The Deep Ownership Paradox: Why It Takes Years to Master What You Think You Already Know

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.

The Expertise Crisis: Why AI’s War on Entry-Level Jobs Threatens Quality Excellence

As pharmaceutical and biotech organizations rush to harness artificial intelligence to eliminate “inefficient” entry-level positions, we are at risk of creating a crisis that threatens the very foundation of quality expertise. The Harvard Business Review’s recent analysis of AI’s impact on entry-level jobs reads like a prophecy of organizational doom—one that quality leaders should heed before it’s too late.

Research from Stanford indicates that there has been a 13% decline in entry-level job opportunities for workers aged 22 to 25 since the widespread adoption of generative AI. The study shows that 50-60% of typical junior tasks—such as report drafting, research synthesis, data cleaning, and scheduling—can now be performed by AI. For high-quality organizations already facing expertise gaps, this trend signals a potential self-destructive path rather than increased efficiency.

Equally concerning, automation is leading to the phasing out of some traditional entry-level professional tasks. When I started in the field, newcomers would gain experience through tasks like batch record reviews and good documentation practices for protocols. However, with the introduction of electronic batch records and electronic validation management, these tasks have largely disappeared. AI is expected to accelerate this trend even further.

Everyone should go and read “The Perils of Using AI to Replace Entry-Level Jobs” by Amy C. Edmondson and Tomas Chamorro-Premuzic and then come back and read this post.

The Apprenticeship Dividend: What We Lose When We Skip the Journey

Every expert in pharmaceutical quality began somewhere. They learned to read batch records, investigated their first deviations, struggled through their first CAPA investigations, and gradually developed the pattern recognition that distinguishes competent from exceptional quality professionals. This journey, what the Edmondson and Chamorro-Premuzic call the “apprenticeship dividend”, cannot be replicated by AI or compressed into senior-level training programs.

Consider commissioning, qualification, and validation (CQV) work in biotech manufacturing. Junior engineers traditionally started by documenting Installation Qualification protocols, learning to recognize when equipment specifications align with user requirements. They progressed to Operational Qualification, developing understanding of how systems behave under various conditions. Only after this foundation could they effectively design Performance Qualification strategies that demonstrate process capability.

When organizations eliminate these entry-level CQV roles in favor of AI-generated documentation and senior engineers managing multiple systems simultaneously, they create what appears to be efficiency. In reality, they’ve severed the pipeline that transforms technical contributors into systems thinkers capable of managing complex manufacturing operations.

The Expertise Pipeline: Building Quality Gardeners

As I’ve written previously about building competency frameworks for quality professionals, true expertise requires integration of technical knowledge, methodological skills, social capabilities, and self-management abilities. This integration occurs through sustained practice, mentorship, and gradual assumption of responsibility—precisely what entry-level positions provide.

The traditional path from Quality specialist to Quality Manager to Quality Director illustrates this progression:

Foundation Level: Learning to execute quality methods methods, understand requirements, and recognize when results fall outside acceptance criteria. Basic deviation investigation and CAPA support.

Intermediate Level: Taking ownership of requirement gathering, leading routine investigations, participating in supplier audits, and beginning to see connections between different quality systems.

Advanced Level: Designing audit activities, facilitating cross-functional investigations, mentoring junior staff, and contributing to strategic quality initiatives.

Leadership Level: Building quality cultures, designing organizational capabilities, and creating systems that enable others to excel.

Each level builds upon the previous, creating what we might call “quality gardeners”—professionals who nurture quality systems as living ecosystems rather than enforcing compliance through rigid oversight. Skip the foundation levels, and you cannot develop the sophisticated understanding required for advanced practice.

The False Economy of AI Substitution

Organizations defending entry-level job elimination often point to cost savings and “efficiency gains.” This thinking reflects a fundamental misunderstanding of how expertise develops and quality systems function. Consider risk management in biotech manufacturing—a domain where pattern recognition and contextual judgment are essential.

A senior risk management professional reviewing a contamination event can quickly identify potential failure modes, assess likelihood and severity, and design effective mitigation strategies. This capability developed through years of investigating routine deviations, participating in CAPA teams, and learning to distinguish significant risks from minor variations.

When AI handles initial risk assessments and senior professionals review only the outputs, we create a dangerous gap. The senior professional lacks the deep familiarity with routine variations that enables recognition of truly significant deviations. Meanwhile, no one is developing the foundational expertise needed to replace retiring experts.

