Navigating the Evolving Landscape of Validation in 2025: Trends, Challenges, and Strategic Imperatives

Hopefully, you’ve been following my journey through the ever-changing world of validation. In that case, you’ll recognize that our field is undergoing transformation under the dual drivers of digital transformation and shifting regulatory expectations. Halfway through 2025, we have another annual report from Kneat, and it is clear that while some of those core challenges remain, companies are reporting that new priorities are emerging—driven by the rapid pace of digital adoption and evolving compliance landscapes.

Audit Readiness Overtakes Compliance Burden as Top Challenge

The 2025 validation landscape reveals a striking reversal: audit readiness has dethroned compliance burden as the industry’s primary concern , marking a fundamental shift in how organizations prioritize regulatory preparedness. While compliance burden dominated in 2024—a reflection of teams grappling with evolving standards during active projects—this year’s data signals a maturation of validation programs. As organizations transition from project execution to operational stewardship, the scramble to pass audits has given way to the imperative to sustain readiness.

Why the Shift Matters

The surge in audit readiness aligns with broader quality challenges outlined in The Challenges Ahead for Quality (2023) , where data integrity and operational resilience emerged as systemic priorities.

Table: Top Validation Challenges (2022–2025)

Rank	2022	2023	2024	2025
1	Human resources	Human resources	Compliance burden	Audit readiness
2	Efficiency	Efficiency	Audit readiness	Compliance burden
3	Technological gaps	Technological gaps	Data integrity	Data integrity

This reversal mirrors a lifecycle progression. During active validation projects, teams focus on navigating procedural requirements (compliance burden). Once operational, the emphasis shifts to sustaining inspection-ready systems—a transition fraught with gaps in metadata governance and decentralized workflows. As noted in Health of the Validation Program, organizations often discover latent weaknesses in change control or data traceability only during audits, underscoring the need for proactive systems.

Next year it could flop back, to be honest these are just two sides of the same coin.

Operational Realities Driving the Change

The 2025 report highlights two critical pain points:

Documentation traceability : 69% of teams using digital validation tools cite automated audit trails as their top benefit, yet only 13% integrate these systems with project management platform . This siloing creates last-minute scrambles to reconcile disparate records.
Experience gaps : With 42% of professionals having 6–15 years of experience, mid-career teams lack the institutional knowledge to prevent audit pitfalls—a vulnerability exacerbated by retiring senior experts .

Organizations that treated compliance as a checkbox exercise now face operational reckoning, as fragmented systems struggle to meet the FDA’s expectations for real-time data access and holistic process understanding.

Similarly, teams that relied on 1 or 2 full-time employees, and leveraged contractors, also struggle with building and retaining expertise.

Strategic Implications

To bridge this gap, forward-thinking teams continue to adopt risk-adaptive validation models that align with ICH Q10’s lifecycle approach. By embedding audit readiness into daily work organizations can transform validation from a cost center to a strategic asset. As argued in Principles-Based Compliance, this shift requires rethinking quality culture: audit preparedness is not a periodic sprint but a byproduct of robust, self-correcting systems.

In essence, audit readiness reflects validation’s evolution from a tactical compliance activity to a cornerstone of enterprise quality—a theme that will continue to dominate the profession’s agenda and reflects the need to drive for maturity.

Digital Validation Adoption Reaches Tipping Point

Digital validation systems have seen a 28% adoption increase since 2024, with 58% of organizations now using these tools . By 2025, 93% of firms either use or plan to adopt digital validation, signaling and sector-wide transformation. Early adopters report significant returns: 63% meet or exceed ROI expectations, achieving 50% faster cycle times and reduced deviations. However, integration gaps persist, as only 13% connect digital validation with project management tools, highlighting siloed workflows.

None of this should be a surprise, especially since Kneat, a provider of an electronic validation management system, sponsored the report.

Table 2: Digital Validation Adoption Metrics (2025)

Metric	Value
Organizations using digital systems	58%
ROI expectations met/exceeded	63%
Integration with project tools	13%

For me, the real challenge here, as I explored in my post “Beyond Documents: Embracing Data-Centric Thinking“, is not just settling for paper-on-glass but to start thinking of your validation data as a larger lifecycle.

Leveraging Data-Centric Thinking for Digital Validation Transformation

The shift from document-centric to data-centric validation represents a paradigm shift in how regulated industries approach compliance, as outlined in Beyond Documents: Embracing Data-Centric Thinking. This transition aligns with the 2025 State of Validation Report’s findings on digital adoption trends and addresses persistent challenges like audit readiness and workforce pressures.

The Paper-on-Glass Trap in Validation

Many organizations remain stuck in “paper-on-glass” validation models, where digital systems replicate paper-based workflows without leveraging data’s full potential. This approach perpetuates inefficiencies such as:

Manual data extraction requiring hours to reconcile disparate records
Inflated validation cycles due to rigid document structures that limit adaptive testing
Increased error rates from static protocols that cannot dynamically respond to process deviations

Principles of Data-Centric Validation

True digital transformation requires reimagining validation through four core data-centric principles:

