When people talk about UX, it’s usually about the things they can see and interact with, like wireframes and prototypes, smart interactions, and design tools like Figma, Miro, or Maze. Some of the outputs are even glamorized, like design systems, research reports, and pixel-perfect UI designs. But here’s the truth I’ve seen again and again in over two decades of working in UX: none of that moves the needle if there is no collaboration. Great UX doesn’t happen in isolation. It happens through conversations with engineers, product managers, customer-facing teams, and the customer support teams who manage support tickets. Amazing UX ideas come alive in messy Miro sessions, cross-functional workshops, and those online chats (e.g., Slack or Teams) where people align, adapt, and co-create. Some of the most impactful moments in my career weren’t when I was “designing” in the traditional sense. They have been gaining incredible insights when discussing problems with teammates who have varied experiences, brainstorming, and coming up with ideas that I never could have come up with on my own. As I always say, ten minds in a room will come up with ten times as many ideas as one mind. Often, many ideas are the most useful outcome. There have been times when a team has helped to reframe a problem in a workshop, taken vague and conflicting feedback, and clarified a path forward, or I’ve sat with a sales rep and heard the same user complaint show up in multiple conversations. This is when design becomes a team sport, and when your ability to capture the outcomes multiplies the UX impact. Why This Article Matters Now The reason collaboration feels so urgent now is that the way we work since COVID has changed, according to a study published by the US Department of Labor. Teams are more cross-functional, often remote, and increasingly complex. Silos are easier to fall into, due to distance or lack of face-to-face contact, and yet alignment has never been more important. We can’t afford to see collaboration as a “nice to have” anymore. It’s a core skill, especially in UX, where our work touches so many parts of an organisation. Let’s break down what collaboration in UX really means, and why it deserves way more attention than it gets. What Is Collaboration In UX, Really? Let’s start by clearing up a misconception. Collaboration is not the same as cooperation. Cooperation: “You do your thing, I’ll do mine, and we’ll check in later.” Collaboration: “Let’s figure this out together and co-own the outcome.” Collaboration, as defined in the book Communication Concepts, published by Deakin University, involves working with others to produce outputs and/or achieve shared goals. The outcome of collaboration is typically a tangible product or a measurable achievement, such as solving a problem or making a decision. Here’s an example from a recent project: Recently, I worked on a fraud alert platform for a fintech business. It was a six-month project, and we had zero access to users, as the product had not yet hit the market. Also, the users were highly specialised in the B2B finance space and were difficult to find. Additionally, the team members I needed to collaborate with were based in Malaysia and Melbourne, while I am located in Sydney. Instead of treating that as a dead end, we turned inward: collaborating with subject matter experts, professional services consultants, compliance specialists, and customer support team members who had deep knowledge of fraud patterns and customer pain points. Through bi-weekly workshops using a Miro board, iterative feedback loops, and sketching sessions, we worked on design solution options. I even asked them to present their own design version as part of the process. After months of iterating on the fraud investigation platform through these collaboration sessions, I ended up with two different design frameworks for the investigator’s dashboard. Instead of just presenting the “best one” and hoping for buy-in, I ran a voting exercise with PMs, engineers, SMEs, and customer support. Everyone had a voice. The winning design was created and validated with the input of the team, resulting in an outcome that solved many problems for the end user and was owned by the entire team. That’s collaboration! It is definitely one of the most satisfying projects of my career. On the other hand, I recently caught up with an old colleague who now serves as a product owner. Her story was a cautionary tale: the design team had gone ahead with a major redesign of an app without looping her in until late in the game. Not surprisingly, the new design missed several key product constraints and business goals. It had to be scrapped and redone, with her now at the table. That experience reinforced what we all know deep down: your best work rarely happens in isolation. As illustrated in my experience, true collaboration can span many roles. It’s not just between designers and PMs. It can also include QA testers who identify real-world issues, content strategists who ensure our language is clear and inclusive, sales representatives who interact with customers on a daily basis, marketers who understand the brand’s voice, and, of course, customer support agents who are often the first to hear when something goes wrong. The best outcomes arrive when we’re open to different perspectives and inputs. Why Collaboration Is So Overlooked? If collaboration is so powerful, why don’t we talk about it more? In my experience, one reason is the myth of the “lone UX hero”. Many of us entered the field inspired by stories of design geniuses revolutionising products on their own. Our portfolios often reflect that as well. We showcase our solo work, our processes, and our wins. Job descriptions often reinforce the idea of the solo UX designer, listing tool proficiency and deliverables more than soft skills and team dynamics. And then there’s the team culture within many organisations of “just get the work done”, which often leads to fewer meetings and tighter deadlines. As a result, a sense of collaboration is inefficient and wasted. I have also experienced working with some designers where perfectionism and territoriality creep in — “This is my design” — which kills the open, communal spirit that collaboration needs. When Collaboration Is The User Research In an ideal world, we’d always have direct access to users. But let’s be real. Sometimes that just