September 21, 2026
5 minutes

Rethinking How We Design and Innovate in an Agentic AI World: Key Takeaways from DDX

Was human-centered design all wrong?

No, but it fails to capture the whole picture.

Don Norman introduced human-centered design (HCD) as we know it to be in the 1980s and almost 50 years later at DDX San Diego 2026, he challenged the philosophy.

Human-centered design gave the industry an important shift in perspective, paying close attention to the people who would actually live with what we create, but there’s tension. Design can create tremendous value for people while also creating unintended consequences for cultures, communities, organizations, and the world around them.

If we optimize for the person in front of us, what happens to everyone and everything outside the frame?

And as technology becomes increasingly agentic, that broader view becomes even more relevant. We're no longer designing only things that people interact with. We're increasingly designing systems that can act, make decisions, and influence what happens next.

Summary

    • Human-centered design does not capture the whole picture
    • New philosophies emerge including life-centered design (widening the lens to the larger impact of design), intelligence-centered and human-agent centered design (AI and humans as co-participants in the process), and agent-centered design (AI agents as the primary participants)
    • Four shifts matter most in the age of agentic AI: design for the system, not just the user; turn frameworks into prototypes; study behavior over stated needs; and translate insights and decisions into the language of your audience
    • The future of design is about what you do with what you know
  • Emerging design philosophies

    Life-Centered Design (LCD) expands the focus from the individual user to the larger living system that the product or service exists within. It seeks to align design decisions with global goals such as the United Nation’s Sustainable Development Goals.

    Intelligence-Centered Design (ICD) and Human-Agent Centered Design (H-ACD) are extensions of HCD where AI plays an active role in the design process alongside human intelligence, each learning from, making decisions, and mediating the process.

    Agent-Centered Design (ACD) puts AI agents at the center as the primary participant in the process, agents building for agents.

    Key elements of design and innovation in an agentic world

    Design for the system around the human

    Good design starts with deep understanding of the people who use a product, service, or system. Their behavior is shaped by their environment, organizational structures, constraints, and the decisions made around them. Those factors are part of what we are designing for.

    Agentic AI makes this deep understanding of context even more important. When AI can act, make decisions, and influence what happens next, it becomes part of the system rather than simply a tool within it. Design therefore has to account for the relationship between humans, each other, and agents including how they work together, what artifacts are created, what each party is responsible for, what decisions AI can make, and how the human is in the loop.

    This means asking questions like:

    • What happens outside the immediate experience?
    • Who is represented in my research repository. What's their environment? Who is missing?
    • How might the system change over time?

    Humans exist within much larger systems.

    Do more "design doing"

    Design has no shortage of frameworks, methodologies, books, and models. They give us useful ways to think, but change happens when teams turn what they've learned into something tangible. It’s about what you create from the knowledge.

    Ask yourself: What's the smallest thing I could do to build or test this idea this week?

    A prototype can reveal what a meeting can't. A real interaction can reveal contradictions to what people say and actually do and why. An experiment can tell a team more than another round of debate or back-and-forth.

    Good design thinking should become good design doing.

    Start with people and context, not the product

    Understanding people is fundamental to good research and design. People's needs, expectations, behaviors, and priorities are shaped by their environment. What works in one market, organization, or culture can't automatically be treated as a universal truth for all. That's why ethnography and other forms of contextual research remain so valuable.

    Before asking what we should build, we need to understand the world people are navigating.

    Questions worth exploring:

    • Who are we really designing for?
    • What assumptions are we bringing from our own environment?
    • What happens when this design enters a different context?
    • Where do people's stated choices differ from their actual behavior?

    One practical takeaway is to spend more time understanding what people actually do, not just what they say. Diary studies, observation, prototype testing, and live site testing can all help surface what people may not be able to articulate and the insights you’re looking for. Probe what was behind the decision they made, not by asking "why", but what drove it.

    Earn influence by translating value into the language of the business

    Research and design often sits in the middle of an organization. Leadership sets direction, research gathers insight, design shapes the experience, then implementation follows. When each function speaks a different language, intent can get lost along the way.

