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.

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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.

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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.

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Why UX Is More Critical Than Ever

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

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

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Key UX Focus Areas for AI-First Products

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To Trust Your AI, Users Need to Understand It

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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.

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What does a transparent experience look like?

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• Show users why AI makes certain decisions (e.g., "Recommended for you because…")
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• Allow users to adjust AI settings to customize their experience
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• Enable users to provide feedback when AI gets something wrong—and offer ways to correct it
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A strong example: Spotify's AI recommendations explain why a song was suggested, helping users understand the reasoning behind specific song recommendations.

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AI Should Augment Human Expertise Not Replace It

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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.

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

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

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Validate and Test AI UX Early and Often

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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.

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Run UX Research to Validate AI Models Throughout Development:

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

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The Future of AI Products Relies on UX

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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.

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

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As AI transforms the UX research landscape, product teams face an important choice that extends far beyond functionality: how to integrate AI while maintaining the security and privacy standards your customers trust you with. At Optimal, we've been navigating these waters for years as we implement AI into our own product, and we want to share the way we view three fundamental approaches to AI integration, and why your choice matters more than you might think.

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Three Paths to AI Integration

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Path 1: Self-Hosting - The Gold Standard 

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Self-hosting AI models represents the holy grail of data security. When you run AI entirely within your own infrastructure, you maintain complete control over your data pipeline. No external parties process your customers' sensitive information, no data crosses third-party boundaries, and your security posture remains entirely under your control.

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The reality? This path is largely theoretical for most organizations today. The most powerful AI models, the ones that deliver the transformative capabilities your users expect, are closely guarded by their creators. Even if these models were available, the computational requirements would be prohibitive for most companies.

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While open-source alternatives exist, they often lag significantly behind proprietary models in capability. 

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Path 2: Established Cloud Providers - The Practical, Secure Choice 

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This is where platforms like AWS Bedrock shine. By working through established cloud infrastructure providers, you gain access to cutting-edge AI capabilities while leveraging enterprise-grade security frameworks that these providers have spent decades perfecting.

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Here's why this approach has become our preferred path at Optimal:

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Unified Security Perimeter: When you're already operating within AWS (or Azure, Google Cloud), keeping your AI processing within the same security boundary maintains consistency. Your data governance policies, access controls, and audit trails remain centralized.

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Proven Enterprise Standards: These providers have demonstrated their security capabilities across thousands of enterprise customers. They're subject to rigorous compliance frameworks (SOC 2, ISO 27001, GDPR, HIPAA) and have the resources to maintain these standards.

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Reduced Risk: Fewer external integrations mean fewer potential points of failure. When your transcription (AWS Transcribe), storage, compute, and AI processing all happen within the same provider's ecosystem, you minimize the number of trust relationships you need to manage.

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Professional Accountability: These providers have binding service agreements, insurance coverage, and legal frameworks that provide recourse if something goes wrong.

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Path 3: Direct Integration - A Risky Shortcut 

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Going directly to AI model creators like OpenAI or Anthropic might seem like the most straightforward approach, but it introduces significant security considerations that many organizations overlook.

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When you send customer data directly to OpenAI's APIs, you're essentially making them a sub-processor of your customers' most sensitive information. Consider what this means:

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• User research recordings containing personal opinions and behaviors
• Prototype feedback revealing strategic product directions
• Customer journey data exposing business intelligence
• Behavioral analytics containing personally identifiable patterns

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While these companies have their own security measures, you're now dependent on their practices, their policy changes, and their business decisions. 

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The Hidden Cost of Taking Shortcuts

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A practical example of this that we’ve come across in the UX tools ecosystem is the way some UX research platforms appear to use direct OpenAI integration for AI features while simultaneously using other services like Rev.ai for transcription. This means sensitive customer recordings touch multiple external services:

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1. Recording capture (your platform)
2. Transcription processing (Rev.ai)
3. AI analysis (OpenAI)
4. Final storage and presentation (back to your platform)

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Each step represents a potential security risk, a new privacy policy to evaluate, and another business relationship to monitor. More critically, it represents multiple points where sensitive customer data exists outside your primary security controls.

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Optimal’s Commitment to Security: Why We Choose the Bedrock Approach

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At Optimal, we've made a deliberate choice to route our AI capabilities through AWS Bedrock rather than direct integration. This isn't just about checking security boxes, although that’s important,  it's about maintaining the trust our customers place in us.

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Consistent Security Posture: Our entire infrastructure operates within AWS. By keeping AI processing within the same boundary, we maintain consistent security policies, monitoring, and incident response procedures.

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Future-Proofing: As new AI models become available through Bedrock, we can evaluate and adopt them without redesigning our security architecture or introducing new external dependencies.

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Customer Confidence: When we tell customers their data stays within our security perimeter, we mean it. No caveats. 

