UXLx 2026 Wrap Up: Masterclasses & Workshops

From 12 to 15 May, Lisbon welcomed hundreds of UX professionals from 29 countries for four inspiring days of learning, collaboration, and fresh perspectives. UXLx 2026 brought together a vibrant international community to explore the evolving world of UX through 8 hands-on workshops and 10 thought-provoking talks.

Whether you joined us in Lisbon, couldn't attend this year, or simply want to catch up on the highlights, this recap offers a glimpse into the ideas, conversations, and experiences that shaped UXLx 2026.

💡 Read through the article or click on a talk title to jump straight to the session that interests you most.

12 MAY - MASTERCLASSES

• Leading with Design: Amplifying Your Impact in Complex Organizations, Doug Powell
• Making UX Strategy Work: Design Ops in Practice, Zalihata Ahamada
• Design Leadership in the AI Era: Re-architecting Teams, Design Systems, and Quality for AI, Dave Brown

13 MAY - HALF-DAY WORKSHOPS

• Content Design and AI, Noz Urbina
• Knowledge Management with AI for Research Ops, Emily DiLeo
• The Psychology of AI Experiences: Designing AI People Trust, Adopt, and Value, Sarah Thompson 
• From Systems Thinking to Systems Doing: A Practical Approach for Experience Designers, Sylvie Abookire

14 MAY - HALF-DAY WORKSHOPS

• Impactful Service Design: Working With the Realities of Organisations, Ayesha Moarif
• Designing with Universal AI Assists, Christopher Noessel
• Designing AI Experiences for Human Agency: A Systems Framework for Human-AI Collaboration, Brandon Hardwood
• Lean Design Systems: Perception, Reality & Direction, Marianne Ashton-Booth

Leading with Design: Amplifying Your Impact in Complex Organizations by Doug Powell

Executive design leader, Doug Powell kicked things off at UXLx 2026 with a masterclass focused on how design leaders can measure, articulate, and expand the value of UX within large-scale corporate environments.

Measuring UX Maturity and Value

Doug introduced frameworks for assessing an organization's design maturity, such as the Design Business Council scale, which ranges from Step 1 (delivering tactical outputs) to Step 5 (full integration into the company). To move up this ladder, he emphasized measuring more than just final outcomes; leaders must track Input, Throughput, and Output:

• Input: Existing conditions (design maturity)
• Throughput: How the work is getting done
• Output: The quality of the work

The Business Impact of Design

Using data from a Forrester study on IBM’s Design Thinking practice, Doug highlighted the tangible economic benefits of design. Cross-functional teams using these practices were found to be 80% more aligned, reduced development time by 33%, and were 2x faster in moving from idea to market. Doug also provided specific KPIs for different sectors, including SaaS (e.g., retention and acquisition cost) and the public sector (e.g., accessibility and trust).

Design Leadership and "Superpowers"

A significant portion of the session addressed the evolution of the design leader. Powell identified two problematic patterns in senior design leadership:

1. Diminishing Superpowers: UX designers often stop using their unique skills, such as storytelling, visualization, critiquing, and prototyping, as they progress into leadership.
2. Impostor Syndrome: Senior design leaders frequently suffer from a sense of not belonging in high-level business roles.

The Future of Leadership

Doug critiqued traditional business schools for teaching outdated 20th-century management principles. He argued that modern leadership requires transparency, authenticity, collaboration, and empathy - qualities that are inherent to design. His final call to action was for designers to emphasize and elevate their unique "superpowers" as they rise into leadership roles, rather than trying to fit into traditional corporate molds.

Making UX Strategy Work: Design Ops in Practice by Zalihata Ahamada

Design Ops leader and service designer, Zalihata focused on operationalizing design leadership to move beyond theoretical strategy into tangible execution.

The Core Problem: Influence and Execution

Data indicates that 70% of design leaders in Europe work in organizations where design lacks a strong influence on key decisions. Zalihata argued that design legitimacy is not a permanent achievement but must be continuously demonstrated and reinforced. Many leaders find that the strategy they defend is not the one they actually execute, leading to lost time, money, and credibility.

Three Pillars for Design Leadership

To bridge the gap between strategy and execution, Zalihata proposed three critical questions for leaders:

1. Are we really listening to our peers? Leaders should use triangulation (combining literature, peer insights, and field research) to turn subjective opinions into undeniable demonstrations of value.
2. Is our strategic framing rigorous? Using a 360° framing approach across nine dimensions, including strategic intent, funding logic, and AI readiness, is not just preparation; it is part of the solution.
3. Is our operating architecture robust? A strategy must be robust enough to survive organizational changes, individual departures, and the integration of AI.

