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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.