Brian Moore

Adaptive structures and operating design are shifting away from rigid, static organizational models toward more fluid and continuously responsive operating systems. One signal supporting this transition: approximately 80% of businesses are already leveraging real-time analytics capabilities to support faster adaptation, coordination, and decision-making in rapidly changing environments. Long gone are monthly and quarterly reviews in favor of real-time, continuous, dynamic models. In this new article, 'Adaptive Structures and Operating Design During The Great Transition,' we introduce and begin exploring why organizations must redesign how they operate in environments defined by accelerating change, rising complexity, and increasing uncertainty. This article explores: - Why traditional organizational structures are struggling under modern conditions - The growing need for flexibility, responsiveness, and distributed coordination - How organizations are redesigning operating models to adapt without creating too much chaos - Why adaptive structures are becoming a strategic requirement during The Great Transition This article is part of the Human Innovation Institute’s ongoing work mapping the frontiers of human, organizational, and systemic advancement during The Great Transition. Through research, publications, and applied frameworks, HII explores how people, organizations, and societies are adapting to rising complexity, technological acceleration, institutional change, and shifting models of value, leadership, and coordination. The Frontier Series was created to help leaders, practitioners, and institutions better understand the emerging conditions shaping the future and the capabilities required to navigate them intentionally. Access the article here: https://lnkd.in/dp3G_rNd or the full series on our website: https://lnkd.in/gh9YuUQB Welcome to Human Innovation. #humaninnovation Human Innovation Institute

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I had the pleasure of interviewing Bryan H. Kelly for the Dongmicus Blog — and now, he’s been featured in CDO Magazine as a thought leader in data literacy. Bryan is the author of Cass Freeman: Decision Trees, a compelling novel that fuses STEM education, ethics, and storytelling to inspire data leaders toward inclusive and accountable decision-making. It’s exciting to see his insights gaining global recognition. You can read my original interview with him on the Dongmicus Blog — link in the comments!

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A Novel Large Language Model–Driven Methodology for Future Disability Support Systems The future of disability support systems must move beyond conventional assistive technologies toward intelligent, adaptive, and context-aware AI ecosystems. In response to this need, we propose a novel Large Language Model (LLM)-driven methodological framework designed to deliver personalized, real-time, and scalable disability assistance. Unlike traditional rule-based systems, this approach integrates multimodal data sources, including speech, text, visual perception, wearable sensors, smart devices, and optional brain–computer interface signals into a unified cognitive architecture capable of contextual reasoning and adaptive response generation. At the core of the framework lies an LLM-based reasoning engine that performs semantic analysis, intent recognition, contextual modeling, and predictive adaptation. This transforms assistive support from reactive automation into proactive cognitive assistance. The system continuously learns through a feedback-driven personalization loop, where user interactions refine contextual understanding and improve adaptive outputs over time. Such a dynamic learning structure ensures that support mechanisms evolve alongside individual needs, environmental changes, and health conditions. The proposed methodology enables intelligent outcome delivery through adaptive communication systems, real-time health monitoring and alerts, smart environment control, and decision-support mechanisms. By integrating continuous personalization, multimodal fusion, and scalable AI deployment, this framework establishes a foundation for next-generation disability support ecosystems. The ultimate objective is not only accessibility but also enhanced autonomy, independence, and quality of life through human-centered artificial intelligence. #AI #LargeLanguageModels #AssistiveTechnology #DisabilitySupport #HealthcareInnovation #InclusiveAI #SmartHealthcare #ResearchInnovation #Hit #rightmindos #romansarkar #roman #HarbinInstituteofTechnology #China

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Are You TRL-Ready? .....A Framework for Smarter Innovation Decisions When you're building or backing new technology, one question always comes up: "How close is this to real-world use?" Technology Readiness Levels (TRLs) provide a clear, shared language to answer that. Developed by NASA and now used globally (EU, UK Gov, etc.), TRLs help gauge the maturity of a technology from an idea to a scalable, operational solution. Why Use TRLs? 👉 Track innovation progress 👉Justify R&D investment stages 👉Align teams, funders, and partners on development status 👉Reduce risk before scaling or commercialising Case Studies on TRL Usage # NASA uses TRLs for space missions to assess risk at each system component. # European Commission’s Horizon 2020 requires TRLs to define funding stages. # UK Ministry of Defence uses TRLs to evaluate military tech and procurement risks. # Clean Energy Sector applies TRLs for hydrogen tech, battery systems, etc. Source Summary NASA’s TRL Handbook European Commission Horizon 2020 Guidelines UK Government – Innovation Funding Service Journal of Technology Management and Innovation OECD Science, Technology and Innovation Outlook #LessonsLearned #Innovation #IPs #R&D

