In this second installment of my conversation with Dr. David Woods, we continue our exploration of resilience engineering and complexity science, focusing on practical applications and actionable strategies. Building on the foundational concepts from part one, Dr. Woods offers deeper insights into how organizations can thrive in unpredictable environments by embracing resilience as a core competency.We dive into the nuts and bolts of designing systems that can adapt and recover, emphasizing the importance of fostering collaboration, continuous learning, and feedback loops. Dr. Woods connects these practices to W. Edwards Deming’s teachings, particularly the interplay between profound knowledge and operational flexibility. Our conversation also underscores the significance of learning from near misses and small failures, treating them as opportunities to strengthen systems rather than vulnerabilities to hide.Key highlights include:The Adaptive Cycle: Dr. Woods introduces a powerful framework for understanding how systems evolve and adapt over time, offering lessons for IT, healthcare, and manufacturing.Learning from Disruption: Examples of organizations that turned crises into growth opportunities by leveraging resilience principles.Operationalizing Resilience: How leaders can embed resilience thinking into daily operations through deliberate design and cultural shifts, echoing Deming’s focus on systems thinking and constancy of purpose.This episode serves as a practical guide for anyone seeking to bridge theoretical concepts with real-world applications. Dr. Woods leaves us with actionable takeaways on how to lead and thrive in an era of constant change, making this a must-listen for leaders and practitioners alike.

In this discussion, Dr. David Woods, a leading authority on resilience engineering and complexity science, shares his insights on resilience in a rapidly changing world. He emphasizes the importance of adaptive leadership in organizations, particularly in IT and healthcare. Dr. Woods provides real-world examples of successful resilience strategies and warns against oversimplifying complex systems. He connects these ideas to W. Edwards Deming’s principles, stressing that fostering a culture of learning and experimentation can drive innovation and improve organizational adaptability.

In this episode, I invite Tracy Ragan, CEO of DeployHub, on the podcast for an in-depth discussion on the evolution of DevOps, the complexities of modern software systems, and the cultural challenges women face in technology. Tracy's rich history in software development, from working on mainframes in the late 1980s to spearheading DevOps advancements, provides a fascinating lens through which to examine the industry.Key Topics Discussed:The Historical Context of DevOps: Tracy recounts the industry's shift from mainframe to distributed systems and the lessons learned (and forgotten) along the way. She highlights the recurring mistakes in adopting "shiny new objects" without addressing foundational issues, such as dependency chaos and scripting overuse.AI and Long-Tail Productivity: Tracy and John explore AI's transformative potential, emphasizing that its real impact lies in long-term gains rather than short-term ROI. Tracy draws parallels to past transitions, like the adoption of relational databases, arguing that AI's value will be fully realized only with improved system architectures.The Persistent Challenge of Women in Tech: Tracy candidly discusses her experiences as a woman in a male-dominated industry, noting a regression in gender diversity, particularly post-COVID. She highlights systemic issues, including exclusion from key networking opportunities and persistent biases, advocating for cultural shifts to empower women in tech.DeployHub’s Role in Tackling DevOps Complexity: Tracy introduces DeployHub's innovative approach to managing software supply chains and SBOMs (Software Bill of Materials). By mapping dependencies and automating vulnerability remediation, DeployHub aims to reduce the time and complexity of patch management, addressing critical gaps in modern software pipelines.

In this episode of The Profound Podcast, I sit down with Dr. Jabe Bloom, a researcher and expert in systems thinking, AI, and digital transformation. We explore Eric Lawson’s book The Myth of AI, tackling the contentious debate around artificial general intelligence (AGI). Dr. Bloom offers insights from his dissertation and divides the ongoing discourse on AI into two camps: dogmatists and pragmatists. Dogmatists believe AGI is inevitable, while pragmatists focus on the practical impacts of current AI technology, such as large language models (LLMs), and how these will reshape businesses, education, and society.Throughout the episode, Dr. Bloom explains his framework for thinking about AI, touching on proactionary versus precautionary approaches to its development and regulation. He also draws connections between these ideas and W. Edwards Deming’s principles, especially around abductive reasoning—a concept that links back to Dr. Bloom’s past discussions about AI’s potential in problem-solving.The conversation takes a critical view of AGI's feasibility, with Dr. Bloom emphasizing the current challenges AI faces in replicating abductive reasoning, which involves making intelligent guesses—a capability he argues machines have yet to achieve. We also dive into examples from fields like DevOps, healthcare, and city planning, discussing where AI has shown great promise and where it still falls short.Key takeaways from the episode include the importance of addressing present AI technologies and their immediate impacts on work and society, as well as the ongoing need for human oversight and critique when using AI systems.

In this episode of the Profound Podcast, I speak with Erik J. Larson, author of The Myth of Artificial Intelligence, about the speculative nature and real limitations of AI, particularly in relation to achieving Artificial General Intelligence (AGI). Larson delves into the philosophical and scientific misunderstandings surrounding AI, challenging the dominant narrative that AGI is just around the corner. Drawing from his expertise and experience in the field, Larson explains why much of the AI hype lacks empirical foundation. He emphasizes the limits of current AI models, particularly their reliance on inductive reasoning, which, though powerful, is insufficient for achieving human-like intelligence.Larson discusses how the field of AI has historically blended speculative futurism with genuine technological advancements, often fueled by financial incentives rather than scientific rigor. He highlights how this approach has led to misconceptions about AI’s capabilities, especially in the context of AGI. Drawing connections to philosophical theories of inference, Larson introduces deductive, inductive, and abductive reasoning, explaining how current AI systems fall short in their over-reliance on inductive methods. The conversation touches on the challenges of abduction (the "broken" form of reasoning humans often use) and the difficulty of replicating this in AI systems.Throughout the discussion, we explore the social and ethical implications of AI, including concerns about data limitations, the dangers of synthetic data, and the looming “data wall” that could hinder future AI progress. We also touch on broader societal impacts, such as how AI’s potential misuse and over-reliance might affect innovation and human intelligence.

