September 25, 2025
5 min read

AI Is Only as Good as Its UX: Why User Experience Tops Everything

AI is transforming how businesses approach product development. From AI-powered chatbots and recommendation engines to predictive analytics and generative models, AI-first products are reshaping user interactions with technology, which in turn impacts every phase of the product development lifecycle.

Whether you're skeptical about AI or enthusiastic about its potential, the fundamental truth about product development in an AI-driven future remains unchanged: a product is only as good as its user experience.

No matter how powerful the underlying AI, if users don't trust it, can't understand it, or struggle to use it, the product will fail. Good UX isn't simply an add-on for AI-first products, it's a fundamental requirement.

Why UX Is More Critical Than Ever

Unlike traditional software, where users typically follow structured, planned workflows, AI-first products introduce dynamic, unpredictable experiences. This creates several unique UX challenges:

  • Users struggle to understand AI's decisions – Why did the AI generate this particular response? Can they trust it?
  • AI doesn't always get it right – How does the product handle mistakes, errors, or bias?
  • Users expect AI to "just work" like magic – If interactions feel confusing, people will abandon the product.

AI only succeeds when it's intuitive, accessible, and easy-to-use: the fundamental components of good user experience. That's why product teams need to embed strong UX research and design into AI development, right from the start.

Key UX Focus Areas for AI-First Products

To Trust Your AI, Users Need to Understand It

AI can feel like a black box, users often don't know how it works or why it's making certain decisions or recommendations. If people don't understand or trust your AI, they simply won't use it. The user experiences you need to build for an AI-first product must be grounded in transparency.

What does a transparent experience look like?

  • Show users why AI makes certain decisions (e.g., "Recommended for you because…")
  • Allow users to adjust AI settings to customize their experience
  • Enable users to provide feedback when AI gets something wrong—and offer ways to correct it

A strong example: Spotify's AI recommendations explain why a song was suggested, helping users understand the reasoning behind specific song recommendations.

AI Should Augment Human Expertise Not Replace It

AI often goes hand-in-hand with automation, but this approach ignores one of AI's biggest limitations: incorporating human nuance and intuition into recommendations or answers. While AI products strive to feel seamless and automated, users need clarity on what's happening when AI makes mistakes.

How can you address this? Design for AI-Human Collaboration:

  • Guide users on the best ways to interact with and extract value from your AI
  • Provide the ability to refine results so users feel in control of the end output
  • Offer a hybrid approach: allow users to combine AI-driven automation with manual/human inputs

Consider Google's Gemini AI, which lets users edit generated responses rather than forcing them to accept AI's output as final, a thoughtful approach to human-AI collaboration.

Validate and Test AI UX Early and Often

Because AI-first products offer dynamic experiences that can behave unpredictably, traditional usability testing isn't sufficient. Product teams need to test AI interactions across multiple real-world scenarios before launch to ensure their product functions properly.

Run UX Research to Validate AI Models Throughout Development:

  • Implement First Click Testing to verify users understand where to interact with AI
  • Use Tree Testing to refine chatbot flows and decision trees
  • Conduct longitudinal studies to observe how users interact with AI over time

One notable example: A leading tech company used Optimal to test their new AI product with 2,400 global participants, helping them refine navigation and conversion points, ultimately leading to improved engagement and retention.

The Future of AI Products Relies on UX

The bottom line is that AI isn't replacing UX, it's making good UX even more essential. The more sophisticated the product, the more product teams need to invest in regular research, transparency, and usability testing to ensure they're building products people genuinely value and enjoy using.

Want to improve your AI product's UX? Start testing with Optimal today.

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1 min read

Measuring the impact of UXR: beyond CSAT and NPS

In the rapidly evolving world of user experience research (UXR), demonstrating value and impact has become more crucial than ever. While traditional metrics like Customer Satisfaction (CSAT) scores and Net Promoter Scores (NPS) have long been the go-to measures for UX professionals, they often fall short in capturing the full scope and depth of UXR's impact. As organizations increasingly recognize the strategic importance of user-centered design, it's time to explore more comprehensive and nuanced approaches to measuring UXR's contribution.