    Designers and researchers are inherently connectors. We bring together different perspectives, make sense of complexity, and help people see how decisions affect one another. But we aren't often in the room when the decisions themselves are being made. Policy, process, business models, and organizational structures are all forms of design, yet designers are often brought in after those choices have already been made. There is an opportunity and a need for design thinking and "design doing" to move further upstream.

    That starts with speaking the language of the people making those decisions: finances and margins.

    Guiding questions:

    • What does this insight or design mean for productivity, adoption, risk, growth, for the people working inside the system and for the people affected by it?
    • What would this save, generate, or protect, in dollars, time, or risk?
    • Can we connect the human story to a number?
    • How can I socialize this upwards in my organization?

    It's about recognizing that business decisions are design decisions, and using our ability to connect perspectives to have a voice in shaping them.

    Designing for what comes next

    The next era of design is expanding what we consider when we design.

    As systems become more autonomous, designers have an opportunity to shape not only experiences, but the policies, services, and the systems around them.

    The future of design is less about finding the right framework and more about what we choose to do with what we know.

    Our monthly newsletter, User Tested is for product, design, and research teams who want to make UX research a strategic accelerator.  Subscribe to User Tested.

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    Header graphic for the article 'The AI Automation Breakthrough: Key Insights from Our Latest Community...'
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    The AI Automation Breakthrough: Key Insights from Our Latest Community Event

    Last night, Optimal brought together an incredible community of product leaders and innovators for "The Automation Breakthrough: Workflows for the AI Era" at Q-Branch in Austin, Texas. This two-hour in-person event featured expert perspectives on how AI and automation are transforming the way we work, create, and lead.

    The event featured a lightning Talk on "Designing for Interfaces" featured Cindy Brummer, Founder of Standard Beagle Studio, followed by a dynamic panel discussion titled "The Automation Breakthrough" with industry leaders including Joe Meersman (Managing Partner, Gyroscope AI), Carmen Broomes (Head of UX, Handshake), Kasey Randall (Product Design Lead, Posh AI), and Prateek Khare (Head of Product, Amazon). We also had a fireside chat with our CEO, Alex Burke and Stu Smith, Head of Design at Atlassian. 

    Here are the key themes and insights that emerged from these conversations:

    Trust & Transparency: The Foundation of AI Adoption

    Cindy emphasized that trust and transparency aren't just nice-to-haves in the AI era, they're essential. As AI tools become more integrated into our workflows, building systems that users can understand and rely on becomes paramount. This theme set the tone for the entire event, reminding us that technological advancement must go hand-in-hand with ethical considerations.

    Automation Liberates Us from Grunt Work

    One of the most resonant themes was how AI fundamentally changes what we spend our time on. As Carmen noted, AI reduces the grunt work and tasks we don't want to do, freeing us to focus on what matters most. This isn't about replacing human workers, it's about eliminating the tedious, repetitive tasks that drain our energy and creativity.

    Enabling Creativity and Higher-Quality Decision-Making

    When automation handles the mundane, something remarkable happens: we gain space for deeper thinking and creativity. The panelists shared powerful examples of this transformation:

    Carmen described how AI and workflows help teams get to insights and execution on a much faster scale, rather than drowning in comments and documentation. Prateek encouraged the audience to use automation to get creative about their work, while Kasey shared how AI and automation have helped him develop different approaches to coaching, mentorship, and problem-solving, ultimately helping him grow as a leader.

    The decision-making benefits were particularly striking. Prateek explained how AI and automation have helped him be more thoughtful about decisions and make higher-quality choices, while Kasey echoed that these tools have helped him be more creative and deliberate in his approach.

    Democratizing Product Development

    Perhaps the most exciting shift discussed was how AI is leveling the playing field across organizations. Carmen emphasized the importance of anyone, regardless of their role, being able to get close to their customers. This democratization means that everyone can get involved in UX, think through user needs, and consider the best experience.

    The panel explored how roles are blurring in productive ways. Kasey noted that "we're all becoming product builders" and that product managers are becoming more central to conversations. Prateek predicted that teams are going to get smaller and achieve more with less as these tools become more accessible.

