User Journey Mapping Tools

This is a pivotal time for business leaders to apply strategic foresight and systems thinking. Go beyond tariffs and stock market trends and consider the broader, longer-term impacts: 1. How might a trend toward AI deregulation in product safety affect the AI products my business relies on? 2. In what ways could shifts in immigration policy influence my workforce strategy for maintaining a competitive edge with emerging technologies? How could these policies reshape PhD talent pipelines? 3. How will evolving U.S. geopolitical relationships impact my third-party suppliers and global partnerships? 4. With the increasing influence of techno-politics, what new considerations emerge for my business strategy? Scenario planning is key in moments of change and uncertainty.

Strategic planning just got an AI upgrade – and it's a game-changer. Thrilled to share my latest #Fortune column, co-authored with some of my former colleagues at Boston Consulting Group (BCG). The reality: Even the best strategic planning suffers from human limitations – our biases, groupthink, and tendency to anchor future scenarios in past experience. When volatility rises, these constraints become dangerous blind spots. The breakthrough: Multi-agent AI platforms that simulate complex strategic scenarios with human-like behavioral patterns, but without human cognitive limitations. Think of it as having a boardroom full of AI agents – each playing regulators, competitors, customers, and other stakeholders – stress-testing your strategy 24/7 at a fraction of traditional costs. What we're seeing in practice: AI simulations identifying the same strategic moves as human workshops – plus new options humans missed entirely "Unknown unknowns" becoming "known unknowns" through expanded scenario modeling Strategic planning becoming more frequent, scalable, and accessible across organizations Leaders building confidence through pattern recognition across multiple simulation runs This isn't about replacing human strategic thinking. It's about augmenting it with tools that can explore a vastly wider range of futures, faster and cheaper than ever before. In an era where resilience drives outperformance, the organizations that upgrade their strategic planning capabilities first will have the advantage. Read the full piece: https://lnkd.in/eUNDT2WZ #AI #StrategicPlanning #BusinessStrategy #Leadership #GenAI #ScenarioPlanning #DigitalTransformation Leonid Zhukov, Ph.D, Maxwell Struever, Alan Iny Elton Parker David Zuluaga Martínez

We recently wrapped up usability testing for a client project. In the fast-paced environment of agency culture, the real challenge isn’t just gathering insights—it’s turning them into actionable outcomes, quickly and efficiently. Here’s how we ensured that no data was lost, priorities were clear, and progress was transparent for all stakeholders: 1️⃣ Organized Documentation: We broke the barriers— and documented on Excel sheet to categorize all observations into usability issues, enhancement ideas, and general comments. Each issue was tagged with severity (critical, high, medium, low) and frequency to highlight trends and prioritize fixes. 2️⃣ Action-Oriented Workflow: For high-severity and high-frequency issues, immediate fixes were planned to minimize potential impact. Ownership was assigned to specific team members, with timelines to ensure quick resolutions, in line with our fast-moving development cycle. 3️⃣ Client Transparency: A summarized report was shared with the client, showing the issues identified, the actions taken, and the progress made. This kept everyone aligned and built confidence in our iterative design process. Previously, I’ve never felt the level of confidence that comes from having such detailed and well-organized documentation. This documentation not only gave us clarity and streamlined our internal processes but also empowered us to communicate progress effectively to the client, reinforcing trust and showcasing the value of our iterative approach. It’s a reminder that thorough documentation isn’t just about organizing data—it’s about enabling smarter, faster decision-making. In agency culture, speed matters—but so does precision. How does your team balance the two during usability testing?