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Moving Forward Responsibly

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The path your organization chooses should align with your risk tolerance, technical capabilities, and customer commitments. The AI revolution in UX research is just beginning, but the security principles that should guide it are timeless. As we see these powerful new capabilities integrated into more UX tools and platforms, we hope businesses choose to resist the temptation to prioritize features over security, or convenience over customer trust.

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At Optimal, we believe the most effective AI implementations are those that enhance user research capabilities while strengthening, not weakening, your security posture. This means making deliberate architectural choices, even when they require more initial work. This alignment of security, depth and quality is something we’re known for in the industry, and it’s a core component of our brand identity. It’s something we will always prioritize. 

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Ready to explore AI-powered UX research that doesn't compromise on security? Learn more about how Optimal integrates cutting-edge AI capabilities within enterprise-grade security frameworks.

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

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

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The Ethical Challenges of AI

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Privacy & Data Ethics

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

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

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

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Bias & Fairness

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

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

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

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User Autonomy & Agency

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

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

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

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Accessibility & Digital Divide

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AI-powered interfaces may create new barriers:

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

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Accountability & Transparency

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

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

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How Product Owners Can Champion Ethical AI Through UX

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

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User-Centered Testing for AI Systems

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

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

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Inclusive Research Practices

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

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

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Transparency in AI Decision-Making

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

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

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The Path Forward: Responsible Innovation

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

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

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

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

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

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

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

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

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Beyond Generative AI: A New Paradigm Emerges

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The AI landscape is undergoing a profound transformation. While generative AI has captured public imagination with its ability to create content, a new paradigm is quietly revolutionizing how we think about human-computer interaction: Agentic AI.

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Unlike traditional software that waits for explicit commands or generative AI focused primarily on content creation, Agentic AI represents a fundamental shift toward truly autonomous systems. These advanced AI agents can independently make decisions, take actions, and solve complex problems with minimal human oversight. Rather than simply responding to prompts, they proactively work toward goals, demonstrating initiative and adaptability that more closely resembles human collaboration than traditional software interaction.

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This evolution is already transforming industries across the board:

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• In customer service, AI agents handle complex inquiries end-to-end
• In software development, they autonomously debug code and suggest improvements
• In healthcare, they monitor patient data and flag concerning patterns
• In finance, they analyze market trends and execute optimized strategies
• In manufacturing and logistics, they orchestrate complex operations with minimal human intervention

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As these autonomous systems become more prevalent, designing exceptional user experiences for them becomes not just important, but essential. The challenge? Traditional UX approaches built around graphical user interfaces and direct manipulation fall short when designing for AI that thinks and acts independently.

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The New Interaction Model: From Commands to Collaboration

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Interacting with Agentic AI represents a fundamental departure from conventional software experiences. The predictable, structured nature of traditional GUIs—with their buttons, menus, and visual feedback—gives way to something more fluid, conversational, and at times, unpredictable.

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The ideal Agentic AI experience feels less like operating a tool and more like collaborating with a capable teammate. This shift demands that UX designers look beyond the visual aspects of interfaces to consider entirely new interaction models that emphasize:

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• Natural language as the primary interface
• The AI's ability to take initiative appropriately
• Establishing the right balance of autonomy and human control
• Building and maintaining trust through transparency
• Adapting to individual user preferences over time

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The core challenge lies in bridging the gap between users accustomed to direct manipulation of software and the more abstract interactions inherent in systems that can think and act independently. How do we design experiences that harness the power of autonomy while maintaining the user's sense of control and understanding?

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Understanding Users in the Age of Autonomous AI

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The foundation of effective Agentic AI design begins with deep user understanding. Expectations for these autonomous agents are shaped by prior experiences with traditional AI assistants but require significant recalibration given their increased autonomy and capability.

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Essential UX Research Methods for Agentic AI

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Several research methodologies prove particularly valuable when designing for autonomous agents:

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User Interviews provide rich qualitative insights into perceptions, trust factors, and control preferences. These conversations reveal the nuanced ways users think about AI autonomy—often accepting it readily for low-stakes tasks like calendar management while requiring more oversight for consequential decisions like financial planning.

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Usability Testing with Agentic AI prototypes reveals how users react to AI initiative in real-time. Observing these interactions highlights moments where users feel empowered versus instances where they experience discomfort or confusion when the AI acts independently.

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Longitudinal Studies track how user perceptions and interaction patterns evolve as the AI learns and adapts to individual preferences. Since Agentic AI improves through use, understanding this relationship over time provides critical design insights.

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Ethnographic Research offers contextual understanding of how autonomous agents integrate into users' daily workflows and environments. This immersive approach reveals unmet needs and potential areas of friction that might not emerge in controlled testing environments.

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Key Questions to Uncover

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Effective research for Agentic AI should focus on several fundamental dimensions:

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Perceived Autonomy: How much independence do users expect and desire from AI agents across different contexts? When does autonomy feel helpful versus intrusive?

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Trust Factors: What elements contribute to users trusting an AI's decisions and actions? How quickly is trust lost when mistakes occur, and what mechanisms help rebuild it?

