Knowing vs Doing Many L&D teams continue to resort to eLearning for their upskilling and reskilling needs. It’s scalable. It’s consistent. It’s easy to deploy. But there’s a catch. eLearning—especially when it's passive and content-heavy—is not enough. If we want to build real capability in our organizations, we must move beyond content consumption and invest in skills practice . In short: e Learning is not doing.  And doing is what changes behavior, builds fluency, and creates value. The Illusion of Learning Traditional eLearning is often structured like a digital textbook. It scaffolds information, reinforces it with short quizzes, and moves learners from one topic to the next in a linear fashion. For example, in a course on project management, learners might move from budgeting to resourcing to scheduling to closing—ticking boxes and passing knowledge checks along the way. How eLearning would typically be used to teach project management On the surface, this looks like progress. People complete the learning. Learners gleaned some good points. L&D reports that “everyone took the training.” But here's the problem: knowing isn’t the same as doing . Without application, that knowledge sits idle. It fades. Or worse—it gives people a false sense of confidence. They’ve passed the quiz, but they haven’t tested their judgment in a dynamic situation. They haven’t made a decision under pressure. They haven’t seen how one choice can ripple across a project—or a team. And in today’s volatile, AI-augmented world, that gap between knowing and doing can be the difference between mediocrity and mastery. What Skills Practice Does Differently Now consider an alternative: skills practice. This approach places learners directly into realistic scenarios where they must make decisions and experience the consequences of those decisions. Take project management again. Instead of simply learning that "resource allocation is important," a learner might be asked to decide whether to divert a key team member from one task to another—and then watch as the timeline slips, morale drops, or another deadline is saved. In this skills practice, the learner quickly realizes that project management is not a checklist—it’s a system . How Skills Practice teaches project management In this system, every decision is interconnected. A trade-off in one area creates tension in another. Learners must balance cost, time, quality, and stakeholder expectations—not in theory, but in context. And when they fail? They learn more deeply. They reflect. They try again. This is the kind of learning that builds capability , not just knowledge. The Power of Systems Thinking Skills practice unlocks what eLearning often cannot: systems thinking . When people engage in real-time simulations or decision-based scenarios, they begin to see how their choices affect the larger system—how short-term wins can create long-term problems, how a quick fix in one area can cascade into another. They move from thinking linearly (If I do X, then Y will happen) to thinking systemically (If I do X, then Y might happen, but Z could be impacted—and that, in turn, affects A, B, and C). This kind of thinking is exactly what modern organizations need: agile, reflective employees who can see the big picture and navigate complexity with confidence. Skills Practice Builds Thinking Abilities What makes skills practice so effective is that it naturally develops the Core Abilities : Critical thinking : Learners must assess information, evaluate trade-offs, and decide what matters most. Creative thinking : They must generate options, adapt when their plan fails, and try unconventional solutions. Systems thinking : They must consider unintended consequences, feedback loops, and long-term outcomes. These abilities are what fuel Value Skills  like problem solving, decision making, and collaboration—skills that AI can’t easily replicate, and that organizations desperately need. What L&D Needs to Do Differently If you’re in learning and development, it’s time to ask some hard questions: Are we teaching content or building capability? Are our learners experiencing the kinds of situations they’ll face on the job—or just reading about them? Are we helping people connect knowledge to action, theory to consequence? To move the needle, we need to: Shift from “knowing” to “doing.”  Stop measuring completions and start measuring application. Invest in immersive practice.  Use simulations, scenario-based learning, and problem-based activities that mimic real decision-making. Teach thinking, not just tasks.  Build the Core Abilities that help people navigate ambiguity and make better choices—especially when the path isn’t clear. Reframe failure as feedback.  Create safe environments where people can get it wrong, reflect, and try again. That’s where the learning really happens. Final Thought: Practice Is the New Learning The future of L&D is not more content. It’s better experiences . Experiences that challenge learners to think, decide, adapt, and grow. In the age of AI, the organizations that thrive won’t be those with the best training libraries. They’ll be the ones with the most capable people—people who’ve had the chance to practice  the skills that matter. So, let’s stop confusing exposure with expertise. Let’s start giving people the opportunity to do the work before they have to do the work . That’s not just better learning. That’s better performance.

The question isn't “Are your people learning?”—it’s “Can they perform ?” And the uncomfortable truth is: most training modalities aren’t built to answer that. Skill Partice Learning goes beyond traditional training modalities by focusing on performance. This isn’t another take on simulations or scenario-based training. This is about creating real moments that matter—where employees engage with complex decisions, test their assumptions, expose their blind spots, and sharpen their ability to think and act with clarity. It’s practice, not content, that changes behavior. And Skill Practice Learning is the modality that makes it possible. Why Traditional Training Falls Short Workshops, eLearning, and coaching are valuable, but they overwhelmingly fall into the “ what-to-think ” category. They tell learners what  great leaders do, or how  to follow a process. The problem? Knowing what  to do doesn’t guarantee knowing how  to think through problems, weigh trade-offs, or act under pressure. Most learners leave training confident—but that confidence is brittle. As soon as conditions change, stress mounts, or decisions deviate from the script, performance falters. Why? Because mental models —the deeply held beliefs that drive our decisions—haven’t been challenged or changed. In other words, people leave training feeling good. But they haven’t practiced being  good. Practice Surfaces Mental Models—So We Can Change Them Mental models are like operating systems. They determine how people interpret feedback, respond to stress, and make sense of complexity. But they’re invisible—and incredibly sticky. Skill Practice Learning is uniquely suited to surface these hidden models. In well-designed practice environments, learners experience the consequences of flawed thinking in real time. They can’t hide behind the right answer. They have to wrestle with the right approach . It’s this friction—the realization that “my usual way isn’t working”—that opens the door to true transformation. No bullet-point list, no polished video, no LMS module can do that. What Makes Skill Practice Learning Different Skill Practice Learning, when done right, replicates the messiness of real-world decision-making. It’s not linear. It’s not passive. And it’s not easy. Instead of teaching people what to do, it places them in dynamic, ambiguous situations and asks them to decide, reflect, adapt, and try