doesn’t happen. Whether it’s due to budget constraints, time limitations, or layers of bureaucracy, talking to end users isn’t always possible. That’s where collaboration with team members becomes even more crucial. The next best thing to talking to users? Talking to the people who talk to users. Sales teams, customer success reps, tech support, and field engineers. They’re all user researchers in disguise! On another B2C project, the end users were having trouble completing the key task. My role was to redesign the onboarding experience for an online identity capture tool for end users. I was unable to schedule interviews with end users due to budget and time constraints, so I turned to the sales and tech support teams. I conducted multiple mini-workshops to identify the most common onboarding issues they had heard directly from our customers. This led to a huge “aha” moment: most users dropped off before the document capture process. They may have been struggling with a lack of instruction, not knowing the required time, or not understanding the steps involved in completing the onboarding process. That insight reframed my approach, and we ultimately redesigned the flow to prioritize orientation and clear instructions before proceeding to the setup steps. Below is an example of one of the screen designs, including some of the instructions we added. This kind of collaboration is user research. It’s not a substitute for talking to users directly, but it’s a powerful proxy when you have limited options. But What About Using AI? Glad you asked! Even AI tools, which are increasingly being used for idea generation, pattern recognition, or rapid prototyping, don’t replace collaboration; they just change the shape of it. AI can help you explore design patterns, draft user flows, or generate multiple variations of a layout in seconds. It’s fantastic for getting past creative blocks or pressure-testing your assumptions. But let’s be clear: these tools are accelerators, not oracles. As an innovation and strategy consultant Nathan Waterhouse points out, AI can point you in a direction, but it can’t tell you which direction is the right one in your specific context. That still requires human judgment, empathy, and an understanding of the messy realities of users and business goals. You still need people, especially those closest to your users, to validate, challenge, and evolve any AI-generated idea. For instance, you might use ChatGPT to brainstorm onboarding flows for a SaaS tool, but if you’re not involving customer support reps who regularly hear “I didn’t know where to start” or “I couldn’t even log in,” you’re just working with assumptions. The same applies to engineers who know what is technically feasible or PMs who understand where the business is headed. AI can generate ideas, but only collaboration turns those ideas into something usable, valuable, and real. Think of it as a powerful ingredient, but not the whole recipe. How To Strengthen Your UX Collaboration Skills? If collaboration doesn’t come naturally or hasn’t been a focus, that’s okay. Like any skill, it can be practiced and improved. Here are a few ways to level up: Cultivate curiosity about your teammates.Ask engineers what keeps them up at night. Learn what metrics your PMs care about. Understand the types of tickets the support team handles most frequently. The more you care about their challenges, the more they'll care about yours. Get comfortable facilitating.You don’t need to be a certified Design Sprint master, but learning how to run a structured conversation, align stakeholders, or synthesize different points of view is hugely valuable. Even a simple “What’s working? What’s not?” retro can be an amazing starting point in identifying where you need to focus next. Share early, share often.Don’t wait until your designs are polished to get input. Messy sketches and rough prototypes invite collaboration. When others feel like they’ve helped shape the work, they’re more invested in its success. Practice active listening.When someone critiques your work, don’t immediately defend. Pause. Ask follow-up questions. Reframe the feedback. Collaboration isn’t about consensus; it’s about finding a shared direction that can honour multiple truths. Co-own the outcome.Let go of your ego. The best UX work isn’t “your” work. It’s the result of many voices, skill sets, and conversations converging toward a solution that helps users. It’s not “I”, it’s “we” that will solve this problem together. Conclusion: UX Is A Team Sport Great design doesn’t emerge from a vacuum. It comes from open dialogue, cross-functional understanding, and a shared commitment to solving real problems for real people. If there’s one thing I wish every early-career designer knew, it’s this: Collaboration is not a side skill. It’s the engine behind every meaningful design outcome. And for seasoned professionals, it’s the superpower that turns good teams into great ones. So next time you’re tempted to go heads-down and just “crank out a design,” pause to reflect. Ask who else should be in the room. And invite them in, not just to review your work, but to help create it. Because in the end, the best UX isn’t just what you make. It’s what you make together. Further Reading On SmashingMag “Presenting UX Research And Design To Stakeholders: The Power Of Persuasion,” Victor Yocco “Transforming The Relationship Between Designers And Developers,” Chris Day “Effective Communication For Everyday Meetings,” Andrii Zhdan “Preventing Bad UX Through Integrated Design Workflows,” Ceara Crawshaw