    Automation also plays a crucial role in iteration, helping teams incorporate customer feedback more effectively, according to Prateek.

    Practical Advice for Navigating the AI Era

    The panelists didn't just share lofty visions, they offered concrete guidance for professionals navigating this transformation:

    Stay perpetually curious. Prateek warned that no acquired knowledge will stay with you for long, so you need to be ready to learn anything at any time.

    Embrace experimentation. "Allow your process to misbehave," Prateek advised, encouraging attendees to break from rigid workflows and explore new approaches.

    Overcome fear. Carmen urged the audience not to be afraid of bringing in new tools or worrying that AI will take their jobs. The technology is here to augment, not replace.

    Just start. Kasey's advice was refreshingly simple: "Just start and do it again." Whether you're experimenting with AI tools or trying "vibe coding," the key is to begin and iterate.

    The energy in the room at Q-Branch reflected a community that's not just adapting to change but actively shaping it. The automation breakthrough isn't just about new tools, it's about reimagining how we work, who gets to participate in product development, and what becomes possible when we free ourselves from repetitive tasks.

    As we continue to navigate the AI era, events like this remind us that the most valuable insights come from bringing diverse perspectives together. The conversation doesn't end here, it's just beginning.

    Interested in joining future Optimal community events? Stay tuned for upcoming gatherings where we'll continue exploring the intersection of design, product, and emerging technologies.

    Header graphic for the article 'Top User Research Platforms 2025'
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    Top User Research Platforms 2025

    User research software isn't what it used to be. The days of insights being locked away in specialist UX research teams are fading fast, replaced by a world where product managers, designers, and even marketers are running their own usability testing, prototype validation, and user interviews. The best UX research platforms powering this shift have evolved from complex enterprise software into tools that genuinely enable teams to test with users, analyze results, and share insights faster.

    This isn't just about better software, it's about a fundamental transformation in how organizations make decisions. Let's explore the top user research tools in 2025, what makes each one worth considering, and how they're changing the research landscape.


    What Makes a UX Research Platform All-in-One?


    The shift toward all-in-one UX research platforms reflects a deeper need: teams want to move from idea to insight without juggling multiple tools, logins, or data silos. A truly comprehensive research platform combines several key capabilities within a unified workflow.

    The best all-in-one platforms integrate study design, participant recruitment, multiple research methods (from usability testing to surveys to interviews to navigation testing to prototype testing), AI-powered analysis, and insight management in one cohesive experience. This isn't just about feature breadth, it's about eliminating the friction that prevents research from influencing decisions. When your entire research workflow lives in one platform, insights move faster from discovery to action.

    What separates genuine all-in-one solutions from feature-heavy tools is thoughtful integration. The best platforms ensure that data flows seamlessly between methods, participants can be recruited consistently across study types, and insights build upon each other rather than existing in isolation. This integrated approach enables both quick validation studies and comprehensive strategic research within the same environment.

    1. Optimal: Best End-to-End UX Research Platform


    Optimal has carved out a unique position in the UX research landscape: it’s powerful enough for enterprise teams at Netflix, HSBC, Lego, and Toyota, yet intuitive enough that anyone, product managers, designers, even marketers, can confidently run usability studies. That balance between depth and accessibility is hard to achieve, and it's where Optimal shines.

    Unlike fragmented tool stacks, Optimal is a complete User Insights Platform that supports the full research workflow. It covers everything from study design and participant recruitment to usability testing, prototype validation, AI-assisted interviews, and a research repository. You don't need multiple logins or wonder where your data lives, it's all in one place.

    Two recent features push the platform even further:

    • Live Site Testing: Run usability studies on your actual live product, capturing real user behavior in production environments.

    • Interviews: AI-assisted analysis dramatically cuts down time-to-insight from moderated sessions, without losing the nuance that makes qualitative research valuable.



    One of Optimal's biggest advantages is its pricing model. There are no per-seat fees, no participant caps, and no limits on the number of users. Pricing is usage-based, so anyone on your team can run a study without needing a separate license or blowing your budget. It's a model built to support research at scale, not gate it behind permissioning.