again. It's an experiential loop that mirrors the way our brains form, challenge, and rewire mental models. This form of learning is grounded in U-shaped development : learners perform well when the scenario is simple, stumble as complexity exposes gaps in their understanding, and rise again as they reconstruct more accurate mental models. Unlike eLearning, which often peaks at awareness, Skill Practice Learning dives you to the bottom of the U (feels like confusion) —and emerges with clarity. Practice Changes the Brain The science backs this up. Neural pathways—our habits of thought—are shaped by repetition. But not just any repetition. For mental models to shift, people need: Emotional engagement : The learning must matter. Cognitive dissonance : Their current thinking must be tested. Reflective feedback : They must understand the impact of their decisions. Systemic insight : They must see the bigger picture behind what’s happening. Skill Practice Learning brings all four together. It’s not a worksheet or a quiz. It’s a living, breathing system where decisions ripple forward, where biases are confronted, and where insight is earned. And perhaps most importantly: learners practice thinking , not just knowing. Behavioral Analytics: The Window into How People Really Learn At the heart of Skill Practice Learning lies a powerful engine: behavioral analytics. This isn’t just data collection—it’s a window into the learner’s mind. Behavioral analytics tracks how learners behave within a practice environment—how they respond to complexity, adapt to feedback, collaborate under pressure, and recover from failure. Rather than measuring what someone knows, it reveals how they think, decide, and act. This data can answer critical questions for L&D teams: Are our people defaulting to reactive decisions or pausing to think systemically? Do they tend to guess when uncertain, or seek out more data? Are they improving their performance across rounds, or repeating the same mistakes? Where do they struggle most: collaboration, problem definition, or prioritization? How do different teams approach the same challenge—and what does that say about our culture? When combined with feedback loops and coaching, behavioral analytics not only illuminates capability gaps—it accelerates development by making invisible thinking patterns visible and actionable. Practice Is the Ultimate Diagnostic Tool There’s another reason Skill Practice Learning matters now more than ever: it reveals what traditional assessments cannot. Most training evaluations measure recall or satisfaction. But recall isn’t readiness. And satisfaction isn’t performance. In contrast, Skill Practice Learning—when paired with behavioral analytics—can measure: Whether a learner reacts, guesses, or fixates under pressure How they filter information and manage competing priorities Whether they collaborate productively or default to command-and-control Whether they apply systemic, critical, and creative thinking These insights go beyond knowledge checks. They diagnose how someone actually thinks , giving organizations data they can use to coach, develop, and invest wisely. Practice Builds Value Workers , Not Just Knowledge Workers In a world where AI can generate slide decks, write reports, and even answer basic customer questions, the differentiator isn’t what employees know—it’s what they can do  with what they know. Value Workers—those who make decisions, solve problems, and collaborate effectively—don’t emerge from lecture halls. They’re forged through practice. Skill Practice Learning builds these Value Workers. It helps employees develop the Core Abilities (critical, creative, and systems thinking) and the Value Skills (decision-making, problem-solving, and collaboration). These are the muscles of modern work—and they don’t grow by watching videos. A Call to Action: Rebuild Your Learning Stack Around Practice If you’re in L&D and still measuring success in completions and Net Promoter Scores, it’s time to shift. AI is coming for content. What it can’t do—yet—is replicate the complex, layered, human act of thinking under pressure  in service of others. Invest in Skill Practice Learning. Design practice environments that reflect your organization’s real challenges. Embed rich feedback, emotional stakes, and decision consequences. Use data to surface the thinking patterns that drive value—or erode it. Equip your people not just to perform tasks, but to reshape how they think . In the age of AI, your competitive advantage isn’t what your people know. It’s what they do.

Overcoming the Noise Today’s workers aren’t suffering from a lack of information—they’re drowning in it. Organizational noise, global instability, and internal doubts create a cognitive fog that clouds decision-making, dampens creativity, and paralyzes progress. In this environment, clarity is a superpower. But here’s the kicker: this mental fog isn’t just a personal productivity issue—it’s systemic. It starts with how we’re taught to think. For over a century, education and corporate training have excelled at teaching people what  to think. Memorize this. Follow that process. Repeat what works. It made sense in the industrial era, where predictability was prized. It made sense in the knowledge era, when expertise was scarce. But in the age of AI? That formula is broken. AI Is Eating “What to Think” learning AI is rapidly mastering the “what-to-think” domain. Need a training manual? An analysis of sales trends? A quick rewrite of a policy? Generative AI can already do much of that work—and do it well. According to recent estimates, AI can now perform at least 30% of tasks in traditional instructional design and content development  roles​. And it’s getting better, fast. Which means this: If your workforce is only trained in what to think, they will be automated. What remains—and what’s rising in value—are the abilities AI struggles to replicate: Drawing connections from ambiguity Adapting quickly in dynamic situations Seeing systems, not silos Asking better questions Thinking critically, creatively, and collaboratively These aren’t knowledge tasks. They’re thinking behaviors . And they require a fundamentally different learning model. Why "How to Think" Is the New Competitive Advantage Training workers how to think  is the L&D opportunity of the decade. It's how we future-proof our organizations and elevate the human-AI partnership. “How to think” skills empower employees to: Navigate complexity  through systems thinking Adapt and innovate  with creative thinking Make better decisions  with critical thinking Collaborate across silos  with mental agility and empathy​KeySkills​Patterns of Thought Think of these as the core cognitive muscles —the foundational strength behind every other skill. Without them, upskilling is like putting icing on a crumbling cake. The Problem: Most Training Still Teaches What to Think Despite these shifts, the vast majority of corporate training remains linear and content-heavy. We’re still teaching workers to follow steps instead of evaluate systems. We train for compliance, not curiosity. Why? Because content is cheap to build and easy to scale. But just like junk food, cheap calories don’t sustain us. And they certainly don’t spark transformation. The deeper issue: most training reinforces surface-level patterns of thought. Participants learn to resolve issues in isolation, follow processes regardless of context, and validate decisions with short-term results​Patterns of Thought. This leads to a workforce that’s “busy,” but not valuable. The Solution: Skills Practice Learning The fastest, most scalable way to teach how to think is through skills practice . Not theory. Not static content. Not checkbox compliance. We’re talking about dynamic, immersive experiences that simulate real-world