Unified Data Layer Architecture: The adoption of unified data layer architectures marks a paradigm shift in validation practices, as highlighted in the 2025 State of Validation Report. By replacing fragmented document-centric models with centralized repositories, organizations can achieve real-time traceability and automated compliance with ALCOA++ principles. The transition to structured data objects over static PDFs directly addresses the audit readiness challenges discussed above, ensuring metadata remains enduring and available across decentralized teams.
Dynamic Protocol Generation: AI-driven dynamic protocol generation may reshape validation efficiency. By leveraging natural language processing and machine learning, the hope is to have systems analyze historical protocols and regulatory guidelines to auto-generate context-aware test scripts. However, regulatory acceptance remains a barrier—only 10% of firms integrate validation systems with AI analytics, highlighting the need for controlled pilots in low-risk scenarios before broader deployment.
Continuous Process Verification: Continuous Process Verification (CPV) has emerged as a cornerstone of the industry as IoT sensors and real-time analytics enabling proactive quality management. Unlike traditional batch-focused validation, CPV systems feed live data from manufacturing equipment into validation platforms, triggering automated discrepancy investigations when parameters exceed thresholds. By aligning with ICH Q10’s lifecycle approach, CPV transforms validation from a compliance exercise into a strategic asset.
Validation as Code: The validation-as-code movement, pioneered in semiconductor and nuclear industries, represents the next frontier in agile compliance. By representing validation requirements as machine-executable code, teams automate regression testing during system updates and enable Git-like version control for protocols. The model’s inherent auditability—with every test result linked to specific code commits—directly addresses the data integrity priorities ranked #1 by 63% of digital validation adopters.

Table 1: Document-Centric vs. Data-Centric Validation Models

Aspect	Document-Centric	Data-Centric
Primary Artifact	PDF/Word Documents	Structured Data Objects
Change Management	Manual Version Control	Git-like Branching/Merging
Audit Readiness	Weeks of Preparation	Real-Time Dashboard Access
AI Compatibility	Limited (OCR-Dependent)	Native Integration (eg, LLM Fine-Tuning)
Cross-System Traceability	Manual Matrix Maintenance	Automated API-Driven Links

Implementation Roadmap

Organizations progressing towards maturity should:

Conduct Data Maturity Assessments
- Map existing validation data flows
- Perform a maturity assessment
Adopt Modular Validation Platforms
- Implement cloud-native solutions
Reskill Teams for Data Fluency
Establish Data Governance Frameworks

AI in Validation: Early Adoption, Strategic Potential

Artificial intelligence (AI) adoption and validation are still in the early stages, though the outlook is promising. Currently, much of the conversation around AI is driven by hype, and while there are encouraging developments, significant questions remain about the fundamental soundness and reliability of AI technologies.

In my view, AI is something to consider for the future rather than immediate implementation, as we still need to fully understand how it functions. There are substantial concerns regarding the validation of AI systems that the industry must address, especially as we approach more advanced stages of integration. Nevertheless, AI holds considerable potential, and leading-edge companies are already exploring a variety of approaches to harness its capabilities.

Table 3: AI Adoption in Validation (2025)

AI Application	Adoption Rate	Impact
Protocol generation	12%	40% faster drafting
Risk assessment automation	9%	30% reduction in deviations
Predictive analytics	5%	25% improvement in audit readiness

Workforce Pressures Intensify Amid Resource Constraints

Workloads increased for 66% of teams in 2025, yet 39% operate with 1–3 members, exacerbating talent gaps . Mid-career professionals (42% with 6–15 years of experience) dominate the workforce, signaling a looming “experience gap” as senior experts retire. This echoes 2023 quality challenges, where turnover risks and knowledge silos threaten operational resilience. Outsourcing has become a critical strategy, with 70% of firms relying on external partners for at least 10% of validation work.

Smart organizations have talent and competency building strategies.

Emerging Challenges and Strategic Responses

From Compliance to Continuous Readiness

Organizations are shifting from reactive compliance to building “always-ready” systems.

From Firefighting to Future-Proofing: The Strategic Shift to “Always-Ready” Quality Systems

The industry’s transition from reactive compliance to “always-ready” systems represents a fundamental reimagining of quality management. This shift aligns with the Excellence Triad framework—efficiency, effectiveness, and elegance—introduced in my 2025 post on elegant quality systems, where elegance is defined as the seamless integration of intuitive design, sustainability, and user-centric workflows. Rather than treating compliance as a series of checkboxes to address during audits, organizations must now prioritize systems that inherently maintain readiness through proactive risk mitigation , real-time data integrity , and self-correcting workflows .

Elegance as the Catalyst for Readiness

The concept of “always-ready” systems draws heavily from the elegance principle, which emphasizes reducing friction while maintaining sophistication. .

Principles-Based Compliance and Quality

The move towards always-ready systems also reflects lessons from principles-based compliance , which prioritizes regulatory intent over prescriptive rules.

Cultural and Structural Enablers

Building always-ready systems demands more than technology—it requires a cultural shift. The 2021 post on quality culture emphasized aligning leadership behavior with quality values, a theme reinforced by the 2025 VUCA/BANI framework , which advocates for “open-book metrics” and cross-functional transparency to prevent brittleness in chaotic environments. F

Outcomes Over Obligation

Ultimately, always-ready systems transform compliance from a cost center into a strategic asset. As noted in the 2025 elegance post , organizations using risk-adaptive documentation practices and API-driven integrations report 35% fewer audit findings, proving that elegance and readiness are mutually reinforcing. This mirrors the semiconductor industry’s success with validation-as-code, where machine-readable protocols enable automated regression testing and real-time traceability.

By marrying elegance with enterprise-wide integration, organizations are not just surviving audits—they’re redefining excellence as a state of perpetual readiness, where quality is woven into the fabric of daily operations rather than bolted on during inspections.

Workforce Resilience in Lean Teams

The imperative for cross-training in digital tools and validation methodologies stems from the interconnected nature of modern quality systems, where validation professionals must act as “system gardeners” nurturing adaptive, resilient processes. This competency framework aligns with the principles outlined in Building a Competency Framework for Quality Professionals as System Gardeners, emphasizing the integration of technical proficiency, regulatory fluency, and collaborative problem-solving.