    Reviews on G2 reflect this balance between power and ease. Users consistently highlight Optimal's intuitive interface, responsive customer support, and fast turnaround from study to insight. Many reviewers also call out its AI-powered features, which help teams synthesize findings and communicate insights more effectively. These reviews reinforce Optimal's position as an all-in-one platform that supports research from everyday usability checks to strategic deep dives.

    The bottom line? Optimal isn't just a suite of user research tools. It's a system that enables anyone in your organization to participate in user-centered decision-making, while giving researchers the advanced features they need to go deeper.

    2. UserTesting: Remote Usability Testing


    UserTesting built its reputation on one thing: remote usability testing with real-time video feedback. Watch people interact with your product, hear them think aloud, see where they get confused. It's immediate and visceral in a way that heat maps and analytics can't match.

    The platform excels at both moderated and unmoderated usability testing, with strong user panel access that enables quick turnaround. Large teams particularly appreciate how fast they can gather sentiment data across UX research studies, marketing campaigns, and product launches. If you need authentic user reactions captured on video, UserTesting delivers consistently.

    That said, reviews on G2 and Capterra note that while video feedback is excellent, teams often need to supplement UserTesting with additional tools for deeper analysis and insight management. The platform's strength is capturing reactions, though some users mention the analysis capabilities and data export features could be more robust for teams running comprehensive research programs.

    A significant consideration: UserTesting operates on a high-cost model with per-user annual fees plus additional session-based charges. This pricing structure can create unpredictable costs that escalate as your research volume grows, teams often report budget surprises when conducting longer studies or more frequent research. For organizations scaling their research practice, transparent and predictable pricing becomes increasingly important.

    3. Maze: Rapid Prototype Testing


    Maze understands that speed matters. Design teams working in agile environments don't have weeks to wait for findings, they need answers now. The platform leans into this reality with rapid prototype testing and continuous discovery research, making it particularly appealing to individual designers and small product teams.

    Its Figma integration is convenient for quick prototype tests. However, the platform's focus on speed involves trade-offs in flexibility as users note rigid question structures and limited test customization options compared to more comprehensive platforms. For straightforward usability tests, this works fine. For complex research requiring custom flows or advanced interactions, the constraints become more apparent.

    User feedback suggests Maze excels at directional insights and quick design validation. However, researchers looking for deep qualitative analysis or longitudinal studies may find the platform limited. As one G2 reviewer noted, "perfect for quick design validation, less so for strategic research." The reporting tends toward surface-level metrics rather than the layered, strategic insights enterprise teams often need for major product decisions.

    For teams scaling their research practice, some considerations emerge. Lower-tier plans limit the number of studies you can run per month, and full access to card sorting, tree testing, and advanced prototype testing requires higher-tier plans. For teams running continuous research or multiple studies weekly, these study caps and feature gates can become restrictive. Users also report prototype stability issues, particularly on mobile devices and with complex design systems, which can disrupt testing sessions. Originally built for individual designers, Maze works well for smaller teams but may lack the enterprise features, security protocols, and dedicated support that large organizations require for comprehensive research programs.

    4. Dovetail: Research Centralization Hub

    Dovetail has positioned itself as the research repository and analysis platform that helps teams make sense of their growing body of insights. Rather than conducting tests directly, Dovetail shines as a centralization hub where research from various sources can be tagged, analyzed, and shared across the organization. Its collaboration features ensure that insights don't get buried in individual files but become organizational knowledge.

    Many teams use Dovetail alongside testing platforms like Optimal, creating a powerful combination where studies are conducted in dedicated research tools and then synthesized in Dovetail's collaborative environment. For organizations struggling with insight fragmentation or research accessibility, Dovetail offers a compelling solution to ensure research actually influences decisions.

    6. Lookback: Moderated User Interviews


    Lookback specializes in moderated user interviews and remote testing, offering a clean, focused interface that stays out of the way of genuine human conversation. The platform is designed specifically for qualitative UX work, where the goal is deep understanding rather than statistical significance. Its streamlined approach to session recording and collaboration makes it easy for teams to conduct and share interview findings.

