Recent Podcast Appearance: Risk Revolution

I’m excited to share that I recently had the opportunity to appear on the Risk Revolution podcast, joining host Valerie Mulholland for what turned out to be a provocative and deeply engaging conversation about the future of pharmaceutical quality management.

The episode, titled “Quality Theatre to Quality Science – Jeremiah Genest’s Playbook,” aired on September 28, 2025, and dives into one of my core arguments: that quality systems should be designed to fail predictably so we can learn purposefully. This isn’t about celebrating failure—it’s about building systems intelligent enough to fail in ways that generate learning rather than hiding in the shadows until catastrophic breakdown occurs.

Why This Conversation Matters

Valerie and I spent over an hour exploring what I call “intelligent failure”—a concept that challenges the feel-good metrics that dominate our industry dashboards. You know the ones I’m talking about: those green lights celebrating zero deviations that make everyone feel accomplished while potentially masking the unknowns lurking beneath the surface. As I argued in the episode, these metrics can hide systemic problems rather than prove actual control.

This discussion connects directly to themes I’ve been developing here on Investigations of a Dog, particularly my thoughts on the effectiveness paradox and the dangerous comfort of “nothing bad happened” thinking. The podcast gave me a chance to explore how zemblanity—the patterned recurrence of unfortunate events that we should have anticipated—manifests in quality systems that prioritize the appearance of control over genuine understanding.

The Perfect Platform for These Ideas

Risk Revolution proved to be the ideal venue for this conversation. Valerie brings over 25 years of hands-on experience across biopharmaceutical, pharmaceutical, medical device, and blood transfusion industries, but what sets her apart is her unique combination of practical expertise and cutting-edge research.

The podcast’s monthly format allows for the kind of deep, nuanced discussions that advance risk management maturity rather than recycling conference presentations. When I wrote about Valerie’s writing on the GI Joe Bias, I noted how her emphasis on systematic interventions rather than individual awareness represents exactly the kind of sophisticated thinking our industry needs. This podcast appearance let us explore these concepts in real-time conversation.

What made the discussion particularly engaging was Valerie’s ability to challenge my thinking while building on it. Her research-backed insights into cognitive bias management created a perfect complement to my practical experience with system failures and investigation patterns. We explored how quality professionals—precisely because of our expertise—become vulnerable to specific blind spots that systematic design can address.

Looking Forward

This Risk Revolution appearance represents more than just a podcast interview—it’s part of a broader conversation about advancing pharmaceutical quality management beyond surface-level compliance toward genuine excellence. The episode includes references to my blog work, the Deming philosophy, and upcoming industry conferences where these ideas will continue to evolve.

If you’re interested in how quality systems can be designed for intelligent learning rather than elegant hiding, this conversation offers both provocative challenges and practical frameworks. Fair warning: you might never look at a green dashboard the same way again.

The episode is available now, and I’d love to hear your thoughts on how we might move from quality theatre toward quality science in your own organization.

Risk Blindness: The Invisible Threat

Risk blindness is an insidious loss of organizational perception—the gradual erosion of a company’s ability to recognize, interpret, and respond to threats that undermine product safety, regulatory compliance, and ultimately, patient trust. It is not merely ignorance or oversight; rather, risk blindness manifests as the cumulative inability to see threats, often resulting from process shortcuts, technology overreliance, and the undervaluing of hands-on learning.

Unlike risk aversion or neglect, which involves conscious choices, risk blindness is an unconscious deficiency. It often stems from structural changes like the automation of foundational jobs, fragmented risk ownership, unchallenged assumptions, and excessive faith in documentation or AI-generated reports. At its core, risk blindness breeds a false sense of security and efficiency while creating unseen vulnerabilities.

