Optimal Blog

Product, design and research teams today are drowning in user data while starving for user understanding. Never before have teams had such access to user information, analytics dashboards, heatmaps, session recordings, survey responses, social media sentiment, support tickets, and endless behavioral data points. Yet despite this volume of data, teams consistently build features users don't want and miss needs hiding in plain sight.

‍

It’s a true paradox for product, design and research teams: more information has made genuine understanding more elusive. 

‍

Because with  all this data, teams feel informed. They can say with confidence: "Users spend 3.2 minutes on this page," "42% abandon at this step," "Power users click here." But what this data doesn't tell you is Why. 

‍

The Difference between Data and Insight

‍

Data tells you what happened. Understanding tells you why it matters.

‍

Here’s a good example of this: Your analytics show that 60% of users abandon a new feature after first use. You know they're leaving. You can see where they click before they go. You have their demographic data and behavioral patterns.

‍

But you don't know:

‍

• Were they confused or simply uninterested?
• Did it solve their problem too slowly or not at all?
• Would they return if one thing changed, or is the entire approach wrong?
• Are they your target users or the wrong segment entirely?

‍

One team sees "60% abandonment" and adds onboarding tooltips. Another talks to users and discovers the feature solves the wrong problem entirely. Same data, completely different understanding.

‍

Modern tools make it dangerously easy to mistake observation for comprehension, but some aspects of user experience exist beyond measurement:

‍

• Emotional context, like the frustration of trying to complete a task while handling a crying baby, or the anxiety of making a financial decision without confidence.
• The unspoken needs of users which can only be demonstrated through real interactions. Users develop workarounds without reporting bugs. They live with friction because they don't know better solutions exist.
• Cultural nuances that numbers don't capture, like how language choice resonates differently across cultures, or how trust signals vary by context.
• Data shows what users do within your current product. It doesn't reveal what they'd do if you solved their problems differently to help you identify new opportunities. 

‍

Why Human Empathy is More Important than Ever 

‍

The teams building truly user-centered products haven't abandoned data but they've learned to combine quantitative and qualitative insights. 

‍

• Combine analytics (what happens), user interviews (why it happens), and observation (context in which it happens).
• Understanding builds over time. A single study provides a snapshot; continuous engagement reveals the movie.
• Use data to form theories, research to validate them, and real-world live testing to confirm understanding.
• Different team members see different aspects. Engineers notice system issues, designers spot usability gaps, PMs identify market fit, researchers uncover needs.

‍

Adding AI into the mix also emphasizes the need for human validation. While AI can help significantly speed up workflows and can augment human expertise, it still requires oversight and review from real people. 

‍

AI can spot trends humans miss, processing millions of data points instantly but it can't understand human emotion, cultural context, or unspoken needs. It can summarize what users say but humans must interpret what they mean.

‍

Understanding users has never been easier from a data perspective. We have tools our predecessors could only dream of.  But understanding users has never been harder from an empathy perspective. The sheer volume of data available to us creates an illusion of knowledge that's more dangerous than ignorance.

‍

The teams succeeding aren't choosing between data and empathy, they're investing equally in both. They use analytics to spot patterns and conversations to understand meaning. They measure behavior and observe context. They quantify outcomes and qualify experiences.

‍

Because at the end of the day, you can track every click, measure every metric, and analyze every behavior, but until you understand why, you're just collecting data, not creating understanding.

‍

Are we exaggerating when we say that the way the researchers run and analyze user interviews hasn’t changed in 20 years? We don’t think so. When we talk to our customers to try and understand their current workflows, they look exactly the same as they did when we started this business 17 years ago: record, transcribe, analyze manually, create reports. See the problem?

‍

Despite  advances in technology across every industry, the fundamental process of conducting and analyzing user interviews has remained largely unchanged. While we've transformed how we design, develop, and deploy products, the way we understand our users is still trapped in workflows that would feel familiar to product, design and research teams from decades ago.

‍

The Same Old Interview Analysis Workflow 

‍

For most researchers, in the best case scenario, Interview analysis can take several hours over the span of multiple days. Yet in that same timeframe, in part thanks to new and emerging AI tools, an engineering team can design, build, test, and deploy new features. That just doesn't make sense.