    For researchers who prioritize depth over breadth and want a tool that facilitates genuine conversation without overwhelming complexity, Lookback delivers a refined experience. It's particularly popular among UX researchers who spend significant time in one-on-one sessions and value tools that respect the craft of qualitative inquiry.

    7. Lyssna: Quick and lite design feedback


    Lyssna (formerly UsabilityHub) positions itself as a straightforward, budget-friendly option for teams needing quick feedback on designs. The platform emphasizes simplicity and fast turnaround, making it accessible for smaller teams or those just starting their research practice.

    The interface is deliberately simple, which reduces the learning curve for new users. For basic preference tests, first-click tests, and simple prototype validation, Lyssna's streamlined approach gets you answers quickly without overwhelming complexity.

    However, this simplicity involves significant trade-offs. The platform operates primarily as a self-service testing tool rather than a comprehensive research platform. Teams report that Lyssna lacks AI-powered analysis, you're working with raw data and manual interpretation rather than automated insight generation. The participant panel is notably smaller (around 530,000 participants) with limited geographic reach compared to enterprise platforms, and users mention quality control issues where participants don't consistently match requested criteria.

    For organizations scaling beyond basic validation, the limitations become more apparent. There's no managed recruitment service for complex targeting needs, no enterprise security certifications, and limited support infrastructure. The reporting stays at a basic metrics level without the layered analysis or strategic insights that inform major product decisions. Lyssna works well for simple, low-stakes testing on limited budgets, but teams with strategic research needs, global requirements, or quality-critical studies typically require more robust capabilities.

    Emerging Trends in User Research for 2025


    The UX and user research industry is shifting in important ways:

    Live environment usability testing is growing. Insights from real users on live sites are proving more reliable than artificial prototype studies. Optimal is leading this shift with dedicated Live Site Testing capabilities that capture authentic behavior where it matters most.

    AI-powered research tools are finally delivering on their promise, speeding up analysis while preserving depth. The best implementations, like Optimal's Interviews, handle time-consuming synthesis without losing the nuanced context that makes qualitative research valuable.

    Research democratization means UX research is no longer locked in specialist teams. Product managers, designers, and marketers are now empowered to run studies. This doesn't replace research expertise; it amplifies it by letting specialists focus on complex strategic questions while teams self-serve for straightforward validation.

    Inclusive, global recruitment is now non-negotiable. Platforms that support accessibility testing and global participant diversity are gaining serious traction. Understanding users across geographies, abilities, and contexts has moved from nice-to-have to essential for building products that truly serve everyone.

    How to Choose the Right Platform for Your Team


    Forget feature checklists. Instead, ask:

    Do you need qualitative vs. quantitative UX research? Some platforms excel at one, while others like Optimal provide robust capabilities for both within a single workflow.

    Will non-researchers be running studies (making ease of use critical)? If this is your goal, prioritize intuitive interfaces that don't require extensive training.

    Do you need global user panels, compliance features, or AI-powered analysis? Consider whether your industry requires specific certifications or if AI-assisted synthesis would meaningfully accelerate your workflow.

    How important is integration with Figma, Slack, Jira, or Notion? The best platform fits naturally into your existing stack, reducing friction and increasing adoption across teams.


    Evaluating All-in-One Research Capabilities

    When assessing comprehensive research platforms, look beyond the feature list to understand how well different capabilities work together. The best all-in-one solutions excel at data continuity, participants recruited for one study can seamlessly participate in follow-up research, and insights from usability tests can inform survey design or interview discussion guides.

    Consider your team's research maturity and growth trajectory. Platforms like Optimal that combine ease of use with advanced capabilities allow teams to start simple and scale sophisticated research methods as their needs evolve, all within the same environment. This approach prevents the costly platform migrations that often occur when teams outgrow point solutions.

    Pay particular attention to analysis and reporting integration. All-in-one platforms should synthesize findings across research methods, not just collect them. The ability to compare prototype testing results with interview insights, or track user sentiment across multiple touchpoints, transforms isolated data points into strategic intelligence.

