Mental Model Alignment

𝗘𝘃𝗼𝗹𝘃𝗶𝗻𝗴 𝘁𝗵𝗲 𝗺𝗲𝗻𝘁𝗮𝗹 𝗺𝗼𝗱𝗲𝗹𝘀 𝗳𝗼𝗿 𝗣𝗿𝗼𝗰𝘂𝗿𝗲𝗧𝗲𝗰𝗵 Are you seeing what i am seeing? There is a gradual shift taking place in the space of Procurement technology. New solutions like a Guided Buying solutions i reviewed recently do not meet any conventions of classical Procurement software solutions any longer. They are breaking the mental models we are used to carry as references of what a good solution look and feels like. How we expect procurement solutions to be used and to work is being redefined. What's driving this shift? I'd say mainly two factors: ▪️ An infusion of AI capabilities into platforms ▪️ Changing expectations by new generations So what's shifting? Our evaluation criteria for Procurement technology. Find here are six key shifts in mental models i see taking place: 1️⃣ 𝗟𝗶𝗻𝗲𝗮𝗿 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀 → 𝗮𝗱𝗮𝗽𝘁𝗶𝘃𝗲 𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗲𝘀 Legacy solutions are based on sequential workflows based on rigid step-by-step tasks. New solutions adapt dynamically to scenarios and context. 2️⃣ 𝗠𝗮𝗻𝘂𝗮𝗹 𝗲𝗳𝗳𝗼𝗿𝘁→ 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝘁 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 Tedious data entry by users is increasingly automated through automation and AI. 3️⃣ 𝗦𝘁𝗮𝘁𝗶𝗰 𝘀𝘂𝗽𝗽𝗼𝗿𝘁 → 𝗽𝗿𝗼𝗮𝗰𝘁𝗶𝘃𝗲 𝗮𝘀𝘀𝗶𝘀𝘁𝗮𝗻𝗰𝗲 Legacy tools provide search-based support options while new tools provide smart, interactive assistance across the process. 4️⃣ 𝗖𝗼𝗺𝗽𝗹𝗲𝘅 𝗺𝗲𝗻𝘂-𝗯𝗮𝘀𝗲𝗱 𝗻𝗮𝘃𝗶𝗴𝗮𝘁𝗶𝗼𝗻 → 𝗰𝗼𝗻𝘃𝗲𝗿𝘀𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗶𝗻𝘁𝗲𝗿𝗳𝗮𝗰𝗲𝘀 Instead of drilling down into complex menus, users can now interact via natural language and ask specific questions such as "What are my top suppliers this month?". 5️⃣ 𝗦𝗶𝗹𝗼𝗲𝗱 𝗱𝗮𝘁𝗮 → 𝗶𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗲𝗱 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀 & 𝗿𝗲𝗰𝗼𝗺𝗺𝗲𝗻𝗱𝗮𝘁𝗶𝗼𝗻𝘀 Disconnected data and guess work has given way to unified, actionable insights that allow procurement professionals to act more quickly. 6️⃣ 𝗚𝗲𝗻𝗲𝗿𝗶𝗰 𝗲𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲 → 𝗵𝘂𝗺𝗮𝗻𝗶𝘀𝗲𝗱 𝗱𝗲𝘀𝗶𝗴𝗻 New generations coming from a mobile first environment, demands sleek, intuitive tools with a focus on personalisation adapted to their own ways of working. Generic solutions and clunky UI's don't cut it any longer. This isn’t just about adopting new tools. It’s about recognizing the fundamental shift in how we design, evaluate and use Procurement technology. The infusion of AI and the expectations of younger generations are forcing us to rethink what makes a solution valuable, effective and user-friendly. A transition where trust in data and responsible, autonomous actions of technology has to be earned. It’s an evolution into next-generation Procurement which cannot be avoided. What’s your view on this? Have you noticed a change od mental models?

Mental Models Are Better Than Market Forecasts Every year I get asked: “Where do you think the market’s going?” Every year, my answer stays the same: “That matters less than how you’ll think when it doesn’t go there.” I’ve learned this both in markets and in managing my own family’s finances: It’s not the forecast that saves you. It’s the framework. Markets will surprise you. That’s their job. But your reaction? That should be scripted. Calm. Boring, even. Because consistent thinking beats clever guessing. Every time. Here’s how I think now: – What if I’m wrong? – What assumptions am I blindly trusting? – Who else does this depend on? That’s a mental model. Not a prediction—just preparation for when reality doesn’t follow your chart. Books like Superforecasting and Poor Charlie’s Almanack prove it: The best aren’t the most accurate. They’re the most mentally organized. What does this look like in real life? – A plan for what to sell before the market drops – A premortem, not just a projection – Written assumptions for every major financial move – Simple “if/then” scripts: “If volatility spikes, then I rebalance — not retreat.” Ask yourself: – Am I planning for what I want—or how I’ll think when I’m wrong? – Do I have more data, or better decisions? – What will I actually lean on when it gets hard? Forecasts feel useful. Until they aren’t. That’s why I use mental models. For myself. For the families I help. For the moments when it matters most. #beprepared (Part of an ongoing series on thinking clearly, planning calmly, and building real resilience.) Image Credit: RobertoFerraro.art