Competency: Digital Validation Cross-Training

Definition : The ability to fluidly navigate and integrate digital validation tools with traditional methodologies while maintaining compliance and fostering system-wide resilience.

Dimensions and Elements

1. Adaptive Technical Mastery

Elements :

Tool Agnosticism : Proficiency across validation platforms and core systems (eQMS, etc) with ability to map workflows between systems.
System Literacy : Competence in configuring integrations between validation tools and electronic systems, such as an MES.
CSA Implementation : Practical application of Computer Software Assurance principles and GAMP 5.

2. Regulatory-DNA Integration

Elements :

ALCOA++ Fluency : Ability to implement data integrity controls that satisfy FDA 21 CFR Part 11 and EU Annex 11.
Inspection Readiness : Implementation of inspection readiness principles
Risk-Based AI Validation : Skills to validate machine learning models per FDA 2024 AI/ML Validation Draft Guidance.

3. Cross-Functional Cultivation

Elements :

Change Control Hybridization : Ability to harmonize agile sprint workflows with ASTM E2500 and GAMP 5 change control requirements.
Knowledge Pollination : Regular rotation through manufacturing/QC roles to contextualize validation decisions.

Validation’s Role in Broader Quality Ecosystems

Data Integrity as a Strategic Asset

The axiom “we are only as good as our data” encapsulates the existential reality of regulated industries, where decisions about product safety, regulatory compliance, and process reliability hinge on the trustworthiness of information. The ALCOA++ framework— Attributable, Legible, Contemporary, Original, Accurate, Complete, Consistent, Enduring, and Available —provides the architectural blueprint for embedding data integrity into every layer of validation and quality systems. As highlighted in the 2025 State of Validation Report , organizations that treat ALCOA++ as a compliance checklist rather than a cultural imperative risk systemic vulnerabilities, while those embracing it as a strategic foundation unlock resilience and innovation.

Cultural Foundations: ALCOA++ as a Mindset, Not a Mandate

The 2025 validation landscape reveals a stark divide: organizations treating ALCOA++ as a technical requirement struggle with recurring findings, while those embedding it into their quality culture thrive. Key cultural drivers include:

Leadership Accountability : Executives who tie KPIs to data integrity metrics (eg, % of unattributed deviations) signal its strategic priority, aligning with Principles-Based Compliance.
Cross-Functional Fluency : Training validation teams in ALCOA++-aligned tools bridges the 2025 report’s noted “experience gap” among mid-career professionals .
Psychological Safety : Encouraging staff to report near-misses without fear—a theme in Health of the Validation Program —prevents data manipulation and fosters trust.

The Cost of Compromise: When Data Integrity Falters

The 2025 report underscores that 25% of organizations spend >10% of project budgets on validation—a figure that balloons when data integrity failures trigger rework. Recent FDA warning letters cite ALCOA++ breaches as root causes for:

Batch rejections due to unverified temperature logs (lack of original records).
Clinical holds from incomplete adverse event reporting (failure of Complete ).
Import bans stemming from inconsistent stability data across sites (breach of Consistent ).

Conclusion: ALCOA++ as the Linchpin of Trust

In an era where AI-driven validation and hybrid inspections redefine compliance, ALCOA++ principles remain the non-negotiable foundation. Organizations must evolve beyond treating these principles as static rules, instead embedding them into the DNA of their quality systems—as emphasized in Pillars of Good Data. When data integrity drives every decision, validation transforms from a cost center into a catalyst for innovation, ensuring that “being as good as our data” means being unquestionably reliable.

Future-Proofing Validation in 2025

The 2025 validation landscape demands a dual focus: accelerating digital/AI adoption while fortifying human expertise . Key recommendations include:

Prioritize Integration : Break down silos by connecting validation tools to data sources and analytics platforms.
Adopt Risk-Based AI : Start with low-risk AI pilots to build regulatory confidence.
Invest in Talent Pipelines : Address mid-career gaps via academic partnerships and reskilling programs.

As the industry navigates these challenges, validation will increasingly serve as a catalyst for quality innovation—transforming from a cost center to a strategic asset.

Business Process Management: The Symbiosis of Framework and Methodology – A Deep Dive into Process Architecture’s Strategic Role

Building on our foundational exploration of process mapping as a scaling solution and the interplay of methodologies, frameworks, and tools in quality management, it is essential to position Business Process Management (BPM) as a dynamic discipline that harmonizes structural guidance with actionable execution. At its core, BPM functions as both an adaptive enterprise framework and a prescriptive methodology, with process architecture as the linchpin connecting strategic vision to operational reality. By integrating insights from our prior examinations of process landscapes, SIPOC analysis, and systems thinking principles, we unravel how organizations can leverage BPM’s dual nature to drive scalable, sustainable transformation.

BPM’s Dual Identity: Structural Framework and Execution Pathway

Business Process Management operates simultaneously as a conceptual framework and an implementation methodology. As a framework, BPM establishes the scaffolding for understanding how processes interact across an organization. It provides standardized visualization templates like BPMN (Business Process Model and Notation) and value chain models, which create a common language for cross-functional collaboration. This framework perspective aligns with our earlier discussion of process landscapes, where hierarchical diagrams map core processes to supporting activities, ensuring alignment with strategic objectives.

Yet BPM transcends abstract structuring by embedding methodological rigor through its improvement lifecycle. This lifecycle-spanning scoping, modeling, automation, monitoring, and optimization-mirrors the DMAIC (Define, Measure, Analyze, Improve, Control) approach applied in quality initiatives. For instance, the “As-Is” modeling phase employs swimlane diagrams to expose inefficiencies in handoffs between departments, while the “To-Be” design phase leverages BPMN simulations to stress-test proposed workflows. These methodological steps operationalize the framework, transforming architectural blueprints into executable workflows.