complexity—where participants must: Diagnose ambiguous problems Collaborate across functions Make decisions under pressure Reflect and iterate In these environments, learners are assessed not by what they know , but by how they think —how they respond, question, analyze, and adapt in unfolding scenarios​NewAssessment​The Thinking Effect. We call this Skills Practice Learning . And it changes everything. The Neuroscience: Practice Changes the Brain Research in neuroplasticity and behavioral science confirms that repeated, immersive practice builds stronger, more flexible mental models. When learners do  rather than just consume , they develop cognitive stamina, form better habits, and surface flawed assumptions before they calcify​Menal Models. And when learners reflect on those experiences—through well-facilitated debriefs or responsive feedback—they strengthen their critical reasoning and systems perspective​. Enter the Richness and Reach Framework Using tools like the Richness and Reach Model , L&D leaders can assess where their programs fall: Quadrants I & II  = High reach, low richness (e.g., eLearning, workshops). Good for scale, but limited in behavior change. Quadrants III & IV  = High richness (e.g., simulations, multiplayer challenges, decision games). These are how-to-think  environments—high in complexity, nuance, and insight generation. To future-proof learning, shift investment to Quadrants III & IV . These solutions unlock thinking patterns that can’t be replicated by AI—and they scale through modern tools like AI-powered microsims and multiplayer simulations​. A Call to Action for L&D Professionals If L&D is to remain a strategic function—not a cost center—it must lead the shift from content delivery to capability development. That means: Stop overproducing knowledge content.  AI has that covered. Start building Skills Practice ecosystems  that develop the thinking muscles: critical, creative, and systems. Use AI as a lever, not a crutch.  Let it automate the low-level tasks so your team can focus on enabling growth, not managing content. In this next era, value isn’t what you know . It’s how you think . Final Word We’re not just training workers—we’re cultivating thinkers.In the age of AI, knowing what to do is table stakes.Knowing how  to think is the differentiator. Let’s stop training for the past. Let’s start preparing for the future.

Engaging System 2 Thinking Through Skills Practice A Guide for Learning Professionals Deep learning begins when we invite people to slow down, wrestle with complexity, and think more intentionally. This kind of cognitive effort—what Nobel Prize-winning psychologist Daniel Kahneman calls System 2 thinking—is where lasting insight and behavioral change take root. It’s not our brain’s default mode, which makes it all the more essential that learning professionals move beyond knowledge delivery and create experiences that challenge assumptions, stretch perspectives, and foster reflection. The most effective way to spark this deeper level of thinking? Purposeful, well-designed Skills Practice. Why Skills Practice Is Ideal for System 2 Activation In our work designing hundreds of high-impact learning programs, we’ve discovered that Skills Practice  — dynamic, immersive experiences where learners make decisions and reflect on outcomes — is one of the most effective tools for engaging deeper thinking. Why? Because Skills Practice offers: Realistic complexity  without real-world consequences Immediate and evolving feedback  that pushes learners out of autopilot Opportunities to pause, reflect, re-evaluate , and try again — the hallmarks of System 2 A participant can run a project into the ground or choose the wrong strategy in a safe, feedback-rich environment. And when the dust settles, the most powerful learning happens: not in the doing, but in the debrief . The Power of the Debrief: Where Mental Models Meet the Mirror The magic of Skills Practice isn’t just in the experience — it’s in the conversation that follows . A well-led debrief is where learners: Confront their mental models  (often flawed or outdated) Surface assumptions and biases Engage in metacognitive reflection Rebuild stronger, more adaptive ways of thinking We all operate from mental models — internalized rules and beliefs about how the world works. But most of us aren’t aware of them until something breaks. A well-crafted Skills Practice followed by a thoughtful debrief creates a safe space for these realizations to occur. System 1 vs. System 2: Why It Matters in Learning Kahneman’s framework offers critical insights for instructional design: System 1  is fast, intuitive, and automatic — it’s the “cruise control” we operate on most of the time. System 2  is slow, effortful, and logical — it kicks in when we face unfamiliar, complex, or risky situations. Most eLearning taps System 1: click-through content, quick quizzes, simple knowledge checks. But real growth? Real behavioral change? That lives in System 2 , and Skills Practice is the gateway. Priming System 2 in the Debrief As a facilitator, your role is to design for and prompt System 2 activation . Here are a few techniques: 1. Priming Questions Planting a thought or inquiry during the Skills Practice that sets up deeper reflection later.Examples: “What information are you missing to make this decision?” “What if your role was reversed with your stakeholder — how would your choice change?” 2. Anticipate System 1 Pitfalls Design experiences that expose common knee-jerk responses or biases. Then, in the debrief, explore: Why did that reaction occur? What assumptions were operating? 3. Mental Model Shifts Use from-to language  to guide reframing.Example: From “Do it yourself to get it right” → To “Delegate strategically for better long-term outcomes” Sample Debrief Questions to Trigger Deeper Thinking What rationale drove your decision? What patterns did you notice in your thinking? What emotions influenced your actions? How might this decision look in another context? What assumptions shaped your perspective? What would failure have looked like here, and why? If you ask a question and are met with silence — good. That’s often the sign System 2 is waking up. Practice That Builds Neural Resilience The brain is like a muscle. The more you activate deep thinking pathways, the more fluent and agile they become. Repeated Skills Practice , paired with reflection, strengthens neural pathways for decision making, problem solving, and adaptive collaboration — the Value Skills every organization needs. By using behavioral analytics  during Skills Practice, facilitators and L&D teams can also begin to surface critical questions: Is this person stuck in reactive decision-making? Are they fixating on short-term results at the expense of long-term strategy? Do they understand the systems they're operating within? System 2 Activities That Work in Skills Practice Debriefs Here are three practical approaches: Present and Defend Learners present their decision and field challenges from peers, prompting self-justification and deeper analysis. Role Play (Then Replay) After a scenario plays out, reassign roles and replay the situation. Compare decisions and reflect on what changed. Structured Debate Two learners argue for opposing decisions. The group then unpacks both arguments, guided by System 2-framed questions. Final Thought: It’s Not the Simulation. It’s the Thinking. Skills Practice isn’t valuable because it’s digital, immersive, or exciting (though it often is all three). It’s valuable because it creates the space and stimulus for better thinking . And in a world where AI does more of the routine, learning professionals must design for the part of the human brain that AI can’t replicate: The ability to pause, reflect, question, reframe, and choose better. That’s the job now.