Ever wonder why behavior change is so difficult in organizations? Whether it's technology adoption or performance challenges, we often oversimplify the solution. As an organizational psychologist, I've spent two decades watching smart leaders struggle with the same question: "Why aren't people doing what we need them to do?" What makes the difference? Having a mental model that captures the complexity of human behavior without being overwhelming. This diagnostic framework has been my secret weapon in countless conversations. Recently, a CTO was frustrated with low adoption rates of their new collaboration platform. Using this model, we quickly identified that users had the knowledge and skills but lacked motivation because the context didn't support performance—their incentives and team structures were misaligned with the new behaviors. The beauty of models like this is they transform vague frustrations into actionable insights. They help us see that performance and behavior change aren't just about individual choices—they're about systems. I've carried frameworks like this in my head throughout my career, but at Fractional Insights we finally put it on paper to support our work with leaders and students. It's amazing how a simple visual can transform a complex conversation. What mental models or frameworks have you found most helpful when diagnosing performance issues or driving behavior change in your organization? #BehaviorChange #PerformanceImprovement #OrganizationalPsychology #LeadershipTools #TechnologyAdoption

The cost of a bad mental model in business can be staggering. One I’ve been thinking about recently is the model we use to judge people in the workplace. Today, in the work environment, we often rely on relics of the primal mental model which doesn't work at all. In hiring, promoting, assigning work and more, we ask ourselves: What does this person look like? Are they imposing and confident, or are they meek and questioning? Do they look like me? Do they remind me of the people I most associate with? These things tell us nothing about how well a person can help the business meet its stated goals. If we don't switch to a better mental model, we will operate with major blind spots and pass up huge opportunities. Our tendency to regard surface-level traits as proxies for performance runs deep. Studies repeatedly show that hiring managers favor people who share lifestyle markers (like hobbies and group affiliations) with them, even though these lifestyle markers have no bearing on the person’s ability to deliver results in the job. Haircuts, clothing, accents, backgrounds, personality quirks—all these factors can drive us toward or away from a person despite their irrelevance to performance. So what’s a better mental model for the work environment? I propose a model something like this: Each individual in the workplace consists of a combination of unique strengths and behaviors. As a leader, it is my job to understand which of those strengths and behaviors best serve our team’s mission. How do you understand those unique strengths and behaviors of other people? One way is through simple observation and thinking, but tools like the User Manual, CliftonStrengths, and DISC can help you apply the more sophisticated model as well. Because “getting the right people on the bus” is mandatory—and because those “right people” are scarcer than ever—CEOs are shooting themselves in the foot when they allow their animal brains to push them toward or away from certain people based on meaningless factors. Much better is to switch to a mental model that allows you to identify the people who will actually move your business forward, even if they look different than you expected.

In my classroom, we face an unexpected challenge: students struggle with what I call the "AI cognitive dance": The complex back-and-forth between asking AI questions and using it to generate content. This journey has revealed something striking: the process is impossible without what decades of research shows us about how students build understanding. As we watch students interact with AI, we're discovering it's not just about teaching AI skills. It's about helping them construct the mental frameworks needed to navigate this new landscape. The solution, I've realized, lies in how students naturally build understanding. Here's a concise 10-list of principles reshaping our approach: 1. Start with familiar ground - connect AI interactions to known learning strategies 2. Build from experience - use students' existing questioning patterns as foundation 3. Create clear pathways - develop explicit connections between inquiry and generation 4. Recognize patterns - identify productive vs. unproductive AI dialogues 5. Practice mode-switching - deliberately toggle between asking and creating 6. Map mental models - visualize the relationship between different AI functions 7. Generate frameworks - help students build personal systems for AI interaction 8. Test and refine - actively check understanding through practical application 9. Build bridges - connect AI outputs to existing knowledge structures 10. Develop intuition - foster natural recognition of when to switch modes These principles guide us as we help students develop stronger mental models for AI interaction. They've transformed our approach from teaching isolated AI skills to building integrated understanding. This journey has reinforced my belief: the challenge isn't teaching AI use - it's helping students construct the mental frameworks needed for effective AI interaction. Important note: Every one of these insights applies to educators, researchers, and professionals learning to integrate AI into their work!!! How are you helping learners develop better mental models for AI interaction? What patterns have you noticed in successful AI learning? Let's continue this crucial conversation. #AIinEducation #LearningScience #EdTech #TeachingInnovation #AILiteracy #PragmaticAISolutions Anna Mills Anna Shildrick Jessica L. Parker, Ed.D. Jessica Maddry, M.EdLT Jessica Ann Amanda Bickerstaff Mike Kentz Lance Eaton, Ph.D. Dr. Lance Cummings Alfonso Mendoza Jr., M.Ed. David H. David Hill Melanie Aco Dr. Sabba Quidwai Sabrina Ramonov 🍄Vriti Saraf