The interdependence between BPM’s framework and methodology becomes evident in regulated industries like pharmaceuticals, where process architectures must align with ICH Q10 guidelines while methodological tools like change control protocols ensure compliance during execution. This duality enables organizations to maintain strategic coherence while adapting tactical approaches to shifting demands.

Process Architecture: The Structural Catalyst for Scalable Operations

Process architecture transcends mere process cataloging; it is the engineered backbone that ensures organizational processes collectively deliver value without redundancy or misalignment. Drawing from our exploration of process mapping as a scaling solution, effective architectures integrate three critical layers:

Strategic Layer: Anchored in Porter’s Value Chain, this layer distinguishes primary activities (e.g., manufacturing, service delivery) from support processes (e.g., HR, IT). By mapping these relationships through high-level process landscapes, leaders can identify which activities directly impact competitive advantage and allocate resources accordingly.
Operational Layer: Here, SIPOC (Supplier-Input-Process-Output-Customer) diagrams define process boundaries, clarifying dependencies between internal workflows and external stakeholders. For example, a SIPOC analysis in a clinical trial supply chain might reveal that delayed reagent shipments from suppliers (an input) directly impact patient enrollment timelines (an output), prompting architectural adjustments to buffer inventory.
Execution Layer: Detailed swimlane maps and BPMN models translate strategic and operational designs into actionable workflows. These tools, as discussed in our process mapping series, prevent scope creep by explicitly assigning responsibilities (via RACI matrices) and specifying decision gates.

Implementing Process Architecture: A Phased Approach
Developing a robust process architecture requires methodical execution:

Value Identification: Begin with value chain analysis to isolate core customer-facing processes. IGOE (Input-Guide-Output-Enabler) diagrams help validate whether each architectural component contributes to customer value. For instance, a pharmaceutical company might use IGOEs to verify that its clinical trial recruitment process directly enables faster drug development (a strategic objective).
Interdependency Mapping: Cross-functional workshops map handoffs between departments using BPMN collaboration diagrams. These sessions often reveal hidden dependencies-such as quality assurance’s role in batch release decisions-that SIPOC analyses might overlook. By embedding RACI matrices into these models, organizations clarify accountability at each process juncture.
Governance Integration: Architectural governance ties process ownership to performance metrics. A biotech firm, for example, might assign a Process Owner for drug substance manufacturing, linking their KPIs (e.g., yield rates) to architectural review cycles. This mirrors our earlier discussions about sustaining process maps through governance protocols.

Sustaining Architecture Through Dynamic Process Mapping

Process architectures are not static artifacts; they require ongoing refinement to remain relevant. Our prior analysis of process mapping as a scaling solution emphasized the need for iterative updates-a principle that applies equally to architectural maintenance:

Quarterly SIPOC Updates: Revisiting supplier and customer relationships ensures inputs/outputs align with evolving conditions. A medical device manufacturer might adjust its SIPOC for component sourcing post-pandemic, substituting single-source suppliers with regional alternatives to mitigate supply chain risks.
Biannual Landscape Revisions: Organizational restructuring (e.g., mergers, departmental realignments) necessitates value chain reassessment. When a diagnostics lab integrates AI-driven pathology services, its process landscape must expand to include data governance workflows, ensuring compliance with new digital health regulations.
Trigger-Based IGOE Analysis: Regulatory changes or technological disruptions (e.g., adopting blockchain for data integrity) demand rapid architectural adjustments. IGOE diagrams help isolate which enablers (e.g., IT infrastructure) require upgrades to support updated processes.

This maintenance cycle transforms process architecture from a passive reference model into an active decision-making tool, echoing our findings on using process maps for real-time operational adjustments.

Unifying Framework and Methodology: A Blueprint for Execution

The true power of BPM emerges when its framework and methodology dimensions converge. Consider a contract manufacturing organization (CMO) implementing BPM to reduce batch release timelines:

Framework Application:
- A value chain model prioritizes “Batch Documentation Review” as a critical path activity.
- SIPOC analysis identifies regulatory agencies as key customers of the release process.
Methodological Execution:
- Swimlane mapping exposes delays in quality control’s document review step.
- BPMN simulation tests a revised workflow where parallel document checks replace sequential approvals.
- The organization automates checklist routing, cutting review time by 40%.
Architectural Evolution:
- Post-implementation, the process landscape is updated to reflect QC’s reduced role in routine reviews.
- KPIs shift from “Documents Reviewed per Day” to “Right-First-Time Documentation Rate,” aligning with strategic goals for quality culture.

Strategic Insights for Practitioners

Architecture-Informed Problem Solving

A truly effective approach to process improvement begins with a clear understanding of the organization’s process architecture. When inefficiencies arise, it is vital to anchor any improvement initiative within the specific architectural layer where the issue is most pronounced. This means that before launching a solution, leaders and process owners should first diagnose whether the root cause of the problem lies at the strategic, operational, or tactical level of the process architecture. For instance, if an organization is consistently experiencing raw material shortages, the problem is situated within the operational layer. Addressing this requires a granular analysis of the supply chain, often using tools like SIPOC (Supplier, Input, Process, Output, Customer) diagrams to map supplier relationships and identify bottlenecks or gaps. The solution might involve renegotiating contracts with suppliers, diversifying the supplier base, or enhancing inventory management systems. On the other hand, if the organization is facing declining customer satisfaction, the issue likely resides at the strategic layer. Here, improvement efforts should focus on value chain realignment-re-examining how the organization delivers value to its customers, possibly by redesigning service offerings, improving customer touchpoints, or shifting strategic priorities. By anchoring problem-solving efforts in the appropriate architectural layer, organizations ensure that solutions are both targeted and effective, addressing the true source of inefficiency rather than just its symptoms.

