Optimal Blog

Your research platform shouldn't be the slowest part of your sprint

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Your team ships every two weeks. Your research platform takes three weeks to get a study live, recruited and analysed. By the time insights land, the roadmap has already moved on.

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Speed is only half of it. A fast study with the wrong participants can be worse than no study at all. Screener gaming, ineligible respondents and AI-written answers are now regular complaints in platform reviews. When your data is skewed, the product decisions built on it are too. Your team pays for that later, in rework, missed adoption targets and a harder conversation with stakeholders.

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In 2026 teams are asking more of their tools. They want studies live in minutes, participants they can trust, and pricing that doesn't punish them for doing more research. They want one place to run the methods they rely on, with no stitching together a recruitment tool, a testing tool and a repository.

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We've compared seven of the most widely used user research platforms against Optimal on the things that decide whether research shapes what ships. Here's what we found, including where other platforms are the better fit.

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How we evaluated each platform

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Feature lists all start to look the same. So we judged each platform on five questions that we believe matters to setting up and running a successful research program from the get go:

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1. Speed to insights. How quickly can you go from a research question to a study in field and results you can share? Approval queues, setup steps and analysis time all count.
2. Participant quality and reach. How big is the panel, how many countries does it cover, and how much can you trust the people in it? We also looked at whether you can bring your own participants.
3. Ease of setup. Can a designer or PM run a study on their own, or does every study need a specialist?
4. Price transparency. Can you see what you'll pay before you talk to sales? Are seats capped, and are core methods locked behind higher tiers?
5. Design tool workflow. How well does the platform fit the way your team designs, including Figma prototypes and early concepts?

We used public product pages, help docs, pricing pages and third-party review sites, current as of November 2026. Platforms change quickly, so check current plans before you buy.

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The 2026 comparison at a glance

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Optimal is the only platform here that combines instant launch, a 20M+ verified panel across 150+ countries, unlimited seats on every plan and core UX research and IA methods with no tier gating.

Sources: vendor product, help and pricing pages, plus G2 reviews, as of November 2026.

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Platform by platform

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Every platform here does something well. The question is whether it does what your team needs, at the pace you work.

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Optimal

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Optimal is a user insights platform and research repository built by research, product, and design leaders over 19+ years. It covers surveys, card sorting, tree testing, first-click testing, prototype testing, live site testing, native mobile app testing, AI Interviewer, and qualitative insights, all in one place.

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• Strengths: Studies go live the moment you're ready, with no review queue. You can recruit from 20M+ verified participants in 150+ countries, or bring your own for free. AI insights, summaries and an AI interviewer help you get from raw data to a shareable finding in minutes. Every plan includes unlimited seats, and the platform holds SOC 2, their management systems and infrastructure is ISO/IEC 27001 and ISO/IEC 27701 certified.
• Limitations: There are no dedicated diary study or five-second test blocks. Teams running long-form diary research may want a specialist tool alongside.
• Best for: Teams that want one platform for evaluative research, IA and recruitment, with room for the whole org to take part.

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UserTesting

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UserTesting is the long-standing name in remote video research. After merging with UserZoom and acquiring User Interviews, it now sits under the Auros brand.

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• Strengths: It covers a broad set of methods, including diary studies and native mobile app testing. Enterprise support and services are deep, and its MCP server connects to AI tools and Figma Make.
• Limitations: Pricing is quote-only and credit-based, and reviewers report yearly price rises. Joining three platforms has left parts of the experience feeling disjointed, and some reviewers flag participant quality and fulfilment delays.
• Best for: Large enterprises with a dedicated research budget and a heavy focus on video usability sessions.

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Maze

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Maze is built for rapid prototype validation inside design sprints.

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• Strengths: It has a well-loved Figma prototype testing flow, a free plan to get started, and a broad AI feature set.
• Limitations: Card sorting, tree testing and its AI Moderator sit on the Enterprise tier. Reviewers report bugs with large prototypes and heatmaps, and first-click tests allow multiple clicks. Panel credits are billed separately.
• Best for: Design teams whose research is mostly quick prototype checks.

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Great Question

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Great Question combines participant recruitment, a research CRM, mixed-method studies and an AI repository.