The relationship between strategists and designers is key to creating effective work that meets clients’ needs. But strategists can feel misunderstood, and even undervalued, in their attempts to set a project’s direction through clear and meaningful thinking. We spoke with a range of in-house and independent strategists about what they wish designers knew about their work. Is the role of strategists changing, like so many design industry roles right now? If so, how? “The lightning speed turnaround” of creative work is creating new pressures, says Gardiner Richardson’s associate director and strategic lead, Matt Forster. Partly this is down to the rise of AI, which is front-of-mind according to independent strategist Manfred Abraham, who has held senior roles at Interbrand and Wolff Olins. The two big shifts, he says, are AI’s potential to bring efficiency to the process – using information gathering and analytics to inform insights – and the dramatic changes that AI will bring to the consumer landscape. “Imagine a world where your personal AI agent makes your life much easier,” he says. “What are consumers going to do with their extra time? Strategists will have to work in close collaboration with creatives to be able to imagine the future for our clients.” Beyond AI, consumers’ withering attention spans, coupled with the proliferating demands on their time, creates a big challenge. “Brands are looking for strategists to show them high interest areas of culture where they have a credible role to play, making it easier for them to reach their audience,” says Matt Boffey, chief strategy officer, UK & Europe, at Design Bridge and Partners. As the world becomes more complex, there is a renewed appetite for clarity, says Polly Clark, a strategy consultant for agencies like Buddy Creative in Cornwall. “I’m seeing that simplicity is even more important than ever,” she says. “Overly complex or convoluted thinking isn’t helpful for anyone, and just slows everything down.” And some strategists have noticed a bit of mission creep. “Increasingly, clients are expecting strategists to contribute at a broader business level not purely brand strategy, design or comms,” says Louise Kennedy, who recently joined Into The Light as head of strategy. What don’t designers understand about your role? “Strategy is not a threat or a limit to designers’ creativity,” says Gardiner Richardson’s Matt Forster. “It’s a springboard to a controlled creative leap.” Into the Light’s Louise Kennedy points out that “designers, on the whole, are visual and often want to get to the ‘creative ask’ very quickly so they can start doing what they do best. “But many of us strategists enjoy taking people on the journey of how we got there by unpacking context and patterns. What designers might see as wordy, we see as fascinating storytelling, but perhaps we tell them more than they need to know, to protect our own egos.” There seems to be a recurring tension between the idea of strategists as left brain thinkers – rigorous, analytical, and logical – against designers as right-brain thinkers – more creative and emotional. But Manfred Abraham points out this is a false – if persistent – way of looking at strategy. “Some designers have missed that there might be a strong right brain there as well!” he says. What don’t clients understand about your role? “Unless clients have experienced it before, they aren’t immediately going to understand the value of strategy,” Gardiner Richardson’s Matt Forster says. “They may have worked with agencies who underpin their creative approaches on little substance. “Once we’ve explained our strategic process, why we follow it and the value it will create for all their creative communications and wider business, it’s a no brainer.” Nor does every client understand the commercial power of great design. “In the brand consulting and growth space specifically, clients often think that strategy is communication strategy,” says Manfred Abraham. “The strategies we develop go much further than that – communications is a part of it.” And adding all this value takes time – more than some clients realise. “I think for clients, it is understanding the need to protect the time and space to do a proper job at this stage and the benefit that will bring,” says Into The Light’s Louise Kennedy. “We might even need to commission new insight work if we feel there are big gaps in knowledge,” she adds. How do you balance multiple client meetings with getting the deep thinking done? This, most strategists agree, is a precarious juggling act. “It sometimes feels like ‘manager time’ has won out over ‘maker time,’” says Design Bridge and Partners’ Matt Boffey. “Days are apportioned into slots, from 30 minutes to an hour, which is perfect for meetings but inadequate for building momentum on substantial projects. The goal, he insists, isn’t to eliminate meetings. “Collaboration remains essential. Rather, it’s to create conditions where both discussion and deep work can thrive. We must be careful that ‘talking’ doesn’t completely squeeze out ‘doing’.” He encourages his team to block time between meetings to mentally stretch, as you might after a gym session. “And I’m a strong advocate for reserving longer periods, either half days or full days, for the ‘deep work’ required when writing a discovery debrief or developing brand strategy.” Although Louise Kennedy blocks out time in this way, she finds it doesn’t always work for her. “Often in those moments I can get brainfreeze as I feel under pressure to produce something smart,” she says. “So I like to read everything on a project then leave it for at least a day so my brain can digest it fully and start working behind the scenes.” External consultants can work the schedule that suits them. On most days, Manfred Abraham gets up at 5.30am because that’s when his brain is at its best. It’s also a time of day free of client meetings, “so it’s great thinking time,” he says. Polly Clark, on the other hand, embraces this juggling act. “It’s always something I’ve needed to do, and actually helps sharpen my thinking. Switching focus means I can come back to things fresher, and stops me getting caught up in the weeds.” What’s the worst thing a designer can say to a strategist? Matt Forster – “That they still don’t get it – which means I haven’t involved them enough, explained it well enough or done a good enough job.” Louise Kennedy – “’I’m confused’ or worse, ‘I’m confused and bored’.” Matt Boffey – ‘“Great, the client’s bought the strategy, now we can really start the work.” “This sounds like strategy has become a hurdle to clear before creativity begins, where it should be the foundation that makes creativity powerful and purposeful. The best work happens when strategists and designers see their contributions as interconnected parts of a unified process, rather than unrelated elements.” Polly Clark – “In the past I’ve heard designers question what strategy brings. That’s been when the strategy hasn’t made sense of the challenge, or is overly convoluted – which is sure to make everyone switch off.” Manfred Abraham – “That great design doesn’t need strategic thinking. It’s simply not true. We are great individually but we are brilliant together.”