Limitations of traditional metrics

CSAT and NPS, while valuable, have significant limitations when it comes to measuring UXR impact. These metrics provide a snapshot of user sentiment but fail to capture the direct influence of research insights on product decisions, business outcomes, or long-term user behavior. Moreover, they can be influenced by factors outside of UXR's control, such as marketing campaigns or competitor actions, making it challenging to isolate the specific impact of research efforts.

Another limitation is the lack of context these metrics provide. They don't offer insights into why users feel a certain way or how specific research-driven improvements contributed to their satisfaction. This absence of depth can lead to misinterpretation of data and missed opportunities for meaningful improvements.

Alternative measurement approaches

To overcome these limitations, UX researchers are exploring alternative approaches to measuring impact. One promising method is the use of proxy measures that more directly tie to research activities. For example, tracking the number of research-driven product improvements implemented or measuring the reduction in customer support tickets related to usability issues can provide more tangible evidence of UXR's impact.

Another approach gaining traction is the integration of qualitative data into impact measurement. By combining quantitative metrics with rich, contextual insights from user interviews and observational studies, researchers can paint a more comprehensive picture of how their work influences user behavior and product success.

Linking UXR to business outcomes

Perhaps the most powerful way to demonstrate UXR's value is by directly connecting research insights to key business outcomes. This requires a deep understanding of organizational goals and close collaboration with stakeholders across functions. For instance, if a key business objective is to increase user retention, UX researchers can focus on identifying drivers of user loyalty and track how research-driven improvements impact retention rates over time.

Risk reduction is another critical area where UXR can demonstrate significant value. By validating product concepts and designs before launch, researchers can help organizations avoid costly mistakes and reputational damage. Tracking the number of potential issues identified and resolved through research can provide a tangible measure of this impact.

Case studies of successful impact measurement

While standardized metrics for UXR impact remain elusive, some organizations have successfully implemented innovative measurement approaches. For example, one technology company developed a "research influence score" that tracks how often research insights are cited in product decision-making processes and the subsequent impact on key performance indicators.

Another case study involves a financial services firm that implemented a "research ROI calculator." This tool estimates the potential cost savings and revenue increases associated with research-driven improvements, providing a clear financial justification for UXR investments.

These case studies highlight the importance of tailoring measurement approaches to the specific context and goals of each organization. By thinking creatively and collaborating closely with stakeholders, UX researchers can develop meaningful ways to quantify their impact and demonstrate the strategic value of their work.

As the field of UXR continues to evolve, so too must our approaches to measuring its impact. By moving beyond traditional metrics and embracing more holistic and business-aligned measurement strategies, we can ensure that the true value of user research is recognized and leveraged to drive organizational success. The future of UXR lies not just in conducting great research, but in effectively communicating its impact and cementing its role as a critical strategic function within modern organizations.

Maximize UXR ROI with Optimal 

While innovative measurement approaches are crucial, having the right tools to conduct and analyze research efficiently is equally important for maximizing UXR's return on investment. This is where the Optimal Workshop platform comes in, offering a comprehensive solution to streamline your UXR efforts and amplify their impact.

The Optimal Platform provides a suite of user-friendly tools designed to support every stage of the research process, from participant recruitment to data analysis and insight sharing. By centralizing your research activities on a single platform, you can significantly reduce the time and resources spent on administrative tasks, allowing your team to focus on generating valuable insights.

Key benefits of using Optimal for improving UXR ROI include:

  • Faster research cycles: With automated participant management and data collection tools, you can complete studies more quickly, enabling faster iteration and decision-making.

  • Enhanced collaboration: The platform's sharing features make it easy to involve stakeholders throughout the research process, increasing buy-in and ensuring insights are actioned promptly.

  • Robust analytics: Advanced data visualization and analysis tools help you uncover deeper insights and communicate them more effectively to decision-makers.

  • Scalable research: The platform's user-friendly interface enables non-researchers to conduct basic studies, democratizing research across your organization and increasing its overall impact.

  • Comprehensive reporting: Generate professional, insightful reports that clearly demonstrate the value of your research to stakeholders at all levels.

By leveraging the Optimal Workshop, you're not just improving your research processes – you're positioning UXR as a strategic driver of business success. Our platform's capabilities align perfectly with the advanced measurement approaches discussed earlier, enabling you to track research influence, calculate ROI, and demonstrate tangible impact on key business outcomes.