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• Strengths: Its participant CRM, API and MCP tooling are strong. Observer seats are unlimited, so stakeholders can follow along.
• Limitations: Self-serve plans cap paid seats at five. Card sorting and tree testing sit on Enterprise. External recruitment runs through a User Interviews integration rather than an in-house panel, and some reviewers report lag on large datasets.
• Best for: Research ops teams managing their own customer panel.

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Lyssna

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Lyssna, formerly UsabilityHub, is a simple, self-serve platform known for five-second and preference testing.

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• Strengths: It's quick to learn, has a free plan, and publishes its self-serve pricing. It recently added mobile testing and observer rooms.
• Limitations: Its 690K+ panel has no managed recruitment, and reviewers say niche audiences can be hard to reach. AI analysis is lighter, and does not yet hold enterprise-grade security certifications.
• Best for: Smaller teams running fast design checks.

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Dscout

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Dscout specialises in in-context, mobile-first qualitative research.

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• Strengths: Diary studies are its flagship, and recent AI moderation and insight features are a real investment.
• Limitations: A Dscout advisor reviews screeners before launch, which can add days. Participants download an app, survey logic is limited, and there's no first-click testing.
• Best for: Teams running longitudinal, in-the-moment qual research.

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UXtweak

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UXtweak is a researcher-founded platform with a broad toolkit, from card sorting to session recordings.

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• Strengths: It offers a free plan and published lower tiers, native mobile app testing without an SDK, and testing for AI-generated prototypes.
• Limitations: AI insight is minimal. You can't combine study types in a single link, and reviewers call the setup interface dated. It's a small team, which can limit enterprise support.
• Best for: Budget-conscious teams that want breadth without enterprise requirements.

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Why Optimal wins in 2026

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If your team runs one kind of study, a specialist tool may be all you need. Most enterprise teams don't work that way. Some weeks you're testing a prototype, other weeks you're restructuring navigation or checking a concept with a hard-to-reach audience. Your team needs a platform that keeps up with all of it.

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We've spent 19+ years listening to researchers describe the same frustrations: waiting on approvals, chasing recruits, rationing seats and defending data quality to stakeholders. Optimal is built to remove that friction.

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• Insights in minutes. Launch the moment your study is ready, with no review queue, and let AI summaries turn results into findings you can share the same day.
• Participants you can trust. Recruit from 20M+ verified participants across 150+ countries, or bring your own for free. Our recruitment team can help with niche and international audiences.
• Simple enough for everyone. Designers, PMs and researchers run studies with guided setup and templates. Unlimited seats mean the whole team can join in without a budget conversation.
• Optimal Intelligence. Optimal's growing AI and automation capabilities help you refine studies, uncover key themes, summaries, and insights, and dive deeper with AI chat and MCP.
• Clear pricing. You pay one platform fee, sized to your research plan. Card sorting, tree testing and first-click testing come included, with no extra tier to unlock them.
• Built into how you design. Test Figma prototypes or images alongside the IA methods that tell you whether people can find what you've built.
• Enterprise ready. SOC 2, ISO/IEC 27001 and ISO/IEC 27701, with dedicated onboarding and a Customer Success Manager.

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Netflix, Nike, Toyota, Apple, LEGO and Workday already trust Optimal with their research. Book a demo and we'll walk through your research plan with you.

Every year on the second Thursday of November, communities around the world come together for World Usability Day to celebrate the practice of designing products for the people they serve. This year, on November 12, 2026, the theme is Designing a Sustainable Future with Ethical AI, exploring how human creativity and intelligent technology can work together to create a better world. It’s a theme that holds particular significance for us, reflecting a shared vision of what’s possible when people and technology come together with purpose.

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Shaping a better future, together

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Responsible innovation is the process of creating products that deliver value while minimizing unintended consequences and potential harm. This means understanding the broader impact of our decisions on people and our planet, mitigating social and ethical risks, and taking responsibility for what we create.

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This work is happening across our industry every day. Researchers, designers, and practitioners are co-designing with users, testing ideas, challenging assumptions, and working to turn what they learn into meaningful improvements. It's an ongoing process of questioning, learning, and advocating, and its impact reaches millions of people through the products and experiences we design. Progress depends on a community that leads with kindness, empathy, and respect; one that shares openly, listens thoughtfully, and lifts others up along the way.

