Human-Centered Design Workshops

🧠 “How We Brainstorm And Choose UX Ideas” (+ Miro template) (https://lnkd.in/eN32hH2x), a practical guide by Booking.com on how to run a rapid UX ideation session with silent brainstorming and “How Might We” (HMW) statements — by clustering data points into themes, reframing each theme and then prioritizing impactful ideas. Shared by Evan Karageorgos, Tori Holmes, Alexandre Benitah. 👏🏼👏🏽👏🏾 Booking.com UX Ideation Template (Miro) https://lnkd.in/eipdgPuC (password: bookingcom) 🚫 Ideas shouldn’t come from assumptions but UX research. ✅ Study past research and conduct a new study if needed. ✅ Cluster data in user needs, business goals, competitive insights. ✅ Best ideas emerge at the intersections of these 3 pillars. ✅ Cluster all data points into themes, prioritize with colors. ✅ Reframe each theme as a “How Might We” (HMW) statement. ✅ Start with the problems (or insights) you’ve uncovered. ✅ Focus on the desired outcomes, rather than symptoms. ✅ Collect and group ideas by relevance for every theme. ✅ Prioritize and visualize ideas with visuals and storytelling. Many brainstorming sessions are an avalanche of unstructured ideas, based on hunches and assumptions. Just like in design work we need constraints to be intentional in our decisions, we need at least some structure to mold realistic and viable ideas. I absolutely love the idea of frame the perspective through the lens of ideation clusters: user needs, business problems and insights. Reframing emerging themes as “How-Might-We”-statements is a neat way to help teams focus on a specific problem at hand and a desired outcome. A simple but very helpful approach — without too much rigidity but just enough structure to generate, prioritize and eventually visualize effective ideas with the entire team. Invite non-designers in the sessions as well, and I wouldn’t be surprised how much value a 2h session might deliver. Useful resources: The Rules of Productive Brainstorming, by Slava Shestopalov https://lnkd.in/eyYZjAz3 On “How Might We” Questions, by Maria Rosala, NN/g https://lnkd.in/ejDnmsRr Ideation for Everyday Design Challenges, by Aurora Harley, NN/g https://lnkd.in/emGtnMyy Brainstorming Exercises for Introverts, by Allison Press https://lnkd.in/eta6YsFJ How To Run Successful Product Design Workshops, by Gustavs Cirulis, Cindy Chang https://lnkd.in/eMtX-xwD Useful Miro Templates For UX Designers, by yours truly https://lnkd.in/eQVxM_Nq #ux #design

We can’t predict the future. But we can approach it more systematically. That’s where futures thinking (or strategic foresight) comes in. And it’s a critical part of good strategic design. You’ll often hear futurists say: “Foresight precedes strategy.” That’s only true if we treat strategy as a fixed plan, built in a linear way. When we instead see strategy as a testable hypothesis, futures thinking becomes more powerful. The two start to shape each other. One of the hardest parts of futures work is that it asks us to question our own values and beliefs. At its best, it creates a scaffold that helps people think the unthinkable. Here’s how futures thinking shows up in my strategic design practice. FRAMING AND SCOPING Getting alignment early matters. Futures tools can be used for different challenges, so framing the right question is essential. Clear scope and shared intent give the work its best chance of success. SCANNING Often called horizon scanning. This is where we lift our gaze and look for weak signals of change. These early signs can point to larger shifts ahead. They form the raw material for scenarios, alongside drivers of change and, to a lesser extent, trends. UNDERSTANDING IMPACT Not all signals matter equally. We explore which ones could have the biggest impact, or where uncertainty is highest. Tools like impact wheels and probability–impact matrices help build shared perspectives and increase situational awareness. SCENARIOS Scenarios turn signals into stories about alternate futures. They help us test assumptions, surface risks, and spot opportunities. Importantly, they let us rehearse decisions before we have to make them. STRATEGY FORMULATION In a linear process, strategy is the end point. In a complex world, that rarely works. Rather than a single plan, I’m interested in strategy as a system. New information about the future feeds into decisions in regular cycles, not as a one-off exercise. This is only a personal snapshot. Each stage has more depth and nuance, and many practitioners would break this into more steps. Because I also work with a complexity lens, I’m less interested in futures as a way to design an ideal future and “close the gap”. For me, the real value of futures thinking is its ability to: - Broaden what we notice - Challenge hidden assumptions - Build resilience in strategic decision-making Futures thinking isn’t a silver bullet. But its value grows when it’s used alongside other complementary practices. It expands what we can imagine, while understanding complex adaptive systems helps us respond to what’s emerging in the present. #StrategicDesign #FuturesThinking #Strategy #DesignThinking #StrategicForesight