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Consultants often peddle AI governance frameworks bloated with committees, review bodies, and oversight structures. The result is not governance but theater. Decision diffusion masquerading as accountability, veto chains posing as deliberation. When everyone must approve, no one can act. These frameworks do not enable AI adoption. They prescribe paralysis. My Stanford Law School essay "Paralysis Prescription" explores this in more detail. Link in the comments ⬇️ #AIGovernance #GovernanceTheater #AIStrategy #ResponsibleAI #AICompliance

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"The last decade has been a stress test for systems designed for smooth conditions. Climate volatility, pandemics, and geopolitical disruptions have made one truth clear: efficiency without resilience is a liability. The new playbook prioritizes: Resilience over speed Distributed innovation over centralized control Regenerative capacity over extractive gain These aren’t theoretical ideals. They are in play, right now, in cities and nations that see no alternative but to adapt.

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Jim Highsmith sees teams turning into tribes wedded to adaptation or optimization - but we should manage the tension between them by blending modes of explore and exploit, based on uncertainty, risk, cost of change, & evidence threshold. https://lnkd.in/e3jcci7f

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Not all AI is created equal. Understanding the three types can transform how you think about implementation. - Reactive Assistants (ChatGPT, Claude): Great brainstorming partners that work when you do. They're reactive - responding to what you tell them to do. - Task Agents: These walk through processes step-by-step. Think moving data from point A to point B. Solid automation, but not much thinking involved. - Digital Staff: This is where it gets exciting. Goal-driven AI that decides what to do and when, using tools and information to achieve objectives - much more like an actual team member. The key difference? Digital staffers don't just execute tasks - they think through problems and make decisions about how to reach their goals. That's what makes them true force multipliers for your team. This philosophy has guided the AI digital staffers we've created at https://aiwhy.io

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📢 Published in collaboration with the International Journal of Project Management 🧠 How do project teams really perceive complexity? A new #IJPMNugget explores how uncovering differences in individual perceptions helps project teams align their mental models and better navigate complexity. 📘 This research shows that perception-based methods can reveal blind spots, challenge assumptions, and drive more adaptive project strategies. 🔍 Key takeaway: Measuring and reflecting on perception gaps creates a powerful feedback loop — enabling teams to think more clearly, act more collaboratively, and manage uncertainty with greater insight. 💡 Embracing double loop learning equips teams to rethink not just actions, but also goals and frameworks guiding decisions. 📖 Read the full open access article: https://lnkd.in/gng3uge8 #ComplexProjects #ProjectManagement #MentalModels #IJPM #IPMA60Years #LearningInProjects #SustainableLeadership

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Stop calling it “AI governance.” What Google just released proves this isn’t about ethics committees anymore. It’s about operational control at scale. The Frontier Safety Framework 3.0 quietly rewrote the playbook. For the first time, Google has drawn hard capability lines: Critical Capability Levels (CCLs). When a model crosses one, everything changes: ☑️ Access locks down like nuclear material ☑️ Audits become mandatory before release ☑️ Governments get notified if public safety is at risk AI has entered its flight-certification era. You don’t just release a model. You prove it can be contained. That’s a seismic shift from the “move fast and break things” decade. Safety isn’t a gate; it’s a continuous telemetry loop. Every upgrade triggers new evaluations, red-team tests, and layered reviews. Google’s running AI like an air-traffic control system: every capability logged, tested, and audited in real time. Here’s why this matters to every operator building with AI: This mindset won’t stay in frontier labs. It’s coming downstream. If you’re deploying AI inside your org, start building your own safety loop: ✅ Define capability thresholds ✅ Log what each agent saw, did, and why ✅ Build rollback plans before you scale Because the winners in this next phase won’t be the ones who move fastest, they’ll be the ones who can prove their systems are safe, measurable, and human-in-the-loop. Innovation without control isn’t innovation. It’s drift. Useful over shiny. One process at a time. Follow Brianna Bentler for clear, measurable AI frameworks from Main Street to frontier systems. P.S. Should “safety cases” like Google’s be mandatory for all AI products or just frontier models?