Pattern Recognition and Risk Blindness: The Cognitive Foundation of Quality Excellence

The Neural Architecture of Risk Detection

Pattern recognition lies at the heart of effective risk management in quality systems. It represents the sophisticated cognitive process by which experienced professionals unconsciously scan operational environments, data trends, and behavioral cues to detect emerging threats before they manifest as full-scale quality events. This capability distinguishes expert practitioners from novices and forms the foundation of what we might call “risk literacy” within quality organizations.

The development of pattern recognition in pharmaceutical quality follows predictable stages. At the most basic level (Level 1 Situational Awareness), professionals learn to perceive individual elements—deviation rates, environmental monitoring trends, supplier performance metrics. However, true expertise emerges at Level 2 (Comprehension), where practitioners begin to understand the relationships between these elements, and Level 3 (Projection), where they can anticipate future system states based on current patterns.

Research in clinical environments demonstrates that expert pattern recognition relies on matching current situational elements with previously stored patterns and knowledge, creating rapid, often unconscious assessments of risk significance. In pharmaceutical quality, this translates to the seasoned professional who notices that “something feels off” about a batch record, even when all individual data points appear within specification, or the environmental monitoring specialist who recognizes subtle trends that precede contamination events.

The Apprenticeship Dividend: Building Pattern Recognition Through Experience

The development of sophisticated pattern recognition capabilities requires what we’ve previously termed the “apprenticeship dividend”—the cumulative learning that occurs through repeated exposure to routine operations, deviations, and corrective actions. This learning cannot be accelerated through technology or condensed into senior-level training programs; it must be built through sustained practice and mentored reflection.

The Stages of Pattern Recognition Development:

Foundation Stage (Years 1-2): New professionals learn to identify individual risk elements—understanding what constitutes a deviation, recognizing out-of-specification results, and following investigation procedures. Their pattern recognition is limited to explicit, documented criteria.

Integration Stage (Years 3-5): Practitioners begin to see relationships between different quality elements. They notice when environmental monitoring trends correlate with equipment issues, or when supplier performance changes precede raw material problems. This represents the emergence of tacit knowledge—insights that are difficult to articulate but guide decision-making.

Mastery Stage (Years 5+): Expert practitioners develop what researchers call “intuitive expertise”—the ability to rapidly assess complex situations and identify subtle risk patterns that others miss. They can sense when a investigation is heading in the wrong direction, recognize when supplier responses are evasive, or detect process drift before it appears in formal metrics.

Tacit Knowledge: The Uncodifiable Foundation of Risk Assessment

Perhaps the most critical aspect of pattern recognition in pharmaceutical quality is the role of tacit knowledge—the experiential wisdom that cannot be fully documented or transmitted through formal training systems. Tacit knowledge encompasses the subtle cues, contextual understanding, and intuitive insights that experienced professionals develop through years of hands-on practice.

In pharmaceutical quality systems, tacit knowledge manifests in numerous ways:

Knowing which equipment is likely to fail after cleaning cycles, based on subtle operational cues rather than formal maintenance schedules
Recognizing when supplier audit responses are technically correct but practically inadequate
Sensing when investigation teams are reaching premature closure without adequate root cause analysis
Detecting process drift through operator reports and informal observations before it appears in formal monitoring data

This tacit knowledge cannot be captured in standard operating procedures or electronic systems. It exists in the experienced professional’s ability to read “between the lines” of formal data, to notice what’s missing from reports, and to sense when organizational pressures are affecting the quality of risk assessments.

The GI Joe Fallacy: The Dangers of “Knowing is Half the Battle”

A persistent—and dangerous—belief in quality organizations is the idea that simply knowing about risks, standards, or biases will prevent us from falling prey to them. This is known as the GI Joe fallacy—the misguided notion that awareness is sufficient to overcome cognitive biases or drive behavioral change.

What is the GI Joe Fallacy?

Inspired by the classic 1980s G.I. Joe cartoons, which ended each episode with “Now you know. And knowing is half the battle,” the GI Joe fallacy describes the disconnect between knowledge and action. Cognitive science consistently shows that knowing about biases or desired actions does not ensure that individuals or organizations will behave accordingly.