Methodology Customization

No two organizations are alike, and the maturity of an organization’s processes should dictate the methods and tools used for business process management (BPM). Methodology customization is about tailoring the BPM lifecycle to fit the unique needs, scale, and sophistication of the organization. For startups and rapidly growing companies, the priority is often speed and adaptability. In these environments, rapid prototyping with BPMN (Business Process Model and Notation) can be invaluable. By quickly modeling and testing critical workflows, startups can iterate and refine their processes in real time, responding nimbly to market feedback and operational challenges. Conversely, larger enterprises with established Quality Management Systems (QMS) and more complex process landscapes require a different approach. Here, the focus shifts to integrating advanced tools such as process mining, which enables organizations to monitor and analyze process performance at scale. Process mining provides data-driven insights into how processes actually operate, uncovering hidden inefficiencies and compliance risks that might not be visible through manual mapping alone. In these mature organizations, BPM methodologies are often more formalized, with structured governance, rigorous documentation, and continuous improvement cycles embedded in the organizational culture. The key is to match the BPM approach to the organization’s stage of development, ensuring that process management practices are both practical and impactful.

Metrics Harmonization

For process improvement initiatives to drive meaningful and sustainable change, it is essential to align key performance indicators (KPIs) with the organization’s process architecture. This harmonization ensures that metrics at each architectural layer support and inform one another, creating a cascade of accountability that links day-to-day operations with strategic objectives. At the strategic layer, high-level metrics such as Time-to-Patient provide a broad view of organizational performance and customer impact. These strategic KPIs should directly influence the targets set at the operational layer, such as Batch Record Completion Rates, On-Time Delivery, or Defect Rates. By establishing this alignment, organizations can ensure that improvements made at the operational level contribute directly to strategic goals, rather than operating in isolation. Our previous work on dashboards for scaling solutions illustrates how visualizing these relationships can enhance transparency and drive performance. Dashboards that integrate metrics from multiple architectural layers enable leaders to quickly identify where breakdowns are occurring and to trace their impact up and down the value chain. This integrated approach to metrics not only supports better decision-making but also fosters a culture of shared accountability, where every team understands how their performance contributes to the organization’s overall success.

Process Boundary

A process boundary is the clear definition of where a process starts and where it ends. It sets the parameters for what is included in the process and, just as importantly, what is not. The boundary marks the transition points: the initial trigger that sets the process in motion and the final output or result that signals its completion. By establishing these boundaries, organizations can identify the interactions and dependencies between processes, ensuring that each process is manageable, measurable, and aligned with objectives.

Why Are Process Boundaries Important?

Defining process boundaries is essential for several reasons:

Clarity and Focus: Boundaries help teams focus on the specific activities, roles, and outcomes that are relevant to the process at hand, avoiding unnecessary complexity and scope creep.
Effective Resource Allocation: With clear boundaries, organizations can allocate resources efficiently and prioritize improvement efforts where they will have the greatest impact.
Accountability: Boundaries clarify who is responsible for each part of the process, making it easier to assign ownership and measure performance.
Process Optimization: Well-defined boundaries make it possible to analyze, improve, and optimize processes systematically, as each process can be evaluated on its own terms before considering its interfaces with others.

How to Determine Process Boundaries

Determining process boundaries is both an art and a science. Here’s a step-by-step approach, drawing on best practices from process mapping and business process analysis:

1. Define the Purpose of the Process

Before mapping, clarify the purpose of the process. What transformation or value does it deliver? For example, is the process about onboarding a new supplier, designing new process equipment, or resolving a non-conformance? Knowing the purpose helps you focus on the relevant start and end points.

2. Identify Inputs and Outputs

Every process transforms inputs into outputs. Clearly articulate what triggers the process (the input) and what constitutes its completion (the output). For instance, in a cake-baking process, the input might be “ingredients assembled,” and the output is “cake baked.” This transformation defines the process boundary.

3. Engage Stakeholders

Involve process owners, participants, and other stakeholders in boundary definition. They bring practical knowledge about where the process naturally starts and ends, as well as insights into handoffs and dependencies with other processes. Workshops, interviews, and surveys can be effective for gathering these perspectives.

4. Map the Actors and Activities

Decide which roles (“actors”) and activities are included within the boundary. Are you mapping only the activities of a laboratory analyst, or also those of supervisors, internal customers who need the results, or external partners? The level of detail should match your mapping purpose-whether you’re looking at a high-level overview or a detailed workflow.

5. Zoom Out, Then Zoom In

Start by zooming out to see the process as a whole in the context of the organization, then zoom in to set precise start and end points. This helps avoid missing upstream dependencies or downstream impacts that could affect the process’s effectiveness.

6. Document and Validate

Once you’ve defined the boundaries, document them clearly in your process map or supporting documentation. Validate your boundaries with stakeholders to ensure accuracy and buy-in. This step helps prevent misunderstandings and ensures the process map will be useful for analysis and improvement.

7. Review and Refine

Process boundaries are not set in stone. As the organization evolves or as you learn more through process analysis, revisit and adjust boundaries as needed to reflect changes in scope, objectives, or business environment.

Common Pitfalls and How to Avoid Them

Scope Creep: Avoid letting the process map expand beyond its intended boundaries. Stick to the defined start and end points unless there’s a compelling reason to adjust them7.
Overlapping Boundaries: Ensure that processes don’t overlap unnecessarily, which can create confusion about ownership and accountability.
Ignoring Interfaces: While focusing on boundaries, don’t neglect to document key interactions and handoffs with other processes. These interfaces are often sources of risk or inefficiency.

