Over the past decades, as I’ve grown and now led quality organizations in biotechnology, I’ve encountered many thinkers who’ve shaped my approach to investigation and risk management. But few have fundamentally altered my perspective like Sidney Dekker. His work didn’t just add to my toolkit—it forced me to question some of my most basic assumptions about human error, system failure, and what it means to create genuinely effective quality systems.
Dekker’s challenge to move beyond “safety theater” toward authentic learning resonates deeply with my own frustrations about quality systems that look impressive on paper but fail when tested by real-world complexity.
Why Dekker Matters for Quality Leaders
Professor Sidney Dekker brings a unique combination of academic rigor and operational experience to safety science. As both a commercial airline pilot and the Director of the Safety Science Innovation Lab at Griffith University, he understands the gap between how work is supposed to happen and how it actually gets done. This dual perspective—practitioner and scholar—gives his critiques of traditional safety approaches unusual credibility.
But what initially drew me to Dekker’s work wasn’t his credentials. It was his ability to articulate something I’d been experiencing but couldn’t quite name: the growing disconnect between our increasingly sophisticated compliance systems and our actual ability to prevent quality problems. His concept of “drift into failure” provided a framework for understanding why organizations with excellent procedures and well-trained personnel still experience systemic breakdowns.
The “New View” Revolution
Dekker’s most fundamental contribution is what he calls the “new view” of human error—a complete reframing of how we understand system failures. Having spent years investigating deviations and CAPAs, I can attest to how transformative this shift in perspective can be.
The Traditional Approach I Used to Take:
Human error causes problems
People are unreliable; systems need protection from human variability
Solutions focus on better training, clearer procedures, more controls
Dekker’s New View That Changed My Practice:
Human error is a symptom of deeper systemic issues
People are the primary source of system reliability, not the threat to it
Variability and adaptation are what make complex systems work
This isn’t just academic theory—it has practical implications for every investigation I lead. When I encounter “operator error” in a deviation investigation, Dekker’s framework pushes me to ask different questions: What made this action reasonable to the operator at the time? What system conditions shaped their decision-making? How did our procedures and training actually perform under real-world conditions?
This shift aligns perfectly with the causal reasoning approaches I’ve been developing on this blog. Instead of stopping at “failure to follow procedure,” we dig into the specific mechanisms that drove the event—exactly what Dekker’s view demands.
Drift Into Failure: Why Good Organizations Go Bad
Perhaps Dekker’s most powerful concept for quality leaders is “drift into failure”—the idea that organizations gradually migrate toward disaster through seemingly rational local decisions. This isn’t sudden catastrophic failure; it’s incremental erosion of safety margins through competitive pressure, resource constraints, and normalized deviance.
I’ve seen this pattern repeatedly. For example, a cleaning validation program starts with robust protocols, but over time, small shortcuts accumulate: sampling points that are “difficult to access” get moved, hold times get shortened when production pressure increases, acceptance criteria get “clarified” in ways that gradually expand limits.
Each individual decision seems reasonable in isolation. But collectively, they represent drift—a gradual migration away from the original safety margins toward conditions that enable failure. The contamination events and data integrity issues that plague our industry often represent the endpoint of these drift processes, not sudden breakdowns in otherwise reliable systems.
Traditional root cause analysis seeks the single factor that “caused” an event, but complex system failures emerge from multiple interacting conditions. The take-the-best heuristic I’ve been exploring on this blog—focusing on the most causally powerful factor—builds directly on Dekker’s insight that we need to understand mechanisms, not hunt for someone to blame.
When I investigate a failure now, I’m not looking for THE root cause. I’m trying to understand how various factors combined to create conditions for failure. What pressures were operators experiencing? How did procedures perform under actual conditions? What information was available to decision-makers? What made their actions reasonable given their understanding of the situation?
This approach generates investigations that actually help prevent recurrence rather than just satisfying regulatory expectations for “complete” investigations.
Just Culture: Moving Beyond Blame
Dekker’s evolution of just culture thinking has been particularly influential in my leadership approach. His latest work moves beyond simple “blame-free” environments toward restorative justice principles—asking not “who broke the rule” but “who was hurt and how can we address underlying needs.”
This shift has practical implications for how I handle deviations and quality events. Instead of focusing on disciplinary action, I’m asking: What systemic conditions contributed to this outcome? What support do people need to succeed? How can we address the underlying vulnerabilities this event revealed?
This doesn’t mean eliminating accountability—it means creating accountability systems that actually improve performance rather than just satisfying our need to assign blame.
Safety Theater: The Problem with Compliance Performance
Dekker’s most recent work on “safety theater” hits particularly close to home in our regulated environment. He defines safety theater as the performance of compliance when under surveillance that retreats to actual work practices when supervision disappears.
I’ve watched organizations prepare for inspections by creating impressive documentation packages that bear little resemblance to how work actually gets done. Procedures get rewritten to sound more rigorous, training records get updated, and everyone rehearses the “right” answers for auditors. But once the inspection ends, work reverts to the adaptive practices that actually make operations function.
This theater emerges from our desire for perfect, controllable systems, but it paradoxically undermines genuine safety by creating inauthenticity. People learn to perform compliance rather than create genuine safety and quality outcomes.
The falsifiable quality systems I’ve been advocating on this blog represent one response to this problem—creating systems that can be tested and potentially proven wrong rather than just demonstrated as compliant.
Six Practical Takeaways for Quality Leaders
After years of applying Dekker’s insights in biotechnology manufacturing, here are the six most practical lessons for quality professionals:
1. Treat “Human Error” as the Beginning of Investigation, Not the End
When investigations conclude with “human error,” they’ve barely started. This should prompt deeper questions: Why did this action make sense? What system conditions shaped this decision? What can we learn about how our procedures and training actually perform under pressure?
2. Understand Work-as-Done, Not Just Work-as-Imagined
There’s always a gap between procedures (work-as-imagined) and actual practice (work-as-done). Understanding this gap and why it exists is more valuable than trying to force compliance with unrealistic procedures. Some of the most important quality improvements I’ve implemented came from understanding how operators actually solve problems under real conditions.
3. Measure Positive Capacities, Not Just Negative Events
Traditional quality metrics focus on what didn’t happen—no deviations, no complaints, no failures. I’ve started developing metrics around investigation quality, learning effectiveness, and adaptive capacity rather than just counting problems. How quickly do we identify and respond to emerging issues? How effectively do we share learning across sites? How well do our people handle unexpected situations?
4. Create Psychological Safety for Learning
Fear and punishment shut down the flow of safety-critical information. Organizations that want to learn from failures must create conditions where people can report problems, admit mistakes, and share concerns without fear of retribution. This is particularly challenging in our regulated environment, but it’s essential for moving beyond compliance theater toward genuine learning.
5. Focus on Contributing Conditions, Not Root Causes
Complex failures emerge from multiple interacting factors, not single root causes. The take-the-best approach I’ve been developing helps identify the most causally powerful factor while avoiding the trap of seeking THE cause. Understanding mechanisms is more valuable than finding someone to blame.
6. Embrace Adaptive Capacity Instead of Fighting Variability
People’s ability to adapt and respond to unexpected conditions is what makes complex systems work, not a threat to be controlled. Rather than trying to eliminate human variability through ever-more-prescriptive procedures, we should understand how that variability creates resilience and design systems that support rather than constrain adaptive problem-solving.
Connection to Investigation Excellence
Dekker’s work provides the theoretical foundation for many approaches I’ve been exploring on this blog. His emphasis on testable hypotheses rather than compliance theater directly supports falsifiable quality systems. His new view framework underlies the causal reasoning methods I’ve been developing. His focus on understanding normal work, not just failures, informs my approach to risk management.
Most importantly, his insistence on moving beyond negative reasoning (“what didn’t happen”) to positive causal statements (“what actually happened and why”) has transformed how I approach investigations. Instead of documenting failures to follow procedures, we’re understanding the specific mechanisms that drove events—and that makes all the difference in preventing recurrence.
Essential Reading for Quality Leaders
If you’re leading quality organizations in today’s complex regulatory environment, these Dekker works are essential:
Dekker’s work challenges us as quality leaders to move beyond the comfortable certainty of compliance-focused approaches toward the more demanding work of creating genuine learning systems. This requires admitting that our procedures and training might not work as intended. It means supporting people when they make mistakes rather than just punishing them. It demands that we measure our success by how well we learn and adapt, not just how well we document compliance.