In many companies, data, findings, and insights are all used interchangeably. Slack conversations circle around convincing data points, statistically significant findings, reliable insights, and emerging trends. Unsurprisingly, conversations often mistake sporadic observations for consistent patterns. But how impactful is the weight that each of them carries? And how do we turn raw data into meaningful insights to make better decisions? Well, let’s find out. Why It All Matters At first, it may seem that the differences are very nuanced and merely technical. But when we review inputs and communicate the outcomes of our UX work, we need to be careful not to conflate terminology — to avoid wrong assumptions, wrong conclusions, and early dismissals. When strong recommendations and bold statements emerge in a big meeting, inevitably, there will be people questioning the decision-making process. More often than not, they will be the loudest voices in the room, often with their own agenda and priorities that they are trying to protect. As UX designers, we need to be prepared for it. The last thing we want is to have a weak line of thinking, easily dismantled under the premise of “weak research”, “unreliable findings”, “poor choice of users” — and hence dismissed straight away. Data ≠ Findings ≠ Insights People with different roles — analysts, data scientists, researchers, strategists — often rely on fine distinctions to make their decisions. The general difference is easy to put together: Data is raw observations (logs, notes, survey answers) (what was recorded). Findings describe emerging patterns in data but aren’t actionable (what happened). Insights are business opportunities (what happened + why + so what). Hindsights are reflections of past actions and outcomes (what we learned in previous work). Foresights are informed projections, insights with extrapolation (what could happen next). Here’s what it then looks like in real life: Data ↓ Six users were looking for ”Money transfer” in “Payments”, and 4 users discovered the feature in their personal dashboard. Finding ↓ 60% of users struggled to find the “Money transfer” feature on a dashboard, often confusing it with the “Payments” section. Insight ↓ Navigation doesn’t match users’ mental models for money transfers, causing confusion and delays. We recommend renaming sections or reorganizing the dashboard to prioritize “Transfer Money”. It could make task completion more intuitive and efficient. Hindsight ↓ After renaming the section to “Transfer Money” and moving it to the main dashboard, task success increased by 12%. User confusion dropped in follow-up tests. It proved to be an effective solution. Foresight ↓ As our financial products become more complex, users will expect simpler task-oriented navigation (e.g., “Send Money”, “Pay Bills“) instead of categories like “Payments”. We should evolve the dashboard towards action-driven IA to meet user expectations. Only insights create understanding and drive strategy. Foresights shape strategy, too, but are always shaped by bets and assumptions. So, unsurprisingly, stakeholders are interested in insights, not findings. They rarely need to dive into raw data points. But often, they do want to make sure that findings are reliable. That’s when, eventually, the big question about statistical significance comes along. And that’s when ideas and recommendations often get dismissed without a chance to be explored or explained. But Is It Statistically Significant? Now, for UX designers, that’s an incredibly difficult question to answer. As Nikki Anderson pointed out, statistical significance was never designed for qualitative research. And with UX work, we’re not trying to publish academic research or prove universal truths. What we are trying to do is reach theoretical saturation, the point where additional research doesn’t give us new insights. Research isn’t about proving something is true. It’s about preventing costly mistakes before they happen. Here are some useful talking points to handle the question: Five users per segment often surface major issues, and 10–15 users per segment usually reach saturation. If we’re still getting new insights after that, our scope is too broad. “If five people hit the same pothole and wreck their car, how many more do you need before fixing the road?” “If three enterprise customers say onboarding is confusing, that’s a churn risk.” “If two usability tests expose a checkout issue, that’s abandoned revenue.” “If one customer interview reveals a security concern, that’s a crisis waiting to happen.” “How many user complaints exactly do we need to take this seriously?” “How much revenue exactly are we willing to lose before fixing this issue?” And: it might not be necessary to focus on the number of participants, but instead, argue about users consistently struggling with a feature, mismatch of expectations, and a clear pattern emerging around a particular pain point. How To Turn Findings Into Insights Once we notice patterns emerging, we need to turn them into actionable recommendations. Surprisingly, this isn’t always easy — we need to avoid easy guesses and assumptions as far as possible, as they will invite wrong conclusions. To do that, you can rely on a very simple but effective framework to turn findings into insights: What Happened + Why + So What: “What happened” covers observed behavior and patterns. “Why” includes beliefs, expectations, or triggers. “So What” addresses impact, risk, and business opportunity. To better assess the “so what” part, we should pay close attention to the impact of what we have noticed on desired business outcomes. It can be anything from high-impact blockers and confusion to hesitation and inaction. I can wholeheartedly recommend exploring Findings → Insights Cheatsheet in Nikki Anderson’s wonderful slide deck, which has examples and prompts to use to turn findings into insights. Stop Sharing Findings — Deliver Insights When presenting the outcomes of your UX work, focus on actionable recommendations and business opportunities rather than patterns that emerged during testing. To me, it’s all about telling a good damn story. Memorable, impactful, feasible, and convincing. Paint the picture of what the future could look like and the difference it would produce. That’s where the biggest impact of UX work emerges. How To Measure UX And Design Impact Meet Measure UX & Design Impact (8h), a practical guide for designers and UX leads to shape, measure, and explain your incredible UX impact on business. Recorded and updated by Vitaly Friedman. Use the friendly code 🎟 IMPACT to save 20% off today. Jump to the details. Video + UX TrainingVideo onlyVideo + UX Training$ 495.00 $ 799.00 Get Video + UX Training25 video lessons (8h) + Live UX Training.100 days money-back-guarantee.Video only$ 250.00$ 395.00 Get the video course25 video lessons (8h). Updated yearly.Also available as a UX Bundle with 2 video courses. Further Reading on Smashing Magazine “The Human Element: Using Research And Psychology To Elevate Data Storytelling,” Victor Yocco & Angelica Lo Duca “Integrations: From Simple Data Transfer To Modern Composable Architectures,” Edoardo Dusi “Scaling Success: Key Insights And Practical Takeaways,” Addy Osmani “Embracing Introversion In UX,” Victor Yocco