Conclusion

Defining process boundaries is a foundational step in business process mapping and analysis. It provides the clarity needed to manage, measure, and improve processes effectively. By following a structured approach-clarifying purpose, identifying inputs and outputs, engaging stakeholders, and validating your work-you set the stage for successful process optimization and organizational growth. Remember: a well-bounded process is a manageable process, and clarity at the boundaries is the first step toward operational excellence.

Why ‘First-Time Right’ is a Dangerous Myth in Continuous Manufacturing

In manufacturing circles, “First-Time Right” (FTR) has become something of a sacred cow-a philosophy so universally accepted that questioning it feels almost heretical. Yet as continuous manufacturing processes increasingly replace traditional batch production, we need to critically examine whether this cherished doctrine serves us well or creates dangerous blind spots in our quality assurance frameworks.

The Seductive Promise of First-Time Right

Let’s start by acknowledging the compelling appeal of FTR. As commonly defined, First-Time Right is both a manufacturing principle and KPI that denotes the percentage of end-products leaving production without quality defects. The concept promises a manufacturing utopia: zero waste, minimal costs, maximum efficiency, and delighted customers receiving perfect products every time.

The math seems straightforward. If you produce 1,000 units and 920 are defect-free, your FTR is 92%. Continuous improvement efforts should steadily drive that percentage upward, reducing the resources wasted on imperfect units.

This principle finds its intellectual foundation in Six Sigma methodology, which can tend to give it an air of scientific inevitability. Yet even Six Sigma acknowledges that perfection remains elusive. This subtle but crucial nuance often gets lost when organizations embrace FTR as an absolute expectation rather than an aspiration.

First-Time Right in biologics drug substance manufacturing refers to the principle and performance metric of producing a biological drug substance that meets all predefined quality attributes and regulatory requirements on the first attempt, without the need for rework, reprocessing, or batch rejection. In this context, FTR emphasizes executing each step of the complex, multi-stage biologics manufacturing process correctly from the outset-starting with cell line development, through upstream (cell culture/fermentation) and downstream (purification, formulation) operations, to the final drug substance release.

Achieving FTR is especially challenging in biologics because these products are made from living systems and are highly sensitive to variations in raw materials, process parameters, and environmental conditions. Even minor deviations can lead to significant quality issues such as contamination, loss of potency, or batch failure, often requiring the entire batch to be discarded.

In biologics manufacturing, FTR is not just about minimizing waste and cost; it is critical for patient safety, regulatory compliance, and maintaining supply reliability. However, due to the inherent variability and complexity of biologics, FTR is best viewed as a continuous improvement goal rather than an absolute expectation. The focus is on designing and controlling processes to consistently deliver drug substances that meet all critical quality attributes-recognizing that, despite best efforts, some level of process variation and deviation is inevitable in biologics production

The Unique Complexities of Continuous Manufacturing

Traditional batch processing creates natural boundaries-discrete points where production pauses, quality can be assessed, and decisions about proceeding can be made. In contrast, continuous manufacturing operates without these convenient checkpoints, as raw materials are continuously fed into the manufacturing system, and finished products are continuously extracted, without interruption over the life of the production run.

This fundamental difference requires a complete rethinking of quality assurance approaches. In continuous environments:

Quality must be monitored and controlled in real-time, without stopping production
Deviations must be detected and addressed while the process continues running
The interconnected nature of production steps means issues can propagate rapidly through the system
Traceability becomes vastly more complex

Regulatory agencies recognize these unique challenges, acknowledging that understanding and managing risks is central to any decision to greenlight CM in a production-ready environment. When manufacturing processes never stop, quality assurance cannot rely on the same methodologies that worked for discrete batches.

The Dangerous Complacency of Perfect-First-Time Thinking

The most insidious danger of treating FTR as an achievable absolute is the complacency it breeds. When leadership becomes fixated on achieving perfect FTR scores, several dangerous patterns emerge:

Overconfidence in Automation

While automation can significantly improve quality, it is important to recognize the irreplaceable value of human oversight. Automated systems, no matter how advanced, are ultimately limited by their programming, design, and maintenance. Human operators bring critical thinking, intuition, and the ability to spot subtle anomalies that machines may overlook. A vigilant human presence can catch emerging defects or process deviations before they escalate, providing a layer of judgment and adaptability that automation alone cannot replicate. Relying solely on automation creates a dangerous blind spot-one where the absence of human insight can allow issues to go undetected until they become major problems. True quality excellence comes from the synergy of advanced technology and engaged, knowledgeable people working together.

Underinvestment in Deviation Management

If perfection is expected, why invest in systems to handle imperfections? Yet robust deviation management-the processes used to identify, document, investigate, and correct deviations becomes even more critical in continuous environments where problems can cascade rapidly. Organizations pursuing FTR often underinvest in the very systems that would help them identify and address the inevitable deviations.

False Sense of Process Robustness

Process robustness refers to the ability of a manufacturing process to tolerate the variability of raw materials, process equipment, operating conditions, environmental conditions and human factors. An obsession with FTR can mask underlying fragility in processes that appear to be performing well under normal conditions. When we pretend our processes are infallible, we stop asking critical questions about their resilience under stress.

Quality Culture Deterioration

When FTR becomes dogma, teams may become reluctant to report or escalate potential issues, fearing they’ll be seen as failures. This creates a culture of silence around deviations-precisely the opposite of what’s needed for effective quality management in continuous manufacturing. When perfection is the only acceptable outcome, people hide imperfections rather than address them.