This isn’t easy work. It requires the kind of organizational humility that Amy Edmondson and other leadership researchers emphasize—the willingness to be proven wrong in service of getting better. But in my experience, organizations that embrace this challenge develop more robust quality systems and, ultimately, better outcomes for patients.
The question isn’t whether Sidney Dekker is right about everything—it’s whether we’re willing to test his ideas and learn from the results. That’s exactly the kind of falsifiable approach that both his work and effective quality systems demand.
Safety science has evolved from a narrow focus on preventing individual errors to a sophisticated understanding of how complex socio-technical systems create both failure and resilience. The intellectual influences explored in this guide represent a paradigm shift from traditional “blame and fix” approaches to nuanced frameworks that recognize safety and quality as emergent properties of system design, organizational culture, and human adaptation.
These thinkers have fundamentally changed how quality professionals understand failure, risk, and the role of human expertise in creating reliable operations. Their work provides the theoretical foundation for moving beyond compliance-driven quality management toward learning-oriented, resilience-based approaches that acknowledge the inherent complexity of modern organizational systems.
System Failure and Accident Causation
Sidney Dekker
The architect of Safety Differently and New View thinking
Sidney Dekker has fundamentally transformed how we understand human error and system failure. His work challenges the traditional focus on individual blame, instead viewing errors as symptoms of deeper system issues. Dekker’s concept of “drift into failure” explains how systems gradually migrate toward unsafe conditions through seemingly rational local adaptations. His framework provides quality professionals with tools for understanding how organizational pressures and system design create the conditions for both success and failure.
The Swiss Cheese model creator and error management pioneer
James Reason’s work provides the foundational framework for understanding how organizational failures create the conditions for accidents. His Swiss Cheese model demonstrates how multiple defensive layers must align for accidents to occur, shifting focus from individual error to organizational defenses. Reason’s 12 principles of error management offer practical guidance for building systems that can contain and learn from human fallibility.
Human Error: Models and Management (2000) – Essential reading on the difference between person-centered and system-centered approaches to error.
Charles Perrow
The normal accidents theorist
Charles Perrow revolutionized safety thinking with his theory of “normal accidents” – the idea that in complex, tightly-coupled systems, catastrophic failures are inevitable rather than preventable. His work demonstrates why traditional engineering approaches to safety often fail in complex systems and why some technologies may be inherently too dangerous to operate safely. For quality professionals, Perrow’s insights are crucial for understanding when system redesign, rather than procedural improvements, becomes necessary.
The resilience engineering pioneer and ETTO principle creator
Erik Hollnagel’s resilience engineering framework fundamentally shifts safety thinking from preventing things from going wrong (Safety-I) to understanding how things go right (Safety-II). His four cornerstones of resilience – the ability to respond, monitor, learn, and anticipate – provide quality professionals with a proactive framework for building adaptive capacity. The ETTO (Efficiency-Thoroughness Trade-Off) principle explains why organizations must balance competing demands and why perfect safety procedures are often impractical.
David Woods co-founded both cognitive systems engineering and resilience engineering, fundamentally changing how we understand human-system interaction. His concept of “graceful extensibility” explains how systems must be designed to adapt beyond their original parameters. Woods’ work on joint cognitive systems provides frameworks for understanding how human expertise and technological systems create integrated performance capabilities.
Nancy Leveson’s Systems-Theoretic Accident Model and Processes (STAMP) provides a approach to understanding accidents in complex systems. Unlike traditional event-chain models, STAMP views accidents as control problems rather than failure problems. Her work is essential for quality professionals dealing with software-intensive systems and complex organizational interfaces where traditional hazard analysis methods prove inadequate.
The Human and Organizational Performance (HOP) advocate
Todd Conklin’s five principles of Human and Organizational Performance represent a contemporary synthesis of decades of safety science research. His approach emphasizes that people make mistakes, blame fixes nothing, learning is vital, context drives behavior, and how we respond to failure shapes future performance. Conklin’s work provides quality professionals with practical frameworks for implementing research-based safety approaches in real organizational settings.
Andrew Hopkins’ detailed analyses of major industrial disasters provide unparalleled insights into how organizational factors create the conditions for catastrophic failure. His work on the BP Texas City refinery disaster, Longford gas plant explosion, and other major accidents demonstrates how regulatory systems, organizational structure, and safety culture interact to create or prevent disasters. Hopkins’ narrative approach makes complex organizational dynamics accessible to quality professionals.
Safety, Culture and Risk: The Organisational Causes of Disasters (2005) – Essential framework for understanding how organizational culture shapes safety outcomes.
Carl Macrae
The healthcare resilience researcher
Carl Macrae’s work bridges safety science and healthcare quality, demonstrating how resilience engineering principles apply to complex care environments. His research on incident reporting, organizational learning, and regulatory systems provides quality professionals with frameworks for building adaptive capacity in highly regulated environments. Macrae’s work is particularly valuable for understanding how to balance compliance requirements with learning-oriented approaches.
Learning from Failure: Building Safer Healthcare through Reporting and Analysis (2016) – Essential guide to building effective organizational learning systems in regulated environments.
Philosophical Foundations of Risk and Speed
Paul Virilio
The dromology and accident philosopher
Paul Virilio’s concept of dromology – the study of speed and its effects – provides profound insights into how technological acceleration creates new forms of risk. His insight that “when you invent the ship, you also invent the shipwreck” explains how every technology simultaneously creates its potential for failure. For quality professionals in rapidly evolving technological environments, Virilio’s work explains how speed itself becomes a source of systemic risk that traditional quality approaches may be inadequate to address.
Essential Books:Speed and Politics (1986) – The foundational text on how technological acceleration reshapes power relationships and risk patterns.
The Information Bomb (2000) – Essential reading on how information technology acceleration creates new forms of systemic vulnerability.
This guide represents a synthesis of influences that have fundamentally transformed safety thinking from individual-focused error prevention to system-based resilience building. Each recommended book offers unique insights that, when combined, provide a comprehensive foundation for quality leadership that acknowledges the complex, adaptive nature of modern organizational systems. These thinkers challenge us to move beyond traditional quality management toward approaches that embrace complexity, foster learning, and build adaptive capacity in an uncertain world.
When I encounter professionals who believe they can master a process in six months, I think of something the great systems thinker W. Edwards Deming once observed: “It is not necessary to change. Survival is not mandatory.” The professionals who survive—and more importantly, who drive genuine improvement—understand something that transcends the checkbox mentality: true ownership takes time, patience, and what some might call “stick-to-itness.”
The uncomfortable truth is that most of us confuse familiarity with mastery. We mistake the ability to execute procedures with the deep understanding required to improve them. This confusion has created a generation of professionals who move from role to role, collecting titles and experiences but never developing the profound process knowledge that enables breakthrough improvement. This is equally true on the consultant side.
The cost of this superficial approach extends far beyond individual career trajectories. When organizations lack deep process owners—people who have lived with systems long enough to understand their subtle rhythms and hidden failure modes—they create what I call “quality theater”: elaborate compliance structures that satisfy auditors but fail to serve patients, customers, or the fundamental purpose of pharmaceutical manufacturing.
The Science of Deep Ownership
Recent research in organizational psychology reveals the profound difference between surface-level knowledge and genuine psychological ownership. When employees develop true psychological ownership of their processes, something remarkable happens: they begin to exhibit behaviors that extend far beyond their job descriptions. They proactively identify risks, champion improvements, and develop the kind of intimate process knowledge that enables predictive rather than reactive management.
But here’s what the research also shows: this psychological ownership doesn’t emerge overnight. Studies examining the relationship between tenure and performance consistently demonstrate nonlinear effects. The correlation between tenure and performance actually decreases exponentially over time—but this isn’t because long-tenured employees become less effective. Instead, it reflects the reality that deep expertise follows a complex curve where initial competence gives way to periods of plateau, followed by breakthrough understanding that emerges only after years of sustained engagement.
Consider the findings from meta-analyses of over 3,600 employees across various industries. The relationship between organizational commitment and job performance shows a very strong nonlinear moderating effect based on tenure. The implications are profound: the value of process ownership isn’t linear, and the greatest insights often emerge after years of what might appear to be steady-state performance.
This aligns with what quality professionals intuitively know but rarely discuss: the most devastating process failures often emerge from interactions and edge cases that only become visible after sustained observation. The process owner who has lived through multiple product campaigns, seasonal variations, and equipment lifecycle transitions develops pattern recognition that cannot be captured in procedures or training materials.
