Science-Based Decision Making

10 𝗜𝗻𝘀𝘁𝗿𝘂𝗺𝗲𝗻𝘁𝘀 𝗳𝗼𝗿 𝗙𝘂𝘁𝘂𝗿𝗲 𝗦𝗲𝗻𝘀𝗲𝗺𝗮𝗸𝗶𝗻𝗴 Navigating uncertainty isn’t just a challenge—it’s an opportunity for visionary leaders. By leveraging foresight-driven sensemaking, you can anticipate change more effectively, develop highly adaptive and antifragile strategies, and unlock transformative innovations in an early stage. Here are 10 essential, field-proven instruments to enhance your foresight and ability to shape a thriving future for you and your organization: 1️⃣ 𝗛𝗼𝗿𝗶𝘇𝗼𝗻 𝗦𝗰𝗮𝗻𝗻𝗶𝗻𝗴 – Detect early signals of emerging trends, risks, and opportunities to stay ahead of the curve. 2️⃣ 𝗧𝗿𝗲𝗻𝗱 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 – Identify ongoing trends, their drivers, and potential impacts on industries and societies. 3️⃣ 𝗖𝗿𝗼𝘀𝘀-𝗜𝗺𝗽𝗮𝗰𝘁 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 – Evaluate how different trends, events, or factors influence each other over time. 4️⃣ 𝗪𝗲𝗮𝗸 𝗦𝗶𝗴𝗻𝗮𝗹𝘀 & 𝗪𝗶𝗹𝗱 𝗖𝗮𝗿𝗱𝘀 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 – Recognize early indicators of change (weak signals) and prepare for high-impact, unexpected events (wild cards). 5️⃣ 𝗙𝘂𝘁𝘂𝗿𝗲𝘀 𝗪𝗵𝗲𝗲𝗹 – A visual brainstorming tool to map out direct and indirect consequences of a change or event. 6️⃣ 𝗗𝗲𝗹𝗽𝗵𝗶 𝗠𝗲𝘁𝗵𝗼𝗱 – A structured forecasting technique that gathers expert consensus to enhance decision-making. 7️⃣ 𝗦𝗰𝗲𝗻𝗮𝗿𝗶𝗼 𝗣𝗹𝗮𝗻𝗻𝗶𝗻𝗴 – Develop multiple plausible future scenarios to prepare for uncertainty and explore strategic options. 8️⃣ 𝗖𝗮𝘂𝘀𝗮𝗹 𝗟𝗮𝘆𝗲𝗿𝗲𝗱 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 (𝗖𝗟𝗔) – A deep analysis framework that uncovers different layers of meaning, systemic causes, and underlying worldviews. 9️⃣ 𝗧𝗵𝗿𝗲𝗲 𝗛𝗼𝗿𝗶𝘇𝗼𝗻𝘀 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 – Helps decision-makers think about present and future simultaneously by categorizing innovation and change into three time-based horizons. 🔟 𝗕𝗮𝗰𝗸𝗰𝗮𝘀𝘁𝗶𝗻𝗴 – Starts with a desirable future vision and works backward to identify necessary steps to achieve it. In an era of constant change and opportunity, these tools help you and your organization move beyond short-term thinking and develop long-term strategic foresight to drive imagination, innovation, and antifragility. 👉 Follow Ewa Lombard, PhD, and Sebastian Baumann for more insights on foresight, visionary leadership, and future-fit decision-making. Press 🔔 to stay updated on upcoming posts, articles, and our peer-reviewed papers on these topics. 👉 Find more info on our 2025 special 𝗙𝗨𝗧𝗨𝗥𝗘 𝗨𝗡𝗙𝗢𝗟𝗗𝗜𝗡𝗚 - exclusive visionary leadership retreats and trainings - at Gravity & Grandeur

The biggest lesson I have learned from years of working with data leaders - more dashboards do not create better decisions. In fact, the opposite is often true. I have seen executives paralyzed in rooms filled with a hundred metrics, each dashboard offering a different slice of the truth. The more dashboards we build, the harder it becomes to know where to look or what to do. What truly matters is not the quantity of dashboards, but the clarity of the narrative. --- One clear signal is worth more than fifty colorful charts --- The best data leaders I know act not as builders of endless dashboards, but as ruthless simplifiers. They kill most dashboards, protect a few, and translate those into a story that drives action. This is not only intuition. Research confirms it. Studies in decision science show that simplified dashboards consistently lead to faster and more accurate decision making than complex ones (for example, https://lnkd.in/dFbkSJjg).