Our capacity to adapt how we think will become the top skill in the next 5 years . At the core of our ability to adapt lie our mental models —the deeply rooted frameworks that shape how we interpret situations, solve problems, and make decisions. For Learning and Development professionals, the imperative is clear: equip people not with static knowledge, but with the tools to challenge, refine, and rebuild their mental models—continuously. What Are Mental Models—and Why Do They Matter More Than Ever? Mental models are internal representations of how things work. They help us interpret complexity, make predictions, and guide our actions. We use them constantly—often unconsciously—to simplify the world around us. But in an era defined by AI and volatility, outdated or incomplete mental models  become liabilities. They lead to misinterpretations, poor decisions, and rigid responses to change. Neuroscience has shown that these models are not fixed. They are formed through experiences, reinforced through repetition, and—most importantly— changeable  through deliberate learning interventions. "Mental models are like trails in the brain—walked frequently, they deepen. But new trails can be formed with effort, practice, and the right learning conditions."— The Thinking Effect The Neuroscience of Changing Mental Models Recent research into neuroplasticity  reveals that the brain’s ability to rewire itself continues well into adulthood. However, changing mental models requires more than exposure to new ideas. It requires disruption  of existing beliefs and sustained practice  with new ways of thinking. Key enablers of mental model change: Engaging Core Abilities : Mental model shifts happen when learners activate critical, creative, and systems thinking together. These Core Abilities  function like neural “muscle groups” that must be exercised in unison. Triggering Self-Generated Insight : True change comes when learners experience “aha” moments—internal insights triggered by reflective challenges, not delivered as conclusions. Overcoming the U-Curve : Learning often follows a U-shaped curve, where performance temporarily dips before rising again. This is when old models break down and new ones begin to form. Behavioral Feedback Loops : Change is cemented when learners receive feedback that exposes the effects  of their decisions and thought processes in real-world or simulated environments. Why This Matters in the Age of AI As AI takes over procedural tasks and information synthesis, humans must excel at what machines cannot easily replicate: adaptive thinking . Now, that's a fancy word that simply means you need to learn new things continually. That hard part, is become aware that you need to learn something. The harder part, is putting the effort to learning something new. Updating mental models enables people to: Make better decisions by seeing beyond visible symptoms to root causes Solve problems more holistically by understanding systemic relationships Collaborate more effectively by surfacing limiting beliefs and aligning around shared understanding The Five-Stage Process for Changing Mental Models Transforming thinking isn’t a linear journey. It’s a continuous cycle of unlearning and relearning. Here's how L&D professionals can design learning experiences that update mental models: Surface Existing Mental Models Use tools like reflective questioning, impact mapping, or behavioral assessments to help learners recognize their current models. Disrupt Assumptions and Limiting Beliefs Apply Core Thinking Practices to challenge default thinking and provoke discomfort with the status quo. Introduce New Perspectives Leverage systems thinking simulations, collaborative exercises, and exposure to diverse viewpoints to expand learners’ conceptual range. Enable Practice and Feedback Practice is the neurobiological engine of change. Skill practice environments—especially simulations—allow learners to test, fail, and refine new models. Reinforce with Behavioral Insights Provide continuous feedback—both data-driven and reflective—to reinforce evolving mental models and guide future action. The New Role of L&D Professionals In this landscape, Learning & Development is not about transferring knowledge. It’s about transforming cognition . L&D leaders must shift from content creators to mental model architects . This means: Facilitating self-awareness  and emotional safety to allow for deep reflection Creating immersive learning environments  that activate Core Abilities Integrating neuroscience-backed feedback  systems into learning design Measuring success not by retention, but by changes in thinking patterns and behaviors A Call to Action Mental models are not just cognitive tools—they’re the foundation for how individuals adapt, organizations evolve, and society progresses. As AI changes the rules of work, the ability to change how we think becomes the most critical skill of all. L&D professionals are uniquely positioned to ignite this transformation. This is the moment to move beyond static training and toward dynamic learning ecosystems that build adaptable thinkers. Not just what-to-think workers. But how-to-think   Value Workers . Because in a world where machines can know more and do more, human thinking —and the ability to reshape it—is our ultimate competitive advantage.