Designing human-centered AI products can be arduous.Keeping up with the overall pace of change isn’t easy. But here’s a bigger challenge:The wildly different paces of change attached to the key elements of AI product strategy, design, and development can make managing those elements — and even thinking about them — overwhelming.Yesterday’s design processes and frameworks offer priceless guidance that still holds. But in many spots, they just don’t fit today’s environment.For instance, designers used to map out and user-test precise, predictable end-to-end screen flows. But flows are no longer precisely predictable. AI generates dynamic dialogues and custom-tailored flows on the fly, rendering much of the old practice unhelpful and infeasible.It’s easy for product teams to feel adrift nowadays — we can hoist the sails, but we’re missing a map and a rudder. We need frameworks tailored to the traits that fundamentally set AI apart from traditional software, including:its capabilities for autonomy and collaboration,its probabilistic nature,its early need for quality data, andits predictable unpredictability. Humans tend to be perpetually surprised by its abilities — and its inabilities.AI pace layers: design for resilienceHere’s a framework to address these challenges.Building on Stewart Brand’s “Shearing Layers” framework, AI Pace Layers helps teams grow thriving AI products by framing them as layered systems with components that function and evolve at different timescales.It helps anticipate points of friction and create resilient and humane products.Each layer represents a specific domain of activity and responsibility, with a distinct pace of change.* Unlike the other layers, Services cuts across multiple layers rather than sitting between them, and its pace of change fluctuates erratically.Boundaries between layers call for special attention and care — friction at these points can produce destructive shearing and constructive turbulence.I’ll dive deeper into this framework with some practical examples showing how it works. But first, a brief review of the precursors that inspired this framework will help you put it to good use.The foundationsThis model builds on the insights of several influential design frameworks from the professions of building architecture and traditional software design.Shearing layers (Duffy and Brand)In his 1994 book How Buildings Learn, Stewart Brand expanded on architect Frank Duffy’s concept of shearing layers. The core insight: buildings consist of components that change at different rates.Shell, Services, Scenery, and Sets. (Frank Duffy, 1992).“…there isn’t any such thing as a building. A building properly conceived is several layers of longevity of built components.” — Frank DuffyShearing Layers of Change, from How Buildings Learn: What Happens after they’re built (Stewart Brand, 1994).Expanding on Duffy’s work, Brand identified six layers, from the slow-changing “Site” to the rapidly evolving “Stuff.”As the layers move at different speeds, friction forms where they meet. Buildings designed without mindful consideration of these different velocities tear themselves apart at these “shearing” points. Before long, they tend to be demolished and replaced.Buildings designed for resiliency allow for “slippage” between the moving layers — flexibility for the different rates of change to unfold with minimal conflict. Such buildings can thrive and remain useful for hundreds of years.Pace layers (Brand)In 1999, Brand drew insights from ecologists to expand this concept beyond buildings and encompass human society. In The Clock Of The Long Now: Time And Responsibility, he proposed “Pace Layers” — six levels ranging from rapid fashion to glacially-slow nature.Brand’s Pace Layers (1999) as sketched by Jono Hey.Brand again pointed out the boundaries, where the most intriguing and consequential changes emerge. Friction at the tension points can tear a building apart — or spur a civilization’s collapse–when we try to bind the layers too tightly together. But with mindful design and planning for slippage, activity along these boundary zones can also generate “constructive turbulence” that keeps systems balanced and resilient.The most successful systems survive and thrive through times of change through resiliency, by absorbing and incorporating shocks.“…a few scientists (such as R. V. O’Neill and C. S. Holling) have been probing the same issue in ecological systems: how do they manage change, how do they absorb and incorporate shocks? The answer appears to lie in the relationship between components in a system that have different change-rates and different scales of size. Instead of breaking under stress like something brittle, these systems yield as if they were soft. Some parts respond quickly to the shock, allowing slower parts to ignore the shock and maintain their steady duties of system continuity.” — Stewart BrandRoles and tendencies of the fast (upper) and slow (lower) layers. (Brand).Slower layers provide constraints and underpinnings for the faster layers, while faster layers induce adaptations in the slower layers that evolve the system.Elements of UX (Garrett)Jesse James Garrett’s classic The Elements of User Experience (2002) presents a five-layer model for digital design:Surface (visual design)Skeleton (interface design, navigation design, information design)Structure (interaction design, information architecture)Scope (functional specs, content requirements)Strategy (user needs, site objectives)Structure, Scope, and Strategy. Each layer answers a different set of questions, with the questions answered at each level setting constraints for the levels above. Lower layers set boundaries and underpinnings that help define the more concrete layers.Jesse James Garrett’s 5 layers from The Elements of User Experience Design (2002)This framework doesn’t focus on time, or on tension points resulting from conflicting velocities. But it provides a comprehensive structure for shaping different aspects of digital product design, from abstract strategy to concrete surface elements.AI Pace Layers: diving deeperBuilding on these foundations, the AI Pace Layers framework adapts these concepts specifically for AI systems design.Let’s explore each layer and understand how design expertise contributes across the framework.SessionsPace of change: Very fast (milliseconds to minutes)Focus: Performance of real-time interactions.This layer encompasses real-time dialogue, reasoning, and processing. These interplays happen between the user and AI, and between AI agents and other services and people, on behalf of the user. Sessions draw on lower-layer capabilities and components to deliver the “moments of truth” where product experiences succeed or fail. Feedback from the Sessions layer is crucial for improving and evolving the lower layers.Key contributors: Users and AI agents — usually with zero direct human involvement backstage.Example actions/decisions/artifacts: User/AI dialogue. Audio, video, text, images, and widgets are rendered on the fly (using building blocks provided by lower levels). Real-time adaptations to context.SkinPace of change: Moderately fast (days to months)Focus: Design patterns, guidelines, and assetsSkin encompasses visual, interaction, and content design.Key contributors: Designers, content strategists, front-end