Optimal Blog

Live site testing is now part of the Optimal platform and is designed to give you real insights from real user interactions without code, installs, or complicated setups. 

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If you missed our recent Live Site Testing Training webinar or want a refresher, we’ll get you up to speed with this recap of all the key insights. 

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What is Live Site Testing?

Optimal’s live site testing lets you watch users navigate any website or web app, including your own staging or production site, or even competitor experiences. It’s all about understanding how people behave in the environments they actually use, helping you identify friction points you might otherwise miss.

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Key Takeaways From the Training

1. Context Is Everything

In usability research, the “real world” often looks very different from controlled prototype tests. People use their own devices, have distractions, and bring patterns and expectations shaped by real life. Foundational research shows the richest insights often come from observing users in these real contexts.

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Live site testing is built to reflect that reality, helping you answer not just if someone completes a task, but how they approach it and why they struggle. 

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2. Testing Is Fast and Friction‑Free

One of the biggest barriers to live site testing historically is complexity, needing code snippets, extensions, or technical setup. Optimal’s tool removes all that friction so you can see natural behaviour without influence or disruption:

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• No code or installs required
• Paste a URL and you’re ready to go
• You can test as often as you want - during discovery, before launch, after launch, or anytime in between - and any site you want
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3. Design Tests With Real‑Life Scenarios

When crafting tasks for live site testing, think about real user goals. Asking people to complete realistic tasks (e.g., find a product, book a flight, compare two pages) and encouraging them to think out loud leads to much richer insights than purely metric‑focused tests. You can also mix tasks with survey questions for quantitative data. 
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4. Participant Experience Is Built for Natural Interaction

A big part of getting real behavior is ensuring participants feel comfortable and unencumbered. Optimal’s built-in task window is readily available when needed but otherwise minimizes to stay out of the way. This flow helps people stay focused and act naturally, which directly improves the quality of insights you collect.
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5. Combine Live Site Testing with Optimal’s Interviews Tool‍

For even deeper insights, pair live site testing with Optimal Interviews. Once you upload live site testing recordings, you get automated insights, transcripts, summaries, as well as highlight reels in Interviews. You can also explore further with AI chat, so you can quickly uncover quotes, compare experiences, and answer ad‑hoc questions.
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This combination doesn’t just make analysis faster; it helps you convince stakeholders with clear, digestible, and compelling evidence from real user behaviour. Instead of long reports, you can present snackable, actionable insights that drive alignment and decisions.

Looking Ahead

We’re evolving live site testing at Optimal with solution testing, a multi-method approach that combines prototypes, live sites, and surveys in a single study. This will let teams capture even richer insights with speak-aloud tasks, automated analysis, highlight reels, and AI chat, making it faster and easier to understand user behavior and share compelling findings.

FAQs Highlights

Can you test staging or test environments and sites behind a password or firewall?

Yes, Optimal's live site testing tool works with any URL, including staging and test environments as well as sites behind a password or firewall.

You can share specific instructions with participants before they start. For example, if participants need to create an account and you don’t want that recorded, you can ask them to do this in advance via the welcome screen. That way, when the study begins, they’re already logged in.
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Will live site testing affect my live website or real data?
No, user testers interacting with a live site test cannot make any changes to your website or its data.

What permissions are needed to test competitor websites?
With Optimal’s live site testing, you don't need special approval or permissions to evaluate public competitors' experiences.

Access the Training

If you want to experience the full walkthrough, demo, and Q&A from the session, we encourage you to watch the full webinar! You’ll learn how to start running your own live site tests and uncover real user behavior, plus pick up tips and best practices straight from the training.

👉 Watch the full training webinar here.

59% of teams say that without research, their decisions would be based on assumptions. Yet only 16% of organizations have user research fully embedded in how they work.

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That gap explains why so much research never influences what gets built.

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Teams do the work – they run studies, gather insights, document findings. But when research tools are too complex for most people to use, when getting insights takes weeks instead of days, when findings arrive after decisions are already made, research becomes documentation, not direction.

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The problem isn't research quality. It's that most user research processes don't match how product teams actually make decisions. Research platforms are too complex, so only specialists can run studies. Analysis takes too long, so teams ship before insights are ready. Findings arrive as 40-slide decks, so they get filed away instead of acted on.