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A partnership built on decades of shared purpose

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For nearly two decades, we've celebrated the work World Usability Day brings into focus: the vital role of user research in shaping technology that makes a meaningful difference in our world and the power of thoughtful design to shape our everyday experiences. This year, we're excited to take that relationship a step further as a Research Sponsor.

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As AI and emerging technologies reshape what's possible, we're also faced with new questions about the choices we make, the responsibilities we hold and the future we're creating. Through this partnership, five conversations are taking shape:

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• Ethical and Responsible AI: How do we share responsibility for the decisions and impacts of AI as it becomes part of our everyday lives?
• AI and Sustainability: How can we harness the potential of AI and emerging technologies to support a more sustainable future?
• Keeping Humans in the Loop: How can human judgment, creativity, and expertise complement AI to shape better outcomes?
• Inclusion and Digital Equity: How do we design and deliver products and services that are accessible, inclusive, and equitable for everyone?
• From Creating to Evaluating: How do we embed standards and meaningfully evaluate AI outputs?

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We are proud to support World Usability Day and the World Usability Initiative, and to contribute to a broader body of work that advances our understanding of how technology should be designed, developed and evaluated as AI transforms our industry.

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To follow our latest updates, reports, and insights, subscribe to our blog.

AI-moderated interviews are moving from experiment to everyday workflow. But for anyone who cares about user experience, the real question isn’t whether an AI can ask good questions. It’s whether the person on the other side wants to answer them. And that comes down to the design more than the model.

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People are more comfortable talking to AI than you might expect. In an Adobe Express survey of 1,005 US consumers, 61% said they were comfortable having conversations with AI-powered systems. A COPC study across six countries found people who knew they were talking to AI reported 34% higher satisfaction than those who weren’t told.

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But a quick chat with a bot isn’t a research interview. So we put it to the test. We ran a study using Optimal’s AI Interviewer with participants at different levels of AI and UX experience, including some who were skeptical going in (and rightfully so!). Because if an AI interview only works for people who already love AI, it doesn’t really work.

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What participants told us

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Most participants described the experience as seamless, smooth, natural and even fun, including several who had reservations going in. Three things stood out.

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1. It felt like a conversation, not a script

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Participants noticed that follow-ups were built on what they had just said. One put it simply:

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“I really felt like I was talking to a real person.”

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Another appreciated that the AI summarised their points before moving on, instead of asking the same question in different ways. That was a frustration they’d had with other AI interview tools.

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2. Less pressure made it easier to open up

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Several participants found the AI easier to talk to than a live interviewer. Without a person across the table, there was less pressure to perform.

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“I have no problems speaking freely.”

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On-screen text alongside the spoken conversation also mattered. One participant with auditory processing difficulties called it out as a genuinely useful accessibility feature.

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Not everyone was fully won over. One participant felt a human would better grasp the emotion behind their answers, though they still rated the experience positively.

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3. Choice built confidence

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Participants could pick how the conversation flowed. One chose “submit response” mode, deciding when each answer was finished. After a good first experience, they said they’d be happy to try “free form” mode next, where the AI responds naturally once they stop speaking.

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Want to hear it from participants directly?
‍Watch the highlight reels in our full study write-up →

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What designers can take from this

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The takeaway reaches well beyond research tools: whether an AI conversation works depends less on the model and more on the experience designed around it. If you’re designing any conversational AI, these are worth stealing.

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• Prove you’re listening. Responses that reference the user’s own words build trust faster than clever phrasing. A short summary before moving on signals “I heard you.”
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• Don’t make people repeat themselves. Rephrasing the same question feels robotic. Move the conversation forward instead.
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• Lower the stakes. Removing social pressure can make people more candid. Clear instructions and a predictable flow help them settle in.
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• Offer more than one channel. Pairing voice with on-screen text supports people who process information differently, and makes the experience easier for everyone.
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• Give users control over pacing. Letting people choose when to submit, or when to go free form, turns uncertainty into confidence.
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• Be honest about limits. Some people will still prefer a human for emotionally nuanced topics. Design AI as an option, not the only path.

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See how Optimal’s AI Interviewer works

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We built these lessons into Optimal’s AI Interviewer, an AI-moderated research tool designed to work with you, not instead of you. It helps teams run more interviews without losing the depth that makes qualitative research valuable.