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Control Mechanisms: What types of controls (pause, override, adjust parameters) do users expect to have over autonomous systems? How can these be implemented without undermining the benefits of autonomy?

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Transparency Needs: What level of insight into the AI's reasoning do users require? How can this information be presented effectively without overwhelming them with technical complexity?

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The answers to these questions vary significantly across user segments, task types, and domains—making comprehensive research essential for designing effective Agentic AI experiences.

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Core UX Principles for Agentic AI Design

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Designing for autonomous agents requires a unique set of principles that address their distinct characteristics and challenges:

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Clear Communication

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Effective Agentic AI interfaces facilitate natural, transparent communication between user and agent. The AI should clearly convey:

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• Its capabilities and limitations upfront
• When it's taking action versus gathering information
• Why it's making specific recommendations or decisions
• What information it's using to inform its actions

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Just as with human collaboration, clear communication forms the foundation of successful human-AI partnerships.

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Robust Feedback Mechanisms

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Agentic AI should provide meaningful feedback about its operations and make it easy for users to provide input on its performance. This bidirectional exchange enables:

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• Continuous learning and refinement of the agent's behavior
• Adaptation to individual user preferences
• Improved accuracy and usefulness over time

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The most effective agents make feedback feel conversational rather than mechanical, encouraging users to shape the AI's behavior through natural interaction.

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Thoughtful Error Handling

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How an autonomous agent handles mistakes significantly impacts user trust and satisfaction. Effective error handling includes:

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• Proactively identifying potential errors before they occur
• Clearly communicating when and why errors happen
• Providing straightforward paths for recovery or human intervention
• Learning from mistakes to prevent recurrence

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The ability to gracefully manage errors and learn from them is often what separates exceptional Agentic AI experiences from frustrating ones.

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Appropriate User Control

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Users need intuitive mechanisms to guide and control autonomous agents, including:

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• Setting goals and parameters for the AI to work within
• The ability to pause or stop actions in progress
• Options to override decisions when necessary
• Preferences that persist across sessions

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The level of control should adapt to both user expertise and task criticality, offering more granular options for advanced users or high-stakes decisions.

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Balanced Transparency

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Effective Agentic AI provides appropriate visibility into its reasoning and decision-making processes without overwhelming users. This involves:

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• Making the AI's "thinking" visible and understandable
• Explaining data sources and how they influence decisions
• Offering progressive disclosure—basic explanations for casual users, deeper insights for those who want them

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Transparency builds trust by demystifying what might otherwise feel like a "black box" of AI decision-making.

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Proactive Assistance

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Perhaps the most distinctive aspect of Agentic AI is its ability to anticipate needs and take initiative, offering:

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• Relevant suggestions based on user context
• Automation of routine tasks without explicit commands
• Timely information that helps users make better decisions

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When implemented thoughtfully, this proactive assistance transforms the AI from a passive tool into a true collaborative partner.

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Building User Confidence Through Transparency and Explainability

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For users to embrace autonomous agents, they need to understand and trust how these systems operate. This requires both transparency (being open about how the system works) and explainability (providing clear reasons for specific decisions).

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Several techniques can enhance these critical qualities:

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• Feature visualization that shows what the AI is "seeing" or focusing on
• Attribution methods that identify influential factors in decisions
• Counterfactual explanations that illustrate "what if" scenarios
• Natural language explanations that translate complex reasoning into simple terms

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From a UX perspective, this means designing interfaces that:

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• Clearly indicate when users are interacting with AI versus human systems
• Make complex decisions accessible through visualizations or natural language
• Offer progressive disclosure—basic explanations by default with deeper insights available on demand
• Implement audit trails documenting the AI's actions and reasoning

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The goal is to provide the right information at the right time, helping users understand the AI's behavior without drowning them in technical details.

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Embracing Iteration and Continuous Testing

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The dynamic, learning nature of Agentic AI makes traditional "design once, deploy forever" approaches inadequate. Instead, successful development requires:

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Iterative Design Processes

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• Starting with minimal viable agents and expanding capabilities based on user feedback
• Incorporating user input at every development stage
• Continuously refining the AI's behavior based on real-world interaction data

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Comprehensive Testing Approaches

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• A/B testing different AI behaviors with actual users
• Implementing feedback loops for ongoing improvement
• Monitoring key performance indicators related to user satisfaction and task completion
• Testing for edge cases, adversarial inputs, and ethical alignment

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Cross-Functional Collaboration

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• Breaking down silos between UX designers, AI engineers, and domain experts
• Ensuring technical capabilities align with user needs
• Creating shared understanding of both technical constraints and user expectations

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This ongoing cycle of design, testing, and refinement ensures Agentic AI continuously evolves to better serve user needs.

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Learning from Real-World Success Stories

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Several existing applications offer valuable lessons for designing effective autonomous systems:

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Autonomous Vehicles demonstrate the importance of clearly communicating intentions, providing reassurance during operation, and offering intuitive override controls for passengers.