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The teams getting research to influence decisions aren't running more studies. They're running connected workflows that answer the right questions at the right time, with insights stakeholders can't ignore.

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Here are five workflows that make this happen.

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1. Understand what competitors get right (and wrong)

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Your team is redesigning checkout, and leadership wants competitive intelligence. But screenshot comparisons and assumptions won't cut it when you're trying to justify engineering time.

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Here's the workflow:

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Start with Live Site Testing to observe how real users navigate competitor experiences. Watch where they hesitate, what they click first, where they abandon the process entirely. You're not analyzing what competitors built, you're seeing how users actually respond to it.

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Follow up with Interviews to understand the why behind the behavior. Upload your live site test recordings and use AI analysis to surface patterns across participants. That random dropout? It's actually a theme: users don't trust the security badge placement because it looks like an ad.

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Validate your redesign with Prototype Testing before you commit to building it. Test your new flow against the competitor's current experience and measure the difference in task success, time on task, and user confidence.

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What stakeholders see: Video evidence of where competitors fail users, quantitative data proving your concept performs better, and AI-generated insights connecting behavior to business impact. 

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2. Ship features users will actually use

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Product wants to launch a new feature. You need to make sure it won't just join the graveyard of functionality nobody touches.

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Here's the workflow:

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Use Surveys to understand current user priorities and pain points. Deploy an intercept survey on your live site to catch people in context, not just those who respond to email campaigns. Find out what problems they're actually trying to solve today.

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Build it out in Prototype Testing to see whether users can find, understand, and successfully use the feature. Validate key interactions and task flows before engineering writes a line of code. Where do users expect to find this feature? Can they actually easily complete the task you're building it for? Do they move through a flow as intended?

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Conduct Interviews to explore the edge cases and mental models you didn't anticipate. Use AI Chat to query across all your interview data: "What concerns did users raise about data privacy?" Get quotes, highlight reels, and themes that answer questions you didn't think to ask upfront.

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What stakeholders see: Evidence that this feature solves a real user problem, proof that users can find it where you're planning to put it, and specific guidance on what could cause adoption to fail.

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3. Fix navigation without rebuilding blindly

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Your information architecture is a mess. Years of adding features means nobody can find anything anymore. But reorganizing based on what makes sense to internal teams is how you end up with labels or structures that don’t resonate with users.

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Here's the workflow:

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Run Card Sorting to understand how users naturally categorize your content. What your team calls "Account Settings," users call "My Profile." What seems logical internally could be completely foreign to the people who actually use your product.

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Validate your structure with Tree Testing before you commit to rebuilding. Test multiple organizational approaches and use the task comparison tool to see which structure helps users complete critical tasks. Can they find what they need, or are you just rearranging deck chairs?

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Use Live Site Testing to see how people struggle with your current navigation in practice. Watch them hunt through menus, resort to search as a last-ditch effort, give up entirely. Then test your new structure the same way to measure actual improvement, not just theoretical better-ness.

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Upload recordings to Interviews for AI-powered analysis. Get clear summaries of common pain points, highlight reels of critical issues, and stakeholder-friendly video clips that make the case for change.

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What stakeholders see: Your redesign isn't based on internal preferences. It's based on how users think about your content, validated with task completion data, and backed by video proof of improvement.

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4. Boost conversions with evidence from users

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Leadership wants to know why conversion rates are stuck. You have theories about friction points, but theories don't justify engineering sprints.

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Here's the workflow:

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Deploy Surveys with intercept snippets on your live site. Ask people in the moment what they're trying to accomplish and what's stopping them. Surface objections and confusion you wouldn't discover through internal speculation. This solves two problems: you get feedback from actual users in context, and you avoid the participant recruitment challenge that 41% of researchers cite as a top obstacle.

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Run Live Site Testing to watch users actually attempt to convert. See where they hesitate before clicking "Continue," what makes them abandon their cart, which form fields cause them to pause and reconsider.