Many CRO/experimentation programmes are an exercise in cycling through random ideas and best practices to see if any move a metric. There is nothing wrong with this per se, and in fact, some very interesting things can come from such accidental finds. But are you really learning anything, or growing and innovating your business strategically? Think about the Apollo moon missions, arguably the greatest of all human achievements. This succeeded not because of some individual experiments on rocket propulsion or anything else, but because of the genius in its systems management and coordinated learning and development. Imagine if they had just told everyone to test a load of ideas and see what happened. True experimentation begins with strategy and theory, and seeks to use experimentation to develop those theories and make bigger strategic decisions and pivots. The example shown is a real example for a retail brand. They wanted to appeal to a younger demographic and did not know how this could be achieved. The chart demonstrates the process of critical thinking that breaks down this challenge, first into strategic hypotheses (that this would be achieved either through changes to product OR to media and targeting) and then into more testable functional hypotheses and then experiments. These experiments are not necessarily A/B tests and run across the whole business. Experimentation is a strategic methodology for innovation and growth, but it requires careful centralised management, coordination and operating systems. Many businesses think they 'tick the box' of experimentation because they have an A/B testing platform and someone in the corner of a marketing team with a login for it. This is not experimentation, and you will not learn or grow with this approach. #experimentation #cro #productmanagement #growth #digitalexperience #experimentationledgrowth #elg #growthexperimentation

Executives 𝗱𝗼𝗻’𝘁 𝘄𝗮𝗻𝘁 more data, they 𝘄𝗮𝗻𝘁 meaning. 𝗗𝗮𝘁𝗮 → 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀 → 𝗦𝘁𝗼𝗿𝘆𝗹𝗶𝗻𝗲 #𝟭 𝗟𝗲𝘁’𝘀 𝘀𝘁𝗮𝗿𝘁 𝘄𝗶𝘁𝗵 𝗗𝗮𝘁𝗮 (𝘁𝗵𝗲 𝗿𝗮𝘄 𝗳𝗮𝗰𝘁𝘀) - This is what happened.  - Numbers, tables, charts, percentages → all factual but not yet meaningful. - Example: “Digital transactions grew 12% last quarter; branch transactions fell 5%.” → By themselves, these don’t tell management why it matters or what to do. #𝟮 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀 (𝘁𝗵𝗲 𝗺𝗲𝗮𝗻𝗶𝗻𝗴) - This is what it implies. - You interpret patterns, connect causes and effects, and draw implications. - Example:  “Customer behavior is shifting toward convenience; traditional customers are adopting digital but still depend on branch staff.” → You’re starting to tell the message, but it’s still fragmented. #𝟯 𝗦𝘁𝗼𝗿𝘆𝗹𝗶𝗻𝗲 (𝘁𝗵𝗲 𝗻𝗮𝗿𝗿𝗮𝘁𝗶𝘃𝗲) - This is how you structure the insights, so people understand the story. - Example: “Digital usage among our customers has grown steadily. ..However, branch workloads remain high because older users haven’t migrated to the app. ..This signals the need for targeted onboarding programs and KPI alignment to drive digital adoption.” Most decks 𝘀𝘁𝗼𝗽 𝗮𝘁 𝗱𝗮𝘁𝗮. Great decks connect the dots into a 𝘀𝘁𝗼𝗿𝘆. 👉 More at: aseptamar.com https://lnkd.in/gMhS9-jK

Human forecasters augmented by GenAI improve performance by 23% and vastly outperform AI-only predictions. Fascinating new research has uncovered important lessons, not just on Humans + AI forecasting, but more generally AI-augmented thinking. 🔮Human forecasters provided an LLM with a 'Superforecaster' prompt substantially improved their prediction performance. 📊In contrast to studies in other domains, the improvement was consistent across more and less skilled forecasters. 🔄Even the use of biased models improves performance to a similar degree, showing that the value was in providing additional perspectives to be assessed by human judgment. 💬Back-and-forth interaction is critical to value creation. Simple Humans + AI thinking processes such as incorporating predictions is of limited use. Forecasters using the models through their thinking process is high value. 🌈Prediction diversity is not degraded by use fo LLMs, with users not letting the models homogenize their thinking. 🚀Forecasting is an excellent use case and example for AI-augmented thinking. High-level human decision-making is highly complex and cannot be delegated to machines, but LLMs, used well, can substantially improve outcomes. The 'Superforecaster' prompt used in the study and a link to the pre-print paper are in the post. #foresight #forecasting #humansplusai #augmentedintelligence