The result is what is called expertise hollowing, organizations that appear capable on the surface but lack the deep competency required to handle complex challenges or adapt to changing conditions.

Building Expertise in a Quality Organization

Creating robust expertise development requires intentional design that recognizes both the value of human development and the capabilities of AI tools. Rather than eliminating entry-level positions, quality organizations should redesign them to maximize learning value while leveraging AI appropriately.

Structured Apprenticeship Programs

Quality organizations should implement formal apprenticeship programs that combine academic learning with progressive practical responsibility. These programs should span 2-3 years and include:

Year 1: Foundation Building

Basic GMP principles and quality systems overview
Hands-on experience with routine quality operations
Mentorship from experienced quality professionals
Participation in investigations under supervision

Year 2: Skill Development

Specialized training in areas like CQV, risk management, or supplier quality
Leading routine activities with oversight
Cross-functional project participation
Beginning to train newer apprentices

Year 3: Integration and Leadership

Independent project leadership
Mentoring responsibilities
Contributing to strategic quality initiatives
Preparation for advanced roles

As I evaluate the organization I am building, this is a critical part of the vision.

Mentorship as Core Competency

Every senior quality professional should be expected to mentor junior colleagues as a core job responsibility, not an additional burden. This requires:

Formal Mentorship Training: Teaching experienced professionals how to transfer tacit knowledge, provide effective feedback, and create learning opportunities.
Protected Time: Ensuring mentors have dedicated time for development activities, not just “additional duties as assigned.”
Measurement Systems: Tracking mentorship effectiveness through apprentice progression, retention rates, and long-term career development.
Recognition Programs: Rewarding excellent mentorship as a valued contribution to organizational capability.

Progressive Responsibility Models

Entry-level roles should be designed with clear progression pathways that gradually increase responsibility and complexity:

CQV Progression Example:

CQV Technician: Executing test protocols, documenting results, supporting commissioning activities
CQV Specialist: Writing protocols, leading qualification activities, interfacing with vendors
CQV Engineer: Designing qualification strategies, managing complex projects, training others
CQV Manager: Building organizational CQV capabilities, strategic planning, external representation

Risk Management Progression:

Risk Analyst: Data collection, basic risk identification, supporting formal assessments
Risk Specialist: Facilitating risk assessments, developing mitigation strategies, training stakeholders
Risk Manager: Designing risk management systems, building organizational capabilities, strategic oversight

AI as Learning Accelerator, Not Replacement

Rather than replacing entry-level workers, AI should be positioned as a learning accelerator that enables junior professionals to handle more complex work earlier in their careers:

Enhanced Analysis Capabilities: AI can help junior professionals identify patterns in large datasets, enabling them to focus on interpretation and decision-making rather than data compilation.
Simulation and Modeling: AI-powered simulations can provide safe environments for junior professionals to practice complex scenarios without real-world consequences.
Knowledge Management: AI can help junior professionals access relevant historical examples, best practices, and regulatory guidance more efficiently.
Quality Control: AI can help ensure that junior professionals’ work meets standards while they’re developing expertise, providing a safety net during the learning process.

The Cost of Expertise Shortcuts

Organizations that eliminate entry-level positions in pursuit of short-term efficiency gains will face predictable long-term consequences:

Expertise Gaps: As senior professionals retire or move to other organizations, there will be no one prepared to replace them.
Reduced Innovation: Innovation often comes from fresh perspectives questioning established practices—precisely what entry-level employees provide.
Cultural Degradation: Quality cultures are maintained through socialization and shared learning experiences that occur naturally in diverse, multi-level teams.
Risk Blindness: Without the deep familiarity that comes from hands-on experience, organizations become vulnerable to risks they cannot recognize or understand.
Competitive Disadvantage: Organizations with strong expertise development programs will attract and retain top talent while building superior capabilities.

Choosing Investment Over Extraction

The decision to eliminate entry-level positions represents a choice between short-term cost extraction and long-term capability investment. For quality organizations, this choice is particularly stark because our work depends fundamentally on human judgment, pattern recognition, and the ability to adapt to novel situations.

AI should augment human capability, not replace the human development process. The organizations that thrive in the next decade will be those that recognize expertise development as a core competency and invest accordingly. They will build “quality gardeners” who can nurture adaptive, resilient quality systems rather than simply enforce compliance.

The expertise crisis is not inevitable—it’s a choice. Quality leaders must choose wisely, before the cost of that choice becomes irreversible.

Tag: expertise hollowing