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1. Add your context. Research goals, audience details and supporting material guide every follow-up.
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2. Build your discussion guide. Set sections, questions, goals and how deeply the AI should probe.
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3. Let it run the interview. The AI listens, adapts and explores relevant threads while staying on your goals.
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4. Get cited insights. An AI-generated summary and key themes, with citations at study, section and question level, plus highlight reels that jump to the moment behind each insight.

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Take this two-minute interactive tour to see it for yourself.

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The bottom line

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AI interviews aren’t about replacing the human side of research. They give teams another way to listen, at a scale that used to need a lot more people and time. And when the experience is designed well, participants are happy to talk.

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Optimal’s AI Interviewer is now in Early Access, book a demo to learn more.

Optimal gives you two ways to use AI to ask questions about your research and explore your findings: AI Chat in Optimal Interviews and Optimal MCP.

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Both let you ask questions in natural language, but they’re designed for different types of research exploration.

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The simple difference: Use AI Chat to explore interview data in depth. Use MCP to explore individual studies, connect research from different studies, connect to other tools, automate existing or new workflows

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AI Chat vs. MCP: Which should I use?

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Use AI Chat when you want to:

• Explore a specific interview topic
• Find supporting evidence in interview sessions
• Find new video clips
• Generate new highlight reels
• Investigate participant reactions in more detail
• Follow evidence back to timestamped interview sessions

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Use MCP when you want to:

• Search across multiple studies
• Compare findings across studies or time periods
• Identify recurring themes and trends
• Combine research from different methods
• Explore individual studies using participant results, study setup and metadata
• Analyse transcripts, task results, user paths and other available research data
• Connect Optimal research to ChatGPT, Claude, Gemini, Cursor or other MCP-compatible tools
• Bring research into broader AI-powered workflows and automate research tasks

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In short

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Go deeper into interviews → AI Chat

Use AI Chat when your question is focused on what participants said, how they reacted, or finding evidence within interview sessions.

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Go broader across research → MCP

Use MCP when your question spans multiple studies, research methods, time periods or data sources, or when you want to bring Optimal research into another AI workflow.

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What is AI Chat in Optimal?

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AI Chat is an AI-powered research assistant built into Optimal Interviews. It helps you explore interview data using natural-language questions.

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Use AI Chat to find what participants said, investigate specific topics, discover supporting evidence and explore interview findings in more depth.

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For example, you can ask:

• “What frustrations did participants mention about checkout?”
• “Show me quotes about trust in the product.”
• “What did participants find confusing about onboarding?”
• “Find examples of participants struggling with the navigation.”

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You can ask follow-up questions to refine your search or investigate a finding further. AI Chat can also surface participant quotes, video clips and highlight reels, with citations that take you back to the supporting evidence in your interviews. Learn more about AI Chat.
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What is Optimal MCP?

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Optimal MCP connects your Optimal research to MCP-compatible AI assistants and tools, including ChatGPT, Claude, Gemini and Cursor.

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MCP is designed for broader research exploration. It lets you work with research data across individual studies or your wider Workspace, making it useful for research synthesis, cross-study analysis and AI-powered research workflows.

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For example, you can ask:

• “What usability issues have appeared across our research this year?”
• “Compare our findings from March and June.”
• “What themes are recurring across our recent studies?”
• “Which studies included feedback about onboarding?”
• “Create a Notion page outline summarising these research findings.”

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Depending on the data available to you, MCP can help you explore study information, participant results, supported survey results, transcripts, task results, user paths, study metadata and AI-generated insights. Learn more about Optimal MCP.

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AI Chat vs. MCP example

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Imagine you want to understand problems with onboarding.

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Use AI Chat

Ask: “What did participants say was confusing about onboarding? Show me examples.”

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Use MCP

Ask: “What onboarding issues have appeared across our research over the past six months?”

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What research data can AI Chat and MCP access?

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AI Chat focuses on your Interviews data, helping you explore interview responses and supporting evidence such as quotes and highlight reels.

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MCP provides read-only access to research data available to you in Optimal, including study information, participant results, supported survey results, and AI insights.

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Best practices for using AI Chat and MCP

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Whichever tool you use, you’ll get more useful results by giving the AI enough context.