Operationalizing the UX Strategy

Zalihata outlined a four-step method to anchor UX within an organization:

• Understand: Read existing documentation and map maturity across value streams.
• Co-design & Co-build: Work with the stakeholders who will carry the strategy, ensuring the Head of UX has autonomy from day one.
• Equip: Provide tools that solve specific problems, such as S/M/L sizing per phase to prevent UX teams from being misused or underestimated by partners.
• Anchor: Integrate UX into existing rituals rather than adding layers of process. This includes using living documentation, light quarterly reviews, and visible indicators like component reuse rates.

Demonstrating Value and the Role of AI

The presentation shifts the value conversation from "Should we keep this?" to "How much do we lose if we stop investing in UX?". Highlighting the risks of disinvestment, such as a potential 4x loss compared to a 5x ROI for continued investment, helps align design with CFO-level priorities.

Regarding Generative AI, Zalihata noted that while 84% of design leaders use AI in workflows, only 13% have truly integrated it. She warned that AI executes architecture - if the design architecture is poor, AI will simply execute it poorly and faster. Robustness in the operating model is what allows a UX practice to survive these technological transformations.

Design Leadership in the AI Era: Re-architecting Teams, Design Systems, and Quality for AI by Dave Brown

Dave Brown, Design Director at Qualtrics, explored how design leaders must re-architect teams, design systems, and quality to effectively leverage AI without compromising excellence. The central thesis was that design leadership is evolving into systems design, where the leader’s role is to create environments where judgment scales, quality compounds, and learning accumulates.

1. Framing the Problem

The design profession has split into thirds: heavy AI adopters, those in the middle, and abstainers. Research shows that "vibe coders" (heavy AI users) report higher workflow satisfaction, indicating a widening gap in operating models.

2. The Diagnostic Lens

To adapt, leaders must analyze the three parts of the product lifecycle:

• Concept: What should be built?
• Build: How is it shipped well?
• Improve: How does it get better over time?

Leaders are encouraged to identify where quality breaks and where decisions are repeatedly made across these phases.

3. The Four-Layer Operating Model

Dave introduced a model to address systemic issues exposed by AI:

• People: Move judgment closer to production. Design should own shipped outcomes (code and behavior) rather than just intent or static artifacts.
• Systems: Design systems should function as organizational memory. Quality must be "built-in" through automated checks (linting, accessibility, regression testing) rather than relying on human review bottlenecks.
• Process: Traditional handoffs are failing. The future involves shared substrates and a "four-legged chair" collaboration model (Product, Engineering, Design, and Science). Workflows should evolve into continuous loops with AI agents.
• Product: AI shifts design from a "single golden path" to a reality where every user experience is an edge case.

4. Re-Architecting for the Future

Leaders are challenged to design their organizations from scratch with AI as a "permanent participant". Key strategies include:

• Not saying “we should use AI more” but “we should —— instead of ——.
• Pick one phase where the system is weakest, identify the most important failure, and consider what would change if you redesigned it with AI in the system. 
• Every review produces a rule, a skill, or a system improvement. 

Conclusion: The Mandate

In the AI era, leadership responsibility increases. Success depends on architecting an environment where judgment scales, quality compounds, and learning accumulates over time.

Content Design and AI by Noz Urbina

Noz Urbina, founder of Urbina Consulting and author of "Content Strategy: Connecting the dots between business, brand, and benefits", provided a half-day deep dive into integrating AI strategically within content and experience design.

Core AI Concepts and Reframing

The workshop transitioned participants from simple "prompting" to more advanced assistant configuration. Key definitions include:

• Prompt vs. Agent: A prompt is like a recipe to follow, while an agent is like a specialist colleague who knows their job and has appropriate tools.
• Assistant vs. Automation: An assistant is a collaborative colleague who adapts to context, whereas automation follows set rules for routine, repetitive work.
• Content as a Product: Content is viewed as a packaged product designed around customer needs, with structure and metadata serving as the production infrastructure.

The HumanLoops Methodology

The core of the presentation was the HumanLoops framework, a reusable AI process and interaction design methodology for strategy, content, and design professionals.