Even the founder of bias research, Daniel Kahneman, has noted that reading about biases doesn’t fundamentally change our tendency to commit them. Organizations often believe that training, SOPs, or system prompts are enough to inoculate staff against error. In reality, knowledge is only a small part of the battle; much larger are the forces of habit, culture, distraction, and deeply rooted heuristics.

GI Joe Fallacy in Quality Risk Management

In pharmaceutical quality risk management, the GI Joe fallacy can have severe consequences. Teams may know the details of risk matrices, deviation procedures, and regulatory requirements, yet repeatedly fail to act with vigilance or critical scrutiny in real situations. Loss aversion, confirmation bias, and overconfidence persist even for those trained in their dangers.

For example, base rate neglect—a bias where salient event data distracts from underlying probabilities—can influence decisions even when staff know better intellectually. This manifests in investigators overreacting to recent dramatic events while ignoring stable process indicators. Knowing about risk frameworks isn’t enough; structures and culture must be designed specifically to challenge these biases in practice, not simply in theory.

Structural Roots of Risk Blindness

The False Economy of Automation and Overconfidence

Risk blindness often arises from a perceived efficiency gained through process automation or the curtailment of on-the-ground learning. When organizations substitute active engagement for passive oversight, staff lose critical exposure to routine deviations and process variables.

Senior staff who only approve system-generated risk assessments lack daily operational familiarity, making them susceptible to unseen vulnerabilities. Real risk assessment requires repeated, active interaction with process data—not just a review of output.

Fragmented Ownership and Deficient Learning Culture

Risk ownership must be robust and proximal. When roles are fragmented—where the “system” manages risk and people become mere approvers—vital warnings can be overlooked. A compliance-oriented learning culture that believes training or SOPs are enough to guard against operational threats falls deeper into the GI Joe fallacy: knowledge is mistaken for vigilance.

Instead, organizations need feedback loops, reflection, and opportunities to surface doubts and uncertainties. Training must be practical and interactive, not limited to information transfer.

Zemblanity: The Shadow of Risk Blindness

Zemblanity is the antithesis of serendipity in the context of pharmaceutical quality—it describes the persistent tendency for organizations to encounter negative, foreseeable outcomes when risk signals are repeatedly ignored, misunderstood, or left unacted upon.

When examining risk blindness, zemblanity stands as the practical outcome: a quality system that, rather than stumbling upon unexpected improvements or positive turns, instead seems trapped in cycles of self-created adversity. Unlike random bad luck, zemblanity results from avoidable and often visible warning signs—deviations that are rationalized, oversight meetings that miss the point, and cognitive biases like the GI Joe fallacy that lull teams into a false sense of mastery

Real-World Manifestations

Case: The Disappearing Deviation

Digital batch records reduced documentation errors and deviation reports, creating an illusion of process control. But when technology transfer led to out-of-spec events, the lack of manually trained eyes meant no one was poised to detect subtle process anomalies. Staff “knew” the process in theory—yet risk blindness set in because the signals were no longer being actively, expertly interpreted. Knowledge alone was not enough.

Case: Supplier Audit Blindness

Virtual audits relying solely on documentation missed chronic training issues that onsite teams would likely have noticed. The belief that checklist knowledge and documentation sufficed prevented the team from recognizing deeper underlying risks. Here, the GI Joe fallacy made the team believe their expertise was shield enough, when in reality, behavioral engagement and observation were necessary.

Counteracting Risk Blindness: Beyond Knowing to Acting

Effective pharmaceutical quality systems must intentionally cultivate and maintain pattern recognition capabilities across their workforce. This requires structured approaches that go beyond traditional training and incorporate the principles of expertise development:

Structured Exposure Programs: New professionals need systematic exposure to diverse risk scenarios—not just successful cases, but also investigations that went wrong, supplier audits that missed problems, and process changes that had unexpected consequences. This exposure must be guided by experienced mentors who can help identify and interpret relevant patterns.