‍

The problem isn't that researchers  lack tools. It's that they haven’t had the right ones. Most tools focus on transcription and storage, treating interviews like static documents rather than dynamic sources of intelligence. Testing with just 5 users can uncover 85% of usability problems, yet most teams struggle to complete even basic analysis in time to influence product decisions. Luckily, things are finally starting to change.

‍

When it comes to user research, three things are happening in the industry right now that are forcing a transformation:

‍

1. The rise of AI means UX research matters more than ever. With AI accelerating product development cycles, the cost of building the wrong thing has never been higher. Companies that invest in UX early cut development time by 33-50%, and with AI, that advantage compounds exponentially.
2. We're drowning in data and have fewer resources.  We’re seeing the need for UX research increase, while simultaneously UX research teams are more resource constrained than ever. Tasks like analyzing hours of video content to gather insights, just isn’t something teams have time for anymore. 
3. AI finally understands research. AI has evolved to a place where it can actually provide valuable insights. Not just transcription. Real research intelligence that recognizes patterns, emotions, and the gap between what users say and what they actually mean.

‍

A Dirty Little Research Secret + A Solution 

‍

We’re just going to say it; most user insights from interviews never make it past the recording stage. When it comes to talking to users, the vast majority of researchers in our audience talk about recruiting pain because the most commonly discussed challenge around interviews is usually finding enough participants who match their criteria. But on top of the challenge of finding the right people to talk to, there’s another challenge that’s even worse: finding time to analyze what users tell us. But, what if you had a tool where using AI, the moment you uploaded an interview video, key themes, pain points, and opportunities surfaced automatically? What if you could ask your interview footage questions and get back evidence-based answers with video citations?

‍

This isn't about replacing human expertise, it's about augmenting  it. AI-powered tools can process and categorize data within hours or days, significantly reducing workload. But more importantly, they can surface patterns and connections that human analysts might miss when rushing through analysis under deadline pressure. Thanks to AI, we're witnessing the beginning of a research renaissance and a big part of that is reimagining the way we do user interviews.

‍

Why AI for User Interviews is a Game Changer 

‍

When interview analysis accelerates from weeks to hours, everything changes.

‍

Product teams can validate ideas before building them. Design teams can test concepts in real-time. Engineering teams can prioritize features based on actual user need, not assumptions. Product, Design and Research teams who embrace AI to help with these workflows, will be surfacing insights, generating evidence-backed recommendations, and influencing product decisions at the speed of thought.

‍

We know that 32% of all customers would stop doing business with a brand they loved after one bad experience. Talking to your users more often makes it possible to prevent these experiences by acting on user feedback before problems become critical. When every user insight comes with video evidence, when every recommendation links to supporting clips, when every user story includes the actual user telling it, research stops being opinion and becomes impossible to ignore. When you can more easily gather, analyze and share the content from user interviews those real user voices start to get referenced in executive meetings. Product decisions begin to include user clips. Engineering sprints start to reference actual user needs. Marketing messages reflect real user voices and language.

The best product, design and research teams are already looking for tools that can support this transformation. They know that when interviews become intelligent, the entire organization becomes more user-centric. At Optimal, we're focused on improving the traditional user interviews workflow by incorporating revolutionary AI features into our tools. Stay tuned for exciting updates on how we're reimagining user interviews.

Despite AI being the buzzword in UX right now, there are still lots of concerns about how it’s going to impact research roles. One of the biggest concerns we hear is: is AI just going to replace UX researchers altogether?

‍

The answer, in our opinion, is no. The longer, more interesting answer is that AI is fundamentally transforming what it means to be a UX researcher, and in ways that make the role more strategic, more impactful, and more interesting than ever before.

‍

What AI Actually Does for Research 

‍

A 2024 survey by the UX Research Collective found that 68% of UX researchers are concerned about AI's impact on their roles. The fear makes sense, we've all seen how automation has transformed other industries. But what's actually happening is that rather than AI replacing researchers, it's eliminating the parts of research that researchers hate most.