• Human-AI Hybrid Teams: HumanLoops aims to create teams where humans are "the loop" rather than just "in the loop," emphasizing human agency and experience grounded in real-world data.
• Specialized Assistants: The framework utilizes specific agents like the Data Assistant (to ingest data), the UX Assistant (to provide UX help), and Persona Sims (simulate audience interactions).
• Persona Simulations vs. Synthetic Users: While synthetic users are generated purely from LLM training data, Persona Sims are built from a synthesis of real research assets like interview transcripts and survey results.

Strategic Workflow: 7 Steps to Productivity

Noz outlined a structured process to move beyond basic "chatting" with AI to achieve professional results:

• Stop "chatting" and start defining structured interactions.
• Define workflows for journey mapping and persona development.
• Define context and knowledge by writing a "job description" and onboarding sheet for the assistant.
• Define required skills, which are templated tasks (e.g., /learn, /j-stage) that avoid constant copy-pasting.
• Educate and test the assistants with various inputs.
• Build copy-paste templates for rendering shareable outputs like journey maps.
• Integrate with enterprise data systems to make assistants part of the broader tech stack.

Addressing Challenges and Bias

Large Language Models (LLMs) are not databases but language interfaces, with known issues such as hallucinations, bias, and generic outputs. Mitigation strategies include:

• Configuring assistants with real brand messages and specific goals.
• Instructing AIs to be more "cautious" to avoid an inherent bias toward over-positivity.
• Generalizing overly narrow recommendations and using real-world data to ground AI responses.

The ultimate goal of the session was to enable non-technical users to build complex, ethical, and AI-enhanced omnichannel solutions that maximize research ROI and elevate the strategic impact of content design.

HumanLoops does not replace you (or your audience). It brings your available research to life.

Knowledge Management with AI for Research Ops by Emily DiLeo

Emily DiLeo is a research operations and knowledge management consultant at The Current, her consulting practice. Emily's workshop at UXLx focused on problem diagnosis, providing a framework of knowledge management principles to help organizations more easily diagnose problems.

The Persistent Challenges of Knowledge Management 

Despite the rise of AI, traditional KM problems remain: knowledge is often spread across disconnected tools, assets are buried due to poor metadata, and tacit knowledge (the "why" behind decisions) frequently disappears. Many teams mistakenly believe that AI's ability to search across everything means they no longer need repositories or organization; however, Emily argues that structure still matters.

How AI Improves KM: From Keywords to Semantic Search

AI facilitates a shift from basic keyword search, where users must guess exact terms, to intelligent, semantic search. Semantic search understands the user's intent and the meaning behind words. This is the foundation for Retrieval Augmented Generation (RAG), where AI retrieves relevant content to generate a response. Crucially, if the underlying information architecture is poor and retrieval fails, the AI's generation will also fail.

The Role of Metadata and Taxonomy

Effective AI implementation depends on how knowledge is structured.

• Metadata: Describes individual items to make them findable and understandable.
• Taxonomy: Organizes these descriptions and represents how an organization understands itself. Emily emphasizes that "more metadata ≠ better metadata"; it must be designed to help users solve specific problems.

Capturing Tacit Knowledge

One of the most significant opportunities for AI is capturing tacit knowledge: the context, reasoning, and "what actually happened" during research that is often shared in conversation but never written down. AI can now "join" Communities of Practice (like Slack channels or meetings) to document this valuable context, turning it into retrievable organizational expertise.

KM is a System, Not Just a Tool

A central theme of the workshop is that KM is a holistic system consisting of five interdependent parts:

• People: Who create, interpret and share knowledge.
• Process: How knowledge is captured and reused.
• Content: What is documented and in what form.
• Technology: Where knowledge lives.
• Culture: Behaviors that are valued, rewarded or ignored.

AI acts as an amplifier: it makes a strong KM system more powerful but will only make the flaws in a weak system more visible and faster. Most organizations over-invest in technology while neglecting the other four pillars.

The goal for Research Ops is to move toward a strategy that balances structure, capture, retrieval, and use. AI is not a standalone solution but a tool that improves search, simplifies processes, and helps capture the human context that was previously lost.

The Psychology of AI Experiences by Sarah Thompson

Sarah Thompson is a behavioral scientist and Senior Experience Specialist at Nielsen Norman Group. Sarah kicked-off the workshop arguing that the success of AI products depends more on human psychology than technological capabilities. Sarah advocates for moving away from tech-driven "AI-washing" toward a value-driven approach that starts with solving real user problems.