The 10,000 Hour Reality in Quality Systems
Malcolm Gladwell’s popularization of the 10,000-hour rule has been both blessing and curse for understanding expertise development. While recent research has shown that deliberate practice accounts for only 18-26% of skill variation—meaning other factors like timing, genetics, and learning environment matter significantly—the core insight remains valid: mastery requires sustained, focused engagement over years, not months.
But the pharmaceutical quality context adds layers of complexity that make the expertise timeline even more demanding. Unlike chess players or musicians who can practice their craft continuously, quality professionals must develop expertise within regulatory frameworks that change, across technologies that evolve, and through organizational transitions that reset context. The “hours” of meaningful practice are often interrupted by compliance activities, reorganizations, and role changes that fragment the learning experience.
More importantly, quality expertise isn’t just about individual skill development—it’s about understanding systems. Deming’s System of Profound Knowledge emphasizes that effective quality management requires appreciation for a system, knowledge about variation, theory of knowledge, and psychology. This multidimensional expertise cannot be compressed into abbreviated timelines, regardless of individual capability or organizational urgency.
The research on mastery learning provides additional insight. True mastery-based approaches require that students achieve deep understanding at each level before progressing to the next. In quality systems, this means that process owners must genuinely understand the current state of their processes—including their failure modes, sources of variation, and improvement potential—before they can effectively drive transformation.
The Hidden Complexity of Process Ownership
Many of our organizations struggle with “iceberg phenomenon”: the visible aspects of process ownership—procedure compliance, metric reporting, incident response—represent only a small fraction of the role’s true complexity and value.
Effective process owners develop several types of knowledge that accumulate over time:
Tacit Process Knowledge: Understanding the subtle indicators that precede process upsets, the informal workarounds that maintain operations, and the human factors that influence process performance. This knowledge emerges through repeated exposure to process variations and cannot be documented or transferred through training.
Systemic Understanding: Comprehending how their process interacts with upstream and downstream activities, how changes in one area create ripple effects throughout the system, and how to navigate the political and technical constraints that shape improvement opportunities. This requires exposure to multiple improvement cycles and organizational changes.
Regulatory Intelligence: Developing nuanced understanding of how regulatory expectations apply to their specific context, how to interpret evolving guidance, and how to balance compliance requirements with operational realities. This expertise emerges through regulatory interactions, inspection experiences, and industry evolution.
Change Leadership Capability: Building the credibility, relationships, and communication skills necessary to drive improvement in complex organizational environments. This requires sustained engagement with stakeholders, demonstrated success in previous initiatives, and deep understanding of organizational dynamics.
Each of these knowledge domains requires years to develop, and they interact synergistically. The process owner who has lived through equipment upgrades, regulatory inspections, organizational changes, and improvement initiatives develops a form of professional judgment that cannot be replicated through rotation or abbreviated assignments.
The Deming Connection: Systems Thinking Requires Time
Deming’s philosophy of continuous improvement provides a crucial framework for understanding why process ownership requires sustained engagement. His approach to quality was holistic, emphasizing systems thinking and long-term perspective over quick fixes and individual blame.
Consider Deming’s first point: “Create constancy of purpose toward improvement of product and service.” This isn’t about maintaining consistency in procedures—it’s about developing the deep understanding necessary to identify genuine improvement opportunities rather than cosmetic changes that satisfy short-term pressures.
The PDCA cycle that underlies Deming’s approach explicitly requires iterative learning over multiple cycles. Each cycle builds on previous learning, and the most valuable insights often emerge after several iterations when patterns become visible and root causes become clear. Process owners who remain with their systems long enough to complete multiple cycles develop qualitatively different understanding than those who implement single improvements and move on.
Deming’s emphasis on driving out fear also connects to the tenure question. Organizations that constantly rotate process owners signal that deep expertise isn’t valued, creating environments where people focus on short-term achievements rather than long-term system health. The psychological safety necessary for honest problem-solving and innovative improvement requires stable relationships built over time.
The Current Context: Why Stick-to-itness is Endangered
The pharmaceutical industry’s current talent management practices work against the development of deep process ownership. Organizations prioritize broad exposure over deep expertise, encourage frequent role changes to accelerate career progression, and reward visible achievements over sustained system stewardship.
This approach has several drivers, most of them understandable but ultimately counterproductive:
Career Development Myths: The belief that career progression requires constant role changes, preventing the development of deep expertise in any single area. This creates professionals with broad but shallow knowledge who lack the depth necessary to drive breakthrough improvement.
Organizational Impatience: Pressure to demonstrate rapid improvement, leading to premature conclusions about process owner effectiveness and frequent role changes before mastery can develop. This prevents organizations from realizing the compound benefits of sustained process ownership.
Risk Aversion: Concern that deep specialization creates single points of failure, leading to policies that distribute knowledge across multiple people rather than developing true expertise. This approach reduces organizational vulnerability to individual departures but eliminates the possibility of breakthrough improvement that requires deep understanding.
Measurement Misalignment: Performance management systems that reward visible activity over sustained stewardship, creating incentives for process owners to focus on quick wins rather than long-term system development.
The result is what I observe throughout the industry: sophisticated quality systems managed by well-intentioned professionals who lack the deep process knowledge necessary to drive genuine improvement. We have created environments where people are rewarded for managing systems they don’t truly understand, leading to the elaborate compliance theater that satisfies auditors but fails to protect patients.
Building Genuine Process Ownership Capability
Creating conditions for deep process ownership requires intentional organizational design that supports sustained engagement rather than constant rotation. This isn’t about keeping people in the same roles indefinitely—it’s about creating career paths that value depth alongside breadth and recognize the compound benefits of sustained expertise development.
Redefining Career Success: Organizations must develop career models that reward deep expertise alongside traditional progression. This means creating senior individual contributor roles, recognizing process mastery in compensation and advancement decisions, and celebrating sustained system stewardship as a form of leadership.
Supporting Long-term Engagement: Process owners need organizational support to sustain motivation through the inevitable plateaus and frustrations of deep system work. This includes providing resources for continuous learning, connecting them with external expertise, and ensuring their contributions are visible to senior leadership.
Creating Learning Infrastructure: Deep process ownership requires systematic approaches to knowledge capture, reflection, and improvement. Organizations must provide time and tools for process owners to document insights, conduct retrospective analyses, and share learning across the organization.
Building Technical Career Paths: The industry needs career models that allow technical professionals to advance without moving into management roles that distance them from process ownership. This requires creating parallel advancement tracks, appropriate compensation structures, and recognition systems that value technical leadership.
Measuring Long-term Value: Performance management systems must evolve to recognize the compound benefits of sustained process ownership. This means developing metrics that capture system stability, improvement consistency, and knowledge development rather than focusing exclusively on short-term achievements.
The Connection to Jobs-to-Be-Done
The Jobs-to-Be-Done tool I explored iprovides valuable insight into why process ownership requires sustained engagement. Organizations don’t hire process owners to execute procedures—they hire them to accomplish several complex jobs that require deep system understanding:
Knowledge Development: Building comprehensive understanding of process behavior, failure modes, and improvement opportunities that enables predictive rather than reactive management.
System Stewardship: Maintaining process health through minor adjustments, preventive actions, and continuous optimization that prevents major failures and enables consistent performance.
Change Leadership: Driving improvements that require deep technical understanding, stakeholder engagement, and change management capabilities developed through sustained experience.
Organizational Memory: Serving as repositories of process history, lessons learned, and contextual knowledge that prevents the repetition of past mistakes and enables informed decision-making.
Each of these jobs requires sustained engagement to accomplish effectively. The process owner who moves to a new role after 18 months may have learned the procedures, but they haven’t developed the deep understanding necessary to excel at these higher-order responsibilities.
The Path Forward: Embracing the Long View
We need to fundamentally rethink how we develop and deploy process ownership capability in pharmaceutical quality systems. This means acknowledging that true expertise takes time, creating organizational conditions that support sustained engagement, and recognizing the compound benefits of deep process knowledge.
The choice is clear: continue cycling process owners through abbreviated assignments that prevent the development of genuine expertise, or build career models and organizational practices that enable deep process ownership to flourish. In an industry where process failures can result in patient harm, product recalls, and regulatory action, only the latter approach offers genuine protection.
True process ownership isn’t something we implement because best practices require it. It’s a capability we actively cultivate because it makes us demonstrably better at protecting patients and ensuring product quality. When we design organizational systems around the jobs that deep process ownership accomplishes—knowledge development, system stewardship, change leadership, and organizational memory—we create competitive advantages that extend far beyond compliance.