Let AI handle the “knowing”—you focus on the “doing.” As artificial intelligence rapidly becomes the co-pilot for knowledge-based work, the game has changed for Learning and Development. Knowledge is no longer the competitive edge—it’s a commodity. AI can now deliver precise, on-demand information faster than any human trainer or eLearning module. What it can’t do is turn that information into behavior change. That’s where you come in. The future of L&D lies not in delivering what to know, but in enabling how to act. Why Knowing Isn’t Enough Anymore eLearning has long been the go-to solution for upskilling—its scalability and consistency made it attractive. But its primary focus is on knowing —what Peter Senge would call “surface-level” understanding. In fact, most eLearning solutions remain stuck in the bottom three layers of Bloom’s Taxonomy: knowledge, comprehension, and application. This kind of learning may teach employees what to think, but not how to think. It may improve awareness, but not action. And it rarely equips people to respond to new, ambiguous, or complex situations. In contrast, Skills Practice Learning  is designed to change behavior. It focuses on doing —the application of knowledge under dynamic and uncertain conditions. And doing is what drives value. From eLearning to Learning, by doing: A Shift That Matters Let’s be clear: we’re not throwing eLearning under the bus. It has its place. But when it comes to preparing employees for a world where they must adapt, respond, and make decisions in real time, Skills Practice beats content delivery every time. Here’s why: eLearning Skills Practice Learning Teaches what to know Builds confidence in what to do Linear and passive Iterative, immersive, and dynamic Limited feedback loops Real-time feedback and reflection Useful for awareness Critical for capability Trains knowledge recall Trains decision-making, problem solving, and collaboration Think of it this way: eLearning is the textbook. Skills Practice is the flight simulator. Just like pilots train for turbulence and engine failure in simulators, business professionals need to train for difficult conversations, tough decisions, ethical dilemmas, and emergent problems. These moments require more than knowledge—they require practiced thinking and adaptive response. The Science Behind Why Practice Works Neuroscience has shown that repetition and real-time feedback  are essential to rewire the brain. Through neuroplasticity , practicing under varied and meaningful conditions strengthens neural pathways and shifts mental models—the filters through which we interpret the world and make decisions. Incorporating Core Abilities  like critical thinking, creative thinking, and systems thinking during Skills Practice leads to long-lasting behavioral change. And when practice includes delayed consequences , feedback loops , and dynamic constraints , it mirrors the real conditions professionals face. Studies confirm that simulations and experiential learning environments—like those built using SimGate—enhance memory retention, accelerate time-to-skill, and increase transfer of learning to the workplace. AI Will Teach the Knowledge. Humans Must Learn to Do. In an AI-powered world, the value of human work lies in adaptability, judgment, collaboration, and creativity. These are the human skills that can’t be encoded—but they can be practiced . AI will surface answers, process data, and even generate learning content. But knowing what to do with that information, how to evaluate it, apply it, and adjust in real time—that’s the new value frontier. In short, AI handles the “what.” Humans must master the “how.” Skills Practice as the Key to Value Creation Skills Practice is not just about learning—it’s about becoming . It develops the Value Skills  that matter most: decision making, problem solving, and collaboration​KeySkills. And it builds the Core Abilities that allow workers to unlearn, relearn, and adapt to new systems and situations​. The most important aspect of Skills Practice in this new era? Speed of acquisition. Organizations don’t need people who slowly absorb knowledge—they need people who can learn new skills quickly  and apply them under pressure. As job roles morph and AI absorbs more tasks, the ability to rapidly build and demonstrate new capabilities becomes the foundation of employability. Why L&D Must Lead the Shift Learning and Development leaders are at a pivotal inflection point. We must stop measuring success by content completion and start measuring it by behavior change and decision quality. To do this, we must: Design simulations and scenarios  that mimic real-life complexity Integrate AI  to provide just-in-time knowledge and intelligent feedback Use behavioral analytics  to assess how people respond under stress, collaborate with others, and adjust their decisions over time​ Foster reflection and iteration , not just content recall Encourage unlearning  outdated mental models and forming new ones​Mental Models The Future of L&D Is Skills Practice What makes Skills Practice revolutionary is not just that it’s effective—it’s that it is adaptive, measurable, and scalable . Platforms like SimGate now allow organizations to practice skills at enterprise scale, tailored by role, industry, and challenge. This isn’t about replacing eLearning. It’s about elevating learning to prepare workers for the unexpected. The next frontier of learning isn’t knowing more. It’s doing better, faster. Let AI handle the knowledge transfer. Let your learners master the action. That’s how we create Value Workers. That’s how we create the future.

Our underlying driver as a learning community is to create positive and lasting behavior change that helps people and, ultimately, organizations perform and treat one another better. Therefore, we aim to have learners transfer what they learn to their lives, careers, and communities. “Transfer,” to the training professional, is the Holy Grail. It refers to how much of what is learned within the learning arena can be applied back to the workplace. We define two levels of transfer: Situational transfer occurs when learners can apply what they learned to similar situations. Adaptive  transfer occurs when learners can adapt what they have learned to a variety of situations. There is another dimension to transfer, which we call capacity.  Capacity correlates with the amount of information that an individual retains and is capable of applying after the learning program. If, for example, an individual can apply many newly learned skills to different situations, then the program is said to have a high adaptive transfer capacity. Part of the goal for training, then, is to increase each learner’s capacity for transfer. Organizations that are trying to get the most out of their employees should note that learning that has a high adaptive transfer capacity is the most desirable for improving each worker’s value potential. When it comes to developing how-to-think workers, this level of transfer is also imperative. With this in mind, my colleagues and I have spent a considerable amount of time and resources rethinking traditional principles and evaluating assessment techniques focused on improving experiential learning. As a result, we created the Neural Coding System (NCS). Our underlying philosophy for developing the NCS is that the training must cause learners to stop and think, reevaluate their mental models, and reach their own insights into how to modify their own thinking or behavior. It must balance learning and practice, leaving students the opportunity to fill gaps and reinforce new skills. It must provide informative and relevant feedback regarding their limiting beliefs within dynamic and complex systems. And, it must be fun and not a waste of time. Neural coding is a neuroscience-related field concerned with how sensory and other information is represented in the brain by networks of neurons. Neural coding describes the process of neural network formation in the brain in response to a stimulus. The formation of these neural networks determines how people respond to future stimuli. With repetition of any stimuli and response, neural pathways are etched deeply and become the default “programming” for how people behave or respond to similar types of stimuli. As noted earlier, this is the neurological basis for habits and mental models. Neural Coding System Self-generated insights The NCS is a design framework that consists of four cognitive conditions that create an optimal learning environment for developing how-to-think workers when the conditions exist together. It is not a step-by-step methodology or series of discrete events. Rather, it is an interconnected system of mental conditions that are created through the artful implementation of various design principles. As the image above illustrates, the NCS is more like a funnel. Learners are placed in the middle of new situations that are evolving in response to their decisions and actions. This spiral approach, moving between the various conditions, is critical to engaging workers as they evaluate their mental models and seek to resolve gaps. Through trial-and-error and reflective dialogue, this approach allows them to work toward that sudden moment of convergence. Real learning happens in the moment when a learner combines knowledge and experiences to create something new, such as a new mental model or belief. I can tell you how a system works, but unless you experiment with it, then you’re merely sharing my perspective of how I think it works. You have your own knowledge and experiences. If you’re going to be effective at making decisions within the system, then you need to construct your own perspective. When learners work with new material, a small burst of adrenaline is released. This action engages their long-term memory as they build new pathways, or “scaffolding,” to help them make decisions. The more people encounter and overcome their own gaps, the more “scaffolding” they build toward retaining and incorporating information that impacts their performance. At this point, real learning begins, and behavior has the potential to change. Check out the power of insights.