The core of the workshop was a framework consisting of eight questions for user-centered AI, organized into three categories:

Expectations

1. Should it even be AI? Designers should identify specific user frictions, such as being uncertain, overwhelmed, stuck, or burdened, and determine if AI is the right fit to solve them. 

Then they have to consider if people want AI to help solve the problem. Consider that people: don’t want AI when they expect warmth (only efficiency); don’t want AI when stakes are high; don’t want AI when they have to take its advice; don’t want AI when it threatens “humanness”. 

2. What’s the mental model? Using metaphors like "Intern," "Tool," or "Companion" sets the relational framing, triggering specific associations and expectations regarding the AI's reliability and function.

3. How salient is AI? Designers must decide how visible the AI should be. Higher salience is recommended when transparency matters for trust or when users need to review output, while lower salience is better for routine, low-stakes tasks where AI visibility might interrupt the user's focus.

Experience

4. What’s the form factor? While chat has been the default, it can introduce articulation barriers and limit user control compared to structured GUIs. Hybrid interfaces can offer a balance of flexibility and efficiency. 

5. How much guidance? Because AI has a "jagged frontier" of uneven capabilities, designers should provide scaffolding to keep users within their "zone of proximal development” - what users can do with a little guidance. 

6. How “human” is it? Humanization is presented as a "dial" that can be turned up to increase initial trust and collaboration or turned down to avoid risks like over-attachment, inflated expectations, and the "uncanny valley".

Engagement

7. How much user control? Sarah warned about the "Brain Drain Hypothesis," where excessive cognitive outsourcing to AI reduces a user's critical thinking and motivation. Designers should preserve autonomy by requiring consent or providing "productive struggle" to ensure users actually learn.

8. How do we manage trust? The goal is calibrated trust, avoiding both automation bias (overtrust) and algorithm aversion (undertrust). This is achieved through capability reminders, confidence scores, source transparency, reasoning explanations, and human escalation. 

With a proper mix of explanation and practice, Sarah guided participants through a design workbook to apply these eight questions to their own AI feature concepts, ultimately generating a "mega prompt" to launch a prototype.

From Systems Thinking to Systems Doing: A Practical Approach for Experience Designers by Sylvie Abookire

Sylvie Abookire, a service and systemic designer, provided a practical framework for experience designers to move beyond theoretical systems analysis into actionable practice.

The "Thinking/Doing Gap"

The core premise is that while many designers recognize the complexity of systems, their efforts often fall short because of three primary barriers:

• Accessibility: Systems thinking is often laden with academic jargon and complex methods rooted in engineering and physics, making it feel intimidating to non-technical practitioners.
• Scope: A common misconception is that systemic change requires massive authority and a vast scope. This leads to overwhelm and the abandonment of the approach for something more "feasible" but less impactful.
• Linear Rationalization: Traditional research often favors linear logic, discounting embodied, intuitive, and emotional knowledge sources that are crucial for navigating human systems.

The "Systems Doing" Approach

To bridge this gap, Sylvie proposed a participatory, active, and flexible approach guided by six principles: being participatory, active, flexible, informed, intuitive, and kind. The framework consists of seven iterative steps:

1. Visualize: Create a flexible visual representation (like a causal loop diagram) to depict the system's dynamics.
2. Position: Identify your own physical and dynamic position within the system, recognizing that you cannot be separated from what you are studying.
3. Define: Choose a focus area with high leverage potential and articulate a clear desired future state.
4. Ideate: Generate design intervention ideas based on your position and available resources.
5. Storytell: Develop a "theory of change" or hypothesis regarding how your intervention will create ripple effects throughout the system.
6. Test & Iterate: Implement the intervention, monitor for signals of change, and continuously adapt based on learnings.

Impactful Service Design: Working With the Realities of Organisations by Ayesha Moarif

Ayesha Moarif, a service designer and transformation coach working in the public sector, explored how service designers can navigate complex organizational structures to create lasting change. She divided the workshop into two primary parts:

Part 1: Organisation Patterns & Lessons Learned

This section addressed the common frustrations in design work, such as discovery phases where outcomes are already decided or strategies that only aim to please the loudest stakeholder.