Organizations that recognize the value of sustained process ownership and create conditions for its development will build capabilities that enable breakthrough improvement and genuine competitive advantage. Those that continue to treat process ownership as a rotational assignment will remain trapped in the cycle of elaborate compliance theater that satisfies auditors but fails to serve the fundamental purpose of pharmaceutical manufacturing.
Process ownership should not be something we implement because organizational charts require it. It should be a capability we actively develop because it makes us demonstrably better at the work that matters: protecting patients, ensuring product quality, and advancing the science of pharmaceutical manufacturing. When we embrace the deep ownership paradox—that mastery requires time, patience, and sustained engagement—we create the conditions for the kind of breakthrough improvement that our industry desperately needs.
In quality systems, as in life, the most valuable capabilities cannot be rushed, shortcuts cannot be taken, and true expertise emerges only through sustained engagement with the work that matters. This isn’t just good advice for individual career development—it’s the foundation for building pharmaceutical quality systems that genuinely serve patients and advance human health.
Further Reading
Kausar, F., Ijaz, M. U., Rasheed, M., Suhail, A., & Islam, U. (2025). Empowered, accountable, and committed? Applying self-determination theory to examine work-place procrastination. BMC Psychology, 13, 620. https://doi.org/10.1186/s40359-025-02968-7
Wright, T. A., & Bonett, D. G. (2002). The moderating effects of employee tenure on the relation between organizational commitment and job performance: A meta-analysis. Journal of Applied Psychology, 87(6), 1183-1190. https://doi.org/10.1037/0021-9010.87.6.1183
I think we all have a central challenge in our professional life: How do we distinguish between genuine scientific insights that enhance our practice and the seductive allure of popularized psychological concepts that promise quick fixes but deliver questionable results. This tension between rigorous evidence and intuitive appeal represents more than an academic debate, it strikes at the heart of our professional identity and effectiveness.
The emergence of emotional intelligence as a dominant workplace paradigm exemplifies this challenge. While interpersonal skills undoubtedly matter in quality management, the uncritical adoption of psychological frameworks without scientific scrutiny creates what Dave Snowden aptly terms the “Woozle effect”—a phenomenon where repeated citation transforms unvalidated concepts into accepted truth. As quality thinkers, we must navigate this landscape with both intellectual honesty and practical wisdom, building systems that honor the genuine insights about human behavior while maintaining rigorous standards for evidence.
This exploration connects directly to the cognitive foundations of risk management excellence we’ve previously examined. The same systematic biases that compromise risk assessments—confirmation bias, anchoring effects, and overconfidence—also make us vulnerable to appealing but unsubstantiated management theories. By understanding these connections, we can develop more robust approaches that integrate the best of scientific evidence with the practical realities of human interaction in quality systems.
The Seductive Appeal of Pop Psychology in Quality Management
The proliferation of psychological concepts in business environments reflects a genuine need. Quality professionals recognize that technical competence alone cannot ensure organizational success. We need effective communication, collaborative problem-solving, and the ability to navigate complex human dynamics. This recognition creates fertile ground for frameworks that promise to unlock the mysteries of human behavior and transform our organizational effectiveness.
However, the popularity of concepts like emotional intelligence often stems from their intuitive appeal rather than their scientific rigor. As Professor Merve Emre’s critique reveals, such frameworks can become “morality plays for a secular era, performed before audiences of mainly white professionals”. They offer the comfortable illusion of control over complex interpersonal dynamics while potentially obscuring more fundamental issues of power, inequality, and systemic dysfunction.
The quality profession’s embrace of these concepts reflects our broader struggle with what researchers call “pseudoscience at work”. Despite our commitment to evidence-based thinking in technical domains, we can fall prey to the same cognitive biases that affect other professionals. The competitive nature of modern quality management creates pressure to adopt the latest insights, leading us to embrace concepts that feel innovative and transformative without subjecting them to the same scrutiny we apply to our technical methodologies.
This phenomenon becomes particularly problematic when we consider the Woozle effect in action. Dave Snowden’s analysis demonstrates how concepts can achieve credibility through repeated citation rather than empirical validation. In the echo chambers of professional conferences and business literature, unvalidated theories gain momentum through repetition, eventually becoming embedded in our standard practices despite lacking scientific foundation.
Understanding why quality professionals become susceptible to popularized psychological concepts requires examining the cognitive architecture underlying our decision-making processes. The same mechanisms that enable our technical expertise can also create vulnerabilities when applied to interpersonal and organizational challenges.
Our professional training emphasizes systematic thinking, data-driven analysis, and evidence-based conclusions. These capabilities serve us well in technical domains where variables can be controlled and measured. However, when confronting the messier realities of human behavior and organizational dynamics, we may unconsciously lower our evidentiary standards, accepting frameworks that align with our intuitions rather than demanding the same level of proof we require for technical decisions.
This shift reflects what cognitive scientists call “domain-specific expertise limitations.” Our deep knowledge in quality systems doesn’t automatically transfer to psychology or organizational behavior. Yet our confidence in our technical judgment can create overconfidence in our ability to evaluate non-technical concepts, leading to what researchers identify as a key vulnerability in professional decision-making.
The research on cognitive biases in professional settings reveals consistent patterns across management, finance, medicine, and law. Overconfidence emerges as the most pervasive bias, leading professionals to overestimate their ability to evaluate evidence outside their domain of expertise. In quality management, this might manifest as quick adoption of communication frameworks without questioning their empirical foundation, or assuming that our systematic thinking skills automatically extend to understanding human psychology.
Confirmation bias compounds this challenge by leading us to seek information that supports our preferred approaches while ignoring contradictory evidence. If we find an interpersonal framework appealing, perhaps because it aligns with our values or promises to solve persistent challenges, we may unconsciously filter available information to support our conclusion. This creates the self-reinforcing cycles that allow questionable concepts to become embedded in our practice.
Evidence-Based Approaches to Interpersonal Effectiveness
The solution to the pop psychology problem doesn’t lie in dismissing the importance of interpersonal skills or communication effectiveness. Instead, it requires applying the same rigorous standards to behavioral insights that we apply to technical knowledge. This means moving beyond frameworks that merely feel right toward approaches grounded in systematic research and validated through empirical study.
Evidence-based management provides a framework for navigating this challenge. Rather than relying solely on intuition, tradition, or popular trends, evidence-based approaches emphasize the systematic use of four sources of evidence: scientific literature, organizational data, professional expertise, and stakeholder perspectives. This framework enables us to evaluate interpersonal and communication concepts with the same rigor we apply to technical decisions.
Scientific literature offers the most robust foundation for understanding interpersonal effectiveness. Research in organizational psychology, communication science, and related fields provides extensive evidence about what actually works in workplace interactions. For example, studies on psychological safety demonstrate clear relationships between specific leadership behaviors and team performance outcomes. This research enables us to move beyond generic concepts like “emotional intelligence” toward specific, actionable insights about creating environments where teams can perform effectively.
Organizational data provides another crucial source of evidence for evaluating interpersonal approaches. Rather than assuming that communication training programs or team-building initiatives are effective, we can measure their actual impact on quality outcomes, employee engagement, and organizational performance. This data-driven approach helps distinguish between interventions that feel good and those that genuinely improve results.
Professional expertise remains valuable, but it must be systematically captured and validated rather than simply accepted as received wisdom. This means documenting the reasoning behind successful interpersonal approaches, testing assumptions about what works, and creating mechanisms for updating our understanding as new evidence emerges. The risk management excellence framework we’ve previously explored provides a model for this systematic approach to knowledge management.
The Integration Challenge: Systematic Thinking Meets Human Reality
The most significant challenge facing quality professionals lies in integrating rigorous, evidence-based approaches with the messy realities of human interaction. Technical systems can be optimized through systematic analysis and controlled improvement, but human systems involve emotions, relationships, and cultural dynamics that resist simple optimization approaches.
This integration challenge requires what we might call “systematic humility“—the recognition that our technical expertise creates capabilities but also limitations. We can apply systematic thinking to interpersonal challenges, but we must acknowledge the increased uncertainty and complexity involved. This doesn’t mean abandoning rigor; instead, it means adapting our approaches to acknowledge the different evidence standards and validation methods required for human-centered interventions.
The cognitive foundations of risk management excellence provide a useful model for this integration. Just as effective risk management requires combining systematic analysis with recognition of cognitive limitations, effective interpersonal approaches require combining evidence-based insights with acknowledgment of human complexity. We can use research on communication effectiveness, team dynamics, and organizational behavior to inform our approaches while remaining humble about the limitations of our knowledge.