To create good policy you need responsible foresight, enabling ethical, sustainble, accountable future design. AI now can massively enable human-centered responsible foresight, in helping address uncertainty, assess risks, and set policies for creating better futures. María Pérez Ortiz's new paper "From Prediction to Foresight: The Role of AI in Designing Responsible Futures" describes responsible foresight in policy and the role of computational foresight tools. Notable approaches to using AI in responsible foresight include: 🤝 Participatory Futures for Inclusive Planning. Engaging diverse stakeholders in foresight practices democratizes the future-planning process. AI tools streamline public participation by analyzing preferences, simulating collective decisions, and creating urban plans that reflect community values, fostering equity and resilience. 🧠 Superforecasting for Precision and Insight. Superforecasting uses disciplined reasoning and probabilistic thinking to predict uncertain events. AI-powered assistants improve human forecasting accuracy by 23%, aggregating data and refining predictions through collective intelligence and advanced analytical models. 🌐 World Simulation for Systemic Insights. Advanced modeling frameworks simulate interconnected global systems, enabling policymakers to test "what-if" scenarios. AI accelerates these simulations, providing precise forecasts and dynamic platforms to visualize the long-term consequences of policy decisions across economic, social, and environmental domains. ⚙️ Simulation Intelligence for Decision Optimization. By integrating AI with high-fidelity simulations, simulation intelligence explores complex systems to uncover optimal strategies. This tool assists in crafting effective policies for urban planning, sustainable agriculture, and climate resilience, offering actionable pathways for addressing systemic challenges. 📜 AI-Assisted Narrative Techniques. Large language models contribute to speculative futures by generating detailed "value scenarios" that integrate ethical, technological, and societal considerations. These AI-driven narratives enable policymakers to visualize desirable outcomes and evaluate potential trade-offs. 🔗 Hybrid Intelligence for Enhanced Foresight. Combining human creativity with AI’s computational strengths creates a robust foresight framework. Intuitive interfaces, explainable AI, and participatory design ensure that tools remain transparent and aligned with ethical considerations, empowering policymakers to navigate complex challenges collaboratively. ♻️ Iterative Foresight with Feedback Loops. Continuous monitoring and real-time adaptation enhance foresight processes. AI’s ability to process evolving data and generate actionable insights ensures policies remain responsive, flexible, and aligned with long-term objectives. The power of AI in assisting foresight is just beginning to come to fruition.