• The Iceberg Model: Ayesha used an iceberg analogy to show that while "Events" (visible behaviors) are what we see, the real drivers are hidden "Patterns," "Structures," and "Mental Models”.
• Five Key Lessons:

1. Be clear on what you want to change: Understand that true change involves how an organization sees itself and operates.
2. Respect the reality, learn the language: Prioritize understanding the organization before trying to be understood.
3. Good work is built on relationships: Focus on "positive deviants" and decision-makers, going where the energy for change is.
4. Solve a real problem for people: Treat colleagues like users and help unblock actual organizational issues.
5. Accept that you'll be annoying: Realize that helping one person may annoy another, and that "not being liked" is not a failure.

Part 2: Organisations as Living Entities

This part advocated for shifting from a "mechanistic" view of organizations to seeing them as living, adaptive systems.

• Alive vs. Mechanistic: An "Alive" approach uses an asset-based model (what’s working) and local wisdom, which produces fewer "antibodies" (resistance) and creates more lasting change.
• A Four-Step Stance: To move forward, designers should Meet people where they are, Reveal the system to itself, Align on what matters, and Act wisely based on what is doable.
• Protecting the Asset: Ayesha emphasized that the practitioner is their own best "asset." It is vital to protect your own energy, mind, and spirit to maintain the ability to contribute effectively.

For the final recap, Ayesha highlighted the necessary shift in values, prioritizing mapping together over just making maps, building understanding over being understood, relationships over roles, and reducing harm over simply adding more.

Designing with Universal AI Assists by Christopher Noessel

Christopher Noessel, a UX veteran focusing on design for AI since 2016, provided a framework for creating artificial intelligence that enhances human intelligence rather than just automating tasks. The core of the presentation was built around three primary concepts:

1. Modes of Agency

A spectrum of autonomy for AI systems, helping designers understand how much work the system should do on the user's behalf:

• Manual: The user does all the work; the system is a passive tool (e.g., a hammer or basic text editor).
• Assistant: The system helps the user while they are working, but the user remains in control (e.g., spellcheck or GPS navigation)
• Agentive: The system acts on the user's behalf based on set preferences (e.g., spam filters or smart thermostats).
• Automatic: The system runs independently, only involving the user during major crises (e.g., anti-lock brakes).

2. The See-Think-Do Loop

This model describes the cycle of human activity: perceiving a situation, thinking to decide on a course of action, and doing (executing) the task. Assistant technology is most effective when it maps to these specific stages of human behavior.

3. The Five Universal Assists

These are five distinct ways AI can assist a user throughout the see-think-do loop:

• Perceive: Helping users see "more signal and less noise" by summarizing data, guiding their attention, extending human senses, or priming users.
• Know: Helping users understand what they are perceiving. This includes answering questions, showing the current state of a system, aiding recall, and helping them "grok" (deeply understand) the implications of data.
• Plan: Helping users decide their next step by displaying available options, previsualizing the outcomes of those choices, and recommending the best actions with clear reasoning.
• Perform: Helping users execute tasks better by providing practice environments, keeping them focused on the task, showing best practices, and benchmarking against prior performance.
• Reflect: Helping users look back on their work. This involves reflecting on their actual performance, the effectiveness of their chosen plan, whether the task itself was correctly framed, and how it all aligns with their long-term goals.

During the workshop, participants used a speculative case study called "Brick House Home Security". Participants were tasked with designing AI assists for a user named Josh to help him secure his home and care for his dog, Milan, while he travels for work. Ideas were documented using "Post-it Pairs" (a title/drawing paired with a descriptive sentence) and scenarios that tell a story of a user solving a problem through a series of see-think-do steps.

Designing AI Experiences for Human Agency: A Systems Framework for Human-AI Collaboration by Brandon Hardwood

Brandon Harwood, founder of quietloudlab, focused on how to intentionally design AI into human creative processes while preserving human agency and intent. The core philosophy was that AI does not communicate meaning, humans are the ones who infer it.

Key Concepts and Frameworks

• The Problem of Homogenization: Brandon cited research showing that Large Language Models (LLMs) can have a "homogenizing effect," potentially reducing creative diversity.
• Semantic Noodling: A method of thinking "new things" by playing with variable concepts or ideas behind words and symbols, rather than just their literal definitions.
• The Five Gaps (AI Capabilities): Brandon categorized AI tasks into a framework for identifying where technology can assist the creative workflow:

1. Perception: Tracking signals humans can't see or hear consistently at scale.
2. Organization: Finding relationships and grouping material by meaning.
3. Inference: Identifying patterns too complex or distributed for reliable human judgment.
4. Production: Generating content at a volume or speed a person cannot sustain.
5. Action: Performing sequences or functions when humans cannot be "in the loop".