‍

According to Gartner's 2024 Market Guide for User Research, AI tools can reduce analysis time by 60-70%, but not by replacing human insight. Instead, they handle:

‍

• Pattern Recognition at Scale: AI can process hundreds of user interviews and identify recurring themes in hours. For a human researcher that same work would take weeks. But those patterns will need human validation because AI doesn't understand why those patterns matter. That's where researchers will continue to add value, and we would argue, become more important than ever. 
• Synthesis Acceleration: According to research by the Nielsen Norman Group, AI can generate first-draft insight summaries 10x faster than humans. But these summaries still need researcher oversight to ensure context, accuracy, and actionable insights aren't lost. 
• Multi-language Analysis: AI can analyze feedback in 50+ languages simultaneously, democratizing global research. But cultural context and nuanced interpretation still require human understanding. 
•  Always-On Insights:  Traditional research is limited by human availability. Tools like AI interviewers can  run 24/7 while your team sleeps, allowing you to get continuous, high-quality user insights. 

‍

AI is Elevating the Role of Researchers 

‍

We think that what AI is actually doing  is making UX researchers more important, not less. By automating the less sophisticated  aspects of research, AI is pushing researchers toward the strategic work that only humans can do.

‍

From Operators to Strategists: McKinsey's 2024 research shows that teams using AI research tools spend 45% more time on strategic planning and only 20% on execution, compared to 30% strategy and 60% execution for traditional teams.

‍

From Reporters  to Storytellers: With AI handling data processing, researchers can focus on crafting compelling narratives. 

‍

From Analysts to Advisors: When freed from manual analysis, researchers become embedded strategic partners. 

‍

Human + AI Collaboration 

‍

The most effective research teams aren't choosing between human or AI, they're creating collaborative workflows that incorporate AI to augment researchers roles, not replace them: 

‍

• AI-Powered Data Collection: Automated transcription, sentiment analysis, and preliminary coding happen in real-time during user sessions.
• Human-Led Interpretation: Researchers review AI-generated insights, add context, challenge assumptions, and identify what AI might have missed.
• Collaborative Synthesis: AI suggests patterns and themes; researchers validate, refine, and connect to business context.
• Human Storytelling: Researchers craft narratives, implications, and recommendations that AI cannot generate.

‍

Is it likely that with AI more and more research tasks will become automated? Absolutely. Basic transcription, preliminary coding, and simple pattern recognition are already AI’s bread and butter. But research has never been about these tasks, it's been about understanding users and driving better decisions and that should always be left to humans. 

‍

The researchers thriving in 2025 and beyond aren't fighting AI, they're embracing it. They're using AI to handle the tedious 40% of their job so they can focus on the strategic 60% that creates real business value. You  have a choice. You can choose to adopt AI as a tool to elevate your role, or you can view it as a threat and get left behind. Our customers tell us that the researchers choosing elevation are finding their roles more strategic, more impactful, and more essential to product success than ever before.

‍

AI isn't replacing UX researchers. It's freeing them to do what they've always done best, understand humans and help build better products. And in a world drowning in data but starving for insight, that human expertise has never been more valuable.

‍

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.

‍

Be honest. Are you guilty of being a gatekeeper? 

‍

For years, UX teams have treated research as a specialized skill that requires extensive training, advanced degrees, and membership in the researcher club. We’re guilty of it too! We've insisted that only "real researchers" can talk to users, conduct studies, or generate insights.

‍

But the problem with this is, this gatekeeping is holding back product development, limiting insights, and ironically, making research less effective.  As a result,  product and design teams are starting to do their own research, bypassing UX because they want to just get things done. 

‍

This shift is happening, and while we could view this as the downfall of traditional UX, we see it more as an evolution. And when done right, with support from UX, this democratization actually leads to better products, more research-informed organizations, and yes, more valuable research roles.

‍

The Problem with Gatekeeping 

‍

Product teams need insights constantly, making decisions daily about features, designs, and priorities. Yet dedicated researchers are outnumbered, often supporting 15-20 product team members each. The math just doesn't work. No matter how talented or efficient researchers are, they can't be everywhere at once, answering every question in real-time. This mismatch between insight demand and research capacity forces teams into an impossible choice: wait for formal research and miss critical decision windows or move forward without insights and risk building the wrong thing.