developers, and user researchers.Design’s role: This is where designers’ traditional expertise shines. They craft the interface elements, establish visual language, define interaction patterns, and create the design systems that represent the product’s capabilities to users.Example actions/decisions/artifacts: UI component libraries, brand guidelines, prompt templates, tone of voice guidelines, navigation systems, visual design systems, patterns (UI, interaction, and conversation), content style guides.ServicesPace of change: Wildly variable (slow to moderately fast)Focus: AI computation capabilities, data systems orchestration, and operational intelligenceThe Services layer provides probabilistic AI capabilities that sometimes feel like superpowers — and like superpowers, they can be difficult to manage. It encompasses foundation models, algorithms, data pipelines, evaluation frameworks, business logic, and computing resources.Services is an outlier that behaves differently from the other layers:• It’s more prone to “shocks” and surprises that can ripple across the rest of the system.• It varies wildly in pace of change. (But its components rarely change faster than Skin, or slower than Skeleton.)• It cuts across multiple layers rather than sitting between two of them. That produces more cross-layer boundaries, more tension points, more risks of destructive friction, and more opportunities for constructive turbulence.Key contributors: Data scientists, engineers, service designers, ethicists, product teamsDesign’s role: Designers partner with technical teams on evaluation frameworks, helping define what “good” looks like from a human experience perspective. They contribute to guardrails, monitoring systems, and multi-agent collaboration patterns, ensuring technical capabilities translate to meaningful human experiences. Service design expertise helps orchestrate complex, multi-touchpoint AI capabilities.Example actions/decisions/artifacts: Foundation model selection, changes, and fine-tuning. Evals, monitoring systems, guardrails, performance metrics. Business rules, workflow orchestration. Multiagent collaboration and use of external tools (APIs, A2A, MCP, etc.) Continual appraisal and adoption of new tools, protocols, and capabilities.SkeletonPace of change: Moderately slow (months) Focus: Fundamental structure and organizationThis layer establishes the foundational architecture — the core interaction models, information architecture and organizing principles.Key contributors: Information architects, information designers, user researchers, system architects, engineersDesign’s role: Designers with information architecture expertise are important in this layer. They design taxonomies, knowledge graphs, and classification systems that make complex AI capabilities comprehensible and usable. UX researchers help ensure these structures fit the audience’s mental models, contexts, and expectations.Example actions/decisions/artifacts: Taxonomies, knowledge graphs, data models, system architecture, classification systems.ScopePace of change: Slow (months to years)Focus: Product requirementsThis layer defines core functional, content, and data requirements, accounting for the probabilistic nature of AI and defining acceptable levels of performance and variance.Key contributors: Product managers, design strategists, design researchers, business stakeholders, data scientists, trust & safety specialistsDesign’s role: Design researchers and strategists contribute to requirements through generative and exploratory research. They help define error taxonomies and acceptable failure modes from a user perspective, informing metrics that capture technical performance and human experience quality. Design strategists balance technical possibilities with human needs and ethical considerations.Example actions/decisions/artifacts: Product requirements documents specifying reliability thresholds, data requirements (volume, diversity, quality standards), error taxonomies and acceptable failure modes, performance metrics frameworks, responsible AI requirements, risk assessment, core user stories and journeys, documentation of expected model variance and handling approaches.StrategyPace of change: Very slow (years)Focus: Long-term vision and business goalsThis foundation layer defines audience needs, core problems to solve, and business goals. In AI products, data strategy is central.Key contributors: Executive leadership, design leaders, product leadership, business strategists, ethics boardsDesign’s role: Design leaders define problem spaces, identify opportunities, and plan roadmaps. They deliver a balance of business needs with human values in strategy development. Designers with expertise in responsible AI help establish ethical frameworks and guiding principles that shape all other layers.Example actions/decisions/artifacts: Problem space and opportunity assessments, market positioning documents, long-term product roadmaps, comprehensive data strategy planning, user research findings on core needs, ethical frameworks and guiding principles, business model documentation, competitive/cooperative AI ecosystem mapping.Practical examples: tension points between layersTension point example 1: Bookmuse’s timeline troubles(Friction between Sessions and Skin)Bookmuse is a promising new AI tool for novelists. Samantha, a writer, tries it out while hashing out the underpinnings of her latest time-travel historical fiction thriller. The Bookmuse team planned for plenty of Samantha’s needs. At first, she considers Bookmuse a handy assistant. It supplements chats with tailored interactive visualizations that efficiently track character personalities, histories, relationships, and dramatic arcs.But Samantha is writing a story about time travelers interfering with World War I events, so she’s constantly juggling dates and timelines. Bookmuse falls short. It’s a tiny startup, and Luke, the harried cofounder who serves as a combination designer/researcher/product manager, hasn’t carved out any date-specific timeline tools or date calculators. He forgot to provide even a basic date picker in the design system.Problem: Bookmuse does its best to help Samantha with her story timeline (Sessions layer). But it lacks effective tools for the job (Skin layer). Its date and time interactions feel confusing, clumsy, and out of step with the rest of its tone, look, and feel. Whenever Samantha consults the timeline, it breaks her out of her creative flow.Constructive turbulence opportunities:a) Present feedback mechanisms that ensure this sort of “missing piece” event results in the product team learning about the type of interaction pothole that appeared — without revealing details or content that compromise Samantha’ privacy and her work. (For instance, a session tagging system can flag all interaction dead-ends during date choice interactions.)b) Improve timeline/date UI and interaction patterns. Table stakes: Standard industry-best-practice date picker components that suit Bookmuse’s style, tone, and voice. Game changers: Widgets, visualizations, and patterns tailored to the special time-tracking/exploration challenges that fiction writers often wrestle with.c) Update the core usability heuristics and universal interaction design patterns baked into the evaluation