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Run First-Click Testing to identify navigation barriers to conversion. Test whether users can find the path that leads to conversion - like locating your pricing page, finding the upgrade plan button, identifying the right product category, or comparing different products to each other. Users who make a correct first click are 3X more likely to complete their task successfully, so this quickly reveals when poor navigation or unclear signage is killing conversion.

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Test proposed fixes with Prototype Testing before rebuilding anything. If you think the problem is an unclear value proposition, test clearer messaging. If you think it's a trust issue, test different social proof placements. Compare task success rates between your current flow and proposed changes.

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Use Interviews to understand the emotional and practical barriers underneath the behavior. AI analysis helps you spot patterns: it's not that your pricing is too high, it's that users don't understand what they're getting for the price, or why your option is better than competitors.

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What stakeholders see: Exactly where users drop off, why they drop off, and measured improvement from your proposed solution, all before engineering builds anything.

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5. Make research fast enough to actually matter

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Your product team ships every two weeks. Research that takes three weeks to complete is documentation of what you already built, not input into decisions.

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Here's the workflow:

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Build research into your sprint cycles by eliminating the manual overhead. Use Surveys for quick pulse checks on assumptions. Deploy a tree test in hours to validate a navigation change before sprint planning, not after the feature ships. Invite your own participants, use Optimal's on-demand panel for fast turnaround, or leverage managed recruitment when you need specific, hard-to-reach audiences.

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Leverage AI to handle the time-consuming work in Interviews. Upload recordings and get automated insights, themes, and highlight reels while you're planning your next study. What used to take days of manual review now takes minutes of focused analysis. AI also automatically surfaces patterns in survey responses and pre/post-task feedback across your studies, so you're finding insights faster regardless of method.

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Test current and proposed experiences in parallel. Use Live Site Testing and Prototype Testing to baseline the problem with your current experience, while simultaneously testing your solution. Compare results side-by-side to show measurable improvement, not just directional feedback. Tree testing has built-in task comparison so you can directly measure navigation performance between your existing structure and proposed changes.

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Share results in tools your team actually uses. Generate highlight reels for stand-ups, pull specific quotes for Slack threads with AI Chat, export data for deeper stakeholder analysis. Research findings that fit into existing workflows get used. Research that requires everyone to change how they work gets ignored.

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What stakeholders see: Research isn't the thing slowing down product velocity. It's the thing making decisions faster and more confident. Teams do more research because research fits their workflow, not because they've been told they should.

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The pattern: What actually makes user research influential

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Most organizations struggle to embed user research into product development. Research happens in disconnected moments rather than integrated workflows, which is why it often feels like it's happening to teams rather than with them.

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Closing that gap requires two shifts: building a user research process that connects insights across the entire product cycle, and making research accessible to everyone who makes product decisions.

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That's the workflow advantage: card sorting reveals how people naturally categorize and label content, tree testing validates structure, surveys surface priorities, live site testing shows real behavior, prototype testing confirms solutions, interviews provide context, and AI analysis handles synthesis. Each method is designed for speed and simplicity, so product managers can validate assumptions, designers can test solutions, and researchers can scale their impact without becoming bottlenecks.

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The workflows we covered - reducing churn, validating roadmaps, boosting conversions, proving impact, and matching product velocity - all follow this same pattern: the right combination of UX research methods, deployed at the right moment, analyzed fast enough to matter, and accessible to the entire product team.

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Ready to see how these user research workflows work for your team? Explore Optimal's platform or talk to our team about your specific research challenges.

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Maze has built a strong reputation for rapid prototype testing and quick design validation. For product teams focused on speed and Figma integration, it offers an appealing workflow. But as research programs mature and teams need deeper insights to inform strategic decisions, many discover that Maze's limitations create friction. Platform reliability issues, restricted research depth, and a narrow focus on unmoderated testing leave gaps that growing teams can't afford.

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If you're exploring Maze alternatives that deliver both speed and substance, here are seven platforms worth evaluating.

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Why Look for a Maze Alternative?