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Smart Assistants like Alexa and Google Assistant highlight the value of natural language processing, personalization based on user preferences, and proactive assistance.

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Robotic Systems in industrial settings showcase the need for glanceable information, simplified task selection, and workflows that ensure safety in shared human-robot environments.

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Healthcare AI emphasizes providing relevant insights to professionals, automating routine tasks to reduce cognitive load, and enhancing patient care through personalized recommendations.

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Customer Service AI prioritizes personalized interactions, 24/7 availability, and the ability to handle both simple requests and complex problem-solving.

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These successful implementations share several common elements:

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• They prioritize transparency about capabilities and limitations
• They provide appropriate user control while maximizing the benefits of autonomy
• They establish clear expectations about what the AI can and cannot do

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Shaping the Future of Human-Agent Interaction

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Designing user experiences for Agentic AI represents a fundamental shift in how we think about human-computer interaction. The evolution from graphical user interfaces to autonomous agents requires UX professionals to:

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• Move beyond traditional design patterns focused on direct manipulation
• Develop new frameworks for building trust in autonomous systems
• Create interaction models that balance AI initiative with user control
• Embrace continuous refinement as both technology and user expectations evolve

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The future of UX in this space will likely explore more natural interaction modalities (voice, gesture, mixed reality), increasingly sophisticated personalization, and thoughtful approaches to ethical considerations around AI autonomy.

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For UX professionals and AI developers alike, this new frontier offers the opportunity to fundamentally reimagine the relationship between humans and technology—moving from tools we use to partners we collaborate with. By focusing on deep user understanding, transparent design, and iterative improvement, we can create autonomous AI experiences that genuinely enhance human capability rather than simply automating tasks.

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The journey has just begun, and how we design these experiences today will shape our relationship with intelligent technology for decades to come.

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At Optimal, we believe in the transformative potential of AI to accelerate your workflow and time to insights. Our goal is simple: keep humans at the heart of every insight while using AI as a powerful partner to amplify your expertise. 

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By automating repetitive tasks, providing suggestions for your studies, and streamlining workflows, AI frees you up to focus on what matters most—delivering impact, making strategic decisions, and building products people love.

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That’s why we’re excited to announce our latest AI feature: AI-Powered Question Simplification. 

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Simplify and Refine Your Questions Instantly

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Ambiguous or overly complex wording can confuse respondents, making it harder to get reliable, accurate insights. Plus, refining survey and question language is manual and can be a time-consuming process with little guidance. To solve this, we built an AI-powered tool to help study creators craft questions that resonate with participants and speed up the process of designing studies.

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Our new AI-powered feature helps with:

• Instant Suggestions: Simplify complex question wording and improve clarity to make your questions easier to understand.
• Seamless Editing: Accept, reject, or regenerate suggestions with just a click, giving you complete control.
• Better Insights: By refining your questions, you’ll gather more accurate responses, leading to higher-quality data that drives better decisions.
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Apply AI-Powered Question Simplification to any of your survey questions or to screening questions, and pre- and post-study questions in prototype tests, surveys, card sorts, tree tests, and first-click tests.
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AI: Your Research Partner, Not a Replacement

AI is at the forefront of our innovation at Optimal this year, and we’re building AI into Optimal with clear principles in mind:
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1. AI does the tedious work: It takes on repetitive, mundane tasks, freeing you to focus on insights and strategy.
2. AI assists, not dictates: You can adapt, change, or ignore AI suggestions entirely.
3. AI is a choice: We recognize that Optimal users have diverse needs and risk appetites. You remain in control of how, when, and if you use AI.
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‍Ready to Get Started? 

Keep an eye out for more updates throughout 2025 as we continue to expand our platform with AI-powered features that help you uncover insights with speed, clarity, and more confidence.

Want to see how AI can speed up your workflow?

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Apply AI-Powered Question Simplification today or check out AI Insights to experience it for yourself!

User research and design doesn’t fail because teams don’t care – it fails because there’s rarely time to explore every option. When deadlines pile up, most teams default to the same familiar research patterns and miss opportunities to get more value from the tools they already have.

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We’ve brought together practical, real-world ways to use Optimal – from tree testing and first-click testing to card sorting, surveys, prototype testing, and interviews. Some of these use cases are obvious, but many aren’t. All of them are designed to help teams move faster, reduce risk, and turn user insights into decisions stakeholders trust.

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We’ve focused on quick wins and flexible examples you can adapt to your own context – whether you’re benchmarking navigation, validating early designs, improving conversion flows, prioritizing work, or proving the ROI of UX. You don’t need more tools or more processes. You just need smarter ways to use what you already have.

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Let’s get into it.