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Using committees when project teams are in fact the right solution risks diffusing accountability for results, undermining empowerment, and wasting a lot of time in bureaucracy. This essay teases apart the appropriate roles of committees versus project teams. https://lnkd.in/evABPEfS #leadership #leaders #management #transformation #organization #committees #projectteams #teamwork #oversight #governance #empowerment #ndma

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Strategy rarely fails from lack of ambition. It fails when the decision system cannot carry the change being pursued. In an environment shaped by AI acceleration and recurring restructuring, scale is no longer the only pressure. Capability is shifting faster than governance and decision systems can adapt. I’ve seen this pattern in two very different environments: • Governance-level transformation at the board level • Scaling a new commercial function inside an enterprise Different contexts. Same structural strain. The framework below outlines a simple 3-layer diagnostic I use to assess where structural strain is emerging inside scaling organisations. As organisations grow - or reallocate resources under pressure - stress concentrates across three layers: 1. Strategic - Mandate Clarity > Who defines direction? > What are the decision boundaries? > What is explicitly out of scope? When this blurs, debates reopen under stress and governance becomes reactive. 2. Operational - Decision Rights > Who owns pricing, scope and trade-offs? > Where does authority sit across functions? When this weakens, revenue may grow — but rework and escalation grow faster. 3. Execution - Decision Velocity > Are teams empowered to decide? > Or are meetings multiplying while follow-through slows? Delay is often the first visible signal of structural drift. Scale does not create dysfunction. It amplifies whatever decision architecture already exists. In practice, restoring clarity at the right layer often unlocks velocity faster than a refresh of another strategy. I’ve applied this 3-layer lens across both board and enterprise contexts. This is not a strategy framework. It is a structural diagnostic. Before rewriting the strategy, I look for where decision pressure is concentrated. If three or more signals cluster in a single layer, the issue is usually structural - not strategic. If you're leading through change right now - growth, restructuring, or both - Where are you seeing the strain? #Leadership #OrganisationalDesign #DecisionMaking

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CIO decision rights determine how fast strategy turns into outcomes. When authority over funding, standards, and exceptions is explicit, execution accelerates and governance simplifies. When it isn’t, priorities stall and accountability blurs. I published a new article on Decision Rights Architecture that breaks down how CIOs design decision ownership so technology strategy stays aligned with enterprise goals… without adding layers or bureaucracy. This is about clarity, not control. Design, not debate. Link to the article in first comment 👇

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Enterprise Architecture Series - Notes #1 The Five Systems That Determine Whether Enterprises Scale or Stall Most organizations slow down as they grow. Not because the strategy was wrong. But because the enterprise architecture was never redesigned for scale. As organizations expand, five systems begin to evolve independently: • workforce capability • organizational structure • governance mechanisms • digital execution systems • leadership decision authority Each system grows. But rarely together. So what emerges is not a coherent operating system. It is structural friction. You see the symptoms everywhere: • escalation replaces authority • meetings replace decisions • transformation programs multiply • execution becomes inconsistent At that point leaders often try to fix the problem with: • restructuring • new processes • new technology platforms But the real issue is deeper. The enterprise lacks an integrated operating architecture. This is what we call the Enterprise Operating Spine™. It aligns five critical systems: Workforce capability Organizational structure Governance rhythm Digital execution systems Leadership decision architecture When these systems operate together, organizations scale with coherence and speed. When they evolve independently, organizations scale with complexity and friction. Over the next few posts, I’ll break down each of these systems. Because strategy rarely fails in design. It fails in execution architecture. Machine Leadership Hacking HR PCSI | People and Culture Strategy Institute SHRM SHRM India Forbes World Economic Forum Vivek Srivastava Dr. David Swanagon Gurpreet Kaur Enrique Rubio #EnterpriseArchitecture #OperatingModel #LeadershipArchitecture #OrganizationalDesign #AIinBusiness #BusinessTransformation #EnterpriseOperatingSpine #LeadershipArchitecture #AscendaraInsights

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Change Structure before Strategy https://lnkd.in/gMNmC7rB A CERM Risk Insights article by Patrick Ow. Change impacts the organisational system and subsystems in different ways and speeds thus impacting strategy

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Most organisations don’t fail because they missed the signals. They fail because the signals had nowhere to land. The paper I published some weeks ago on SSRN is addressing that gap and describes what governance architecture looks like when you take it seriously. Not more foresight. A different relationship between what you sense and what you decide. It’s a working paper. Academic in register, 19 pages, formal in tone . Not a quick read. But the argument is one I haven’t seen made elsewhere. “Temporalities of Collapse: Superposed Futures, Soft Attractors, and the Governance of Premature Convergence” Keep thinking and follow the camel. 🐪

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