One practical approach involves treating interpersonal interventions as experiments rather than solutions. Instead of implementing communication training programs or team-building initiatives based on popular frameworks, we can design systematic pilots that test specific hypotheses about what will improve outcomes in our particular context. This experimental approach enables us to learn from both successes and failures while building organizational knowledge about what actually works.
The systems thinking perspective offers another valuable framework for integration. Rather than viewing interpersonal skills as individual capabilities separate from technical systems, we can understand them as components of larger organizational systems. This perspective helps us recognize how communication patterns, relationship dynamics, and cultural factors interact with technical processes to influence quality outcomes.
Systems thinking also emphasizes feedback loops and emergent properties that can’t be predicted from individual components. In interpersonal contexts, this means recognizing that the effectiveness of communication approaches depends on context, relationships, and organizational culture in ways that may not be immediately apparent. This systemic perspective encourages more nuanced approaches that consider the broader organizational ecosystem rather than assuming that generic interpersonal frameworks will work universally.
Building Knowledge-Enabled Quality Systems
The path forward requires developing what we can call “knowledge-enabled quality systems“—organizational approaches that systematically integrate evidence about both technical and interpersonal effectiveness while maintaining appropriate skepticism about unvalidated claims. These systems combine the rigorous analysis we apply to technical challenges with equally systematic approaches to understanding and improving human dynamics.
Knowledge-enabled systems begin with systematic evidence requirements that apply across all domains of quality management. Whether evaluating a new measurement technology or a communication framework, we should require similar levels of evidence about effectiveness, limitations, and appropriate application contexts. This doesn’t mean identical evidence—the nature of proof differs between technical and behavioral domains—but it does mean consistent standards for what constitutes adequate justification for adopting new approaches.
These systems also require structured approaches to capturing and validating organizational knowledge about interpersonal effectiveness. Rather than relying on informal networks or individual expertise, we need systematic methods for documenting what works in specific contexts, testing assumptions about effective approaches, and updating our understanding as conditions change. The knowledge management principles discussed in our risk management excellence framework provide a foundation for these systematic approaches.
Cognitive bias mitigation becomes particularly important in knowledge-enabled systems because the stakes of interpersonal decisions can be as significant as technical ones. Poor communication can undermine the best technical solutions, while ineffective team dynamics can prevent organizations from identifying and addressing quality risks. This means applying the same systematic approaches to bias recognition and mitigation that we use in technical risk assessment.
The development of these systems requires what we might call “transdisciplinary competence”—the ability to work effectively across technical and behavioral domains while maintaining appropriate standards for evidence and validation in each. This competence involves understanding the different types of evidence available in different domains, recognizing the limitations of our expertise across domains, and developing systematic approaches to learning and validation that work across different types of challenges.
From Theory to Organizational Reality
Translating these concepts into practical organizational improvements requires systematic approaches that can be implemented incrementally while building toward more comprehensive transformation. The maturity model framework provides a useful structure for understanding this progression.
Continuing ineffective programs due to past investment
Defending communication strategies despite poor results
Regular program evaluation with clear exit criteria
Organizations beginning this journey typically operate at the reactive level, where interpersonal approaches are adopted based on popularity, intuition, or immediate perceived need rather than systematic evaluation. Moving toward evidence-based interpersonal effectiveness requires progressing through increasingly sophisticated approaches to evidence gathering, validation, and integration.
The developing level involves beginning to apply evidence standards to interpersonal approaches while maintaining flexibility about the types of evidence required. This might include piloting communication frameworks with clear success metrics, gathering feedback data about team effectiveness initiatives, or systematically documenting the outcomes of different approaches to stakeholder engagement.
Systematic-level organizations develop formal processes for evaluating and implementing interpersonal interventions with the same rigor applied to technical improvements. This includes structured approaches to literature review, systematic pilot design, clear success criteria, and documented decision rationales. At this level, organizations treat interpersonal effectiveness as a systematic capability rather than a collection of individual skills.
Integration-level organizations embed evidence-based approaches to interpersonal effectiveness throughout their quality systems. Communication training becomes part of comprehensive competency development programs grounded in learning science. Team dynamics initiatives connect directly to quality outcomes through systematic measurement and feedback. Stakeholder engagement approaches are selected and refined based on empirical evidence about effectiveness in specific contexts.
The optimizing level involves sophisticated approaches to learning and adaptation that treat both technical and interpersonal challenges as part of integrated quality systems. Organizations at this level use predictive analytics to identify potential interpersonal challenges before they impact quality outcomes, apply systematic approaches to cultural change and development, and contribute to broader professional knowledge about effective integration of technical and behavioral approaches.
Level
Approach to Evidence
Interpersonal Communication
Risk Management
Knowledge Management
1 – Reactive
Ad-hoc, opinion-based decisions
Relies on traditional hierarchies, informal networks
Reactive problem-solving, limited risk awareness
Tacit knowledge silos, informal transfer
2 – Developing
Occasional use of data, mixed with intuition
Recognizes communication importance, limited training
Cognitive Bias Recognition and Mitigation in Practice
Understanding cognitive biases intellectually is different from developing practical capabilities to recognize and address them in real-world quality management situations. The research on professional decision-making reveals that even when people understand cognitive biases conceptually, they often fail to recognize them in their own decision-making processes.
This challenge requires systematic approaches to bias recognition and mitigation that can be embedded in routine quality management processes. Rather than relying on individual awareness or good intentions, we need organizational systems that prompt systematic consideration of potential biases and provide structured approaches to counter them.
The development of bias-resistant processes requires understanding the specific contexts where different biases are most likely to emerge. Confirmation bias becomes particularly problematic when evaluating approaches that align with our existing beliefs or preferences. Anchoring bias affects situations where initial information heavily influences subsequent analysis. Availability bias impacts decisions where recent or memorable experiences overshadow systematic data analysis.
Effective countermeasures must be tailored to specific biases and integrated into routine processes rather than applied as separate activities. Devil’s advocate processes work well for confirmation bias but may be less effective for anchoring bias, which requires multiple perspective requirements and systematic questioning of initial assumptions. Availability bias requires structured approaches to data analysis that emphasize patterns over individual incidents.
The key insight from cognitive bias research is that awareness alone is insufficient for bias mitigation. Effective approaches require systematic processes that make bias recognition routine and provide concrete steps for addressing identified biases. This means embedding bias checks into standard procedures, training teams in specific bias recognition techniques, and creating organizational cultures that reward systematic thinking over quick decision-making.
The Future of Evidence-Based Quality Practice
The evolution toward evidence-based quality practice represents more than a methodological shift—it reflects a fundamental maturation of our profession. As quality management becomes increasingly complex and consequential, we must develop more sophisticated approaches to distinguishing between genuine insights and appealing but unsubstantiated concepts.
This evolution requires what we might call “methodological pluralism”—the recognition that different types of questions require different approaches to evidence gathering and validation while maintaining consistent standards for rigor and critical evaluation. Technical questions can often be answered through controlled experiments and statistical analysis, while interpersonal effectiveness may require ethnographic study, longitudinal observation, and systematic case analysis.
The development of this methodological sophistication will likely involve closer collaboration between quality professionals and researchers in organizational psychology, communication science, and related fields. Rather than adopting popularized versions of behavioral insights, we can engage directly with the underlying research to understand both the validated findings and their limitations.
Technology will play an increasingly important role in enabling evidence-based approaches to interpersonal effectiveness. Communication analytics can provide objective data about information flow and interaction patterns. Sentiment analysis and engagement measurement can offer insights into the effectiveness of different approaches to stakeholder communication. Machine learning can help identify patterns in organizational behavior that might not be apparent through traditional analysis.
However, technology alone cannot address the fundamental challenge of developing organizational cultures that value evidence over intuition, systematic analysis over quick solutions, and intellectual humility over overconfident assertion. This cultural transformation requires leadership commitment, systematic training, and organizational systems that reinforce evidence-based thinking across all domains of quality management.
Organizational Learning and Knowledge Management
The systematic integration of evidence-based approaches to interpersonal effectiveness requires sophisticated approaches to organizational learning that can capture insights from both technical and behavioral domains while maintaining appropriate standards for validation and application.
Traditional approaches to organizational learning often treat interpersonal insights as informal knowledge that spreads through networks and mentoring relationships. While these mechanisms have value, they also create vulnerabilities to the transmission of unvalidated concepts and the perpetuation of approaches that feel effective but lack empirical support.