In today’s hyper-connected, AI-augmented workplace, it’s easy to be dazzled by sophisticated tech, flashy learning platforms, and real-time analytics. But amidst all this digital firepower, one deceptively simple—and often overlooked—practice remains one of the most powerful tools for learning and behavior change: the debrief. A well-facilitated debrief isn’t just a recap. It’s a cognitive reset —a moment to trigger deeper, more analytical thinking that challenges our assumptions, surfaces blind spots, and drives meaningful change. It’s where learning gets metabolized into wisdom. And as AI continues to automate tasks, flood us with information, and challenge human relevance, this kind of reflective thinking is no longer optional—it’s mission-critical. The Science of Deep Thinking: System 2 in Action Nobel Laureate Daniel Kahneman introduced the world to two modes of thinking: System 1 (fast, intuitive, automatic) and System 2 (slow, deliberate, effortful). While System 1 helps us survive and react in everyday life, System 2 is where real learning, growth, and behavior change occur . System 2 is activated when we: Slow down to reflect Challenge assumptions Consider alternative outcomes Wrestle with ambiguity Reframe our mental models Debriefs, when designed well, are triggers for System 2 . They create the psychological space for learners to move beyond recall and into reflection—something AI tools can’t do for us. They nudge learners to think about their thinking , which is essential for breaking habitual patterns and building more adaptive, future-ready mental models. Mental Models: The Invisible Barrier to Better Performance Every learner operates through a set of internal "filters"—mental models that help them make sense of complex environments. These models influence how they: Interpret information React to uncertainty Make decisions Collaborate with others The problem? Most mental models run on autopilot, rooted in past experiences, outdated beliefs, or organizational dogma. They resist change—unless we deliberately surface and challenge them. A well-led debrief, infused with questions that trigger System 2 thinking , becomes a lever for change. It helps learners unearth their limiting beliefs, recognize flawed logic, and build stronger frameworks for future decisions. This is essential for transforming from what-to-think employees into how-to-think Value Workers . Why This Matters More Now—In the AI Era AI is rapidly absorbing knowledge-level work: recalling facts, analyzing patterns, even generating ideas. But AI can’t (yet) reflect, empathize, or challenge a mental model. That’s still a human skill—and it’s becoming the new premium currency in learning. To stay valuable in a world where machines think fast, humans must learn to think slow , reflect deeply, and adapt continually. That’s why debriefs, with their power to activate System 2 thinking, are not just a best practice. They’re a strategic imperative. Making System 2 Stick: Questions that Trigger Reflective Thinking To design effective debriefs, we need to ask better questions —the kind that disrupt cognitive autopilot and demand deliberate analysis. Here are some examples: What was your rationale behind this decision? How does this compare to a past experience—and what might that say about your mental model? Imagine this decision failed completely. What contributed to that outcome? If you had no past experience to draw from, what would you do? How did your emotions shape your choices during this scenario? What signals did you miss—and why might that be? How would you communicate this decision to a skeptical audience? These questions pull learners into the deep end of thought , creating tension between what they did and what they believe . That tension is productive—it opens the door to behavioral change, and it makes learning stick .

Leaders are seeking new ways to increase efficiency and automate tasks using AI. As AI takes over automates tasks, their will be a shift to developing higher cognitive skills, such as, working together to solve complex problems. These employees, known as Value Workers , are set apart by their approach to learning and their impact on organizations. Why AI is Changing the Way We Work AI is transforming industries by automating tasks that once required human effort, from data analysis and customer service to even aspects of creative and strategic planning. As AI technology continues to improve, it’s expected to handle more complex functions, creating a need for employees who bring skills beyond those that can be automated. This shift means that, while certain jobs may disappear, new roles will emerge that demand adaptability, critical thinking, and the ability to learn continually. The Role of Value Workers in an AI-Driven World Value Workers are the employees who go beyond completing tasks—they bring problem-solving abilities, innovation, and the skills to make informed decisions. In an AI-driven workplace, these qualities are more essential than ever. Instead of focusing on routine tasks that machines can do, Value Workers focus on higher-level contributions, such as: Strategic thinking : Understanding how AI outputs can be applied to benefit the organization. Creative problem-solving : Bringing fresh ideas to solve issues that AI may not be equipped to handle. Adaptability : Learning new skills and adjusting to emerging technologies and trends. In short, Value Workers provide the human touch—empathy, collaboration, and big-picture thinking—that AI lacks. Lifelong Learning: The Key to Staying Relevant In a world where technology is constantly changing, the ability to learn, unlearn, and relearn is crucial. Value Workers are committed to lifelong learning, meaning they don’t stop building skills after formal education or initial job training. Instead, they are continuously evolving by: Acquiring new technical skills  to work alongside AI and understand its capabilities. Strengthening soft skills  like communication and teamwork, which are essential in areas where AI falls short. Developing critical thinking and systems thinking skills  to interpret AI-generated data and make decisions considering broader organizational goals. By embracing continuous learning, Value Workers remain adaptable and are always prepared to take on new challenges that arise from advancements in AI. Embracing AI as a Tool, Not a Threat For Value Workers, AI is not a competitor but a powerful tool. By mastering how to work alongside AI, they can leverage its strengths—such as processing vast amounts of data at incredible speed—to enhance their own contributions. AI can assist with analysis, repetitive processes, and data insights, allowing Value Workers to focus on creative, complex, and meaningful work. For instance, instead of spending time on data entry or simple analysis, a Value Worker might use AI to gather insights and then apply their expertise to interpret those findings and make strategic decisions. In this way, they add value that AI alone cannot. Building a Future-Proof Workforce As companies integrate AI into their operations, they are increasingly looking for Value Workers who bring unique, irreplaceable skills to the table. By focusing on adaptability, problem-solving, and continuous learning, these employees are well-equipped to thrive in an AI-driven world. They add value in ways that machines cannot replicate, ensuring that their roles evolve alongside technology rather than being replaced by it. The future of work is one where AI and humans collaborate, each playing to their strengths. Value Workers, with their commitment to lifelong learning and the ability to adapt, are key to building a resilient, innovative workforce that can meet the demands of a constantly evolving world.