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Teams typically start searching for Maze alternatives when they encounter these constraints:

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• ‍Limited research depth: Maze does well at at surface-level feedback on prototypes but struggles with the qualitative depth needed for strategic product decisions. Teams often supplement Maze with additional tools for interviews, surveys, or advanced analysis.
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• ‍Platform stability concerns: Users report inconsistent reliability, particularly with complex prototypes and enterprise-scale studies. When research drives major business decisions, platform dependability becomes critical.
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• ‍Narrow testing scope: While Maze handles prototype validation well, it lacks sophistication in other research methods and the ability to do deep analytics. These are all things that comprehensive product development requires. 
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• Enterprise feature gaps: Organizations with compliance requirements, global research needs, or complex team structures find Maze's enterprise offerings lacking. SSO, role-based access and dedicated support come only at the highest tiers, if at all.
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• Surface-level analysis and reporting capabilities: Once an organization reaches a certain stage, they start needing in-depth analysis and results visualizations. Maze currently only provides basic metrics and surface-level analysis without the depth required for strategic decision-making or comprehensive user insight.

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What to Consider When Choosing a Maze Alternative

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Before committing to a new platform, evaluate these key factors:

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• ‍Range of research methods: Does the platform support your full research lifecycle? Look for tools that handle prototype testing, information architecture validation, live site testing, surveys, and qualitative analysis.
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• Analysis and insight generation: Surface-level metrics tell only part of the story. Platforms with AI-powered analysis, automated reporting, and sophisticated visualizations transform raw data into actionable business intelligence.
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• Participant recruitment capabilities: Consider both panel size and quality. Global reach, precise targeting, fraud prevention, and verification processes determine whether your research reflects real user perspectives.
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• Enterprise readiness: For organizations with compliance requirements, evaluate security certifications (SOC 2, ISO), SSO support, role-based permissions, and dedicated account management.
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• Platform reliability and support: Research drives product strategy. Choose platforms with proven stability, comprehensive documentation, and responsive support that ensures your research operations run smoothly.
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• Scalability and team collaboration: As research programs grow, platforms should accommodate multiple concurrent studies, cross-functional collaboration, and shared workspaces without performance degradation.

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Top Alternatives to Maze

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1. Optimal: Comprehensive User Insights Platform That Scales

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All-in-one research platform from discovery through delivery

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Optimal delivers end-to-end research capabilities that teams commonly piece together from multiple tools. Optimal supports the complete research lifecycle: participant recruitment, prototype testing, live site testing, card sorting, tree testing, surveys, and AI-powered interview analysis.

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Where Optimal outperforms Maze:

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Broader research methods: Optimal provides specialized tools and in-depth analysis and visualizations that Maze simply doesn't offer. Card sorting and tree testing validate information architecture before you build. Live site testing lets you evaluate actual websites and applications without code, enabling continuous optimization post-launch. This breadth means teams can conduct comprehensive research without switching platforms or compromising study quality.

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Deeper qualitative insights: Optimal's new Interviews tool revolutionizes how teams extract value from user research. Upload interview videos and AI automatically surfaces key themes, generates smart highlight reels with timestamped evidence, and produces actionable insights in hours instead of weeks. Every insight comes with supporting video evidence, making stakeholder buy-in effortless.

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AI-powered analysis: While Maze provides basic metrics and surface-level reporting, Optimal delivers sophisticated AI analysis that automatically generates insights, identifies patterns, and creates export-ready reports. This transforms research from data collection into strategic intelligence.

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Global participant recruitment: Access to over 100 million verified participants across 150+ countries enables sophisticated targeting for any demographic or market. Optimal's fraud prevention and quality assurance processes ensure participant authenticity, something teams consistently report as problematic with Maze's smaller panel.

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Enterprise-grade reliability: Optimal serves Fortune 500 companies including Netflix, LEGO, and Apple with SOC 2 compliance, SSO, role-based permissions, and dedicated enterprise support. The platform was built for scale, not retrofitted for it.

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Best for: UX researchers, design and product teams, and enterprise organizations requiring comprehensive research capabilities, deeper insights, and proven enterprise reliability.

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2. UserTesting: Enterprise Video Feedback at Scale

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Established platform for moderated and unmoderated usability testing

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UserTesting remains one of the most recognized platforms for gathering video feedback from participants. It excels at capturing user reactions and verbal feedback during task completion.

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Strengths: Large participant pool with strong demographic filters, robust support for moderated sessions and live interviews, integrations with Figma and Miro.