Evidence-based organizational learning requires systematic approaches to capturing, validating, and disseminating insights about interpersonal effectiveness. This includes documenting the reasoning behind successful communication approaches, testing assumptions about what works in different contexts, and creating systematic mechanisms for updating understanding as new evidence emerges.
The knowledge management principles from our risk management excellence work provide a foundation for these systematic approaches. Just as effective risk management requires systematic capture and validation of technical knowledge, effective interpersonal approaches require similar systems for behavioral insights. This means creating repositories of validated communication approaches, systematic documentation of context-specific effectiveness, and structured approaches to knowledge transfer and application.
One particularly important aspect of this knowledge management involves tacit knowledge: the experiential insights that effective practitioners develop but often cannot articulate explicitly. While tacit knowledge has value, it also creates vulnerabilities when it embeds unvalidated assumptions or biases. Systematic approaches to making tacit knowledge explicit enable organizations to subject experiential insights to the same validation processes applied to other forms of evidence.
The development of effective knowledge management systems also requires recognition of the different types of evidence available in interpersonal domains. Unlike technical knowledge, which can often be validated through controlled experiments, behavioral insights may require longitudinal observation, systematic case analysis, or ethnographic study. Organizations need to develop competencies in evaluating these different types of evidence while maintaining appropriate standards for validation and application.
Measurement and Continuous Improvement
The application of evidence-based approaches to interpersonal effectiveness requires sophisticated measurement systems that can capture both qualitative and quantitative aspects of communication, collaboration, and organizational culture while avoiding the reductionism that can make measurement counterproductive.
Traditional quality metrics focus on technical outcomes that can be measured objectively and tracked over time. Interpersonal effectiveness involves more complex phenomena that may require different measurement approaches while maintaining similar standards for validity and reliability. This includes developing metrics that capture communication effectiveness, team performance, stakeholder satisfaction, and cultural indicators while recognizing the limitations and potential unintended consequences of measurement systems.
One promising approach involves what researchers call “multi-method assessment”—the use of multiple measurement techniques to triangulate insights about interpersonal effectiveness. This might include quantitative metrics like response times and engagement levels, qualitative assessment through systematic observation and feedback, and longitudinal tracking of relationship quality and collaboration effectiveness.
The key insight from measurement research is that effective metrics must balance precision with validity—the ability to capture what actually matters rather than just what can be easily measured. In interpersonal contexts, this often means accepting greater measurement uncertainty in exchange for metrics that better reflect the complex realities of human interaction and organizational culture.
Continuous improvement in interpersonal effectiveness also requires systematic approaches to experimentation and learning that can test specific hypotheses about what works while building broader organizational capabilities over time. This experimental approach treats interpersonal interventions as systematic tests of specific assumptions rather than permanent solutions, enabling organizations to learn from both successes and failures while building knowledge about what works in their particular context.
Integration with the Quality System
The ultimate goal of evidence-based approaches to interpersonal effectiveness is not to create separate systems for behavioral and technical aspects of quality management, but to develop integrated approaches that recognize the interconnections between technical excellence and interpersonal effectiveness.
This integration requires understanding how communication patterns, relationship dynamics, and cultural factors interact with technical processes to influence quality outcomes. Poor communication can undermine the best technical solutions, while ineffective stakeholder engagement can prevent organizations from identifying and addressing quality risks. Conversely, technical problems can create interpersonal tensions that affect team performance and organizational culture.
Systems thinking provides a valuable framework for understanding these interconnections. Rather than treating technical and interpersonal aspects as separate domains, systems thinking helps us recognize how they function as components of larger organizational systems with complex feedback loops and emergent properties.
This systematic perspective also helps us avoid the reductionism that can make both technical and interpersonal approaches less effective. Technical solutions that ignore human factors often fail in implementation, while interpersonal approaches that ignore technical realities may improve relationships without enhancing quality outcomes. Integrated approaches recognize that sustainable quality improvement requires attention to both technical excellence and the human systems that implement and maintain technical solutions.
The development of integrated approaches requires what we might call “transdisciplinary competence”—the ability to work effectively across technical and behavioral domains while maintaining appropriate standards for evidence and validation in each. This competence involves understanding the different types of evidence available in different domains, recognizing the limitations of expertise across domains, and developing systematic approaches to learning and validation that work across different types of challenges.
Building Professional Maturity Through Evidence-Based Practice
The challenge of distinguishing between genuine scientific insights and popularized psychological concepts represents a crucial test of our profession’s maturity. As quality management becomes increasingly complex and consequential, we must develop more sophisticated approaches to evidence evaluation that can work across technical and interpersonal domains while maintaining consistent standards for rigor and validation.
This evolution requires moving beyond the comfortable dichotomy between technical expertise and interpersonal skills toward integrated approaches that apply systematic thinking to both domains. We must develop capabilities to evaluate behavioral insights with the same rigor we apply to technical knowledge while recognizing the different types of evidence and validation methods required in each domain.
The path forward involves building organizational cultures that value evidence over intuition, systematic analysis over quick solutions, and intellectual humility over overconfident assertion. This cultural transformation requires leadership commitment, systematic training, and organizational systems that reinforce evidence-based thinking across all aspects of quality management.
The cognitive foundations of risk management excellence provide a model for this evolution. Just as effective risk management requires systematic approaches to bias recognition and knowledge validation, effective interpersonal practice requires similar systematic approaches adapted to the complexities of human behavior and organizational culture.
The ultimate goal is not to eliminate the human elements that make quality management challenging and rewarding, but to develop more sophisticated ways of understanding and working with human reality while maintaining the intellectual honesty and systematic thinking that define our profession at its best. This represents not a rejection of interpersonal effectiveness, but its elevation to the same standards of evidence and validation that characterize our technical practice.
As we continue to evolve as a profession, our ability to navigate the evidence-practice divide will determine whether we develop into sophisticated practitioners capable of addressing complex challenges with both technical excellence and interpersonal effectiveness, or remain vulnerable to the latest trends and popularized concepts that promise easy solutions to difficult problems. The choice, and the opportunity, remains ours to make.
The future of quality management depends not on choosing between technical rigor and interpersonal effectiveness, but on developing integrated approaches that bring the best of both domains together in service of genuine organizational improvement and sustainable quality excellence. This integration requires ongoing commitment to learning, systematic approaches to evidence evaluation, and the intellectual courage to question even our most cherished assumptions about what works in human systems.
Through this commitment to evidence-based practice across all domains of quality management, we can build more robust, effective, and genuinely transformative approaches that honor both the complexity of technical systems and the richness of human experience while maintaining the intellectual honesty and systematic thinking that define excellence in our profession.
The pharmaceutical industry has long operated under what Michael Hudson aptly describes in his recent Forbes article as “symphonic control, “carefully orchestrated strategies executed with rigid precision, where quality units can function like conductors trying to control every note. But as Hudson observes, when our meticulously crafted risk assessments collide with chaotic reality, what emerges is often discordant. The time has come for quality risk management to embrace what I am going to call “rhythmic excellence,” a jazz-inspired approach that maintains rigorous standards while enabling adaptive performance in our increasingly BANI (Brittle, Anxious, Non-linear, and Incomprehensible) regulatory and manufacturing environment.
And since I love a good metaphor, I bring you:
Rhythmic Quality Risk Management
Recent research by Amy Edmondson and colleagues at Harvard Business School provides compelling evidence for rhythmic approaches to complex work. After studying more than 160 innovation teams, they found that performance suffered when teams mixed reflective activities (like risk assessments and control strategy development) with exploratory activities (like hazard identification and opportunity analysis) in the same time period. The highest-performing teams established rhythms that alternated between exploration and reflection, creating distinct beats for different quality activities.
This finding resonates deeply with the challenges we face in pharmaceutical quality risk management. Too often, our risk assessment meetings become frantic affairs where hazard identification, risk analysis, control strategy development, and regulatory communication all happen simultaneously. Teams push through these sessions exhausted and unsatisfied, delivering risk assessments they aren’t proud of—what Hudson describes as “cognitive whiplash”.
From Symphonic Control to Jazz-Based Quality Leadership
The traditional approach to pharmaceutical quality risk management mirrors what Hudson calls symphonic leadership—attempting to impose top-down structure as if more constraint and direction are what teams need to work with confidence. We create detailed risk assessment procedures, prescriptive FMEA templates, and rigid review schedules, then wonder why our teams struggle to adapt when new hazards emerge or when manufacturing conditions change unexpectedly.