Cross-Functional Pattern Sharing: Different functional areas—manufacturing, quality control, regulatory affairs, supplier management—develop specialized pattern recognition capabilities. Organizations need systematic mechanisms for sharing these patterns across functions, ensuring that insights from one area can inform risk assessment in others.

Cognitive Diversity in Assessment Teams: Research demonstrates that diverse teams are better at pattern recognition than homogeneous groups, as different perspectives help identify patterns that might be missed by individuals with similar backgrounds and experience. Quality organizations should intentionally structure assessment teams to maximize cognitive diversity.

Systematic Challenge Processes: Pattern recognition can become biased or incomplete over time. Organizations need systematic processes for challenging established patterns—regular “red team” exercises, external perspectives, and structured devil’s advocate processes that test whether recognized patterns remain valid.

Reflective Practice Integration: Pattern recognition improves through reflection on both successes and failures. Organizations should create systematic opportunities for professionals to analyze their pattern recognition decisions, understand when their assessments were accurate or inaccurate, and refine their capabilities accordingly.

Using AI as a Learning Accelerator

AI and automation should support, not replace, human risk assessment. Tools can help new professionals identify patterns in data, but must be employed as aids to learning—not as substitutes for judgment or action.

Assess organizational risk literacy not by the presence of knowledge, but by the frequency of active, critical engagement with real risks. Use self-assessment questions such as:

Do deviation investigations include frontline voices, not just system reviewers?
Are new staff exposed to real processes and deviations, not just theoretical scenarios?
Are risk reviews structured to challenge assumptions, not merely confirm them?
Is there evidence that knowledge is regularly translated into action?

Why Preventing Risk Blindness Matters

Regulators evaluate quality maturity not simply by compliance, but by demonstrable capability to anticipate and mitigate risks. AI and digital transformation are intensifying the risk of the GI Joe fallacy by tempting organizations to substitute data and technology for judgment and action.

As experienced professionals retire, the gap between knowing and doing risks widening. Only organizations invested in hands-on learning, mentorship, and behavioral feedback will sustain true resilience.

Choosing Sight

Risk blindness is perpetuated by the dangerous notion that knowing is enough. The GI Joe fallacy teaches that organizational memory, vigilance, and capability require much more than knowledge—they demand deliberate structures, engaged cultures, and repeated practice that link theory to action.

Quality leaders must invest in real development, relentless engagement, and humility about the limits of their own knowledge. Only then will risk blindness be cured, and resilience secured.

When 483s Reveal Zemblanity: The Catalent Investigation – A Case Study in Systemic Quality Failure

The Catalent Indiana 483 form from July 2025 reads like a textbook example of my newest word, zemblanity, in risk management—the patterned, preventable misfortune that accrues not from blind chance, but from human agency and organizational design choices that quietly hardwire failure into our operations.

Twenty hair contamination deviations. Seven months to notify suppliers. Critical equipment failures dismissed as “not impacting SISPQ.” Media fill programs missing the very interventions they should validate. This isn’t random bad luck—it’s a quality system that has systematically normalized exactly the kinds of deviations that create inspection findings.

The Architecture of Inevitable Failure

Reading through the six major observations, three systemic patterns emerge that align perfectly with the hidden architecture of failure I discussed in my recent post on zemblanity.

Pattern 1: Investigation Theatre Over Causal Understanding

Observation 1 reveals what happens when investigations become compliance exercises rather than learning tools. The hair contamination trend—20 deviations spanning multiple product codes—received investigation resources proportional to internal requirement, not actual risk. As I’ve written about causal reasoning versus negative reasoning, these investigations focused on what didn’t happen rather than understanding the causal mechanisms that allowed hair to systematically enter sterile products.