frameworks (in the Services layer), as part of regular eval reviews and updates. Result: When the team learns about a friction moment like this, they can prevent a host of future similar issues before they emerge.These improvements will make Bookmuse more resilient and useful.Tension point example 2: MedicalMind’s diagnostic dilemma(Friction between Services and Skeleton)Thousands of healthcare providers use MedicalMind, an AI-powered clinical decision support tool. Dr. Rina Patel, an internal medicine physician at a busy community hospital, relies on it to stay current with rapidly evolving medical research while managing her patient load.Thanks to a groundbreaking update, a MedicalMind AI model (Services layer) is familiar with new medical research data and can recognize newly discovered connections between previously unrelated symptoms across different medical specialties. For example, it identified patterns linking certain dermatological symptoms to early indicators of cardiovascular issues — connections not yet widely recognized in standard medical taxonomies.But MedicalMind’s information architecture (Skeleton layer) was tailored to traditional medical classification systems, so it’s organized by body system, conditions by specialty, and treatments by mechanism of action. The MedicalMind team constructed this structure based on how doctors were traditionally trained to approach medical knowledge.Problem: When Dr. Patel enters a patient’s constellation of symptoms (Sessions layer), MedicalMind’s AI can recognize potentially valuable cross-specialty patterns (Services layer). But these insights can’t be optimally organized and presented because the underlying information architecture (Skeleton layer) doesn’t easily accommodate the new findings and relationships. The AI either forces the insights into ill-fitting categories or presents them as disconnected “additional notes” that tend to be overlooked. That reduces their clinical utility and Dr. Patel’s trust in the system.Constructive turbulence opportunities:a) Create an “emerging patterns” framework within the information architecture (Skeleton layer) that can accommodate new AI-identified patterns in ways that augment, rather than disrupt, the familiar classification systems that doctors rely on.b) Design flexible visualization components and interaction patterns and styles (in the Skin layer) specifically for exploring, discussing, and documenting cross-category relationships. Let doctors toggle between traditional taxonomies and newer, AI-generated knowledge maps depending on their needs and comfort level.c) Implement a clinician feedback loop where specialists can validate and discuss new AI-surfaced relationships, gradually promoting validated patterns into the main classification system.These improvements will make MedicalMind more adaptive to emerging medical knowledge while maintaining the structural integrity that healthcare professionals rely on for critical decisions. This provides more efficient assistants for clinicians and better health for patients.Tension point example 3: ScienceSeeker’s hypothesis bottleneck(Friction between Skin and Services)ScienceSeeker is an AI research assistant used by scientists worldwide. Dr. Elena Rodriguez, a molecular biologist, uses it to investigate protein interactions for targeted cancer drug delivery.The AI engine (Services layer) recently gained the ability to generate sophisticated hypothesis trees with multiple competing explanations, track confidence levels for each branch, and identify which experiments would most efficiently disambiguate between theories. It can reason across scientific domains, connecting molecular biology with physics, chemistry, and computational modeling.But the interface (Skin layer) remains locked in a traditional chatbot paradigm — a single-threaded exchange with responses appearing sequentially in a scrolling window.Problem: The AI engine and the problem space are natively multithreaded and multimodal, but the UI is limited to single-threaded conversation. When Dr. Rodriguez inputs her experimental results (Sessions layer), the AI generates a rich, multidimensional analysis (Services layer), but must flatten this complex reasoning into linear text (Skin layer). Critical relationships between hypotheses become buried in paragraphs, probability comparisons are difficult, and the holistic picture of how variables influence multiple hypotheses is lost. Dr. Rodriguez resorts to taking screenshots and manually drawing diagrams to reconstruct the reasoning that the AI possesses but cannot visually express.Constructive turbulence opportunities:a) Develop an expandable, interactive, infinite-canvas “hypothesis tree” visualization (Skin) that helps the AI dynamically represent multiple competing explanations and their relationships. Scientists can interact with this to explore different branches spatially rather than sequentially.b) Create a dual-pane interface that maintains the chat for simple queries but provides the infinite canvas for complex reasoning, transitioning seamlessly based on response complexity.c) Implement collaborative, interactive node-based diagrams for multi-contributor experiment planning, where potential experiments appear as nodes showing how they would affect confidence in different hypothesis branches.This would transform ScienceSeeker’s limited text assistant into a scientific reasoning partner. It would help researchers visualize and interact with complex possibilities in ways that better fit how they tackle multidimensional problems.Navigating the future with AI Pace LayersAI Pace Layers offers product teams a new framework for seeing and shaping the bewildering structures and dynamics that power AI products.By recognizing the evolving layers and heeding and designing for their boundaries, AI design teams can:Transform tension points into constructive innovationAnticipate friction before it damages the product experienceGrow resilient and humane AI systems that absorb and integrate rapid technological change without losing sight of human needs.The framework’s value isn’t in rigid categorization, but in recognizing how components interact across timescales. For AI product teams, this awareness enables more thoughtful design choices that prevent destructive shearing that can tear apart an AI system.This framework is a work in progress, evolving alongside the AI landscape it describes.I’d love to hear from you, especially if you’ve built successful AI products and have insights on how this model could better reflect your experience. Please drop me a line or add a comment. Let’s develop more effective approaches to creating AI systems that enhance human potential while respecting human agency.Part of the Mindful AI Design series. Also see:The effort paradox in AI design: Why making things too easy can backfireBlack Mirror: “Override”. Dystopian storytelling for humane AI designStay updatedSubscribe to be notified when new articles in the series are published. Join our community of designers, product managers, founders and ethicists as we shape the future of mindful AI design.AI Pace Layers: a framework for resilient product design was originally published in UX Collective on Medium, where people are continuing the conversation by highlighting and responding to this story.