Magical Thinking in Quality Management

The belief that we can eliminate all errors in complex manufacturing processes amounts to what organizational psychologists call “magical thinking” – the delusional belief that one can do the impossible. In manufacturing, this often manifests as pretending that doing more tasks with less resources will not hurt the work quality.

This is a pattern I’ve observed repeatedly in my investigations of quality failures. When leadership subscribes to the myth that perfection is not just desirable but achievable, they create the conditions for quality disasters. Teams stop preparing for how to handle deviations and start pretending deviations won’t occur.

The irony is that this approach actually undermines the very goal of FTR. By acknowledging the possibility of failure and building systems to detect and learn from it quickly, we actually increase the likelihood of getting things right.

Building a Healthier Quality Culture for Continuous Manufacturing

Rather than chasing the mirage of perfect FTR, organizations should focus on creating systems and cultures that:

Detect deviations rapidly: Continuous monitoring through advanced process control systems becomes essential for monitoring and regulating critical parameters throughout the production process. The question isn’t whether deviations will occur but how quickly you’ll know about them.
Investigate transparently: When issues occur, the focus should be on understanding root causes rather than assigning blame. The culture must prioritize learning over blame.
Implement robust corrective actions: Deviations should be thoroughly documented including details about when and where it occurred, who identified it, a detailed description of the nonconformance, initial actions taken, results of the investigation into the cause, actions taken to correct and prevent recurrence, and a final evaluation of the effectiveness of these actions.
Learn systematically: Each deviation represents a valuable opportunity to strengthen processes and prevent similar issues in the future. The organization that learns fastest wins, not the one that pretends to be perfect.

Breaking the Groupthink Cycle

The FTR myth thrives in environments characterized by groupthink, where challenging the prevailing wisdom is discouraged. When leaders obsess over FTR metrics while punishing those who report deviations, they create the perfect conditions for quality disasters.

This connects to a theme I’ve explored repeatedly on this blog: the dangers of losing institutional memory and critical thinking in quality organizations. When we forget that imperfection is inevitable, we stop building the systems and cultures needed to manage it effectively.

Embracing Humility, Vigilance, and Continuous Learning

True quality excellence comes not from pretending that errors don’t occur, but from embracing a more nuanced reality:

Perfection is a worthy aspiration but an impossible standard
Systems must be designed not just to prevent errors but to detect and address them
A healthy quality culture prizes transparency and learning over the appearance of perfection
Continuous improvement comes from acknowledging and understanding imperfections, not denying them

The path forward requires humility to recognize the limitations of our processes, vigilance to catch deviations quickly when they occur, and an unwavering commitment to learning and improving from each experience.

In the end, the most dangerous quality issues aren’t the ones we detect and address-they’re the ones our systems and culture allow to remain hidden because we’re too invested in the myth that they shouldn’t exist at all. First-Time Right should remain an aspiration that drives improvement, not a dogma that blinds us to reality.

From Perfect to Perpetually Improving

As continuous manufacturing becomes the norm rather than the exception, we need to move beyond the simplistic FTR myth toward a more sophisticated understanding of quality. Rather than asking, “Did we get it perfect the first time?” we should be asking:

How quickly do we detect when things go wrong?
How effectively do we contain and remediate issues?
How systematically do we learn from each deviation?
How resilient are our processes to the variations they inevitably encounter?

These questions acknowledge the reality of manufacturing-that imperfection is inevitable-while focusing our efforts on what truly matters: building systems and cultures capable of detecting, addressing, and learning from deviations to drive continuous improvement.

The companies that thrive in the continuous manufacturing future won’t be those with the most impressive FTR metrics on paper. They’ll be those with the humility to acknowledge imperfection, the systems to detect and address it quickly, and the learning cultures that turn each deviation into an opportunity for improvement.

Understanding the Distinction Between Impact and Risk

Two concepts—impact and risk — are often discussed but sometimes conflated within quality systems. While related, these concepts serve distinct purposes and drive different decisions throughout the quality system. Let’s explore.

The Fundamental Difference: Impact vs. Risk

The difference between impact and risk is fundamental to effective quality management. The difference between impact and risk is critical. Impact is best thought of as ‘What do I need to do to make the change.’ Risk is ‘What could go wrong in making this change?'”

Impact assessment focuses on evaluating the effects of a proposed change on various elements such as documentation, equipment, processes, and training. It helps identify the scope and reach of a change. Risk assessment, by contrast, looks ahead to identify potential failures that might occur due to the change – it’s preventive and focused on possible consequences.

This distinction isn’t merely academic – it directly affects how we approach actions and decisions in our quality systems, impacting core functions of CAPA, Change Control and Management Review.

Aspect	Impact	Risk
Definition	The effect or influence a change, event, or deviation has on product quality, process, or system	The probability and severity of harm or failure occurring as a result of a change, event, or deviation
Focus	What is affected and to what extent (scope and magnitude of consequences)	What could go wrong, how likely it is to happen, and how severe the outcome could be
Assessment Type	Evaluates the direct consequences of an action or event	Evaluates the likelihood and severity of potential adverse outcomes
Typical Use	Used in change control to determine which documents, systems, or processes are impacted	Used to prioritize actions, allocate resources, and implement controls to minimize negative outcomes
Measurement	Usually described qualitatively (e.g., minor, moderate, major, critical)	Often quantified by combining probability and impact scores to assign a risk level (e.g., low, medium, high)
Example	A change in raw material supplier impacts the manufacturing process and documentation.	The risk is that the new supplier’s material could fail to meet quality standards, leading to product defects.