The tribal knowledge around plunger seating issues exemplifies this perfectly. Operators developed informal workarounds because the formal system failed them, yet when this surfaced during an investigation, it wasn’t captured as a separate deviation worthy of systematic analysis. The investigation closed the immediate problem without addressing the systemic failure that created the conditions for operator innovation in the first place.

Pattern 2: Trend Blindness and Pattern Fragmentation

The most striking aspect of this 483 is how pattern recognition failed across multiple observations. Twenty-three work orders on critical air handling systems. Ten work orders on a single critical water system. Recurring membrane failures. Each treated as isolated maintenance issues rather than signals of systematic degradation.

This mirrors what I’ve discussed about normalization of deviance—where repeated occurrences of problems that don’t immediately cause catastrophe gradually shift our risk threshold. The work orders document a clear pattern of equipment degradation, yet each was risk-assessed as “not impacting SISPQ” without apparent consideration of cumulative or interactive effects.

Pattern 3: Control System Fragmentation

Perhaps most revealing is how different control systems operated in silos. Visual inspection systems that couldn’t detect the very defects found during manual inspection. Environmental monitoring that didn’t include the most critical surfaces. Media fills that omitted interventions documented as root causes of previous failures.

This isn’t about individual system inadequacy—it’s about what happens when quality systems evolve as collections of independent controls rather than integrated barriers designed to work together.

Solutions: From Zemblanity to Serendipity

Drawing from the approaches I’ve developed on this blog, here’s how Catalent could transform their quality system from one that breeds inevitable failure to one that creates conditions for quality serendipity:

Implement Causally Reasoned Investigations

The Energy Safety Canada white paper I discussed earlier this year offers a powerful framework for moving beyond counterfactual analysis. Instead of concluding that operators “failed to follow procedure” regarding stopper installation, investigate why the procedure was inadequate for the equipment configuration. Instead of noting that supplier notification was delayed seven months, understand the systemic factors that made immediate notification unlikely.

Practical Implementation:

Retrain investigators in causal reasoning techniques
Require investigation sponsors (area managers) to set clear expectations for causal analysis
Implement structured causal analysis tools like Cause-Consequence Analysis
Focus on what actually happened and why it made sense to people at the time
Implement rubrics to guide consistency

Build Integrated Barrier Systems

The take-the-best heuristic I recently explored offers a powerful lens for barrier analysis. Rather than implementing multiple independent controls, identify the single most causally powerful barrier that would prevent each failure type, then design supporting barriers that enhance rather than compete with the primary control.

For hair contamination specifically:

Implement direct stopper surface monitoring as the primary barrier
Design visual inspection systems specifically to detect proteinaceous particles
Create supplier qualification that includes contamination risk assessment
Establish real-time trend analysis linking supplier lots to contamination events

Establish Dynamic Trend Integration

Traditional trending treats each system in isolation—environmental monitoring trends, deviation trends, CAPA trends, maintenance trends. The Catalent 483 shows what happens when these parallel trend systems fail to converge into integrated risk assessment.

Integrated Trending Framework:

Create cross-functional trend review combining all quality data streams
Implement predictive analytics linking maintenance patterns to quality risks
Establish trigger points where equipment degradation patterns automatically initiate quality investigations
Design Product Quality Reviews that explicitly correlate equipment performance with product quality data

Transform CAPA from Compliance to Learning

The recurring failures documented in this 483—repeated hair findings after CAPA implementation, continued equipment failures after “repair”—reflect what I’ve called the effectiveness paradox. Traditional CAPA focuses on thoroughness over causal accuracy.

CAPA Transformation Strategy:

Implement a proper CAPA hierarchy, prioritizing elimination and replacement over detection and mitigation
Establish effectiveness criteria before implementation, not after
Create learning-oriented CAPA reviews that ask “What did this teach us about our system?”
Link CAPA effectiveness directly to recurrence prevention rather than procedural compliance

Build Anticipatory Quality Architecture

The most sophisticated element would be creating what I call “quality serendipity”—systems that create conditions for positive surprises rather than inevitable failures. This requires moving from reactive compliance to anticipatory risk architecture.