Karl Weick’s work on organizational sensemaking reveals why this approach undermines our quality objectives: complex manufacturing environments require “mindful organizing” and the ability to notice subtle changes and respond fluidly. Setting a quality rhythm and letting go of excessive control provides support without constraint, giving teams the freedom to explore emerging risks, experiment with novel control strategies, and make sense of the quality challenges they face.
This represents a fundamental shift in how we conceptualize quality risk management leadership. Instead of being the conductor trying to orchestrate every risk assessment note, quality leaders should function as the rhythm section—establishing predictable beats that keep everyone synchronized while allowing individual expertise to flourish.
The Quality Rhythm Framework: Four Essential Beats
Drawing from Hudson’s research-backed insights and integrating them with ICH Q9(R1) requirements, I envision a Quality Rhythm Framework built on four essential beats:
Beat 1: Find Your Risk Cadence
Establish predictable rhythms that create temporal anchors for your quality team while maintaining ICH Q9 compliance. Weekly hazard identification sessions, daily deviation assessments, monthly control strategy reviews, and quarterly risk communication cycles aren’t just meetings—they’re the beats that keep everyone synchronized while allowing individual risk management expression.
The ICH Q9(R1) revision’s emphasis on proportional formality aligns perfectly with this rhythmic approach. High-risk processes require more frequent beats, while lower-risk areas can operate with extended rhythms. The key is consistency within each risk category, creating what Weick calls “structured flexibility”—the ability to respond creatively within clear boundaries.
Consider implementing these quality-specific rhythmic structures:
Daily Risk Pulse: Brief stand-ups focused on emerging quality signals—not comprehensive risk assessments, but awareness-building sessions that keep the team attuned to the manufacturing environment.
Weekly Hazard Identification Sessions: Dedicated time for exploring “what could go wrong” and, following ISO 31000 principles, “what could go better than expected.” These sessions should alternate between different product lines or process areas to maintain focus.
Monthly Control Strategy Reviews: Deeper evaluations of existing risk controls, including assessment of whether they remain appropriate and identification of optimization opportunities.
Quarterly Risk Communication Cycles: Structured information sharing with stakeholders, including regulatory bodies when appropriate, ensuring that risk insights flow effectively throughout the organization.
Beat 2: Pause for Quality Breaths
Hudson emphasizes that jazz musicians know silence is as important as sound, and quality risk management desperately needs structured pauses. Build quality breaths into your organizational rhythm—moments for reflection, integration, and recovery from the intense focus required for effective risk assessment.
Research by performance expert Jim Loehr demonstrates that sustainable excellence requires oscillation, not relentless execution. In quality contexts, this means creating space between intensive risk assessment activities and implementation of control strategies. These pauses allow teams to process complex risk information, integrate diverse perspectives, and avoid the decision fatigue that leads to poor risk judgments.
Practical quality breaths include:
Post-Assessment Integration Time: Following comprehensive risk assessments, build in periods where team members can reflect on findings, consult additional resources, and refine their thinking before finalizing control strategies.
Cross-Functional Synthesis Sessions: Regular meetings where different functions (Quality, Operations, Regulatory, Technical) come together not to make decisions, but to share perspectives and build collective understanding of quality risks.
Knowledge Capture Moments: Structured time for documenting lessons learned, updating risk models based on new experience, and creating institutional memory that enhances future risk assessments.
Beat 3: Encourage Quality Experimentation
Within your rhythmic structure, create psychological safety and confidence that team members can explore novel risk identification approaches without fear of hitting “wrong notes.” When learning and reflection are part of a predictable beat, trust grows and experimentation becomes part of the quality flow.
The ICH Q9(R1) revision’s focus on managing subjectivity in risk assessments creates opportunities for experimental approaches. Instead of viewing subjectivity as a problem to eliminate, we can experiment with structured methods for harnessing diverse perspectives while maintaining analytical rigor.
Hudson’s research shows that predictable rhythm facilitates innovation—when people are comfortable with the rhythm, they’re free to experiment with the melody. In quality risk management, this means establishing consistent frameworks that enable creative hazard identification and innovative control strategy development.
Experimental approaches might include:
Success Mode and Benefits Analysis (SMBA): As I’ve discussed previously, complement traditional FMEA with systematic identification of positive potential outcomes. Experiment with different SMBA formats and approaches to find what works best for specific process areas.
Cross-Industry Risk Insights: Dedicate portions of risk assessment sessions to exploring how other industries handle similar quality challenges. These experiments in perspective-taking can reveal blind spots in traditional pharmaceutical approaches.
Scenario-Based Risk Planning: Experiment with “what if” exercises that go beyond traditional failure modes to explore complex, interdependent risk situations that might emerge in dynamic manufacturing environments.
Beat 4: Enable Quality Solos
Just as jazz musicians trade solos while the ensemble provides support, look for opportunities for individual quality team members to drive specific risk management initiatives. This distributed leadership approach builds capability while maintaining collective coherence around quality objectives.
Hudson’s framework emphasizes that adaptive leaders don’t try to be conductors but create conditions for others to lead. In quality risk management, this means identifying team members with specific expertise or interest areas and empowering them to lead risk assessments in those domains.
Quality leadership solos might include:
Process Expert Risk Leadership: Assign experienced operators or engineers to lead risk assessments for processes they know intimately, with quality professionals providing methodological support.
Cross-Functional Risk Coordination: Empower individuals to coordinate risk management across organizational boundaries, taking ownership for ensuring all relevant perspectives are incorporated.
Innovation Risk Championship: Designate team members to lead risk assessments for new technologies or novel approaches, building expertise in emerging quality challenges.
The Rhythmic Advantage: Three Quality Transformation Benefits
Mastering these rhythmic approaches to quality risk management provide three advantages that mirror Hudson’s leadership research:
Fluid Quality Structure
A jazz ensemble can improvise because musicians share a rhythm. Similarly, quality rhythms keep teams functioning together while offering freedom to adapt to emerging risks, changing regulatory requirements, or novel manufacturing challenges. Management researchers call this “structured flexibility”—exactly what ICH Q9(R1) envisions when it emphasizes proportional formality.
When quality teams operate with shared rhythms, they can respond more effectively to unexpected events. A contamination incident doesn’t require completely reinventing risk assessment approaches—teams can accelerate their established rhythms, bringing familiar frameworks to bear on novel challenges while maintaining analytical rigor.
Sustainable Quality Energy
Quality risk management is inherently demanding work that requires sustained attention to complex, interconnected risks. Traditional approaches often lead to burnout as teams struggle with relentless pressure to identify every possible hazard and implement perfect controls. Rhythmic approaches prevent this exhaustion by regulating pace and integrating recovery.
More importantly, rhythmic quality management aligns teams around purpose and vision rather than merely compliance deadlines. This enables what performance researchers call “sustainable high performance”—quality excellence that endures rather than depletes organizational energy.
When quality professionals find rhythm in their risk management work, they develop what Mihaly Csikszentmihalyi identified as “flow state,” moments when attention is fully focused and performance feels effortless. These states are crucial for the deep thinking required for effective hazard identification and the creative problem-solving needed for innovative control strategies.
Enhanced Quality Trust and Innovation
The paradox Hudson identifies, that some constraint enables creativity, applies directly to quality risk management. Predictable rhythms don’t stifle innovation; they provide the stable foundation from which teams can explore novel approaches to quality challenges.
When quality teams know they have regular, structured opportunities for risk exploration, they’re more willing to raise difficult questions, challenge assumptions, and propose unconventional solutions. The rhythm creates psychological safety for intellectual risk-taking within the controlled environment of systematic risk assessment.
This enhanced innovation capability is particularly crucial as pharmaceutical manufacturing becomes increasingly complex, with continuous manufacturing, advanced process controls, and novel drug modalities creating quality challenges that traditional risk management approaches weren’t designed to address.
Integrating Rhythmic Principles with ICH Q9(R1) Compliance
The beauty of rhythmic quality risk management lies in its fundamental compatibility with ICH Q9(R1) requirements. The revision’s emphasis on scientific knowledge, proportional formality, and risk-based decision-making aligns perfectly with rhythmic approaches that create structured flexibility for quality teams.
Rhythmic Risk Assessment Enhancement
ICH Q9 requires systematic hazard identification, risk analysis, and risk evaluation. Rhythmic approaches enhance these activities by establishing regular, focused sessions for each component rather than trying to accomplish everything in marathon meetings.
During dedicated hazard identification beats, teams can employ diverse techniques—traditional brainstorming, structured what-if analysis, cross-industry benchmarking, and the Success Mode and Benefits Analysis I’ve advocated. The rhythm ensures these activities receive appropriate attention while preventing the cognitive overload that reduces identification effectiveness.