The Creative Workshop Process

Brandon outlined a four-step practical approach for designers to build co-creative AI workflows:

1. Identifying & Modeling Creative Action: Mapping out where "meaning-making" (direction setting, evaluating, reflecting) actually happens in a workflow.
2. Identifying Gaps: Determining which of the five capabilities (Perception, Organization, etc.) AI should fill to solve specific capacity or judgment struggles.
3. Mapping Data: Analyzing what information is consumed, used as context, or produced as a result of the creative act.
4. Task & Interaction Design: Assigning specific roles to the Human, the AI, and the System to ensure the user's capabilities are expanded without losing their agency.

The workshop concluded with an emphasis on critical questions for designers:

• Will AI remove a "struggle" that is actually generative to the creative process?
• Will it seed bias before the person forms their own intent?
• How can we prevent the blurring of attribution between the human and the machine?

Lean Design Systems: Perception, Reality & Direction by Marianne Ashton-Booth

Marianne Ashton-Booth, Head of Product Design and Design Systems at ITVX, focused on the organizational and strategic challenges of design systems, moving beyond technical components to ensure long-term alignment with business goals.

The Root Cause of Failure

Design systems typically do not fail due to a lack of components; rather, they fail when they lose alignment with the organization. A critical "gap" often exists between the perception of the design system team and the reality of how it is used or viewed by the business, which is the primary cause of buy-in issues.

The LeanDS Framework

The workshop introduced a structured approach to bridge this gap through three main steps:

1. Naming the Gap: Unpacking the difference between perception and reality.
2. Diagnosing the Phase: Identifying where a system sits in its lifecycle, which includes Adoption (starting out), Optimization, Expansion, and Maintenance.
3. Shaping the Direction: Utilizing a 11-section canvas across three key areas:

• Why? (Purpose): Understanding the wider organization, business goals, and the specific system problems to solve.
• What? (Drivers & Motives): Identifying system users (primary and secondary), user benefits, metrics, solutions, and drafting a hypothesis for improvement.
• How? (Feasibility): Defining the team model (centralized vs. distributed), assessing scope and risks, and creating a concrete plan.

Planning for Longevity

Longevity requires balancing three types of planning:

• Tactical Planning: Short-term execution and daily operations.
• Strategic Planning: Medium-to-long-term objectives aligned with vision and resources.
• Normative Planning: The foundational "why," focusing on long-term vision, purpose, and values.

Stakeholder Influence and Communication

Marianne emphasized that different stakeholders require different communication strategies based on their influence (on the direction and growth of the DS) and frequency of system use:

• Primary Users: High frequency and organizational influence; they care most about "What" the system does and how it helps them build faster.
• Product Stakeholders: High influence and low frequency user; they focus on "How" the system integrates and meets project standards.
• Business Stakeholders: Low influence and frequency of use; they need to hear about "Impact" and how the system contributes to business goals.
• Secondary Users: High frequency of use but low influence; the focus is on the “why” and the metrics to assess the system’s impact.

Marianne ended the workshop with three guiding principles for design system success:

• Research and plan first to maintain a "guiding north star".
• Speak business, not tokens, tailoring the message to what motivates specific audiences (e.g., CPOs vs. designers).
• Prioritize relationships over components, as success depends entirely on buy-in and adoption across the business.

. . .

24 MAY - Talks Day

If your where at UXLx 2026 revisit the key takeaways. If you couldn’t make it, catch a glimpse of the conversations shaping the future of UX 👉 UXLx 2026 Wrap Up: Talks Day Notes

. . .

👀 Stay tuned!

We’ll start releasing the videos from the Talks Day on our UXLx Videos page, gradually, when we launch the next UXLx event. In the meantime you can check hours of content from the previous editions.

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. . .

🙌 Last but not least, we’d like to thank:

• Our Gold Sponsor AutonomyAI, and our Partners for their support.
• Our incredible speakers who so passionately shared their knowledge.
• The hundreds of attendees from all around the world who chose UXLx to help expand their knowledge.
• The entire UXLx team who always goes all out to bring the best UX content to sunny Lisbon and give everyone an incredible experience.