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Limitations: Significantly higher cost at enterprise scale, less flexible for navigation testing or survey-driven research compared to platforms like Optimal, increasingly complex UI following multiple acquisitions (UserZoom, Validately) creates usability issues.

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Best for: Large enterprises prioritizing high-volume video feedback and willing to invest in premium pricing for moderated session capabilities.

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3. Lookback: Deep Qualitative Discovery

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Live moderated sessions with narrative insights

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Lookback specializes in live user interviews and moderated testing sessions, emphasizing rich qualitative feedback over quantitative metrics.

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Strengths: Excellent for in-depth qualitative discovery, strong recording and note-taking features, good for teams prioritizing narrative insights over metrics.

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Limitations: Narrow focus on moderated research limits versatility, lacks quantitative testing methods, smaller participant pool requires external recruitment for most studies.

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Best for: Research teams conducting primarily qualitative discovery work and willing to manage recruitment separately.

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4. PlaybookUX: Bundled Recruitment and Testing

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Built-in participant panel for streamlined research

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PlaybookUX combines usability testing with integrated participant recruitment, appealing to teams wanting simplified procurement.

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Strengths: Bundled recruitment reduces vendor management, straightforward pricing model, decent for basic unmoderated studies.

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Limitations: Limited research method variety compared to comprehensive platforms, smaller panel size restricts targeting options, basic analysis capabilities require manual synthesis.

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Best for: Small teams needing recruitment and basic testing in one package without advanced research requirements.

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5. Lyssna: Rapid UI Pattern Validation

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Quick-turn preference testing and first-click studies

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Lyssna (formerly UsabilityHub) focuses on fast, lightweight tests for design validation; preference tests, first-click tests, and five-second tests.

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Strengths: Fast turnaround for simple validation, intuitive interface, affordable entry point for small teams.

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Limitations: Limited scope beyond basic design feedback, small participant panel with quality control issues, lacks sophisticated analysis or enterprise features.

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Best for: Designers running lightweight validation tests on UI patterns and early-stage concepts.

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6. Hotjar: Behavioral Analytics and Heatmaps

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Quantitative behavior tracking with qualitative context

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Hotjar specializes in on-site behavior analytics; heatmaps, session recordings, and feedback widgets that reveal how users interact with live websites.

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Strengths: Valuable behavioral data from actual site visitors, seamless integration with existing websites, combines quantitative patterns with qualitative feedback.

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Limitations: Focuses on post-launch observation rather than pre-launch validation, doesn't support prototype testing or information architecture validation, requires separate tools for recruitment-based research.

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Best for: Teams optimizing live websites and wanting to understand actual user behavior patterns post-launch.

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7. UserZoom: Enterprise Research at Global Scale

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Comprehensive platform for large research organizations

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UserZoom (now part of UserTesting) targets enterprise research programs requiring governance, global reach, and sophisticated study design.

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Strengths: Extensive research methods and study templates, strong enterprise governance features, supports complex global research operations.

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Limitations: Significantly higher cost than Maze or comparable platforms, complex interface with steep learning curve, integration with UserTesting creates platform uncertainty.

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Best for: Global research teams at large enterprises with complex governance requirements and substantial research budgets.

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Final Thoughts: Choosing the Right Maze Alternative

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Maze serves a specific need: rapid prototype validation for design-focused teams. But as research programs mature and insights drive strategic decisions, teams need platforms that deliver depth alongside speed.

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Optimal stands out by combining Maze's prototype testing capabilities with the comprehensive research methods, AI-powered analysis, and enterprise reliability that growing teams require. Whether you're validating information architecture through card sorting, testing live websites without code, or extracting insights from interview videos, Optimal provides the depth and breadth that transforms research from validation into strategic advantage.

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If you're evaluating Maze alternatives, consider what your research program needs six months from now, not just today. The right platform scales with your team, deepens your insights, and becomes more valuable as your research practice matures.

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Try Optimal for free to experience how comprehensive research capabilities transform user insights from validation into strategic intelligence.

As we close out 2025, we’ve been reflecting on what we’ve achieved together and where we’re headed next. 