Think about the one course or experience that truly changed your life. How did it make you feel, think, and act differently? If you had to boil it down to a few words, how would you describe that experience? For most, it’s impossible to distill into a single phrase. Instead, people share emotionally rich stories—moments of clarity, connection, and change. Consider these: “I realized how my thinking patterns were holding me back. Just yesterday, I responded to a client in a way that reflected the new mindset I’ve adopted—I was more present, thoughtful, and effective.” “It was like we became a real team. The decisions we faced surfaced so many perspectives—even after all my years in the role, I heard things I’d never considered. And we built a bond that’s helping us weather today’s turbulence.” “The activity seemed straightforward… until it wasn’t. The decisions got messy. We didn’t always agree, but we learned from each other. We stretched. We adapted. And we grew—together.” These aren't just anecdotes. They're windows into something deeper—what we call the Trifecta of Great Learning : Reflective, Social, and Generative Learning . These three strategies, when embedded into learning design, don't just teach content—they change people. Reflective Learning: Insight Begins with Awareness Reflection is more than a journal entry. Neuroscience shows that when people pause to analyze their own thinking , they activate the brain’s default mode network—engaged during self-referential thought and future planning. Reflection isn't a pause from learning; it is learning. In skill pratice learning and workplace assessments, we've observed that learners who pause to reflect  are more likely to adapt their mental models—the internal maps they use to navigate the world​Mental Models. This ability to shift perspective is at the core of sustainable growth. Story:  One participant reflected after a tough team discussion, “I realized I wasn’t really listening—I was defending. That moment changed how I lead my team.” Design Tip: Incorporate structured debriefs and “pause and predict” moments. Don’t just ask what happened. Ask why  it happened—and what might happen next. Social Learning: Hardwired for Connection The last few years have underscored a truth many in L&D have long known: learning is social . And it’s not just a “nice to have”—it’s a neurological imperative. Research from the field of social neuroscience shows that social connection triggers the brain’s reward centers , enhancing motivation, memory consolidation, and behavior change​Thinking Energy. Simply put: people remember better when they learn together. In skill practice environments—where teams face realistic, ambiguous problems—learners report a deeper sense of meaning and shared purpose. They build not just knowledge, but trust. Story:  “I didn’t expect to connect with my colleagues like this. We opened up, challenged each other, and created something meaningful. That bond is still helping us today.” Design Tip: Use collaborative decision-making, peer coaching, and shared problem-solving challenges. Layer in tension to simulate real-world stakes—but make it safe enough to experiment. Generative Learning: The Power of Creating Together Generative learning isn’t about repeating information—it’s about constructing something new from it. And it’s essential for developing skills that endure. Constructivist theories of learning—and more recent findings in neuroplasticity —show that mental models change most when learners actively generate and test new ideas ​Mental Models​NewAssessment. In team settings, this becomes even more powerful, as learners bounce off each other’s thinking, surface biases, and collectively reach higher-order insights. These are the experiences that trigger what we call self-generated insight —those “aha” moments that energize and stick. They don’t come from being told the answer. They come from wrestling with the problem and making the discovery on your own​Glossary. Story:  “We thought we had the answer. But then someone asked a new question that shifted the whole conversation. From that point on, we weren’t just solving the task—we were solving the right problem.” Design Tip: Replace content delivery with decision-making, trade-offs, and long-term consequences. Great learning should mimic the complexity of the real world—because that’s what we’re preparing people for. Why These Strategies Work (Especially Now) In a world of increasing complexity, information overload, and AI-generated answers, how we learn  matters more than what we learn . ✔️ Reflective learning builds self-awareness—the foundation of all growth. ✔️ Social learning builds shared mental models—crucial for collaboration. ✔️ Generative learning builds adaptability—the muscle for modern work. Together, they help people unlearn old habits, rethink assumptions, and create new patterns of thought —the very definition of becoming a Value Worker ​Patterns of Thought​The Thinking Effect. This isn’t theory. It’s practice. It’s neuroscience. It’s human. And it’s transformational. Final Thought: The Course That Changes Everything So, think back again to that course that changed your life. What made it so powerful? Chances are, it wasn’t the slides or the framework. It was the moment something clicked. You saw yourself differently. You connected with others. You created something meaningful. And you felt  it. That’s the trifecta in action. Reflective. Social. Generative.That’s not just great learning. That’s life-changing learning .

Core Abilities for the AI-Transformed Workforce As artificial intelligence reshapes the world of work—automating routine tasks, augmenting decisions, and in some cases replacing entire job functions—the value of human talent is undergoing a fundamental shift. In this new landscape, employees won't differentiate themselves by what they know, but by how they think. For learning and development professionals, this shift demands a transformation in how we approach upskilling and reskilling. We must prioritize the development of Core Abilities: critical thinking , creative thinking , and systems thinking . These abilities are not “nice to have”—they are now the engine of value creation. Let’s explore why these three abilities matter and how they prepare individuals to thrive in the age of AI. The New Learning Mandate Historically, L&D programs have focused on technical skills, job-specific knowledge, and “what-to-think” training. These remain important, but they are increasingly becoming table stakes—easily codified, standardized, and, now, automated. What can’t be automated is the ability to frame the right problem, generate innovative ideas, understand complexity, and navigate uncertainty. These are human capabilities, and they are enabled by three interdependent thinking abilities: Critical Thinking  – Understanding problems deeply and forming sound judgments. Creative Thinking  – Imagining new possibilities and novel solutions. Systems Thinking  – Seeing the interconnections within complex environments. Together, these abilities form the thinking infrastructure  required for higher-order skills like decision-making, problem solving, and collaboration—what we call the Value Skills . Why Core Abilities Matter More in an AI World AI excels at answering predefined questions with known variables. It can analyze data, forecast trends, and simulate scenarios. But AI lacks context. It doesn’t know which  questions to ask, why  a problem matters, or how  short-term gains might create long-term harm. Humans bring that value. But only if they think well. A Value Worker—one who can make smart decisions and solve complex problems collaboratively—must learn how to ask better questions , suspend assumptions , and anticipate unintended consequences . These are not just soft skills. They’re survival skills in an AI-driven economy. The Core Abilities in Practice Critical Thinking: Asking Why and How Critical thinking is not about being skeptical or overanalyzing data. It’s about disciplined