8 Mental Models That Made a Difference in Biotech and Boardrooms I've helped launch 9 global therapies-and I'd say the biggest lessons had nothing to do with science. They had more to do with how we think when the pressure hits. Here are 8 mental models I rely on - whether I'm running clinical ops, in a board room, or advising founders. 1. Inversion 🔲 Ask: What would cause this to fail? Then solve backward. In biotech, the riskiest problems are often predictable - but avoided. 2. First Principles Thinking 🔲 Strip it down to fundamentals. Forget "this is how it's always done." Start with: What are we solving, and why? I've restructured entire timelines because "standard" wasn't logical for our patient population, and reduced a 5 year study to 2.5 years. 3. The 80/20 Rule 🔲 Focus on what matters most. In trials: 20% of sites often deliver 80% of enrollment. 4. The Law of Compounding 🔲 Small issues - unresolved - become cliffs. Data delays, slow amendments, unchecked assumptions: they add up. Don't let "it's just one week" fool you. 5. Second-and Third-Order Thinking 🔲 Don't solve the problem. Anticipate the ripple. If we drop this cohort, what will that signal to investors? What doors will it close? 6. Complication Tendency 🔲 Biotech loves complexity. But complexity kills speed. Clarity clearly beats when timelines matter. 7. The Integrity Gauge 🔲 Say the hard thing early. In crisis moments, the most strategic act is honesty - delivered with diplomacy. 8. Multidisciplinary Thinking 🔲 No one function sees the full picture. The best decisions I've made came from integrating safety, biostats, data science, medical, regulatory and much more - and patient voice. What mental model changed the way you lead or invest? Would love to hear yours! Especially the ones that helped in high-stakes moments. These models were shaped by trial-and-error, and helped me build, lead, and recover - again and again. Heavily inspired by Charlie Munger's Poor Charlie's Almanac - a blueprint for clear, multidisciplinary thinking. If you've never read it, it's a masterclass in mental clarity and multidisciplinary thinking: https://lnkd.in/eSvWXB5V

Mental Models vs. Beliefs: A $1M Difference in Therapy Words shape outcomes. Sometimes a million dollars worth. Most successful people don't fail because they're wrong. They fail because they labeled their thoughts wrong. Call it a "mental model": - Your brain treats it like software - Upgradeable - Temporary - Easy to discard Call it a "conviction": - Becomes your identity - Feels permanent - Personal - Hurts to question A founder's story: Version 1 (stuck): "My conviction is that great products always win." Version 2 (freed): "My mental model suggests great products have advantages." Same meaning. Different weight. Massive impact. The first version broke him. The second version let him adapt. Think of it this way: Your beliefs aren't you. They're maps you use to navigate. Maps can be wrong without the navigator being wrong. The best founders I know: - Treat everything as mental models - Even their core beliefs - Even their identity - Not because they believe less - But because they want to believe better Smart people don't have better ideas. They have better ways of updating them. Words shape reality. But more importantly: They shape how easily you can reshape reality.

Ever notice how some apps seem like no-brainers, while others just make you feel clueless? Most often, the difference can be found in design's ability to match up with our mental models 👉What are Mental Models? A mental model is what the user believes about the system (web, application, or other kind of product) at hand. Mental models help the user predict how a system will work and, therefore, influence how they interact with an interface. [Definition by nngroup] 👉 The Significance of Mental Models in UX Aligning UX with users' mental models means designing interfaces that instinctively feel natural and intuitive, delving into the pre-existing beliefs and expectations of the users. The path that the user follows within the application or web is continuous since, for him, the known design pattern exists, and this unburdens the mind, increasing even more satisfaction with the product. 👉 Examples in Action Navigation Menus: If the menus are placed where they are supposed to be, for example, on top of the pages, they give the site a chance of using mental models in web browsing, hence making it easier for users to navigate through the site. Iconography: The use of well-understood icons (e.g., trash for delete) reduces learning time and builds on users' past experience. Hence, during user testing, we always ask users to think aloud so that we can understand what they think, believe, and predict about what will happen next in the interface. What all do you notice while users think aloud during user testing? Follow Megha Dokekar🍁for more UX related content.