Change Control: Different Questions, Different Purposes

Within change management, the PIC/S Recommendation PI 054-1 notes that “In some cases, especially for simple and minor/low risk changes, an impact assessment is sufficient to document the risk-based rationale for a change without the use of more formal risk assessment tools or approaches.”

Impact Assessment in Change Control

Determines what documentation requires updating
Identifies affected systems, equipment, and processes
Establishes validation requirements
Determines training needs

Risk Assessment in Change Control

Identifies potential failures that could result from the change
Evaluates possible consequences to product quality and patient safety
Determines likelihood of those consequences occurring
Guides preventive measures

A common mistake is conflating these concepts or shortcutting one assessment. For example, companies often rush to designate changes as “like-for-like” without supporting data, effectively bypassing proper risk assessment. This highlights why maintaining the distinction is crucial.

Validation: Complementary Approaches

In validation, the impact-risk distinction shapes our entire approach.

Impact in validation relates to identifying what aspects of product quality could be affected by a system or process. For example, when qualifying manufacturing equipment, we determine which critical quality attributes (CQAs) might be influenced by the equipment’s performance.

Risk assessment in validation explores what could go wrong with the equipment or process that might lead to quality failures. Risk management plays a pivotal role in validation by enabling a risk-based approach to defining validation strategies, ensuring regulatory compliance, mitigating product quality and safety risks, facilitating continuous improvement, and promoting cross-functional collaboration.

In Design Qualification, we verify that the critical aspects (CAs) and critical design elements (CDEs) necessary to control risks identified during the quality risk assessment (QRA) are present in the design. This illustrates how impact assessment (identifying critical aspects) works together with risk assessment (identifying what could go wrong).

When we perform Design Review and Design Qualification, we focus on Critical Aspects: Prioritize design elements that directly impact product quality and patient safety. Here, impact assessment identifies critical aspects, while risk assessment helps prioritize based on potential consequences.

Following Design Qualification, Verification activities such as Installation Qualification (IQ), Operational Qualification (OQ), and Performance Qualification (PQ) serve to confirm that the system or equipment performs as intended under actual operating conditions. Here, impact assessment identifies the specific parameters and functions that must be verified to ensure no critical quality attributes are compromised. Simultaneously, risk assessment guides the selection and extent of tests by focusing on areas with the highest potential for failure or deviation. This dual approach ensures that verification not only confirms the intended impact of the design but also proactively mitigates risks before routine use.

Validation does not end with initial qualification. Continuous Validation involves ongoing monitoring and trending of process performance and product quality to confirm that the validated state is maintained over time. Impact assessment plays a role in identifying which parameters and quality attributes require ongoing scrutiny, while risk assessment helps prioritize monitoring efforts based on the likelihood and severity of potential deviations. This continuous cycle allows quality systems to detect emerging risks early and implement corrective actions promptly, reinforcing a proactive, risk-based culture that safeguards product quality throughout the product lifecycle.

Data Integrity: A Clear Example

Data integrity offers perhaps the clearest illustration of the impact-risk distinction.

As I’ve previously noted, Data quality is not a risk. It is a causal factor in the failure or severity. Poor data quality isn’t itself a risk; rather, it’s a factor that can influence the severity or likelihood of risks.

When assessing data integrity issues:

Impact assessment identifies what data is affected and which processes rely on that data
Risk assessment evaluates potential consequences of data integrity lapses

In my risk-based data integrity assessment methodology, I use a risk rating system that considers both impact and risk factors:

Risk Rating	Action	Mitigation
>25	High Risk-Potential Impact to Patient Safety or Product Quality	Mandatory
12-25	Moderate Risk-No Impact to Patient Safety or Product Quality but Potential Regulatory Risk	Recommended
<12	Negligible DI Risk	Not Required

This system integrates both impact (on patient safety or product quality) and risk (likelihood and detectability of issues) to guide mitigation decisions.

The Golden Day: Impact and Risk in Deviation Management

The Golden Day concept for deviation management provides an excellent practical example. Within the first 24 hours of discovering a deviation, we conduct:

An impact assessment to determine:
- Which products, materials, or batches are affected
- Potential effects on critical quality attributes
- Possible regulatory implications
A risk assessment to evaluate:
- Patient safety implications
- Product quality impact
- Compliance with registered specifications
- Level of investigation required

This impact assessment is also the initial risk assessment, which will help guide the level of effort put into the deviation. This statement shows how the two concepts, while distinct, work together to inform quality decisions.

Quality Escalation: When Impact Triggers a Response

In quality escalation, we often use specific criteria based on both impact and risk:

Escalation Criteria	Examples of Quality Events for Escalation
Potential to adversely affect quality, safety, efficacy, performance or compliance of product	– Contamination – Product defect/deviation from process parameters or specification – Significant GMP deviations
Product counterfeiting, tampering, theft	– Product counterfeiting, tampering, theft reportable to Health Authority – Lost/stolen IMP
Product shortage likely to disrupt patient care	– Disruption of product supply due to product quality events
Potential to cause patient harm associated with a product quality event	– Urgent Safety Measure, Serious Breach, Significant Product Complaint

These criteria demonstrate how we use both impact (what’s affected) and risk (potential consequences) to determine when issues require escalation.

Both Are Essential

Understanding the difference between impact and risk fundamentally changes how we approach quality management. Impact assessment without risk assessment may identify what’s affected but fails to prevent potential issues. Risk assessment without impact assessment might focus on theoretical problems without understanding the actual scope.

The pharmaceutical quality system requires both perspectives:

Impact tells us the scope – what’s affected
Risk tells us the consequences – what could go wrong

By maintaining this distinction and applying both concepts appropriately across change control, validation, and data integrity management, we build more robust quality systems that not only comply with regulations but actually protect product quality and patient safety.