reasoning. It starts by resisting the urge to leap to conclusions and instead pursuing a deep understanding of causes, context, and implications. In the workplace, critical thinking looks like: Identifying the real problem beneath surface symptoms Challenging inherited assumptions Making decisions with incomplete or conflicting data Asking, “What is true here?”  and “Why does this matter?” Without critical thinking, employees default to old playbooks or accept AI outputs without question—dangerous in dynamic environments. Creative Thinking: Generating Possibilities Creative thinking complements critical thinking. Where critical thinking dissects, creative thinking expands. It helps people imagine new approaches, connect the unconnected, and see opportunity where others see obstacles. But creativity isn’t just for designers or marketers. In a business context, it's a mindset that: Sees failure as a source of insight Explores multiple alternatives instead of defaulting to the first idea Suspends judgment long enough for ideas to take shape Asks, “What else is possible?” With AI handling routine ideation and optimization, human value will increasingly lie in nonlinear  thinking—the spark that leads to breakthrough strategies or business model reinvention. Balacing Critical and Creative Thinking Systems Thinking: Seeing the Whole Picture Systems thinking is the glue that binds the other two abilities. It allows people to connect dots, trace ripple effects, and understand how individual decisions influence the broader system. In the age of AI and constant disruption, systems thinking helps learners: Spot patterns across time and functions Anticipate second- and third-order consequences Understand how small changes in one part of the business affect others Ask, “How do these elements interact over time?” Without systems thinking, well-intentioned actions often create new problems. L&D professionals can’t afford to train people to solve one problem only to cause three more. If creative thinking and critical thinking are like two sides of a scale, then systems thinking is the fulcrum, balancing both creative and critical approaches based upon the situation at hand (see Figure 17). The Fulcrum: Systems Thinking balaces both critical and creative thinking Systems thinking balances critical thinking and creative thinking by providing context and perspective. In the table below, the middle column between critical and creative thinking provides guidance for using systems thinking to balance critical and creative thinking. Thinkers may gravitate to the left or to the right, but the goal is to return to the middle to keep thinking in balance. This balance should also allow thinkers to respond more quickly to certain situations. Critical Thinking Systems Thinking Creative Thinking Analytic Systemic Generative Convergent Concurrent Divergent Probability Feasibility Possibility Judgment Perspective Open Focused Integrated Scattered Objective Underlying dynamics Subjective The answer Leverage point An answer Left brain Whole brain Right brain Linear Structure Associative Yes, but How and why Yes, and What This Means for Learning and Development If AI is transforming the work, then L&D must transform the worker. That starts with investing in how  people think—not just what they know. Here are four shifts for L&D professionals to consider: Move beyond teaching tools and procedures. Embed experiences that challenge mental models and surface assumptions. Use real-world scenarios, simulations, and reflective practice to build resilience in ambiguous environments. Help learners not just gain new knowledge but reshape how they perceive problems and opportunities. Foster cross-functional collaboration and systems literacy. Equip teams to think beyond their silos.

The most essential human skill is becoming clearer by the day : the ability to ask good questions . This might sound simple. It’s not. We are entering a new era where the value we bring is no longer in recalling facts or performing routine tasks—those are AI’s domain. Instead, what distinguishes the highest-performing learners, teams, and leaders is their ability to frame questions that drive discovery, insight, and action . For L&D professionals, this shift opens a powerful opportunity: to make “questioning” a cornerstone of your learning strategy. The Learning Superpower AI Can’t Replicate (Yet) Today’s AI tools—from large language models to machine learning-based analytics—are remarkably good at surfacing information, analyzing trends, and even generating insights. But here’s the catch: AI is only as good as the questions we feed it. When learners ask shallow, reactive, or misdirected questions, AI gives back shallow answers. But when learners ask insightful, layered, and context-aware questions, AI becomes a catalyst for higher-level thinking and learning. It’s not that AI replaces thinking. AI amplifies  thinking—but only when the thinking begins with a good question. Why Learning to Ask Good Questions Is Now a Critical Learning Outcome Decades of cognitive science and systems thinking have taught us that better questions lead to better thinking . Good questions: Uncover assumptions Expose systemic dynamics Reveal limiting beliefs Expand perspective Enable creative solutions This is especially true in environments filled with complexity, ambiguity, and noise —conditions that are now the norm in modern organizations. In fact, L&D teams who focus only on “what-to-think” training—memorizing frameworks, processes, or tools—are rapidly becoming outdated. What modern learners need is “how-to-think” development: the ability to ask, reframe, and evolve better questions over time. Here are some examples of questions you can ask in different situations. Situation Common Question Better Question You Why do I always procrastinate? How am I going to get that done today? Team What should we do about the problem? What could we do about the problem? Friend Why does this always happen to me? What can I learn from this? Boos How can I help? If I could do this, would that be helpful? Child How was school today? What made you smile today? Partner What’s the matter? How can we resolve this together? PRO TIP: The quality of the answer is proportional to the quality of the question. Core Thinking Practice: Framework for thinking through good questions The Core Thinking Practices  provide a roadmap for building the muscle of questioning: Seek to understand the big picture What problem are we really solving?What’s the system doing—not just the people in it? Seek to surface limiting beliefs What assumptions are we making that could be wrong? Seek systemic change What actions will change the system, not just patch a symptom? Seek to evolve a shared vision Are we aligned on success? What does that look like for each of us? With these patterns embedded into learning programs—especially simulations, coaching conversations, and reflective practices—learners don’t just gain knowledge. They learn how to think. L&D's Role in the AI Era: Cultivate the Curious As AI handles more cognitive heavy lifting, humans must shift from “answer seekers” to curiosity explorers . L&D teams should ask: Are we rewarding memorization, or cultivating inquiry? Are we teaching learners to reflect on how  they think? Are our assessments measuring recall, or are they surfacing limiting beliefs? Are we helping learners understand systems, not just symptoms? Programs like responsive modeling, impact mapping , and Core Abilities development  (critical, creative, and systems thinking) all start with—and build on—the act of questioning. The Bottom Line for L&D In the age of AI, the question is the new curriculum . The L&D teams who will thrive in the coming years are not just creating content. They are cultivating a culture of inquiry—teaching learners how to ask better, deeper, more system-aware questions. They are building value workers —individuals who bring thinking, not just knowledge. In a world where answers are cheap, questions are gold .