You’ve got no business trying to change anyone’s behavior... Unless you have a compelling reason for doing so. - If you’re planning on introducing a marketplace disruption... You need to know what your customers’ mental models are and whether your solution violates their “rules” for how they think it should be used. (Note: Mental models are representations of assumptions and habits that you formed from your experience or borrowed from the experience of others.) Asking research participants to verbally describe a product or service won’t do the job. Having them physically draw it out (even in the most rudimentary way) is critical. You need to see how it’s laid out, how it functions, and how they see it in action. - We call this technique "Memory Mapping." You ask your research participants to think about the situation you’re focused on and draw—from memory—the product or service they currently use. This technique won’t give you a reliable indication of consumers’ preferences or purchase intentions. But you’re not using it for that, anyway. Running them through this activity at the START, helps the interpretation of results at the END. That way, if any of the participants say they hate your idea (which is usually the case with disruptive ideas)... You’ll have a much better idea why. (See post about the Aeron chair: https://bit.ly/3NySc69) - Problems happen when there’s a disconnect between your end user’s mental model and yours. Designers know a lot about how their new ideas will work, but little about how people will actually interact with them. Conversely, end users know how they will (or would like to) interact with things, but not much about how you’d like to have them work. - Just to be clear, I’m not saying that you should never violate your consumers’ rules or mental models. Not at all. The point I’m making is that breaking models can be a good thing... As long as you have a compelling reason for doing so. - Imagine that one of your ideas is a new remote control with the power button in the lower left corner. If all of your participants drew remotes with power buttons in the upper RIGHT... You’ll know that if you insist on the lower LEFT placement... You’re going to need a much better reason than: “It looks kinda cool there.” If you don’t have one, you’ve got no business trying to change anyone’s mental models. - I know that may sound like a traditional focus group, but it’s not. Participants actually become part of a collaborative, creative process. To reach a feasible solution, you need to balance the magic, mystery, and creative intuition that lead you to your disruptive ideas... With the messy, real, unpredictable pressures of the market. In other words, you need to close the loop between the end user’s mental model and yours. - Curious if you've used this technique or anything like it? Let me know in the comments. #ideaskills #innovation #research #disruptiveinnovation #designthinking

As we transition from traditional task-based automation to 𝗮𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀, understanding 𝘩𝘰𝘸 an agent cognitively processes its environment is no longer optional — it's strategic. This diagram distills the mental model that underpins every intelligent agent architecture — from LangGraph and CrewAI to RAG-based systems and autonomous multi-agent orchestration. The Workflow at a Glance 1. 𝗣𝗲𝗿𝗰𝗲𝗽𝘁𝗶𝗼𝗻 – The agent observes its environment using sensors or inputs (text, APIs, context, tools). 2. 𝗕𝗿𝗮𝗶𝗻 (𝗥𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 𝗘𝗻𝗴𝗶𝗻𝗲) – It processes observations via a core LLM, enhanced with memory, planning, and retrieval components. 3. 𝗔𝗰𝘁𝗶𝗼𝗻 – It executes a task, invokes a tool, or responds — influencing the environment. 4. 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 (Implicit or Explicit) – Feedback is integrated to improve future decisions.     This feedback loop mirrors principles from: • The 𝗢𝗢𝗗𝗔 𝗹𝗼𝗼𝗽 (Observe–Orient–Decide–Act) • 𝗖𝗼𝗴𝗻𝗶𝘁𝗶𝘃𝗲 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲𝘀 used in robotics and AI • 𝗚𝗼𝗮𝗹-𝗰𝗼𝗻𝗱𝗶𝘁𝗶𝗼𝗻𝗲𝗱 𝗿𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 in agent frameworks Most AI applications today are still “reactive.” But agentic AI — autonomous systems that operate continuously and adaptively — requires: • A 𝗰𝗼𝗴𝗻𝗶𝘁𝗶𝘃𝗲 𝗹𝗼𝗼𝗽 for decision-making • Persistent 𝗺𝗲𝗺𝗼𝗿𝘆 and contextual awareness • Tool-use and reasoning across multiple steps • 𝗣𝗹𝗮𝗻𝗻𝗶𝗻𝗴 for dynamic goal completion • The ability to 𝗹𝗲𝗮𝗿𝗻 from experience and feedback    This model helps developers, researchers, and architects 𝗿𝗲𝗮𝘀𝗼𝗻 𝗰𝗹𝗲𝗮𝗿𝗹𝘆 𝗮𝗯𝗼𝘂𝘁 𝘄𝗵𝗲𝗿𝗲 𝘁𝗼 𝗲𝗺𝗯𝗲𝗱 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 — and where things tend to break. Whether you’re building agentic workflows, orchestrating LLM-powered systems, or designing AI-native applications — I hope this framework adds value to your thinking. Let’s elevate the conversation around how AI systems 𝘳𝘦𝘢𝘴𝘰𝘯. Curious to hear how you're modeling cognition in your systems.