The 𝐏𝐚𝐭𝐢𝐞𝐧𝐭 𝐑𝐨𝐨𝐦 𝐨𝐟 𝐭𝐡𝐞 𝐅𝐮𝐭𝐮𝐫𝐞 (𝐏𝐑𝐨𝐅) teaching case shows how a large healthcare consortium and a small group of manufacturers collaborated to rethink innovation in a highly regulated sector. At its core, the case demonstrates how PRoF turned the interaction between two very different communities into its main innovation engine. The large consortium represents the healthcare user community: nurses, doctors, caregivers, patients, and hospital managers who express the lived reality of care. Their contribution is experiential and value-based. Through structured “brainwave sessions,” they surface latent needs and convert them into broad keywords such as comfort, privacy, dignity, or anti-loneliness. These keywords form a shared language that avoids technical jargon and allows hundreds of users with diverse perspectives to converge around common priorities. The small consortium consists of manufacturers, architects, and designers who have the capabilities to transform these user insights into concrete room concepts. Their commercial goals are kept strictly outside the creative process, allowing trust to grow between the groups. Once the user community defines the keywords, the producer community develops prototypes, after which the large consortium returns to evaluate and refine them. This modular sequencing keeps tensions low, ensures rapid progress, and prevents commercial logic from dominating user needs. The interaction between these two communities solves a longstanding problem in healthcare innovation: suppliers often misunderstand user needs, while users lack the means to innovate. PRoF bridges this gap by letting users drive ideation and letting producers translate that insight into solutions. What emerges is a genuinely user-oriented innovation ecosystem in which neither community could succeed alone, but together they generate concepts that reshape expectations of care design. You can find the case study at HBSP: https://lnkd.in/e6nxTFM7 #UserCentricInnovation #Collaboration #OpenInnovation #CrossCommunityCollaboration #HealthcareEcosystems #CoCreation #Ideation

It is impossible to predict the future with certainty—yet businesses, especially in industries like oil and energy, must form a clear view of what lies ahead. Pierre Wack, the pioneer of scenario planning at Shell, argued that traditional forecasting often fails at the most critical moments. Here’s why: - Forecasts assume stability, but the world is constantly changing. - When major shifts occur, forecasts break down—leaving businesses unprepared. - Decision-makers often struggle with uncertainty because they cannot exercise their judgment. So how do you plan for the future when the future is unknowable? Wack’s answer was scenario planning—an approach that moves beyond forecasting and focuses on understanding the forces that drive change. Key principles of scenario planning: 1. Identify predetermined elements—events that will happen, regardless of uncertainty. 2. Recognize critical uncertainties—factors that could shape the future in different ways. 3. Avoid single-line strategies—build flexible plans that account for multiple possibilities. 4. Change decision-makers’ mental models—because real planning is about shaping perception, not just producing documents. Traditional strategic planning often relies on numbers and projections, but Wack believed that real foresight comes from wisdom. It’s not about predicting what will happen—it’s about preparing for what could happen. Are you making decisions based on forecasts, or are you building the flexibility to adapt to change? P.S. If you like content like this, please follow me.

Are we realising the potential of our networks to make change happen? Most innovation emerges from collaborative projects where teams openly “borrow” & adapt each other’s (often small but powerful) ideas. Many networks & communities of practice could achieve so much more by experimenting together around collective priorities to generate & share new solutions. This is beyond spreading known “best” or “good” practices. It is about innovating to design new solutions collectively. So I appreciated this piece from Ed Morrison about three different kinds of networks: - Advocacy networks are communities that seek to mobilise people, creating pressure to shift policies, priorities or messages in a particular direction. Their aim is to connect & influence rather than to change how they themselves work. - Learning networks are communities of practice. They share knowledge, compare practice & build shared capability. Learning networks often excel at spread & improvement of existing practice, but only sometimes move into structured innovation work. - Innovating (or transforming) networks are communities that combine their assets - ideas, relationships, data, capabilities - to create new value that none could produce alone. They manage collaboration as a process of experimentation: agreeing a shared outcome, running multiple connected tests of change, learning by doing & amplifying what works across the network. https://lnkd.in/edbbexiG. Every learning network has the potential to become an innovating/transforming network. Some actions to enable this: 1. Build a foundation of strong, trusting relationships within the network, understanding each member’s starting point & motivation for change 2. Focus on helping each other to succeed; listen to each others’ stories & plans, co-coach, give advice to each other & build shared inquiry 3. Move from “sharing” or “raising awareness” to some concrete outcomes the network want to change together through collective experimentation 4. Agree some simple norms for the network so that members help each other to make progress, make it safe to try things, fail fast & share incomplete work 5. Encourage multiple, parallel tests of change around similar outcome so projects can “steal with pride” from one another & quickly refine promising ideas 6. Put simple routines in place for noticing patterns (what is shifting where & why), capturing these insights & amplifying them across the network 7. Add additional success metrics including innovations tested, adapted & adopted in multiple places Graphic by Ed Morrison. Content with added inspiration from June Holley.