Customer discovery via functional prototypes + PostHog is night & day better than the old school way of asking for feedback on Figma mockups. Here's why: I get to observe actual user behavior instead of asking the user to guess how they might use my product. My favorite example of why this matters comes from a Sony Walkman user study. They asked a bunch of people what they thought about a yellow walkman and they said "so sporty! not boring like the black one!". And yet, when they were given the opportunity to take a walkman home after the study, everyone picked the black one. We learned a lot more from user behavior than we did expressed preferences. Here's my setup for now observing user behavior from prototypes: 1. Create a functional prototype in your favorite prototyping tool (Bolt, Lovable, Reforge Build, Magic Patterns, Claude Code) 2. Ask the prototyping tool to integrate PostHog analytics 3. Ask the prototyping tool to instrument key user actions in PostHog Then you get all of these ways of observing actual behavior: - DAUs \ WAUs \ retention curves - I can actually see if people come back and use my prototype instead of taking their word for it - Action metrics dashboards - I can see what actions people are taking vs not - Post-usage survey - I can add a built-in pop-up survey to ask the user a question about the experience after they have engaged with the prototype - Session replays - I can see exactly where people are clicking and how they are using the product to identify usability issues - Heatmaps - I can see what part of my design is working across all sessions I'd never go back to testing with just a mockup after this.

What happens to a company’s financial health when the economy takes a turn for the worse? Imagine: A business starts the year with a healthy cash reserve and manageable debt. But As the market shifts, they’re forced to dip into their revolving credit line. The cash cushion starts to shrink, and by the end of the forecast period, it’s gone. Meanwhile, current liabilities and short-term obligations that must be paid within a year remain high, putting added pressure on their liquidity. Now, Here’s where it gets tricky. Even though the company was paying dividends every year, their retained earnings were growing thanks to steady profits. But under this downside scenario, profits turn into losses. Retained earnings reverse course, and equity erodes. The balance sheet starts to tilt: liabilities rise, equity falls, and the company edges closer to breaching financial covenants. The lenders aren’t blind to these risks. They lower the loan-to-value (LTV) ratio meaning the company can borrow less against its capital expenditures. In the best-case scenario, they could secure 75% financing. But as the risk climbs, the LTV drops to 65%. Lenders also shorten the debt repayment period, ensuring they get their money back faster. This shift in capital structure is a stark reminder of how quickly financial stability can unravel. It underscores the importance of scenario planning in financial modeling preparing not just for growth but also for the storms that might come. According to a recent survey, 77% of CFOs identify liquidity management as their top priority during economic downturns. And yet, many companies still underestimate how quickly their cash position can deteriorate under pressure. This is why building a robust forecast, stress-testing your financials, and maintaining a proactive dialogue with lenders are more critical than ever. Have you experienced a shift in your company’s capital structure during challenging times? How did you navigate it?

The Microsoft-CrowdStrike "blue screen of death" crisis  (2024), Heathrow airport shutdown and Spain’s grid collapse reveal a brutal truth: risks cascade faster than most organizations anticipate. Are your crisis simulations still rehearsing textbook scenarios, or are they stress-testing against today’s interconnected threat landscape? Why Traditional Playbooks Fail ❌Static Assumptions: Most drills ignore how third-party risks intersect with regulatory non-compliance, supply chain bottlenecks, and operational dependencies, creating compounding vulnerabilities. ❌Overlooking Cascades: A single vendor failure (e.g., a critical supplier’s bankruptcy) can trigger multi-system breakdowns, disrupting production, logistics, and customer delivery networks. ❌Linear Thinking: Siloed scenarios (e.g., “cyberattack”) fail to simulate real-world chaos, such as unsecured endpoints enabling breaches that cascade into regulatory penalties, supplier delays, and revenue loss. Here's what we recommend-Crisis Backcasting Instead of just predicting the future (which is also important), backcasting works backward from worst-case scenarios to identify preventive actions. The Framework includes: ✅ Nonlinear Scenario Planning: Test how cloud outages, regulatory shocks, and infrastructure failures collide.   ✅✅ Dependency Mapping: Identify choke points (e.g., single-cloud vendors, centralized grids)... The next crisis won’t wait. Is your playbook ready? #CrisisPreparedness #CrisisSimulation #ThirdPartyRisk #OperationalResilience #RiskManagement #InterconnectedRisks #Backcasting #BusinessContinuity #LeadershipInCrisis