    Most importantly, the best platform is the one your team will actually use. Trial multiple options, involve stakeholders from different disciplines, and evaluate not just features but how well each tool fits your team's natural workflow.

    The Bottom Line: Powering Better Decisions Through Research


    Each of these platforms brings strengths. But Optimal stands out for a rare combination: end-to-end research capabilities, AI-powered insights, and usability testing at scale in an all-in-one interface designed for all teams, not just specialists.

    With the additions of Live Site Testing capturing authentic user behavior in production environments, and Interviews delivering rapid qualitative synthesis, Optimal helps teams make faster, better product decisions. The platform removes the friction that typically prevents research from influencing decisions, whether you're running quick usability tests or comprehensive mixed-methods studies.

    The right UX research platform doesn't just collect data. It ensures user insights shape every product decision your team makes, building experiences that genuinely serve the people using them. That's the transformation happening at the moment; Research is becoming central to how we build, not an afterthought.

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    Frequently Asked Questions about Optimal’s New Mixed-Methods Usability Testing Tool

    We recently hosted a live webinar introducing Optimal's new Usability Testing tool, a powerful solution that brings multiple research methods together in a single study, helping you get better insights, faster.

    Looking for the highlights? We've rounded up answers to the most common questions from the session.

    What is Usability Testing?

    Optimal's new Usability Testing tool is a mixed-methods research tool that brings Prototype Testing, Live Site Testing, and Surveys into a single, end-to-end study workflow. Instead of treating each method as its own initiative, you can combine them inside a single study to allow participants to move naturally between tasks, experiences, and questions.

    With this tool, you can compare multiple prototypes side by side, benchmark a current live experience against a redesigned concept, evaluate a competitor's experience, and more. And researchers get everything analyzed in one place with AI-powered summaries, task results, video clips, and evidence-backed insights surfaced automatically.

    Is Usability Testing supported for mobile testing?

    Yes, participants can complete Usability Testing studies on mobile devices using their mobile browser or the Optimal Participant App. If screen recording is required, participants are prompted to download the Optimal Participant App, available for both iOS and Android. 

    Do you have to use multiple methods, or can you run just one?

    You can keep it simple and run a single survey, a standalone prototype test, or a live site session on its own. Or, mix methods or run multiple of the same method, such as multiple prototypes or live website tests in one study. The tool supports however your study needs to be shaped.

    Can you run bilingual studies?

    Usability Testing currently supports over 30 languages enabling what participants see and guiding how AI models interpret responses, generate summaries, identify themes, and surface insights. Today, studies are configured around a single language, so participants are expected to respond in the chosen language. That said, multilingual study support is something we're exploring for our roadmap.

    Are participants recruited once across all methods, or separately for each?

    Just once. From the participant's perspective, this looks and feels like a single study regardless of how many methods are included. They move through the experience naturally from start to finish.

    To what extent can sections and questions be randomized?

    Section-level randomization shuffles the order of any sections, while question-level randomization works within a specific section, shuffling the order of tasks and follow-up questions. Both are supported, giving researchers the flexibility to reduce order bias, particularly useful when comparing multiple experiences.

    Can you test multiple prototypes within the same study?

    Yes, with no limitations on the number of prototypes you can link to a single study so you can add multiple Figma prototype sections and connect a different prototype to each one.

    Can you reorder sections and questions in a study?

    Given that Usability Testing studies can grow complex, the ability to reorder things quickly was a priority. You can reorder individual tasks and questions within a section, and sections themselves by dragging them in the Build panel.

    How effectively can Usability Testing scale across a business?

    Scaling research isn't just about running more studies, it's about helping more people across the business access insights, understand them, and use them to make decisions. With Usability Testing, product managers, designers, and stakeholders can quickly understand what happened and why without having to see hours of recording through the automatically generated highlight reels, key quotes, and transcripts.

    Watch the full webinar

    If you want to experience the full walkthrough, demo, and Q&A, watch the recording to see Usability Testing in action and pick up tips and best practices straight from the session.

    👉 Watch the full webinar here.

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