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Practical ways to use Optimal for user research and UX design

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#1 Benchmark your information architecture (IA)

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Without a baseline for your navigation or information architecture (IA), you can’t easily tell if any changes you make have a positive effect. If you haven’t done so, benchmark your existing website on tree testing now. Upload your site structure and get results the same day. Now you’ll have IA scores to beat each month. Easy.

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#2 Find out precisely where people get lost

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Watch video recordings of real people interacting with your sites with live site testing. Combine this with surveys and user interviews to understand where users struggled. You can also use the tree testing pietree to find out exactly where people are getting lost in your website structure and where they go instead.

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#3 Start with one screenshot

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If you’re just not sure where to begin then take a screenshot of your homepage, or any page that you think might have some issues and get going with first-click testing. Write up a string of things that people might want to do when they find themselves on this page and use these as your tasks. Surprise all your colleagues with a maddening heatmap or video recordings showing where people actually clicked in response to your tasks or where they struggle. Now you’ll have a better idea of which area of your site to focus on for your next step.

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#4 Test live sites during discovery

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You can run live site testing as part of your discovery phase to baseline your live experiences and see how well your current site supports real user goals. Test competitors' sites to see how you stack up. You’ll quickly uncover opportunities to differentiate your site, all before a single wireframe is drawn. All that's required is a URL and then you're set to go. No code needed.

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#5 A/B test your site structure

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Tree testing is great for testing more than one content structure. It’s easy to run two separate tree testing studies, even more than two. It’ll help you decide which structure you and your team should run with, and it won’t take you long to set them up.

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#6 Optimize sign-up flows

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Discover how easy (or not) it is for users to navigate your sign up experience to ensure it works exactly as intended. Create a live site or prototype test to identify any confusion or points of friction. You could also use this test to understand users' first impressions of your home or landing page. Where do they click first and what information is valuable to them?

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#7 Make collaborative design decisions‍

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Use surveys, first-click tests, and card sorting to get your team involved and let their feedback feed your designs: logos, icons, banners, images, the list goes on... For example, by creating a closed image sort with categories, your team can group designs based on their preferences, you can get some quick feedback to help you figure out where you should focus your efforts.

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#8 Do your (market) research

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Get a better sense of your users and customers’ motivations with surveys and user interviews. You can also find out what people actually want to see on your website with a card sort, by conducting an image sort of potential products. By providing categories like ‘I would buy this’, ‘I wouldn’t buy this’ to indicate their preferences for each item, you can figure out what types of products appeal to your customers.

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#9 Customer satisfaction surveys with surveys and interviews

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The thoughts and feelings of your users are always important. A simple survey or user interview can help you take a deeper look at your checkout process, a recently launched product or service, or even the packaging your product arrives in. Your options are endless.

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#10 Start testing prototypes

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Companies that incorporate prototype testing in their design process can reduce development costs by 33%. Use prototype testing to ensure your designs hit the mark before you invest too heavily in the build. Build your own prototype with images in Optimal or import a Figma file. You can even test AI-generated prototypes from tools like Lovable or Magic Patterns by dropping the URL into live site testing.

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#11 Crowdsource content ideas

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Whether you’re running a blog or a UX conference, surveys can help you generate content ideas and understand any knowledge gaps that might be out there. Figure out what your users and attendees like to read on your blog, or what they want to hear about at your event, and let this feed into what you offer.

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#12 Evaluate user flows

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Sometimes a change in your product or service means you have to change how it’s presented to your existing customers.  Ensure your customers understand the changes to your product or service with prototype and live site testing. Identify issues with user flow, content, or layout that may confuse them. Discover which options they’re most likely to choose with the updates. Uncover what truly matters to your customers.

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#13 Quantify the return on investment of UX

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Some people, including UX Agony Aunt, define return on UX as time saved, money made, and people engaged. By attaching a value to the time spent completing tasks, or to successful completion of tasks, you can approximate an ROI or at least illustrate the difference between two options.

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#14 Convince your stakeholders with highlight reels

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User interviews are teeming with insights but can be time and resource intensive to analyze without automation. Use Optimal Interviews tool to capture key moments, reactions, and pain points with automated highlight reels and clips. These are perfect for storytelling, stakeholder buy-in, and keeping teams connected to who they’re building for.

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#15 Prioritize upcoming work 

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Survey your organization to build a list of ideas for upcoming work. Understand your audience’s priorities with card sorting to inform your feature development. Categorize your upcoming work ideas to decide collectively what’s best to take on next. Great for clarifying what the team considers the most valuable or pressing work to be done.

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#16 Reduce content on landing pages to what people access regularly

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Before you run an open card sort to generate new category ideas, you can run a closed card sort to find out if you have any redundant content. Say you wanted to simplify the homepage of your intranet. You can ask participants to sort cards (containing homepage links) based on how often they use them. You could compare this card sort data with analytics from your intranet and see if people’s actual behavior and perception are well aligned.

