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Solutions Review Executive Editor Tim King offers this primer on learning analytics from people in motion as a powerful capability only found in data-centric peer advisory groups.<\/strong><\/em><\/p>\n Enterprise learning is undergoing a structural shift. For decades, professional development followed a predictable model in that practices emerged inside companies, were studied and documented by analysts and consultants, and eventually became curriculum in universities, executive programs, and online courses. That system worked in a world where change moved at a manageable pace. Our opinion is that AI is now breaking that model in real-time<\/a><\/strong><\/span>.<\/p>\n Across industries, new approaches to leadership, governance, and decision-making are emerging faster than traditional education channels can capture them. The result is a growing gap between what is being taught and what is actually happening<\/a><\/strong><\/span> inside organizations. In this environment, a new form of learning is gaining importance\u2014one built not on retrospective knowledge, but on proximity to leaders actively navigating change.<\/p>\n This is what can be defined as learning from people in motion.<\/p>\n As the pace of change accelerates, learning from people in motion becomes more of a requirement for staying aligned with the state of play<\/a><\/strong><\/span>.<\/p>\n Learning from people in motion is a leadership learning model<\/a><\/strong><\/span> centered on gaining insight from professionals who are actively operating at the frontier of change, rather than relying solely on retrospective education.<\/p>\n Learning from people in motion (in one sentence): <\/strong>Learning from people in motion is how leaders stay aligned with the true state of play in rapidly evolving fields like artificial intelligence.<\/p>\n While there has always been a delay between innovation and education (knowledge latency). A new practice would emerge inside companies, mature over time, and only later be documented, taught, and distributed at scale. We see now how quickly AI compresses that latency.<\/p>\n New operating models, governance frameworks, and decision systems are being tested and refined continuously. By the time these approaches are formally documented, they may already be outdated. Leaders relying solely on traditional education risk learning from a version of reality that no longer reflects current conditions.<\/p>\n As the pace of change accelerates, the value of learning shifts toward sources that minimize this delay.<\/p>\n To understand the significance of the shift, it helps to distinguish between two modes of learning.<\/p>\n Retrospective learning is based on established knowledge. It includes university programs, books, recorded courses, and consultant frameworks. These sources are essential for building a foundational understanding and identifying durable patterns. However, they are inherently backward-looking.<\/p>\n Real-time learning, by contrast, is based on active experimentation. It comes from professionals who are currently testing ideas inside operating environments. This form of learning captures emerging practices before they are fully codified.<\/p>\n Neither model replaces the other, while foundational knowledge remains critical. But in rapidly evolving fields, real-time learning provides a level of relevance and immediacy that retrospective learning cannot match.<\/p>\n As knowledge latency collapses, a third category is emerging between traditional education and informal experience<\/a><\/strong><\/span>: state-of-play learning.<\/p>\n State-of-play learning focuses on understanding how a domain is evolving in the present moment. It reflects the collective experience of professionals actively navigating change, rather than a distilled version of past outcomes.<\/p>\n This type of learning is particularly valuable in environments shaped by artificial intelligence, where practices are still forming, and consensus has not yet been established. In these conditions, the most useful insights often come not from finalized frameworks, but from ongoing experimentation.<\/p>\n AI is reshaping how decisions are made, how organizations are structured, and how value is created. Leaders across industries are being asked to navigate questions that have not yet been fully answered.<\/p>\n These are not static problems and evolve as technologies and use cases evolve.<\/p>\n In this context, the ability to learn quickly (and from the right sources) becomes a competitive advantage. Leaders who rely exclusively on retrospective knowledge risk falling behind the pace of change. Those who engage with peers actively navigating similar challenges gain earlier visibility into emerging patterns and more practical insight into what works.<\/p>\n Peer “mastermind” advisory groups (like this one we launched with futurist Donald Farmer<\/a><\/strong><\/span>) are uniquely positioned to facilitate learning from people in motion.<\/p>\n Unlike large conferences or one-to-many learning formats, leadership development groups<\/a><\/strong><\/span> bring together small numbers of experienced professionals who are actively engaged in similar challenges. Because participants operate within their own organizations, they contribute insights derived from real-world experimentation rather than theoretical models.<\/p>\n These groups create an environment where leaders can exchange perspectives on emerging practices, compare approaches across organizations, and refine their thinking based on current realities. Over time, this continuous exchange allows insights to compound, reducing the distance between experience and understanding.<\/p>\n And the value of these groups is not simply networking. It is access to live knowledge; insight that reflects the current state of a field rather than a historical snapshot.<\/p>\n Peer advisory group<\/a><\/strong><\/span>s, community networks (like ours at Insight Jam<\/em><\/a><\/strong><\/span>) are becoming central to how leaders stay current, test ideas, and refine strategy. These environments provide access to people in motion.<\/p>\n In an economy where the playbook is still being written, the most valuable insights are being found in what is actively being discovered.<\/p>\n","protected":false},"excerpt":{"rendered":" Solutions Review Executive Editor Tim King offers this primer on learning analytics from people in motion as a powerful capability only found in data-centric peer advisory groups. Enterprise learning is undergoing a structural shift. For decades, professional development followed a predictable model in that practices emerged inside companies, were studied and documented by analysts and […]<\/p>\n","protected":false},"author":23,"featured_media":10625,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"footnotes":""},"categories":[4],"tags":[],"acf":[],"yoast_head":"\nWhat Does Learning Analytics from People in Motion Mean?<\/strong><\/h2>\n
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Knowledge Latency Fails<\/strong><\/h3>\n
Retrospective Analytic Learning vs. Real-Time Learning<\/strong><\/h3>\n
Rise of State-of-Play Learning in Data<\/strong><\/h3>\n
Why This Matters Now<\/strong><\/h3>\n
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Why Peer Advisory Groups Enable This<\/strong><\/h3>\n
The Bottom Line<\/strong><\/h3>\n