• Ask specific questions. Include the product, feature, audience, study or timeframe where relevant.
• Start broad, then dig deeper. Use follow-up questions to explore interesting findings.
• Ask for evidence. Request quotes, examples, citations or the studies supporting a finding.
• Specify the format. Ask for a summary, presentation-ready bullets, or another format that suits your needs. With MCP, you further specify formats, such as charts, tables, or interactive deliverables. 
• Check important findings. Use AI to accelerate analysis, but review the underlying research before making important decisions.

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Find out more in our AI Chat best practices.

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Already using AI Chat or MCP? We’d love to hear how you’re using AI to explore your research. Share your use case, workflow or favorite way to work with AI with our team.

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Not using Optimal yet? See how Optimal can help you bring AI into your research workflow.
Book a demo.

What if you could turn real user feedback into your next design iteration with a single prompt?

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With Optimal MCP and Figma Agent, you can turn user research to design updates in minutes. Pull insights from your Optimal studies into Figma, then let its AI design agent update multiple frames and screens based on real participant feedback. As you gather more feedback, you can keep refining your designs, testing updated prototypes with real users and using what you learn to shape the next iteration.

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Together, Optimal MCP and Figma Agent help close the loop between research and design, helping you turn real user feedback into better designs with every iteration.
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How does the Optimal MCP & Figma Agent work? 

Optimal MCP and Figma Agent help you connect the dots between research and design. Connect Optimal to Figma and draw on findings from one or multiple studies, including participant feedback, usability insights, transcripts and prototype task results. Figma’s AI design agent can use these insights to update existing designs or generate new ones based on your prompts.
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Then, bring your updated prototypes back into Optimal to test them with real participants and see how your changes perform.
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So what does this look like in practice? Imagine you've run a usability study and discovered that participants are struggling to find an important action in your product. Here’s how to put this research design loop into action.

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From research insight to your next breakthrough
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1. Turn user insights into new design possibilities

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Reference Optimal MCP and one or more Optimal studies in Figma’s agent chat. Ask the agent to explore solutions to the usability issues your research revealed and generate design changes to address them.

Example prompt: "Connect to Optimal Workshop MCP, look at study "Mobile banking app", read the feedback and update the prototype based on the participants' feedback. Add these updated prototype screens to a new page in this file, side-by-side with the original artboard."

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2. Explore further. Iterate faster.

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Use AI to explore alternatives, challenge your first approach and move through design possibilities faster. Then bring your expertise to the process. Evaluate the trade-offs, review the prototype flows, refine the details and decide which ideas best serve your users.

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As more participant feedback to your Optimal studies comes through, ask the agent to pull feedback from those new participants to bring those fresh insights into your Figma iterations.

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3. Put your changes to the test

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Here's where the loop gets even more exciting.

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Put your newly updated prototype to the test with a new Optimal study. Have you made that important action easier to find? Does the new design solve the original problem? What could be even better?

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You can test multiple prototypes or combine multiple methods in one study, whether you're working with a prototype, live website or web app, or AI prototype with Optimal’s Usability Testing tool or keep it focused on one prototype with Optimal’s Prototype Testing tool.

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Every round of testing gives you more to work with. Every new insight creates another opportunity to improve.
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Ready to bring user research into your next design iteration?

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AI is changing how quickly we can move from an idea to a design. Make the most of your research by connecting Optimal to Figma’s AI Design Agent with Optimal MCP to build experiences and products people love.

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Get started with the Optimal MCP setup guide or check out the interactive demo.

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AI is opening up new possibilities for qualitative research. But for researchers, it’s not enough for an AI Interviewer to ask relevant questions. An effective AI Interviewer needs to create an experience where participants feel comfortable sharing their perspectives, have the space to explain their thinking and explore their answers in more depth.

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To find out more, we ran a study using Optimal’s AI Interviewer to look at general attitudes towards AI, better understand what participants valued about an AI Interviewer experience, and what worked well and where they saw room for improvement.

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One theme stood out: the value of AI interviews depends not just on the technology, but on the experience it creates for the people taking part.

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What do people think about interacting with AI?

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As AI becomes more familiar in everyday life, the conversation is shifting from whether people will use it to when, why and under what circumstances they find it useful. In a survey of 1,005 US consumers led by Adobe Express, 61% said they were comfortable having conversations with AI-powered systems.

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Another survey of more than 1,000 participants across six countries found that people who knew they were interacting with AI reported satisfaction rates 34% higher than those who weren't informed.