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#17 Create tests to fit in your onboarding process

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Onboarding new customers is crucial to keeping them engaged with your product, especially if it involves your users learning how to use it. You can set up a quick study to help your users stay on track with onboarding. For example, say your company provided online email marketing software. You can set up a first-click testing study using a photo of your app, with a task asking your participants where they’d click to see the open rates for a particular email that went out.

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#18 Input your learnings and observations from a UX conference with qualitative insights

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If you're lucky enough to attend a UX conference, you can now share the experience with your colleagues. You can easily jot down ideas, quotes and key takeaways in a Qualitative Insights project and keep your notes organized by using a new session for each presenter Bonus, if you’re part of a team, they can watch the live feed rolling into Qualitative Insights!

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#19 Multivariate testing

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Tree testing and first-click testing allow you to compare multiple versions of content structures, designs, or flows. You can also compare how users engage with different live websites in one study. This helps decide the best-performing option without guessing.

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#20 Do some sociological research

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Using card sorting for sociological research is a great way to deepen your understanding of how different groups may categorize information. For example, by looking at how young people group popular social media platforms, you can understand the relationships between them, and identify where your product may fit in the mix. Then, follow up with surveys or moderated interviews for deeper insights. 

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#21 Test your FAQs page with new users

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Your support and knowledge base within your website can be just as important as any other core action on your website. If your support site is lacking in navigation and UX, this will no doubt increase support tickets and resources. Make sure your online support section is up to scratch. Here’s an article on how to do it quickly.

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#22 Establish which tags or filters people consider to be the most important

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Create a card sort with your search filters or tags as labels, and have participants rank them according to how important they consider them to be. Analytics can tell you half of the story (where people actually click), so the card sort can give another side: a better idea of what people actually think or want. Follow up with surveys or interviews to confirm insights.

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#23 Figure out if your icons need labels

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‍Figure out if your icons are doing their job by testing whether your users are understanding them as intended. Uploading icons you currently use, or plan to use in your interface to first-click testing, and ask your users to identify their meaning by making use of post-task questions.

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#24 Get straight to the aha! moments

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Optimal Interviews gives you automated insights but you can also engage with AI Chat to dive deeper. Ask AI specific questions about a feature or process or request quotes or examples. Then, get highlight reels and clips to match.

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#25 Improve website conversions

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Make the marketing team’s day by doing a fast improvement on some core conversions on your website. Now, there are loads of ways to improve conversions for a check out cart or signup form, but using first-click testing to test out ideas before you start going live A/B test can take mere minutes and give your B version a confidence boost. For deeper insights, try a live site test. 

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#26 Test your mobile experience or web app

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As more and more people are using their smartphones for apps and to browse sites, you need to ensure its design gives your users a great experience. Test your mobile site to ensure people aren’t getting lost in the mobile version of your site. If you haven’t got a mobile-friendly design yet, now’s the time to start designing it!

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#27 Get automated transcripts

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Have a number of interviews you need to transcribe quickly? Upload up to 20 interviews at once in Optimal Interviews and get automated transcripts, so you can spend less time on admin and more time digging into insights.

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#28 Reduce the bounce rates of certain sections of your website‍

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People jumping off your website and not continuing their experience is something (depending on the landing page) everyone tries to improve. The metric ‘time on site’ and ‘average page views’ is a metric that shows the value your whole website has to offer. Again, there are many different ways to do this, but one big reason for people jumping off the website is not being able to find what they’re looking for. Use prototype testing or live site testing to watch users in action and understand where things break down.

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#29 Test your website in different countries‍

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No, you don’t have to spend thousands of dollars to go to all these countries to test, although that’d be pretty sweet. You can remotely research participants from all over the world, using our integrated recruitment panel. Start seeing how different cultures, languages, and countries interact with your website. 

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#30 Preference test

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Whether you’re coming up with a new logo design, headline, featured image, or anything, you can preference test it with first-click testing. Create an image that shows the two designs side by side and upload it to first-click testing. From there, you can ask people to click whichever one they prefer!  If you want to track multiple clicks per task or watch recordings, use prototype testing instead.

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#31 Test visual hierarchy with first-click testing

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Use first-click testing to understand which elements draw users' attention first on your page. Upload your design and ask participants to click on the most important element, or what catches their eye first. The resulting heatmap will show you if your visual hierarchy is working as intended - are users clicking where you expect them to? This technique helps validate design decisions about sizing, color, positioning, and contrast without needing to build the actual page.

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#32 Tame your blog or knowledge base

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Get the tags and categories in your blog under control to make life easier for your readers. Set up a card sort and use all your tags and categories as card labels. Either use your existing ones or test a fresh set of new tags and categories.

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#33 Use AI Chat for stakeholder-ready outputs

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Use AI-powered chat to instantly reformat interview insights and fast-track deliverables for different audiences. Simply specify the details of the deliverable you would like. For example: “Turn this into a 3-sentence Slack summary (no citations).” or “Rewrite this as an exec-ready insight with a clear recommendation.”