The world's most valuable skill is critical thinking. Here are 3 decision-making frameworks that will save you dozens of painful hours trying to learn critical thinking for yourself: 1. Chip and Dan Heath's WRAP Framework The measure of a good decision isn't the outcome you produce, but the process you use to make it. Learning this completely changed the way I thought about decision-making, and the importance I placed on process. According to the Heath Brothers, you can overcome common decision biases like narrow framing, confirmation bias, short-term emotions and over-confidence by using these four steps for every significant choice you make. W - Widen your Options R - Reality Test Your Assumptions A - Attain Distance P - Prepare for the Worst. --- 2. Greg McKeown's Essentialism Framework Hang this up in your room somewhere—and stare at it everyday. Greg McKeown, in his book Essentialism, makes the case that the highest point of frustration occurs when we're trying to do everything, now, because we feel like we should. In order to reach the highest point of contribution, we need to do: The Right Thing, at The Right Time, for The Right Reason. When we focus on these three variables, we don't waste time and energy on activities and decisions that aren't a right-fit.  --- 3. Tim Ferris' Fear-Setting Framework I consider this the gold-standard of strategic risk management and contingency planning. Important decisions will always come with risks, consequences and unforeseen problems. Instead of trying to eliminate the negative and plan for the best, Ferris advises people to complete a pre-mortem that simulates potential responses. By drawing up a three column table with: The worst things that might happen The steps you can take to prevent those The ways you will respond if they do happen You're able to prepare for a more pragmatic future, rather than being thrown off course at the first unexpected obstacle. For more information on fear setting, and some useful downloads, check out Tim's blog here. These three frameworks completely changed the way I thought about decision-making, and the support I was able to offer leaders in developing the skills they needed to keep tricky programmes on track. I hope they're useful for you. #leadership #decisions #NotAnMBA

🧠 Cognitive Bias Index (https://lnkd.in/eXVaxUHh), a helpful little tool to identify and mitigate systematic errors in thinking that affect the decisions and judgments that humans make — explaining how it occurs, how to avoid it, tools to help counteract cognitive biases and useful articles for further reading. Put together by Jon Yablonski. No product is neutral, and no design decisions exist independently from the environment in which they are made. Every person working on a product contributes to it — through their own perspective, opinion, experience, point of view, but also personal biases and assumptions. If we can spot cognitive biases early in conversation and put them in question — similar to how we deal with big hunches and lack of research — we can increase the chances of solving a problem that's worth solving. And we can find an effective solution that works for people, not just for *some* people. In many teams, designers and researchers are the only folks who frequently flag those biases and misconceptions. Which is why we are often perceived as disruptors, difficult people, people with strong opinions. In wise words of 👨🏻💻 Andy Budd: “Designers sound negative (and that’s a good thing). The best teams don’t treat pessimism as a threat. They treat it as insurance. Optimism gets you off the runway. Pessimism keeps you from flying blind. So if a designer raises a concern, don’t see it as friction. See it as foresight. They’re not trying to kill your vision. They’re trying to make sure it survives first contact with reality.” --- 💎 Useful resources: Cognitive Bias Patterns (Cards), by Robert Meza 👍🏽 https://lnkd.in/d5ACMEEa Cognitive Biases In Product Design, by Jeremy Miller 👍🏽 https://lnkd.in/eWZDE8T8 How To Reduce Cognitive Load In UX, by yours truly https://lnkd.in/eR9MEXJF Psychology Insights Cookbook, by Jerome Ribot 💎 👍🏽 Docs: https://lnkd.in/epGNUd9j Figma: https://lnkd.in/gHEU4R9Y Laws of UX, by Jon Yablonski 👍🏽 https://lawsofux.com UX & Psychology: Guides and Cheatsheets (Miro, Figjam) https://lnkd.in/ejNCiNSn Why Designers Sound Negative (And That's A Good Thing), by 👨🏻💻 Andy Budd https://lnkd.in/emAGy8uB ↓