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These findings suggest that interacting with AI isn't inherently uncomfortable for everyone. But comfort with AI doesn't automatically translate into a positive research experience. A quick customer-service interaction is different from a qualitative research interview, where people need to reflect on their experiences, explain their reasoning and sometimes share sensitive or complicated perspectives.

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For researchers, the more useful question is: what makes an AI-led conversation feel comfortable, natural and valuable for participants? 

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What makes an AI-moderated research experience successful?

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In qualitative research, the quality of the conversation matters just as much as the number of responses collected.

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A good AI-moderated interview should give participants the freedom to express themselves in their own words, ask relevant follow-up questions and create opportunities to explore an answer beyond the initial response, and go beyond simply moving people through a series of questions.

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Here are considerations that stood out from participants interacting with Optimal’s AI Interviewer for the first time.

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1. Making AI interviews feel natural and conversational

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Qualitative research aims to uncover the reasoning behind people's choices, not just capture their first response. Relevant follow-up questions can encourage participants to elaborate, clarify an opinion or explore an experience in more depth. The ability to answer in their own words, rather than select from predefined options, is also central to gathering richer feedback.

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In our study, the majority of participants described their experience with Optimal’s AI Interviewer as “seamless,” “smooth”, “natural”, and even "fun". Several were pleasantly surprised by how the interaction felt, including participants who had reservations beforehand. One participant said, "I have no problems speaking freely."

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Automated highlight reel: "The overall experience was widely rated as positive and enjoyable"

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On participant remarked, "It was pleasant and I enjoyed it. And you were great." Others described the AI interview format as something they had “come to expect,” framing it as routine rather than novel. 

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Automated highlight reel: "Overall satisfaction with the AI interview experience was high"

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These responses offer an encouraging perspective on what AI-moderated research can feel like for participants with the right cadence and context. Multiple participants also highlighted that the AI Interviewer’s replies were clearly based on what they had just said, rather than following a rigid script.

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One participant noted it felt like talking to a real person because responses were grounded in their own words: "I really felt like I was talking to a real person and that the responses were based off of what I what I said."

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Automated highlight reel: "Contextual follow-up questions made the conversation feel natural and responsive."

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Another participant highlighted how the AI concisely summarized their points before moving on, without asking the same question in different ways — something they’d found frustrating when using other AI interview tools.

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2. Helping participants feel comfortable and confident

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People are more likely to engage when they understand what to expect and feel comfortable taking part. Several participants found the AI interview easier to talk to than a traditional interview. For some, the absence of a human interviewer reduced the pressure of the interaction, making it easier to share their thoughts. 

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Automated highlight reel: "Talking to an AI felt easier than a live human interview"

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The experience wasn't just about how the conversation sounded either. Participants also found the on-screen text helpful for following along with the spoken interaction. One participant who experiences auditory processing difficulties specifically highlighted this as a useful accessibility feature.

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These observations suggest that small details in the participant experience can make a meaningful difference. Clear instructions, the ability to follow a conversation in more than one way, and a format that feels comfortable can all help people engage with a study.

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One participant noted that they felt a person would more deeply grasp the feelings and emotions behind their answers, but overall rated their experience with the AI Interviewer positively.

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Comfort will vary by person and research context, so the goal isn't to assume AI moderation will suit everyone and every situation, but to provide another considered way for people to share their perspectives. 

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3. Giving participants control over their experience

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A positive AI interview experience doesn't mean every participant will prefer the same format.

With Optimal’s experience, participants appreciated the ability to choose a preferred conversational style that works best for them. One participant, who preferred greater control, initially chose the “submit response” mode. This allowed them to decide when to submit an answer and move on to the AI Interviewer's next question or follow-up.

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After a positive experience with the AI Interviewer, they said they would be willing to try the “free form” option. In this mode, the AI Interviewer responds naturally when the participant finishes speaking. This highlights the value of offering choice and helping participants gain familiarity and comfort.

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Clip: New comfort and willingness to try "free form"

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A new way to engage with participants

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For UX research, design, and product teams, AI Interviews offer another option for gathering qualitative feedback, creating more flexibility in how conversations take place, and supporting gathering evidence at scale.

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The opportunity isn't to replace the human aspect of research. It's to give researchers more ways to listen, learn and make informed decisions.

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Ready to explore AI interviews? Get in touch for a demo or find out how to access Optimal’s AI Interviewer in Early Access.