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‍#34 Validate the designs in your head

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As designers, you’ve probably got umpteen designs floating around in your head at any one time. But which of these are really worth pursuing? Figure this out by using Optimal to test out wireframes of new designs before putting any more work into them.

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#35 Optimize the support escalation flow

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Understand how users navigate help resources, report issues, and conceptualize support categories, especially when they need to locate assistance quickly in time-sensitive situations.

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#36 Improve your search engine optimization (SEO) with tree testing

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Yes, a good IA improves your SEO. Tree testing helps you understand how people navigate throughout your site. It also helps search engines better understand and index your content, making it more discoverable and relevant in search results. Make sure people can easily find what they’re looking for, and you’ll start to see improvement in your search engine ranking.

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#37 Feature prioritization and get some help for your roadmap

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Find out what people think are the most important next steps for your team. Set up a survey or card sort and ask people to categorize items and rank them in descending order of importance or impact on their work. This can also help you gauge their thoughts on potential new features for your site, and for bonus points compare team responses with customer responses.

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#38 Define your brand tone of voice

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Use a card sort to understand how people perceive your brand, so you can shape or refine your brand personality, tone of voice, and style guidelines. Run this with stakeholders or your audience to uncover current perceptions and where they’d like your brand to go next.

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#39 Run an Easter egg hunt using the correct areas in first-click testing

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Liven up the workday by creating a fun Easter egg hunt in first-click testing. Simply upload a photo (like those really hard “spot the X” photos), set the correct area of your target, then send out your study with participant identifiers enabled. You can also send these out as competitions and have closing rules based on time, number of participants, or both.

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#40 Test your home button

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Would an icon or text link work better for navigating to your home page? Before you go ahead and make changes to your site, you can find out by setting up a first-click testing test.

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#41 Improve team structure and clarity role expectations

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Run a card sort, survey, or internal interviews to understand how responsibilities are perceived across different roles. Work with team leaders and managers to clarify role definitions, reporting lines, and decision-making authority. This helps uncover overlapping responsibilities and opportunities to streamline management and support team workflows.

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#42 ‘Buy now’ button shopping cart visibility‍

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If you’re running an e-commerce site, ease of use and a great user experience are crucial. To see if your shopping cart and checkout processes are as good as they can be, look into running a live site, prototype or first-click test.

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#43 Website periodic health checks

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Raise the visibility of good IA by running periodic IA health checks using tree testing and reporting the results. Proactively identifying structural issues early, and backing decisions with clear metrics, helps drive alignment and build confidence across stakeholders.

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‍#44 Use heatmaps to get the first impressions of designs

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Heatmaps in our first-click testing tool are a great way of getting first impressions of any design. You can see where people clicked (correctly and incorrectly), giving you insights on what works and doesn’t work with your designs. Because it’s so fast to test, you can iterate until your designs start singing.

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#45 Focus groups with interviews

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Thinking of launching a new product, app or website, or seeking opinions on an existing one? Remote focus groups can provide you with a lot of candid information that may help get your project off the ground. They’re also dangerous because they’re susceptible to groupthink, design by committee, and tunnel vision. Use with caution, but if you do then upload your recordings to Interviews for automated insights! Find patterns across sessions and use AI Chat to dig deeper. Pay attention to emotional triggers.

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#46 Gather opinions with surveys

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Whether you want the opinions of your users or from members of your team, you can set up a quick and simple survey. It’s super useful for getting opinions on new ideas (consider it almost like a mini-focus group), or even for brainstorming with teammates.

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#47 Prioritise content

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Use a card sort to understand what content matters most to people, so you can plan what to write first. Ask participants which information is most useful or which tasks they do most often. You can also run this after a top tasks survey to help shape your long list of content.

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#48 Test a new concept

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Got an idea you want to sanity-check before investing more time? Use surveys, first-click testing, or prototype testing to see if people understand the concept and find it valuable. A quick test now can save a lot of rework later.

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#49 Run an image card sort to organize products into groups

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You can add images to each card that allows you to understand how your participants may organize and label particular items. Very useful if you want to organize some retail products and want to find out how other people would organize them given a visual including shape, color, and other potential context.

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#50 Guerrilla testing with first-click testing

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For really quick first-click testing, take first-click testing on a tablet, mobile device or laptop to a local coffee shop. Ask people standing in line if they’d like to take part in your super quick test in exchange for a cup of joe. Easy!

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#51 Test your search box

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Case study by Viget: “One of the most heavily used features of the website is its keyword search, so we wanted to make absolutely certain that our redesigned search box didn’t make search harder for users to find and use.” Use first-click testing to test different variations. 

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#52 Run a Net Promoter Score (NPS) survey

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Optimal surveys give you plenty of question options, but one of the simplest ways to take the pulse of your product is an NPS survey to find out how likely they would recommend your product or brand. Use the out-of-the-box NPS question type question to quickly understand customer sentiment and track it over time.