In this episode of The Profound Podcast, I have an enlightening conversation with Dr. Jabe Bloom, a prominent voice in the fields of DevOps and digital transformation. The discussion revolves around the philosophical underpinnings of scientific reasoning and its application to complex systems, particularly through the lens of Charles Sanders Peirce's work on abductive reasoning.Jabe Bloom begins by exploring Peirce’s contributions to philosophy, particularly how Peirce's concept of abductive reasoning offers a framework for making educated guesses in situations where data is incomplete or variables are unknown. This idea becomes especially pertinent when Bloom contrasts the scientific method typically used in complicated domains, like Lean manufacturing, with the unpredictability of complex systems, where multiple hypotheses might be equally valid.The conversation further delves into how these ideas connect to digital transformation, especially in organizations navigating the complexities of modern IT and business environments. Bloom highlights the importance of fostering environments where experimentation and educated guessing are encouraged, as this aligns with Peirce's pragmatic approach, which values the usefulness of an idea over its absolute truth.To wrap up, we also discuss the broader implications of Peirce’s work on modern AI and socio-technical systems, emphasizing the need for a deeper understanding of how these systems operate and how to integrate artificial intelligence into complex human processes.

Andrew Clay Shafer, a pivotal figure in the DevOps community, discusses the evolution of DevOps and its role in digital transformation. He highlights the importance of foundational principles like those of W. Edwards Deming and critiques superficial adoption of methodologies. The conversation touches on the impact of AI and the need for true organizational learning to navigate challenges. Shafer emphasizes that platform engineering is an extension of DevOps philosophy and addresses the dynamics of marketing in the tech landscape.

In this episode, I speak with Joseph Enochs, Managing Director of AI/ML and Emerging Technologies at Enterprise Vision Technologies. Known for his extensive background in DevOps and digital transformation, Joseph shares his remarkable journey transitioning into the AI domain.Joseph begins by recounting how his interest in AI was sparked, notably influenced by the foundational concepts of W. Edwards Deming and the subsequent developments in DevOps. He details the pivotal moments that led him to pursue a master's degree in AI, highlighting the importance of continuous learning and the foresight needed to anticipate technological trends.The discussion covers the evolution and integration of AI within large enterprises, emphasizing the challenges and strategies for incorporating AI into existing systems. Joseph explains the significance of vector databases, context windows, and the roles of orchestrators and agents in enhancing AI capabilities. He also delves into the practical applications of AI in business, such as improving call center efficiency and automating complex tasks.In the final segment, Joseph offers practical advice on how to start learning about AI, emphasizing the importance of experimentation and validation of assumptions. The episode concludes with a reflection on the profound impact AI is set to have on the future of technology and business.

In this episode, I sit down with Dr. Barbara Lawton, a distinguished statistician and expert in experimental design. Dr. Lawton shares her journey from studying biology and ecology to diving deep into statistics, eventually finding herself at the Rocky Flats nuclear weapons facility. It was here she experienced a pivotal moment that led her to W. Edwards Deming's teachings.Dr. Lawton recounts how a significant project at Rocky Flats fell apart due to budget cuts, illustrating Deming's principle that quality is determined in the boardroom. This realization propelled her to attend a seminar by Deming, which profoundly changed her perspective on quality and management. She describes her experience of traveling with Deming, learning from his insights, and understanding the importance of continuous learning and adaptation.The conversation delves into Deming's System of Profound Knowledge (SoPK), highlighting its relevance in today's fast-evolving world. Dr. Lawton emphasizes the need for an appreciation of systems, understanding of variation, psychology, and theory of knowledge, and expands on these concepts by integrating ideas from physics and ecology. She discusses the role of entropy, thermodynamics, and the dynamic nature of systems, explaining how these principles apply to modern organizations and leadership.Dr. Lawton also touches on the importance of emotional intelligence, mindfulness, and effective communication in leadership. She advocates for creating environments that foster continuous learning and adaptability, essential for navigating the complexities of today's digital landscape.You can find Dr. Barbara Lawton's LinkedIn Below:https://www.linkedin.com/in/barbara-lawton-8b920b6/Show Notes:https://www.youtube.com/watch?v=sZ2U7F0RJd0https://www.youtube.com/watch?v=WWiH5S55iDg&t=25m28s

Angela Montgomery, co-founder of Intelligent Management and author, shares her fascinating journey from the arts to collaborating with physicists. She discusses the integration of Deming's philosophy with Goldratt's Theory of Constraints to drive organizational change. Montgomery highlights the significance of identifying constraints over mere bottlenecks and the challenges of systemic thinking in traditional structures. Through engaging narratives, she introduces the Decalogue Method and advocates for sustainable practices in management, emphasizing the importance of flow and innovation.