Ready to transform how you measure and communicate the impact of your UX research? Sign up for a free trial of the Optimal platform today and experience firsthand how it can drive your UXR efforts to new heights of efficiency and effectiveness. 

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1 min read

The AI Automation Breakthrough: Key Insights from Our Latest Community Event

Last night, Optimal brought together an incredible community of product leaders and innovators for "The Automation Breakthrough: Workflows for the AI Era" at Q-Branch in Austin, Texas. This two-hour in-person event featured expert perspectives on how AI and automation are transforming the way we work, create, and lead.

The event featured a lightning Talk on "Designing for Interfaces" featured Cindy Brummer, Founder of Standard Beagle Studio, followed by a dynamic panel discussion titled "The Automation Breakthrough" with industry leaders including Joe Meersman (Managing Partner, Gyroscope AI), Carmen Broomes (Head of UX, Handshake), Kasey Randall (Product Design Lead, Posh AI), and Prateek Khare (Head of Product, Amazon). We also had a fireside chat with our CEO, Alex Burke and Stu Smith, Head of Design at Atlassian. 

Here are the key themes and insights that emerged from these conversations:

Trust & Transparency: The Foundation of AI Adoption

Cindy emphasized that trust and transparency aren't just nice-to-haves in the AI era, they're essential. As AI tools become more integrated into our workflows, building systems that users can understand and rely on becomes paramount. This theme set the tone for the entire event, reminding us that technological advancement must go hand-in-hand with ethical considerations.

Automation Liberates Us from Grunt Work

One of the most resonant themes was how AI fundamentally changes what we spend our time on. As Carmen noted, AI reduces the grunt work and tasks we don't want to do, freeing us to focus on what matters most. This isn't about replacing human workers, it's about eliminating the tedious, repetitive tasks that drain our energy and creativity.

Enabling Creativity and Higher-Quality Decision-Making

When automation handles the mundane, something remarkable happens: we gain space for deeper thinking and creativity. The panelists shared powerful examples of this transformation:

Carmen described how AI and workflows help teams get to insights and execution on a much faster scale, rather than drowning in comments and documentation. Prateek encouraged the audience to use automation to get creative about their work, while Kasey shared how AI and automation have helped him develop different approaches to coaching, mentorship, and problem-solving, ultimately helping him grow as a leader.

The decision-making benefits were particularly striking. Prateek explained how AI and automation have helped him be more thoughtful about decisions and make higher-quality choices, while Kasey echoed that these tools have helped him be more creative and deliberate in his approach.

Democratizing Product Development

Perhaps the most exciting shift discussed was how AI is leveling the playing field across organizations. Carmen emphasized the importance of anyone, regardless of their role, being able to get close to their customers. This democratization means that everyone can get involved in UX, think through user needs, and consider the best experience.

The panel explored how roles are blurring in productive ways. Kasey noted that "we're all becoming product builders" and that product managers are becoming more central to conversations. Prateek predicted that teams are going to get smaller and achieve more with less as these tools become more accessible.

Automation also plays a crucial role in iteration, helping teams incorporate customer feedback more effectively, according to Prateek.

Practical Advice for Navigating the AI Era

The panelists didn't just share lofty visions, they offered concrete guidance for professionals navigating this transformation:

Stay perpetually curious. Prateek warned that no acquired knowledge will stay with you for long, so you need to be ready to learn anything at any time.

Embrace experimentation. "Allow your process to misbehave," Prateek advised, encouraging attendees to break from rigid workflows and explore new approaches.

Overcome fear. Carmen urged the audience not to be afraid of bringing in new tools or worrying that AI will take their jobs. The technology is here to augment, not replace.

Just start. Kasey's advice was refreshingly simple: "Just start and do it again." Whether you're experimenting with AI tools or trying "vibe coding," the key is to begin and iterate.

The energy in the room at Q-Branch reflected a community that's not just adapting to change but actively shaping it. The automation breakthrough isn't just about new tools, it's about reimagining how we work, who gets to participate in product development, and what becomes possible when we free ourselves from repetitive tasks.

As we continue to navigate the AI era, events like this remind us that the most valuable insights come from bringing diverse perspectives together. The conversation doesn't end here, it's just beginning.

Interested in joining future Optimal community events? Stay tuned for upcoming gatherings where we'll continue exploring the intersection of design, product, and emerging technologies.

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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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