‍

Since product teams often don’t have the time to wait, teams make decisions anyway, without research. A Forrester study found that 73% of product decisions happen without any user input, not because teams don't value research, but because they can't wait weeks for formal research cycles.

‍

In organizations where this is already happening (it’s most of them!) teams have two choices, accept that their research to insight to development workflow is broken, or accept that things need to change and embrace the new era of research democratization. 

‍

In Support of  Research Democratization

‍

The most research-informed organizations aren't those with the most researchers, they're those where research skills are distributed throughout the team. When Product Managers and Designers talk directly to users, with researchers providing frameworks and quality control they make more research-informed decisions which result in better product performance and lower business risk. 

‍

When PMs and designers conduct their own research, context doesn't get lost in translation. They hear the user's words, see their frustrations, and understand nuances that don't survive summarization. But there is a right way to democratize, which not all organizations are doing. 

‍

Democratization as a consequence instead of as an intentional strategy, is chaos. Without frameworks and support from experienced researchers, it just won’t work. The goal isn't to turn everyone into researchers, it's to empower more teams to do their own research, while maintaining quality and rigor. In this model, the researcher becomes an advisor instead of a gatekeeper and the researcher's role evolves from conducting all studies to enabling teams to conduct their own. 

‍

Not all questions need expert researchers. Intercom uses a three-tier model:

‍

• Tier 1 (70% of questions): Teams handle with proven templates
• Tier 2 (20% of questions): Researcher-supported team execution
• Tier 3 (10% of questions): Researcher-led complex studies

‍

This model increased research output by 300% while improving quality scores by 25%.

‍

In a model like this, the researcher becomes more important than ever because democratization needs quality assurance. 

‍

Elevating the Role of Researchers 

‍

Democratization requires researchers to shift from "protectors of methodology" to "enablers of insight." It means:

‍

• Not seeking perfection because an imperfect study done today beats a perfect study done never.
• Acknowledging that 80% confidence on 100% of decisions beats 100% confidence on 20% of decisions.
• Measuring success by the "number of research-informed decisions made” instea dof the "number of studies conducted" 
• Deciding that more research happening is good, even if researchers aren't doing it all.

‍

By enabling teams to handle routine research, professional researchers focus on:

‍

• Complex, strategic research that requires deep expertise
• Building research capabilities across the organization
• Ensuring research quality and methodology standards
• Connecting insights across teams and products
• Driving research-informed culture change

‍

In truly research-informed organizations, everyone has user conversations. PMs do quick validation calls. Designers run lightweight usability tests. Engineers observe user sessions. Customer success shares user feedback.

‍

And researchers? They design the systems, ensure quality, tackle complex questions, and turn this distributed insight into strategic direction.

‍

Research democratization isn't about devaluing research expertise, it's about scaling research impact. It's recognizing that in today's product development pace, the choice isn't between formal research and democratized research. It's between democratized research and no research at all.

‍

Done right, democratization isn't the end of UX research as a profession. It's the beginning of research as a competitive advantage.

‍

The decision between interview-focused research tools and comprehensive user insight platforms fundamentally shapes how teams generate, analyze, and act on user feedback. This choice affects not only immediate research capabilities but also long-term strategic planning and organizational impact. While Great Question focuses primarily on customer interviews and basic panel management with streamlined functionality, Optimal provides more robust capabilities, global participant reach, and advanced analytics infrastructure that the world's biggest brands rely on to build products users genuinely love. Optimal's platform enables teams to conduct sophisticated research, integrate insights across departments, and deliver actionable recommendations that drive meaningful business outcomes.

‍

Why Choose Optimal over Great Question?

‍

Strategic Research Capabilities vs. Interview-Centric Tools

‍

Optimal's Research Leadership: Optimal delivers complete research capabilities spanning information architecture testing, prototype validation, card sorting, tree testing, first-click analysis, live site testing, and qualitative insights, all powered by AI-driven analysis and backed by 17 years of specialized research expertise that transforms user feedback into actionable business intelligence. Optimal's live site testing allows you to test actual websites and web apps without code, enabling continuous optimization and real-time insights post-launch.