Designers, I used to think UX research was about talking to users as fast as possible. So I’d jump straight into interviews, surveys, usability tests, whatever felt right at the time. The problem? I’d come out with a lot of insights… and still struggle to make clear decisions. What I learned the hard way: Most research doesn’t fail because of bad tools. It fails because there’s no clear research plan. I made every classic mistake: → Vague research goals → Questions that quietly confirmed my own assumptions → Methods chosen because they sounded impressive → No clear idea of how insights would actually influence design Everything changed when I started planning research properly. Recently, I revisited my process using a guide from Lyssna, and it reinforced what experience had already taught me: a good research plan doesn’t slow you down, it saves weeks of rework. For example, on a checkout redesign project, my original question was: “Why are users dropping off?” Using a structured plan helped me reframe it into: → What assumptions are we testing? → Which user segment matters most right now? → What decision will this research unlock? That shift alone made the research more focused, easier to explain to stakeholders, and far more actionable. One quick tip that’s had the biggest impact on my work: Write research questions that drive decisions, not curiosity. Asking “Do users like this?” rarely helps. Asking “What prevents users from completing this on mobile?” actually does. If your research ever feels messy, hard to justify, or disconnected from design decisions, this is a solid reset. Lyssna’s user research plan guide walks through the process step by step, with real examples, and includes a free, practical template you can use immediately. User research plan guide + free template → https://lnkd.in/dtEB9p7e I hope that this will help you. Like & Repost, If you find this helpful. Share your thoughts in the comments. Enable notification 🔔 Don't forget to follow Abraham John #uiux #design #designgod #uidesign #uiuxdesign #uidesign #ui #uxdesign

Enterprise architecture initiatives are often planned around one future: a single target state and a single roadmap. In practice, the environment does not sit still long enough for that to hold. New regulations emerge, and advancements in AI alter where and how you create value. Most enterprise architects are trained as technologists. We are comfortable with facts, logic and pursuing certainty, yet less often with change and its implications for the organisation. Short-term delivery pressure then pushes many organisations to use EA as a drawing service or a technical advisor at best, focused solely on delivery rather than on how the enterprise adapts over time and strategic decision-making support. That is a missed opportunity. Enterprise architecture is both science and art: grounded in rigour, but dependent on innovation and creativity. Enterprise architects do not need to be the smartest people in the room, but they should be among the most imaginative — seeing patterns others miss, and helping leaders make sense of emerging possibilities. The job is to stay open to multiple futures while guiding decisions with the best knowledge the organisation has at that moment. Scenario planning gives enterprise architects a way to do exactly that. Scenarios can help create better-prepared organisations. When using scenarios in practice, here are some related concepts EA practitioners need to be aware of: → Horizon Scanning – Frame your focal question, scan for weak signals and emerging trends, then assess what each one demands of your strategy and architecture. → Scenario Development – Build 3–4 coherent, plausible futures (many foresight practitioners argue for at least four). These are not predictions or simple best- or worst-case scenarios, but distinct stories or narratives of how the future could unfold. → Wind-Tunnelling – Take your current strategies and test each against your scenarios. Find what breaks, what holds, and what you would regret locking in before you commit. Shape and test the strategy first, then design the technology to support it. → Designing for Optionality – Classify decisions by how hard they are to undo. Build multiple transition paths. Depending on whether you are exploring or shaping the future, consider using backcasting or wind-tunnelling to inform your roadmap with several ways of looking ahead, not just one. Visualise your roadmaps and options using artefacts such as sun-ray diagrams to improve communication. The infographic illustrates these four concepts and shows how to think about futures using the Futures Cone, with sources for each concept if you want to learn more.

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?