Anticipatory Elements:

Implement supplier performance modeling that predicts contamination risk before it manifests
Create equipment degradation models that trigger quality assessment before failure
Establish operator feedback systems that capture emerging risks in real-time
Design quality reviews that explicitly seek weak signals of system stress

The Cultural Foundation

None of these technical solutions will work without addressing the cultural foundation that allowed this level of systematic failure to persist. The 483’s most telling detail isn’t any single observation—it’s the cumulative picture of an organization where quality indicators were consistently rationalized rather than interrogated.

As I’ve written about quality culture, without psychological safety and learning orientation, people won’t commit to building and supporting robust quality systems. The tribal knowledge around plunger seating, the normalization of recurring equipment failures, the seven-month delay in supplier notification—these suggest a culture where adaptation to system inadequacy became preferable to system improvement.

The path forward requires leadership that creates conditions for quality serendipity: reward pattern recognition over problem solving, celebrate early identification of weak signals, and create systems that make the right choice the easy choice.

Beyond Compliance: Building Anti-Fragile Quality

The Catalent 483 offers more than a cautionary tale—it provides a roadmap for quality transformation. Every observation represents an invitation to build quality systems that become stronger under stress rather than more brittle.

Organizations that master this transformation—moving from zemblanity-generating systems to serendipity-creating ones—will find that quality becomes not just a regulatory requirement but a competitive advantage. They’ll detect risks earlier, respond more effectively, and create the kind of operational resilience that turns disruption into opportunity.

The choice is clear: continue managing quality as a collection of independent compliance activities, or build integrated systems designed to create the conditions for sustained quality success. The Catalent case shows us what happens when we choose poorly. The frameworks exist to choose better.

What patterns of “inevitable failure” do you see in your own quality systems? How might shifting from negative reasoning to causal understanding transform your approach to investigations? Share your thoughts—this conversation about quality transformation is one we need to have across the industry.

Zemblanity

William Boyd is a favorite author for me, so I was pleased to read The hidden architecture of failure – understanding “zemblanity”. While I’ve read Armadillio, I missed the applicability of the word.

Zemblanity is actually a pretty good word for our field. I’m going to test it out, see if it has legs.

Zemblanity in Risk Management: Turning the Mirror on Hidden System Fragility

If you’re reading this blog, you already know that risk management isn’t about tallying up hypothetical hazards and ticking regulatory boxes. But have you ever stopped to ask whether your systems are quietly hardwiring failure—almost by design? Christian Busch’s recent LSE Business Review article lands on a word for this: zemblanity—the “opposite of serendipity,” or, more pointedly, bad luck that’s neither blind nor random, but structured right into the bones of our operations.

This idea resonates powerfully with the transformations occurring in pharmaceutical quality systems—the same evolution guiding the draft revision of Eudralex Volume 4 Chapter 1. In both Busch’s analysis and regulatory trends, we’re urged to confront root causes, trace risk back to its hidden architecture, and actively dismantle the quiet routines and incentives that breed failure. This isn’t mere thought leadership; it’s a call to reexamine how our own practices may be cultivating fields of inevitable misfortune—the very zemblanity that keeps reputational harm and catastrophic events just a few triggers away.

The Zemblanity Field: Where Routine Becomes Risk

Let’s be honest: the ghosts in our machines are rarely accidents. They don’t erupt out of blue-sky randomness. They were grown in cultures that prized efficiency over resilience, chased short-term gains, and normalized critical knowledge gaps. In my blog post on normalization of deviance (see: “Why Normalization of Deviance Threatens your CAPA Logic”), I map out how subtle cues and “business as usual” thinking produce exactly these sorts of landmines.