‍

Great Question's Limited Research Scope: In contrast, Great Question operates primarily as an interview scheduling and panel management tool with basic survey capabilities, lacking the comprehensive research methodologies and specialized testing tools that enterprise research programs require for strategic impact across the full product development lifecycle.

‍

Enterprise-Ready Research Suite: Optimal serves Fortune 500 clients including Lego, Nike, and Netflix with SOC 2 compliance, enterprise security protocols, and a comprehensive research toolkit that scales with organizational growth and research sophistication.

‍

Workflow Limitations: Great Question's interview-focused approach restricts teams to primarily qualitative methods, requiring additional tools for quantitative validation and specialized testing scenarios that modern product teams demand for comprehensive user understanding.

‍

Participant Quality and Global Reach

‍

Global Research Network: Optimal's 10M+ verified participants across 150+ countries enable sophisticated audience targeting, international market research, and reliable recruitment for any demographic or geographic requirement, from enterprise software buyers in Germany to mobile gamers in Southeast Asia.

‍

Limited Panel Access: Great Question provides access to 3M+ participants with basic recruitment capabilities focused primarily on existing customer panels, limiting research scope for complex audience requirements and international market validation.

‍

Advanced Participant Targeting: Optimal includes sophisticated recruitment filters, managed recruitment services, and quality assurance protocols that ensure research validity and participant engagement across diverse study requirements.

‍

Basic Recruitment Features: Great Question focuses on CRM integration and existing customer recruitment without advanced screening capabilities or specialized audience targeting that complex research studies require.

‍

Research Methodology Depth and Platform Capabilities

‍

Complete Research Methodology Suite: Optimal provides full-spectrum research capabilities including advanced card sorting, tree testing, prototype validation, first-click testing, surveys, and qualitative insights with integrated AI analysis across all methodologies and specialized tools designed for specific research challenges.

‍

Interview-Focused Limitations: Great Question offers elementary research capabilities centered on customer interviews and basic surveys, lacking the specialized testing tools enterprise teams need for information architecture, prototype validation, and quantitative user behavior analysis.

‍

AI-Powered Research Operations: Optimal streamlines research workflows with automated analysis, AI-powered insights, advanced statistical reporting, and seamless collaboration tools that accelerate insight delivery while maintaining analytical rigor. Our new Interviews tool revolutionizes qualitative research, upload interview videos and let AI automatically surface key themes, generate smart highlight reels with timestamped evidence, and produce actionable insights in hours instead of weeks, eliminating the manual synthesis bottleneck.

‍

Manual Analysis Dependencies: Great Question requires significant manual effort for insight synthesis beyond interview transcription, creating workflow inefficiencies that slow research velocity and limit the depth of analysis possible across large datasets.

‍

Where Great Question Falls Short

‍

Great Question may be a good choice for teams who are looking for:

‍

• Simple customer interview management without complex research requirements
• Basic panel recruitment focused on existing customers
• Streamlined workflows for small-scale qualitative studies
• Budget-conscious solutions prioritizing low cost over comprehensive capabilities
• Teams primarily focused on customer development rather than strategic UX research

‍

When Optimal Delivers Strategic Value

‍

Optimal becomes essential for:

‍

• ‍Strategic Research Programs: When user insights drive business strategy, product decisions, and require diverse research methodologies beyond interviews
• ‍Information Architecture Excellence: Teams requiring specialized testing for navigation, content organization, and user mental models that directly impact product usability
• ‍Global Organizations: Requiring international research capabilities, market validation, and diverse participant recruitment across multiple regions
• ‍Quality-Critical Studies: Where participant verification, advanced analytics, statistical rigor, and research validity matter for strategic decision-making
• ‍Enterprise Compliance: Organizations with security, privacy, and regulatory requirements demanding SOC 2 compliance and enterprise-grade infrastructur
• ‍Advanced Research Operations: Teams requiring AI-powered insights, comprehensive analytics, specialized testing methodologies, and scalable research capabilities
• ‍Prototype and Design Validation: Product teams needing early-stage testing, iterative validation, and quantitative feedback on design concepts and user flows

‍

Ready to see how leading brands including Lego, Netflix and Nike achieve better research outcomes? Experience how Optimal's platform delivers user insights that adapt to your team's growing needs and research sophistication.