𝐖𝐡𝐲 𝐭𝐡𝐞 ‘𝐓𝐡𝐫𝐞𝐞 𝐀𝐥𝐭𝐞𝐫𝐧𝐚𝐭𝐢𝐯𝐞𝐬’ 𝐌𝐨𝐝𝐞𝐥 𝐢𝐬 𝐊𝐢𝐥𝐥𝐢𝐧𝐠 𝐂𝐢𝐭𝐲 𝐏𝐥𝐚𝐧𝐧𝐢𝐧𝐠 – 𝐀𝐧𝐝 𝐖𝐡𝐚𝐭 𝐖𝐞 𝐒𝐡𝐨𝐮𝐥𝐝 𝐃𝐨 𝐈𝐧𝐬𝐭𝐞𝐚𝐝 For decades, urban planning has followed the three-alternatives model, often leading to a hybrid fourth option—sometimes strategic, but often a reactionary mix of ideas. When done right, alternatives provide flexibility, but when built without data, scenario testing, or probability modeling, they can kill a city’s potential before it even takes shape. 𝗪𝗵𝗮𝘁 𝗚𝗼𝗲𝘀 𝗪𝗿𝗼𝗻𝗴? - Alternatives without scenario-driven foundations lead to fragmented, uncoordinated urban growth. - Decisions based on hybridizing weak ideas instead of selecting the best-tested option. - Lack of probability-based forecasting, making urban expansion a guessing game. 𝟭𝟮 𝗦𝘁𝗲𝗽𝘀 𝘁𝗼 𝗖𝗿𝗲𝗮𝘁𝗲 𝗦𝗺𝗮𝗿𝘁𝗲𝗿, 𝗦𝗰𝗲𝗻𝗮𝗿𝗶𝗼-𝗗𝗿𝗶𝘃𝗲𝗻 𝗔𝗹𝘁𝗲𝗿𝗻𝗮𝘁𝗶𝘃𝗲𝘀 To prevent urban failure, alternatives must be built on data, probability models, and scenario forecasting. Here’s how to do it right: 𝟭) Define Key Drivers Using Probabilistic Analysis – Identify economic, demographic, and climate trends using Monte Carlo simulations and historical data. 𝟮) Set Scenario Time Horizons & Probability Weights – Assign likelihood scores to different futures (Compact City = 60%, Sprawl = 30%, Decentralized Nodes = 10%). 𝟯) Use Bayesian Forecasting for Data-Driven Projections – Refine infrastructure demand and land use forecasts based on real estate and economic trends. 4) Develop Multiple Scenarios with Risk Probability Scores – Avoid single-outcome planning by testing multiple futures under different policy and economic stress tests. 5) Translate Scenarios into Spatial Alternatives – Ensure each alternative directly reflects a tested scenario, not just an arbitrary layout. 𝟲) Test Alternatives Against Economic & Environmental KPIs – Use real estate absorption models, climate risk scores, and probability-adjusted cost-benefit analysis. 𝟳) Factor in Policy & Regulatory Risks – Model zoning law changes, governance shifts, and regulatory enforcement trends to prevent future conflicts. 𝟴) Incorporate Economic Feasibility & ROI Projections – Use discounted cash flow (DCF) modeling to assess long-term financial sustainability 𝟵) Avoid Arbitrary Hybridization—Use Data to Justify Merging Alternatives – Only combine alternatives if probability models show compatibility, not as a political compromise. 𝟭𝟬) Engage Stakeholders & Test Probabilities with Digital Simulations. 𝟭𝟭) Plan Phased Implementation Based on Infrastructure Readiness – Align urban expansion with stochastic forecasting of infrastructure demand. 𝟭𝟮) 𝗦𝘁𝗿𝗲𝘀𝘀-𝗧𝗲𝘀𝘁 𝗦𝗰𝗲𝗻𝗮𝗿𝗶𝗼𝘀 for Black Swan Events – Model low-probability, high-impact disruptions 𝙏𝙝𝙚 𝘽𝙤𝙩𝙩𝙤𝙢 𝙇𝙞𝙣𝙚: 𝙋𝙡𝙖𝙣𝙣𝙞𝙣𝙜 𝙒𝙞𝙩𝙝𝙤𝙪𝙩 𝙎𝙘𝙚𝙣𝙖𝙧𝙞𝙤𝙨 𝙇𝙚𝙖𝙙𝙨 𝙩𝙤 𝙐𝙣𝙘𝙚𝙧𝙩𝙖𝙞𝙣𝙩𝙮 #urban_planning #Urban_design #cityplanning