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We’re proud to have supported customers in 45+ countries and nearly 400 cities, powering insights for teams at LEGO, Google, Apple, Nike, and many more. Over the last 12 months alone, more than 1.2 million participants completed studies on Optimal, shaping decisions that lead to better, more intuitive products and experiences around the world.

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We also strengthened and brought our community together. We attended 10 industry events, launched 4 leadership circle breakfasts for senior leaders in UX, product and design, and hosted 19 webinars, creating spaces to exchange ideas, share best practices, and explore the future of our changing landscape across topics like AI, automation, and accessibility.

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But the real story isn't in the numbers. It's in what we built to meet this moment.

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Entering a New Era for Insights

This year, we introduced a completely refreshed Optimal experience - a new Home and Studies interface designed to remove friction and help teams move faster. Clean, calm, intentional. Built not just to look modern, but to feel effortless.

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Optimal: From Discovery to Delivery

2025 was a milestone year: it marked the most significant expansion of the Optimal platform we think we’ve ever accomplished, with an introduction of automation powered by AI.

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Interviews

A transformative way to accelerate insights from interviews and videos through automated highlight reels, instant transcripts, summaries, and AI chat, eliminating days and weeks of manual work.

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Prototype Testing

Test designs early and often. Capture the nuance of user interactions with screen, audio, and/or video recording.

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Live Site Testing

Watch real people interact with any website and web app to see what’s actually happening. Your direct window into reality.

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We also continued enhancing our core toolkit, adding display logic to surveys and launching a new study creation flow to help teams move quickly and confidently across the platform.

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AI: Automate the Busywork, Focus on the Breakthroughs

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The next era of research isn't about replacing humans with AI. It’s about making room for the work humans do best. In 2025, we were intentional with where we added AI to Optimal, guided by our core principle to automate your research. Our ever-growing AI toolkit helps you:

• accelerate your analysis and uncover key insights with automated insights
• transcribe interviews 
• refine study questions for clarity
• dig deeper with AI chat 
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AI handles the tedious parts so you can focus on the meaningful ones.

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Looking Ahead: Raising the Bar for UX Research & Insights

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2025 built out our foundation. The next will raise the bar.

We're entering a phase where research and insights becomes:
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• faster to run
• easier to communicate
• available to everyone on your team
• and infinitely more powerful with AI woven throughout your workflow

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To everyone who ran a study, shared feedback, or pushed us to do better: thank you. You make Optimal what it is. Here’s to an even faster, clearer, more impactful year of insights.


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Onwards and upwards.

We’ve talked a lot this year about the ways that research platforms and other product and design tools have evolved to meet the needs of modern teams.

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This includes: 

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• Reimagining how user interviews should work for 2026 

• How vibe coding tools like Lovable are changing the way design teams work 

• How AI is automating and speeding up product, design and research workflows 

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As we wrap up 2025 and look more broadly at the ideal research tech stack going into 2026, we think the characteristics that teams should be looking for are: an integrated ecosystem of AI-powered platforms, automated synthesis engines, real-time collaboration spaces, and intelligent insight repositories that work together seamlessly. The ideal research toolkit In 2026, will include tools that help you think, synthesize, and scale insight across your entire organization.

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Most research teams today suffer from tool proliferation, 12 different platforms that don't talk to each other, forcing researchers to become data archaeologists, hunting across systems to piece together user understanding.

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The typical team uses:

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• One platform for user interviews

• Another for usability testing

• A third for surveys

• A fourth for card sorting

• A fifth for participant recruitment

• Plus separate tools for transcription, analysis, storage, and sharing

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Each tool solves one problem perfectly while creating integration nightmares. Insights get trapped in silos. Context gets lost in translation. Teams waste hours moving data between systems instead of generating understanding.

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The research teams winning in 2026 aren't using the most tools, they're using unified platforms that support product, design and research teams across the entire product lifecycle. If this isn’t an option, then at a minimum teams need unified tools that: 

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• Reduces friction between research question and actionable insight

• Scales impact beyond individual researcher capacity

• Connects insights across methods, teams, and time

• Drives decisions by bringing research into product development workflows

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Your 2026 research stack shouldn't just help you do research, it should help you think better, synthesize faster, and impact more decisions. The future belongs to research teams that treat their toolkit as an integrated insight-generation system, not a collection of separate tools. Because in a world where every team needs user understanding, the research teams with the best systems will have the biggest impact.