Busch’s zemblanity—the patterned and preventable misfortune that accrues from human agency—makes for a brutal mirror. Risk managers must ask: Which of our controls are truly protective, and which merely deliver the warm glow of compliance while quietly amplifying vulnerability? If serendipity is a lucky break, zemblanity is the misstep built into the schedule, the fragility we invite by squeezing the system too hard.

From Hypotheticals to Archaeology: How to Evaluate Zemblanity

So, how does one bring zemblanity into practical risk management? It starts by shifting the focus from cataloguing theoretical events to archaeology: uncovering the layered decisions, assumptions, and interdependencies that have silently locked in failure modes.

1. Map Near Misses and Routine Workarounds

Stop treating near misses as flukes. Every recurrence is a signpost pointing to underlying zemblanity. Investigate not just what happened, but why the system allowed it in the first place. High-performing teams capture these “almost events” the way a root cause analyst mines deviations for actionable knowledge .

2. Scrutinize Margins and Slack

Where are your processes running on fumes? Organizations that cut every buffer in service of “efficiency” are constructing perfect conditions for zemblanity. Whether it’s staffing, redundancy in critical utilities, or quality reserves, scrutinize these margins. If slim tolerances have become your operating norm, you’re nurturing the zemblanity field.

3. Map Hidden Interdependencies

Borrowing from system dynamics and failure mode mapping, draw out the connections you typically overlook and the informal routes by which information or pressure travels. Build reverse timelines—starting at failure—to trace seemingly disparate weak points back to core drivers.

4. Interrogate Culture and Incentives

A robust risk culture isn’t measured by the thoroughness of your SOPs, but by whether staff feel safe raising “bad news” and questioning assumptions.

5. Audit Cost-Cutting and “Optimizations”

Lean initiatives and cost-cutting programs can easily morph from margin enhancement to zemblanity engines. Run post-implementation reviews of such changes: was resilience sacrificed for pennywise savings? If so, add these to your risk register, and reframe “efficiency” in light of the total cost of a fragile response to disruption.

6. Challenge “Never Happen Here” Assumptions

Every mature risk program needs a cadence of challenging assumptions. Run pre-mortem workshops with line staff and cross-functional teams to simulate how multi-factor failures could cascade. Spotlight scenarios previously dismissed as “impossible” and ask why. Highlight usage in quality system design.

Operationalizing Zemblanity in PQS

The Eudralex Chapter 1 draft’s movement from static compliance to dynamic, knowledge-centric risk management lines up perfectly here. Embedding zemblanity analysis is less about new tools and more about repurposing familiar practices: after-action reviews, bowtie diagrams, CAPA trend analysis, incident logs—all sharpened with explicit attention to how our actions and routines cultivate not just risk, but structural misfortune.

Your Product Quality Review (PQR) process, for instance, should now interrogate near misses, not just reject rates or OOS incidents. It is time to pivot from dull data reviews reviews to causal inference—asking how past knowledge blind spots or hasty “efficiencies” became hazards.

And as pharmaceutical supply chains grow ever more interdependent and brittle, proactive risk detection needs routine revisiting. Integrate zemblanity logic into your risk and resilience dashboards—flag not just frequency, but pattern, agency, and the cultural drivers of preventable failures.

Toward Serendipity: Dismantle Zemblanity, Build Quality Luck

Risk professionals can no longer limit themselves to identifying hazards and correcting defects post hoc. Proactive knowledge management and an appetite for self-interrogation will mark the difference between organizations set up for breakthroughs and those unwittingly primed for avoidable disaster.

The challenge—echoed in both Busch’s argument and the emergent GMP landscape—is clear: shrink the zemblanity field. Turn pattern-seeking into your default. Reward curiosity within your team. Build analytic vigilance into every level of the organization. Only then can resilience move from rhetoric to reality, and only then can your PQS become not just a bulwark against failure, but a platform for continuous, serendipitous improvement.