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#53 Run an empathy test

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Empathy – the ability to understand and share the experience of another person – is central to the design process. An empathy test is another great tool to use in the design phase because it enables you to find out if you are creating the right kind of feelings with your user. Take your design and show it to users. Provide them with a variety of words that could represent the design – for example “minimalistic”, “dynamic”, or “professional” – and ask them to pick out which words which they think are best suited to their experience.

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#54 Compare and test email designs

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Drop your email designs into first-click testing to see which version people prefer and where they click first. Use these insights to refine your layout, hierarchy, and calls to action to improve engagement and conversions.

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#55 Source-specific data with an online survey

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Online survey tools can complement your existing research by sourcing specific information from your participants. For example, if you need to find out more about how your participants use social media, which sites they use, and on which devices, you can do it all through a simple survey questionnaire. Additionally, if you need to identify usage patterns, device preferences or get information on what other products/websites your users are aware of/are using, a questionnaire is the ticket.

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#56 Make sure you get the user's first-click right

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Like most things, read a little, and then it’s all about practice. We’ve found that people who get the first click correct are almost three times as likely to complete a task successfully. Get your first clicks right in tree testing and first-click testing and you’ll start seeing your customers smile.

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#57 Destroy evil attractors in your tree

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Evil attractors are those labels in your IA that attract unjustified clicks across tasks. This usually means the chosen label is ambiguous, or possibly a catch-all phrase like ‘Resources’. Read how to quickly identify evil attractors in the Destinations table of tree test results and how to fix them.

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#58 Ensure accessibility and inclusion

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Check how people with different physical, visual, or cognitive needs move through your content, and spot any areas that might slow them down or cause confusion. Use what you uncover to remove friction and support all users.

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#59 Add moderated card sort results to your card sort‍

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An excellent way of gathering valuable qualitative insights alongside the results of your remote card sorts is to run a moderated version of the sorts with a smaller group of participants. When you can observe and interact with your participants as they complete the sort, you’ll be able to ask questions and learn more about their thought processes and the reasons why they have categorized things in a particular way.

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#60 Test your customers' perceptions of different logo and brand image designs

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Understand how customers perceive your brand by creating a closed card sort. Come up with a list of categories, and ask participants to sort images such as logos, and branded images.

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#61 Run an open image card sort to classify images into groups based on the emotions they elicit

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‍Are these pictures exhilarating, or terrifying? Are they humorous, or offensive? Relaxing, or boring? Productive, or frantic? Happy memories, or a deep sigh?

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#62 Crowd-source the values you want your team/brand/product to represent

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‍Card sorting is a well-established technique in the ‘company values’ realm, and there are some great resources to help you and your team brainstorm the values you represent. These ‘in-person’ brainstorm sessions are great, and you can run a remote closed card sort to support your findings. And if you want feedback from more than a small group of people (if your company has, say, more than 15 staff) you can run a remote closed card sort on its own. Use Microsoft’s Reaction Card Method as card inspiration.

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#63 Test physical and digital experiences together

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Use recorded videos and interviews to observe people interacting with physical products, kiosks, or mobile apps in real-world contexts. Record sessions, capture moments of friction, and bring those insights back into Optimal’s Interviews tool for automated insights.

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#64 HR exercises to determine the motivations of your team

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It’s simple to ask your team about their thoughts, feelings, and motivations with a survey. You can choose to leave participant identifiers blank (so responses are anonymous), or you can ask for a name/email address. As a bonus, you can set up a calendar reminder to send out a new survey in the next quarter. Duplicate the survey and send it out again!

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#65 Designing physical environments

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‍If your company has a physical environment in which your customers visit, you can research new structures using a mixture of tools in Optimal. This especially comes in handy if your customers require certain information within the physical environment in order to make decisions. For example, picture a retail store. Are all the signs clear and communicate the right information? Are people overwhelmed by the physical environment?

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#66 Run an image card sort to organize your library

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Whether it’s a physical library of books, or a digital drive full of ebooks, you can run a card sort to help organize them in a way that makes sense. Will it be by genre, author name, color or topic? Send out the study to your coworkers to get their input! You can also do this at home for your own personal library, and you can include music/CDs/vinyl records and movies!

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#67 Use tree testing to refine an interactive phone menu system

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Similar to how you’d design an IA, you can create a tree test to design an automated phone system. Whether you’re designing from the ground up, or improving your existing system, you will be able to find out if people are getting lost.

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Practical ways to use Optimal for user research (and get value fast)

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And that’s the list. This is not everything you can do with Optimal, but a solid reminder that meaningful user insights don’t have to be slow, heavy, or overcomplicated. Small, well-timed studies can uncover friction, validate decisions, and create momentum across teams.

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Ready to get started?

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• Start your free trial and try one idea today
• Talk to an Optimal expert to tailor a workflow for your team
• Explore how other teams use Optimal to scale research across organizations

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Have a creative use case we missed? Let us know, we’re always learning from the ways our customers push research further, faster, and smarter.

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