It’s one thing to invent something cool within controlled laboratory environments. It’s entirely another to scale that new baby for sale.  The tension between innovation and commercialization is something we regularly wrestle with at Abstrax. Every morning, we don lab coats and ask the same question: “How do you make money from research done in a lab?”  Balance innovation with commercial reality  Discovery for its own sake isn’t enough. Many R&D-heavy companies discover that brilliant ideas can languish for years if they don’t have a system for bringing them to market.   We decided early on to build that bridge proactively. This meant investing heavily not just in research, but in the systems and machinery that connect lab work to real-world products. Our scientists don’t concoct in ivory towers, they work hand-in-hand with product developers to ensure (most) experiments align with market needs.  This pragmatic approach focuses our curiosity. We poured resources into advanced analytical technology. Among other exquisite toys, we operate an ultra-sensitive aroma analysis platform nicknamed OMNI. With it, we can break down a complex flavor into hundreds of molecular components and pinpoint the ones that matter. By capturing a “3D metabolite fingerprint” of a material (cannabis flower, hop varietals, etc.), we get a complete map of that sample’s aroma chemistry.  Why go to these extremes? Because understanding a flavor at that granular level is the key to replicating it, enhancing it, and ultimately monetizing it. We can identify over 500 distinct compounds in a single hop strain. That level of resolution lets us see opportunities others might miss, like the trace molecules that round out pineapple, or a sulfur compound responsible for “skunky” notes.   From breakthrough to beer glass  To illustrate how lab research turns into revenue, take our recent work with Citra, one of the most celebrated hops in craft brewing. Citra’s appeal lies in its remarkably juicy, complex flavor—think grapefruit, lime, peach, and passionfruit steamrolled into one. This tropical medley makes a Citra-hopped beer delicious. But here’s the rub: Achieving that same flavor consistently at scale is hard. Hops are agricultural products, subject to the whims of weather and yearly variation. The Citra you get this year might not taste exactly like last year’s crop.  This is where our lab-to-market philosophy shines. Using OMNI, we profiled Citra’s chemical makeup in exquisite detail. Armed with that “blueprint,” we developed an Omni Hop Profile extract that mirrors Citra’s flavor profile with uncanny accuracy. For brewers, this is a game-changer. Instead of being at the mercy of Mother Nature, they can rely on our Citra extract to deliver the exact same flavor in every batch, forever. And because it’s made from botanically derived ingredients, it stays true to the clean-label standards brewers abide by. We even worked with veteran brewmasters on pilot brews to fine-tune the extract’s performance in different beer styles. By the time our Citra profile hit the market, it was brewer-tested and production-ready.   Our Citra victory highlights our core principle of reasonable innovation. We didn’t stop at discovering what makes that hop special, we pushed to make it a tangible solution to a real problem. That is the essence of monetizing R&D: moving from “Eureka!” to a viable SKU.   No fluff, only real solutions  In avant-garde industries like cannabis and craft beer, it’s easy to get caught up in hype and bold claims. We prefer a different tack: Let the results speak. If we say our new formulation improves a beer’s shelf-life or an extract boosts an IPA’s aroma, we’ve got the data to back it up. Grounding innovation in evidence keeps us credible and ensures we stay focused on real market value.  We also recognize that not every experiment will pan out, and that’s okay. Part of our system is knowing never to become 100% pot committed. We’ll test 10 ideas, then swiftly double down on the one or two that show commercial promise. By failing fast and smart, we conserve resources for the innovations that count.  The new R&D playbook  Our journey from lab to market hasn’t been quick or easy. It took patience and a willingness to invest up front. But that patience is paying off. Today, Abstrax’s approach is turning niche scientific insights into mainstream products. What others consider to be a cost center is our engine for growth.   When scientists and strategists work in sync, every discovery is viewed through the lens of real-world impact. The healthy tension between invention and commercialization keeps us sharp. As it turns out, the lab and the market are pretty good at balancing each other.   Kevin Koby is CEO and cofounder of Abstrax. 

Get your gaming gear primed and wallets at the ready, because today’s lineup of stellar discounts is too good to miss. From adrenaline‑pumping races on Switch to monstrous PC conquests, there’s sweet, economical escapism for all. Nothing but games worth owning. Let's get to it.This Day in Gaming 🎂In retro news, I've baked a cake with a big 16 on it for Pokémon Platinum. Part of the DS Gen IV series, this sprawling Sinnoh shenanigan served as a third version to Pokémon Diamond and Pearl. My memories of it include a gleeful hunt for the 59 extra Pokémon added and dipping into the new M.C. Escher-esque Distortion World to bag a Giratina. It also had some of the best post-game content in the series, and, oh crap—I've just talked myself into a replay...Aussie bdays for notable games- Castlevania (N64) 1999. eBay- R-Type Delta (PS) 1999. Sequel- Ninja Gaiden (XB) 2004. Redux- The Suffering (XB) 2004. Get- Pokémon Platinum (DS) 2009. 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He plays practically everything, often on YouTube.