In every scenario workshop, someone asks the same question: Which future will actually happen? Wrong question. That question belongs to forecasting. Forecasting bets on a single future, hoping it turns up. Foresight works differently. It prepares you for several, so no single one can blindside you. For a regulated incumbent, that isn't a luxury. It's risk reduction. Here's a process you can run in a day: 1. Start with a decision. Which choice are you trying to make robust? Over which horizon? A scenario with no decision attached is just entertainment. 2. Map the driving forces. What shapes your world over that horizon? Regulation, technology, customer behavior, supply, capital. Write them all down. 3. Split the certain from the uncertain. Some forces are fairly predetermined. Demographics, for example. Others are genuinely open, high impact and hard to call. You build scenarios only on the open ones. 4. Pick two key uncertainties and cross them. Two axes, four quadrants, four plausible futures side by side. 5. Make each future concrete. Give it a name. Write a short story for it. A vague scenario moves no decisions. 6. Pull the decisions back out. For each future, ask: what would we do here? Then find the moves that hold up across all four. Those are your robust bets. Make them now. 7. Set your early warning signals. Which indicators tell you which future is arriving? Watch them on a rhythm. Adjust as reality picks a door. None of this is new. The method traces back to Shell's scenario planning in the 1970s and Peter Schwartz's work later. I run a leaner version, built for decisions under time pressure. Where does your planning stop today? At the forecast, or at the decisions that survive more than one future?

A question in one of my facilitation communities this morning on clustering spurred some interesting thoughts for me. Clustering is part of many frameworks, and it’s not wrong, there are just things to consider when using it (or whether to use it at all.) I am very aware of what happens in the room when the phrase ‘group similar ideas’ gets thrown around. With a fuzzy statement like that, people tend to fall back on comfortable patterns, instead of discovering new ones. They might slip into autopilot and group only by similar language, by assumed priorities, or even by team (even if the ideas aren’t related in other ways.) And sometimes the team freezes completely, not even sure how to start, blank stares usually enter here.  So what actually helps when this happens? When you choose to cluster, make sure there is clarity: ↳ ‘We're clustering to find _____’ (put the spotlight on desired outcome, consider sharing what happens with these ideas next)  ↳ Give the team a lens to focus through, and maybe switch up the lens for different rounds (ex: focus on user challenges in ideas not solutions)  ↳ Let people absorb the ideas before jumping right into clustering (give time to scan the wall of thoughts, some breathing room)  ↳ Narrow right after divergence. If there's too much noise, eliminate some options before clustering. ↳ Use multiple rounds. Do a fast scrappy round of clustering to get bias out, then regroup with clear criteria. But clustering isn't always the most effective step directly after ideation. Many people default to it because that’s how the process goes right? However that's just one framework, what might we do instead? 🤔 ↳ Pointstorm as part of ideation - This method starts with categories for your ideation. With those up front, followup steps like mashup of ideas flows naturally from ideation.   ↳ Spectrums-  Arrange ideas along a dimension (quick-win to long-term, user facing to internal, easy energizing to draining…) ↳ Now/Next/Later - Sort ideas into timeframes based on urgency and readiness. ↳ How/Now/Wow - Sort ideas into three buckets: breakthrough but can't do yet (How), safe and doable now (Now), or fresh and feasible (Wow) Making clustering work better comes down to being intentional. What are you actually trying to discover? What lens helps people see past first answers and assumptions? And when does it make sense to just scrap clustering and do something different? What does your team reach for after ideation? I'm curious what's been working (or not working) for you. ⬇️

