’ reminds us that it’s just one suggestion that may or may not work (and that’s OK). These questions then get voted on and moved into a workshop for generating ideas (see Crazy 8s).Read a more detailed instructions on how to run a ‘How might we’ session on the Design Sprint Kit website.

Crazy 8s

This activity is a super quick-fire idea generation technique. The gist of it is that each person gets a piece of paper that has been folded 8 times and has 8 minutes to come up with eight ideas (really rough sketches). When time is up, it’s all pens down and the rest of the team gets to review each other's ideas.In our sprint, we gave each person Post-it notes, paper, and set the timer for 8 minutes. At the end of the activity, we put all the sketches on a wall (this is where the art gallery exercise comes in).

Mila our data scientist sketching intensely during Crazy 8s
 Mila our data scientist sketching intensely during Crazy 8s

A close up of some sketches from the team
 A close up of some sketches from the team

Art gallery/Silent critique

The art gallery is the place where all the sketches go. We give everyone dot stickers so they can vote and pull out key ideas from each sketch. This is done silently, as the ideas should be understood without needing explanation from the person who made them. At the end of it you’ve got a kind of heat map, and you can see the ideas that stand out the most. After this first round of voting, the authors of the sketches get to talk through their ideas, then another round of voting begins.

Mila putting some sticky dots on some sketches
 Mila putting some sticky dots on some sketches

Bowie, our head of security/office dog, even took part in the sprint...kind of.
 Bowie, our head of security, even took part in the sprint...kind of

Usability testing and validation

The key part of a design sprint is validation. For one of our sprints we had two parts of our concept that needed validating. To test one part we conducted simple user tests with other members of Optimal Workshop (the feature was an internal tool). For the second part we needed to validate whether we had the data to continue with this project, so we had our data scientist run some numbers and predictions for us.

6-dan-design-sprintOur remote worker Rebecca dialed in to watch one of our user tests live
 Our remote worker Rebecca dialed in to watch one of our user tests live
"I'm pretty bloody happy" — Actual feedback.
 Actual feedback

Challenges and outcomes

One of our key team members, Rebecca, was working remotely during the sprint. To make things easier for her, we set up 2 cameras: one pointed to the whiteboard, the other was focused on the rest of the sprint team sitting at the table. Next to that, we set up a monitor so we could see Rebecca.

Engaging in workshop activities is a lot harder when working remotely. Rebecca would get around this by completing the activities and take photos to send to us.

8-rebecca-design-sprint
 For more information, read this great Medium post about running design sprints remotely

Lessons

  • Lightning talks are a great way to have each person contribute up front and feel invested in the process.
  • Sprints are energy intensive. Make sure you’re in a good place with plenty of fresh air with comfortable chairs and a break out space. We like to split the five days up so that we get a weekend break.
  • Give people plenty of notice to clear their schedules. Asking busy people to take five days from their schedule might not go down too well. Make sure they know why you’d like them there and what they should expect from the week. Send them an outline of the agenda. Ideally, have a chat in person and get them excited to be part of it.
  • Invite the right people. It’s important that you get the right kind of people from different parts of the company involved in your sprint. The role they play in day-to-day work doesn’t matter too much for this. We’re all mainly using pens and paper and the more types of brains in the room the better. Looking back, what we really needed on our team was a customer support team member. They have the experience and knowledge about our customers that we don’t have.
  • Choose the right sprint problem. The project we chose for our first sprint wasn’t really suited for a design sprint. We went in with a well defined problem and a suggested solution from the team instead of having a project that needed fresh ideas. This made the activities like ‘How Might We’ seem very redundant. The challenge we decided to tackle ended up being more of a data prototype (spreadsheets!). We used the week to validate assumptions around how we can better use data and how we can write a script to automate some internal processes. We got the prototype working and tested but due to the nature of the project we will have to run this experiment in the background for a few months before any building happens.

Overall, this design sprint was a great team bonding experience and we felt pleased with what we achieved in such a short amount of time. Naturally, here at Optimal Workshop, we're experimenters at heart and we will keep exploring new ways to work across teams and find a good middle ground.

Further reading

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1 min read

From Projects to Products: A Growing Career Trend

Introduction

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.

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.

Another Talent Migration? We’ve Seen This Before.

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.

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

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.

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.

Why Is This Happening? The Data Tells the Story.

📈 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).

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

💰 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.

Three Structural Forces Driving the Shift

  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.
  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.
  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.

How This Mirrors the UX Migration of 2019

Source 1 - Source 2

Same pattern. Different discipline.

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

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

🛠️ 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.

😰 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.

What Comes Next? The Smartest Companies Are Already Preparing.

🏆 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.

📊 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.

🚀 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.

Conclusion: The Cycle Continues

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.

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.

This article was researched with the help of Perplexity.ai

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