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Ready to consolidate your research stack? Try Optimal free for 7 days.

We believe that the role of UX researchers is at an inflection point. Researchers are evolving from being conductors of studies and authors of reports to strategic product partners, and organizational change agents.

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At the beginning of 2025 we heard a lot of fear that UX research and traditional research roles were disappearing because of democratization but we think what we're actually seeing is the evolution of those roles into something more powerful and more essential than ever before.

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Traditional research operated on a service model: Teams submit requests, researchers conduct studies, insights get delivered, rinse and repeat. The researcher was the bottleneck through which all user understanding flowed. This model worked when product development moved slowly, when research questions were infrequent, and when user insights could be batched into quarterly releases.

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Unfortunately this model fails in new, fast-paced product development where decisions happen daily, features ship continuously, and competitive advantage depends on rapid learning. The math just ain’t mathing: one researcher can't support 20 product team members making hundreds of decisions per quarter. Something has to change.

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The Shift From Doing to Empowering

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The best and most progressive research teams are transforming their model to one where researchers play a role more focused on empowering and enabling the teams they support to do more of their own research. 

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In this new model: 

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• Researchers enable teams to conduct studies

• Teams generate insights continuously

• Knowledge spreads throughout organization

• Research scales exponentially with systems

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This isn't about researchers doing less, it's about achieving more through strategic democratization.

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What does empowerment really look like? 

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One of the keys to empowerment is creating a self-service model for research, where anyone can run studies with some boundaries and infrastructure to help them do it successfully.

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In this model, researchers can:

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• Creating research templates teams can execute independently

• Choosing a research platform that offers easy recruitment options teams can self-serve (Optimal does that - read more here). 

• Implementing easy tools that make basic research accessible regardless of users experience with running research 

• Educating teams on which types of research and methods are best for which types of questions 

• Creating some quality standards and review processes that make sense depending on the type of research being run and by which team 

• Running workshops on research fundamentals and  insight generation

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If that enablement is set up effectively it allows researchers to focus on more strategic research initiatives and on: handling complex studies that require deep expertise connecting insights across products and teams, identifying organizational knowledge gaps and answering strategic questions that guide product direction. 

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Does this new model require different skills? Yes, and if you focus on building these skills now you’ll be well placed to be the strategic research partner your product and design teams need in 2026.

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The researcher of 2026 needs different capabilities:

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• Systems Thinking: Understanding how to scale research impact through infrastructure and processes, not just individual studies.

• Teaching & Coaching: Ability to transfer research skills to non-researchers effectively.

• Strategic Influence: Connecting user insights to business strategy and organizational priorities.

• Technology Fluency: Leveraging AI, automation, and research platforms to multiply impact.

• Change Management: Driving cultural transformation toward research-informed decision-making.

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When it comes to research transformation like this, researchers know it needs to happen, but are also their own worst enemies. Some of the biggest pushback we hear is from researchers who are resistant to these changes because of fear it will reduce their value as well as a desire to maintain control over the quality and rigor around research. We’ve talked about how we think this transformation actually increases the value of researchers, but when it comes to concerns around quality control, let’s talk through some of the biggest concerns we hear below: 

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"They'll do it wrong": Yes, some team-conducted research will be imperfect. But imperfect research done today beats perfect research done never. Create quality frameworks and review processes rather than preventing action.

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"I'll be less valuable": Actually, researchers become more valuable by enabling 50 decisions instead of informing 5. Strategic insight work is more impactful than routine execution.

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"We'll lose control": Control is an illusion when most decisions happen without research anyway. Better to provide frameworks for good research than prevent any research from happening.

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The future of research is here, and it’s a world where researchers are more strategic and valuable to businesses than ever before. For most businesses the shift toward research democratization is happening whether researchers want it to or not, and the best path forward is for researchers to embrace the change, and get ahead of it by intentionally shifting their role toward a more strategic research partnership, enabling the broader business to do more, better research. We can help with that.

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