Risk analysis benefits from rhythmic separation between data gathering and interpretation activities. Teams can establish rhythms for collecting process data, manufacturing experience, and regulatory intelligence, followed by separate beats for analyzing this information and developing risk models.
Rhythmic Risk Control Development
The ICH Q9(R1) emphasis on risk-based decision-making aligns perfectly with rhythmic approaches to control strategy development. Instead of rushing from risk assessment to control implementation, rhythmic approaches create space for thoughtful strategy development that considers multiple options and their implications.
Rhythmic control development might include beats for:
Control Strategy Ideation: Creative sessions focused on generating potential control approaches without immediate evaluation of feasibility or cost.
Implementation Planning: Separate sessions for detailed planning of selected control strategies, including resource requirements, timeline development, and change management considerations.
Effectiveness Assessment: Regular rhythms for evaluating implemented controls, gathering performance data, and identifying optimization opportunities.
Rhythmic Risk Communication
ICH Q9’s communication requirements benefit significantly from rhythmic approaches. Instead of ad hoc communication when problems arise, establish regular rhythms for sharing risk insights, control strategy updates, and lessons learned.
Quality communication rhythms should align with organizational decision-making cycles, ensuring that risk insights reach stakeholders when they’re most useful for decision-making. This might include monthly updates to senior leadership, quarterly reports to regulatory affairs, and annual comprehensive risk reviews for long-term strategic planning.
Practical Implementation: Building Your Quality Rhythm
Implementing rhythmic quality risk management requires systematic integration rather than wholesale replacement of existing approaches. Start by evaluating your current risk management processes to identify natural rhythm points and opportunities for enhancement.
Phase 1: Rhythm Assessment and Planning
Map your existing quality risk management activities against rhythmic principles. Identify where teams experience the cognitive whiplash Hudson describes—trying to accomplish too many different types of thinking in single sessions. Look for opportunities to separate exploration from analysis, strategy development from implementation planning, and individual reflection from group decision-making.
Establish criteria for quality rhythm frequency based on risk significance, process complexity, and organizational capacity. High-risk processes might require daily pulse checks and weekly deep dives, while lower-risk areas might operate effectively with monthly assessment rhythms.
Train quality teams on rhythmic principles and their application to risk management. Help them understand how rhythm enhances rather than constrains their analytical capabilities, providing structure that enables deeper thinking and more creative problem-solving.
Phase 2: Pilot Program Development
Select pilot areas where rhythmic approaches are most likely to demonstrate clear benefits. New product development projects, technology implementation initiatives, or process improvement activities often provide ideal testing grounds because their inherent uncertainty creates natural opportunities for both risk management and opportunity identification.
Design pilot programs to test specific rhythmic principles:
Rhythm Separation: Compare traditional comprehensive risk assessment meetings with rhythmic approaches that separate hazard identification, risk analysis, and control strategy development into distinct sessions.
Quality Breathing: Experiment with structured pauses between intensive risk assessment activities and measure their impact on decision quality and team satisfaction.
Distributed Leadership: Identify opportunities for team members to lead specific aspects of risk management and evaluate the impact on engagement and expertise development.
Phase 3: Organizational Integration
Based on pilot results, develop systematic approaches for scaling rhythmic quality risk management across the organization. This requires integration with existing quality systems, regulatory processes, and organizational governance structures.
Consider how rhythmic approaches will interact with regulatory inspection activities, change control processes, and continuous improvement initiatives. Ensure that rhythmic flexibility doesn’t compromise documentation requirements or audit trail integrity.
Establish metrics for evaluating rhythmic quality risk management effectiveness, including both traditional risk management indicators (incident rates, control effectiveness, regulatory compliance) and rhythm-specific measures (team engagement, innovation frequency, decision speed).
Phase 4: Continuous Enhancement and Cultural Integration
Like all aspects of quality risk management, rhythmic approaches require continuous improvement based on experience and changing needs. Regular assessment of rhythm effectiveness helps refine approaches over time and ensures sustained benefits.
The ultimate goal is cultural integration—making rhythmic thinking a natural part of how quality professionals approach risk management challenges. This requires consistent leadership modeling, recognition of rhythmic successes, and integration of rhythmic principles into performance expectations and career development.
Measuring Rhythmic Quality Success
Traditional quality metrics focus primarily on negative outcome prevention: deviation rates, batch failures, regulatory findings, and compliance scores. While these remain important, rhythmic quality risk management requires expanded measurement approaches that capture both defensive effectiveness and adaptive capability.
Enhanced metrics should include:
Rhythm Consistency Indicators: Frequency of established quality rhythms, participation rates in rhythmic activities, and adherence to planned cadences.
Innovation and Adaptation Measures: Number of novel risk identification approaches tested, implementation rate of creative control strategies, and frequency of process improvements emerging from risk management activities.
Team Engagement and Development: Participation in quality leadership opportunities, cross-functional collaboration frequency, and professional development within risk management capabilities.
Decision Quality Indicators: Time from risk identification to control implementation, stakeholder satisfaction with risk communication, and long-term effectiveness of implemented controls.
Regulatory Considerations: Communicating Rhythmic Value
Regulatory agencies are increasingly interested in risk-based approaches that demonstrate genuine process understanding and continuous improvement capabilities. Rhythmic quality risk management strengthens regulatory relationships by showing sophisticated thinking about process optimization and quality enhancement within established frameworks.
When communicating with regulatory agencies, emphasize how rhythmic approaches improve process understanding, enhance control strategy development, and support continuous improvement objectives. Show how structured flexibility leads to better patient protection through more responsive and adaptive quality systems.
Focus regulatory communications on how enhanced risk understanding leads to better quality outcomes rather than on operational efficiency benefits that might appear secondary to regulatory objectives. Demonstrate how rhythmic approaches maintain analytical rigor while enabling more effective responses to emerging quality challenges.
The Future of Quality Risk Management: Beyond Rhythm to Resonance
As we master rhythmic approaches to quality risk management, the next evolution involves what I call “quality resonance”—the phenomenon that occurs when individual quality rhythms align and amplify each other across organizational boundaries. Just as musical instruments can create resonance that produces sounds more powerful than any individual instrument, quality organizations can achieve resonant states where risk management effectiveness transcends the sum of individual contributions.
Resonant quality organizations share several characteristics:
Synchronized Rhythm Networks: Quality rhythms in different departments, processes, and product lines align to create organization-wide patterns of risk awareness and response capability.
Harmonic Risk Communication: Information flows between quality functions create harmonics that amplify important signals while filtering noise, enabling more effective decision-making at all organizational levels.
Emergent Quality Intelligence: The interaction of multiple rhythmic quality processes generates insights and capabilities that wouldn’t be possible through individual efforts alone.
Building toward quality resonance requires sustained commitment to rhythmic principles, continuous refinement of quality cadences, and patient development of organizational capability. The payoff, however, is transformational: quality risk management that not only prevents problems but actively creates value through enhanced understanding, improved processes, and strengthened competitive position.
Finding Your Quality Beat
Uncertainty is inevitable in pharmaceutical manufacturing, regulatory environments, and global supply chains. As Hudson emphasizes, the choice is whether to exhaust ourselves trying to conduct every quality note or to lay down rhythms that enable entire teams to create something extraordinary together.
Tomorrow morning, when you walk into that risk assessment meeting, you’ll face this choice in real time. Will you pick up the conductor’s baton, trying to control every analytical voice? Or will you sit at the back of the stage and create the beat on which your quality team can find its flow?
The research is clear: rhythmic approaches to complex work create better outcomes, higher engagement, and more sustainable performance. The ICH Q9(R1) framework provides the flexibility needed to implement rhythmic quality risk management while maintaining regulatory compliance. The tools and techniques exist to transform quality risk management from a defensive necessity into an adaptive capability that drives innovation and competitive advantage.
The question isn’t whether rhythmic quality risk management will emerge—it’s whether your organization will lead this transformation or struggle to catch up. The teams that master quality rhythm first will be best positioned to thrive in our increasingly BANI pharmaceutical world, turning uncertainty into opportunity while maintaining the rigorous standards our patients deserve.
Start with one beat. Find one aspect of your current quality risk management where you can separate exploration from analysis, create space for reflection, or enable someone to lead. Feel the difference that rhythm makes. Then gradually expand, building the quality jazz ensemble that our complex manufacturing world demands.
The rhythm section is waiting. It’s time to find your quality beat.
Investigations of a Dog 