Forget Strategic Planning. The Future Demands Something Different. At dinner recently, a friend mentioned that her organization was deep in a strategic planning process. My ears perked up. Does it involve scenario planning? I asked. Yes, she said. I hadn’t realized I was holding my breath until I exhaled. I was genuinely concerned for her. Today's unpredictability makes relying on a long-term plan very risky. I used to help organizations develop strategic plans. Back then, a solid 5 - or 10-year roadmap felt like a responsible way to navigate the future. Today, I’d give those same organizations very different advice: There is no more business as usual. Stop planning for “the future” and start preparing for multiple possible futures. So how do we lead in an era of chaos and disruption?  📉 New tariffs imposed and markets decline immediately. ⚖️ Government “efficencies” lead to programs stopping overnight. 🌍 Climate disasters accelerate, displacing communities and reshaping economies. We prepare. ✅ Scenario Planning: Don’t bet on one version of the future—map out multiple possibilities and build flexibility into your approach. ✅ Build Agility as a Core Competency: Organizations that can pivot quickly will outlast those clinging to outdated strategies. ✅ Strengthen Emotional Resilience: Leaders must be equipped to navigate disappointment, disruption, and conflict—not just operational challenges. The organizations that survive and thrive will be the ones that train for uncertainty instead of pretending they can control it. Are you preparing for multiple futures, or just hoping your plan holds?

I lead a 9-person creative team. Here’s the process we’ve used for years to turn loose ideas into some of our best work. Many great thinkers had their inspiration rituals. Einstein played the violin. Da Vinci filled notebooks with scribbles and sketches. Ben Franklin took “air baths” (don’t look that up). Ours isn’t quite as weird. But it 𝘩𝘢𝘴 sparked breakthroughs, especially when the brief is vague or open-ended. So for the first installment of 𝘗𝘶𝘭𝘭𝘪𝘯𝘨 𝘉𝘢𝘤𝘬 𝘵𝘩𝘦 𝘊𝘶𝘳𝘵𝘢𝘪𝘯, here’s how we approach ideation for a new data story. 𝗦𝘁𝗲𝗽 𝟭: 𝗦𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 𝗕𝗿𝗮𝗶𝗻𝘀𝘁𝗼𝗿𝗺𝗶𝗻𝗴 We kick things off with a team brainstorm, which is usually remote, often messy, but always productive. We’ll start assembling our thoughts in Figma, guided by 5 prompts: 🎯 What’s the goal of this project? ⚙️ What functionality is a *must-have*? 📊 What data do we have? 👥 Who’s the audience, and what do they want to learn? 🗣️ Is there a core message or CTA? 𝗦𝘁𝗲𝗽 𝟮: 𝗠𝗼𝗼𝗱𝗯𝗼𝗮𝗿𝗱𝘀 Once we have a direction, we start pulling visual inspiration. We’ll scour media outlets, design platforms, blogs and of course, our 𝘋𝘢𝘵𝘢 & 𝘌𝘨𝘨𝘴 newsletter. 🥚 𝗦𝘁𝗲𝗽 𝟯: 𝗗𝗮𝘁𝗮 𝗜𝗻𝘃𝗲𝘀𝘁𝗶𝗴𝗮𝘁𝗶𝗼𝗻 Now it’s time to dig into the data and look for trends, outliers, and storylines to highlight. We’ll put together a short doc or deck with the insights we’ve found. 𝗦𝘁𝗲𝗽 𝟰: 𝗡𝗮𝗿𝗿𝗮𝘁𝗶𝘃𝗲 𝗢𝘂𝘁𝗹𝗶𝗻𝗲 We draft a loose outline to align on the story structure. It includes a written description of all the charts and features we’re imagining, which helps guide the design process. Ideation is one of the most energizing parts of our work. And this process helps keep us grounded and creative. How do 𝘺𝘰𝘶 approach ideation? Any rituals, tools, or prompts that help you think better?