When you're launching something new, you want to be sure it's going to work. Running in-market experiments prior to launch confirms hypotheses before you commit resources. Just as important,  experiments can often prevent big missteps. Here are four rules of thumb that make for powerful experimentation: 1. Test more than one concept or proposition with more than one target market segment. Sure, you can test just one concept with just one target, but you'll only learn if it succeeded or failed. If you test several concepts in parallel with more than one target, you can compare performance by audience and start to understand the drivers of success across concepts. 2. Make sure that tested concepts are distinct and differentiated. Each concept should be unique because the goal is to learn as much as possible. If you only test three shades of blue, you'll never learn that people actually want red. 3. Test more than once. As you see 'hot spots' form between concept and audience, test variations of your winning concept. Let’s say, for example, that you test three distinct versions of your new product concept—let’s call them Red, Yellow, and Blue. In the first experiment, Red tests well with all three of your target audience segments. In the next experiment, test three versions of Red with all three segments. This next experiment might explore value propositions or particular features or positioning. It’s a way to generate additional learning about strategy: →What problem does Red solve for customers? →Which features drive interest in Red? →Which positioning helps to interest people in Red? 4. Be aware of your testing environment and how it creates bias (or not) for your experiment. I prefer real-life in-market experiments, with just enough exposure to generate statistically valid results; others prefer ‘lab-based’ testing. Either way, think about how representative your environment is of your eventual launch. The next time you’re making a big move, remember: experiments are a powerful way to reduce risk, whether you are launching a new product, repositioning a brand, or prioritizing a product pipeline. Happy experimenting! #LIPostingDayJune

When belief filters the evidence. The bias that protects its own view. A contributor misses a deadline. One mistake. The label forms: “Not ready.” Past wins lose weight. New input gets read to match the label. Confirmation bias: judgment that looks for support. Favored hires get credit for small wins. Others get flagged for the same behavior. Women marked “collaborative” rarely get tagged “leader.” Neurodivergent talent delivers results and still gets called inconsistent. Early views resist change even when stronger data shows up. Hiring leans on “fit” from limited input. Investors overlook flaws in those already favored. Then the system scales it. Models learn from past decisions. The same errors repeat. Evidence gets filtered. Judgment holds. What to do as an individual: Show results that don’t match the label. “Q3 revenue doubled. Does that change it?” State it once. Let it land. What to shift as a leader: Slow the call. For each yes, ask what you may miss. For each concern, ask what would disprove it. Review decisions without names. What to test in systems: Change the order of review. Require one counterexample. Test identical cases with new framing. Track what changes. Belief shapes what gets seen. Opposing evidence corrects it. Where are you protecting a view instead of testing it?

Your Team’s Brain is Leaking. Here’s How to Stop It Your company’s intelligence is leaking every single day. You’re hiring great people, they’re learning on the job, making decisions, solving problems… and then? That knowledge evaporates into thin air the moment they move on, switch roles, or simply forget. Meanwhile, you’re constantly asking, Why are we solving the same problems over and over? The truth is, most organisations treat knowledge like a one-time transaction instead of a strategic asset. → Training programs? Already outdated by the time they’re implemented. → Standard knowledge management? Too rigid. → SOPs? Too static. What we need is 'corporate collective intelligence'. An evolving, self-scaling system that captures, refines, and distributes knowledge seamlessly, so our team gets smarter as it grows. Here’s how you start: - Turn conversations into intelligence. Your best insights happen in Slack threads, meetings, and problem-solving sessions. Capture and refine them as they happen. - Make tacit knowledge explicit. The way your best performers make decisions? That’s gold. Codify it before it disappears. - Use AI and automation wisely. Stop treating AI as a gimmick. It should be actively structuring, indexing, and surfacing knowledge, not just summarising documents. - Create a feedback loop. Your organisation should be learning from itself in real-time. No more one-and-done knowledge drops, continuous refinement is key. → Teams that scale without bottlenecks. → Faster decision-making with fewer mistakes. → Institutional knowledge that doesn’t walk out the door. The companies that master this won’t just scale - they compound. Those that don’t? They will keep reinventing the wheel. Which one do you want to be? Found this useful? Repost ♻️ to help your network.