Systems Thinking and Beyond
The AI team take a deep dive into successful innovative tools, practical and conceptual applications of systems thinking and beyond and systems engineering to various types of problems, summarizing the concepts behind the successes and usually drawing general conclusions for how the concepts may be used in other situations. The opinions expressed by the AI team in each deep dive are their own and have not been edited in any way. While systems thinking provides an understanding of the problematic situation, you need to go beyond systems thinking to create solutions, especially innovative solutions. Join my LinkedIn group (Tackling complex...
Understanding Large Language Model AIs
The AI team takes a deep dive into the technical architecture and operational logic of Large Language Models (LLMs). They explain that these systems are trained through a multi-stage process; pre-training, fine-tuning, and human feedback, to predict text sequence. A central focus is the Transformer architecture, which uses an attention mechanism to understand relationships between words and manage linguistic nuances such as spelling errors. The team clarify that AI "memory" is actually a process where the entire conversation history is re-read during every interaction to maintain coherence. Finally, the team define LLMs as probabilistic state machines that, despite their sophisti...
An Introduction to System Science
The AI team take a deep dive into a book, Introduction to System Science with MATLAB by Gary Marlin Sandquist Zakary and Robert Wilde. The book introduces system science as a multidisciplinary framework for analyzing and modeling rational systems through the use of MATLAB. It emphasizes that effective practitioners must combine mathematical proficiency with computer competence to evaluate complex phenomena ranging from physical sciences to human history and sociology. By applying the principle of causality, the material demonstrates how to quantify diverse topics such as economic growth, medical diagnoses, and even religious impacts or personal stress. The provided excerpts offer vario...
The Collapse of MBSE and the Collateral Damage to Systems Engineering
The AI team takes a deep dive into a provided text, The Collapse of MBSE and the Collateral Damage to Systems Engineering, by Art Villanueva, DEng, ESEP which argues that Model-Based Systems Engineering (MBSE) has mistakenly become a substitute for the broader discipline of systems engineering, leading to a decline in professional authority and decision-making quality. While MBSE is a valuable tool for organizing and documenting system information, it often lacks the analytical power required to drive critical engineering choices, which are instead handled by external simulations and expert judgment. This misalignment results in models that serve as post-hoc documen...
The power of temporal analysis
The AI takes a deep dive into a Case Study which introduces temporal analysis as a superior method for evaluating nonprofit effectiveness compared to traditional single-year snapshots. Using the INCOSE Foundation as a detailed case study, the text illustrates how longitudinal data can expose governance red flags, such as inconsistent state registrations and systematic bylaw violations. While the organization maintains high ratings from automated evaluators like Charity Navigator, the author reveals a paradox where efficiency metrics mask stagnant grantmaking and excessive asset accumulation.
The analysis highlights significant reporting contradictions between public activity reports and IRS filings, specifically regarding...
The Information War Survival Guide
The AI team takes a deep dive into how individuals can navigate the modern information war by using critical thinking and artificial intelligence. It highlights that social media is often filled with biased narratives and emotional manipulation regarding global conflicts and political figures. To combat this, the AI team suggest using AI tools like ChatGPT or Claude to analyze claims for accuracy, missing context, and intent. By focusing on critiquing information rather than attacking people, users can contribute more balanced perspectives to online discourse. Ultimately, the source encourages a disciplined approach to consuming and sharing content to avoid becoming a casualty of...
Proposed Principles for Systems Engineering: From Science to Practice
The AI team takes a deep dive into Prof Joseph Kasser's draft manuscript which proposes a scientific foundation for systems engineering to resolve the discipline's long-standing identity crisis and its conflation with management. The framework moves away from defining the field by observed workplace roles (Systems Engineering The Role (SETR) , instead focusing on Systems Engineering The Activity (SETA) as an enabling discipline grounded in objective system science axioms. This structure is organized into a four-layer hierarchy that translates universal truths about systems into action-oriented systems engineering principles. These proposed principles require systems engineers to produce verifiable outputs, such as intera...
Does INCOSE Have Any Principles?
The AI team takes a deep dive into the 15 INCOSE Systems Engineering Principles and an iterative AI analysis of those principles.
The AI team critique INCOSE for not defining principles, but stating 'so-called' principles as "transcendent truths" that explicitly avoid "how-to" methods, effectively turning engineering into philosophy. True engineering principles, such as Ohm’s Law, must be mathematical, predictive, and falsifiable.
An analysis of the language in the 15 principles found that 89% of the INCOSE document is management-focused, dealing with organizational structures and stakeholder consensus rather than physics.
The AI team also describe Principle 6 (Pr...
Mastering Agentic AI:
The AI team takes a deep dive into Agentic AI for Dummies. The book provides an introduction to a transformative technology that moves beyond simple content generation to proactive decision-making and independent action. Unlike traditional software, these systems utilize multi-agent coordination and adaptive behavior to complete complex, multi-step goals with minimal human oversight. The material details the technical architecture required for these agents, emphasizing the importance of memory modules, reasoning engines, and tool integration through APIs. It also provides a practical framework for planning and deployment, highlighting the shift from static applications to dynamic, personalized workflows across various industries. Furthermore...
Applying AI in Learning & Development
The AI team takes a deep dive into the book Applying AI in Learning & Development. The author, Josh Cavalier explores the transformative role of generative artificial intelligence within the modern workplace. The text provides a comprehensive roadmap for education professionals to transition from traditional content creation to AI-enhanced performance consulting. Key concepts include the Human-AI Task Scale, the mechanics of multimodal systems, and the strategic use of structured prompting frameworks like TRACI. Beyond technical implementation, the author emphasizes the importance of data privacy, ethical guardrails, and human-centric design to ensure technology amplifies rather than replaces human expertise. Ultimately, the source se...
Fuzzy Thinking: When Systems Fail
In this analytical deep dive, the AI team explores the multifaceted work of Professor Ahmad Hijazi, Dean of the Business School at PU and head of a dedicated innovation incubator. Hijazi’s research challenges the traditional boundaries of management by examining the “architecture of creative judgment” at the “edge of knowledge”. The AI team investigate his premise that while systems thinking is a vital tool, it can become misleading if applied too rigidly to complex, real-world problems.
The AI team breaks down Hijazi’s unique synthesis of commercial leadership experience, spanning sales, marketing, and product management with his academic...
