How Digital Transformation Is Outpacing Validation

Many regulated organizations require a higher level of digitalization than they’re achieving today. The mass transition of techno-logical infrastructure from on-premises to cloud-based; the use of DevOps to allow Software-as-a-Service (SaaS) platforms to release upgrades and updates frequently; and artificial intelligence (AI) and machine learning (ML) models’ transition from research curiosities to enablers of production quality workflows have compounded the need for companies to quicken their digital pace. However, validation practices at many regulated organizations remain frozen in time. The status quo for validation can’t keep up, and resourcing alone can’t solve it.

Background

Digital transformation is already heavily under way in the life sciences areas. Kneat Solutions’ State of Validation 2026 survey introduced a formal digital maturity scale aligned with the ISPE Good Practice Guide: Digital Validation, in which 60% of respondents reported being already on at least a hybrid paper–digital model, with more than 80% on the journey to digital maturity in at least an early adoption level or higher.1^, 2 Furthermore, as portrayed in Figure 1, at least 64% of respondents in 2026 reported that they are using or applying a dedicated digital validation tool (DVT), up from 12% in 2022, reflecting recognition for this growing industry need.1

This suggests the industry deems this concept innovative, with most regulated organizations eager to adopt modernized, digitally native ecosystems, infrastructure, tools, and solutions. Nevertheless, the validation approach to this shift continues to lag and may hinder adoption.

Many validation programs still approach validation as if software releases occur every 18 months and production systems are static. This is despite the volume of regulatory guidance and industry standards that have emerged over the last few years in response to new technologies that allow for more dynamic and targeted life-cycle management.

Regulators are increasingly promoting risk-based, life-cycle-oriented approaches, and encouraging science- and risk-driven assurance models. However, some regulated organizations continue to rely heavily on legacy validation processes and document-centric practices. Clinging to this older approach can create friction between evolving regulatory expectations and operational reality.

The industry’s growing regulatory emphasis on life-cycle management supports supplier leverage, critical thinking, and risk-based assurance approaches for computerized systems. However, regulated organizations may still misinterpret regulatory expectation, as they are not prescriptive because of the uniqueness of individual circumstances, use cases, and risk tolerances.

Figure 1. Adoption of DVTs among regulated organizations (2022–2026)

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Current State: Snapshots in a Streaming World

Traditional validation was built for stability. It emerged when systems were deployed infrequently, infrastructure was static, and major releases were separated by months or years. Validation assumed discrete project phases, stable baselines, fixed releases, maximum test-case protocol execution, and manual periodic review cycles. That model made sense when change was episodic. Modern digital systems operate differently.

Cloud-native platforms deploy with a higher frequency than before, and functionality is configuration-driven rather than code-frozen. Even AI systems now evolve based on data and retraining strategies. As reflected in the ISPE GAMP^® Guide: Artificial Intelligence, AI-enabled computerized systems may exhibit evolving characteristics that require life-cycle-based oversight rather than one-time validation assumptions.3 Change is no longer an exception; it’s the normal operating condition. Nevertheless, some companies continue to treat it as an anomaly.

We’re trying to validate a streaming world using a snapshot methodology. When validation frameworks designed for static systems are grafted onto live digital ecosystems, predictably problematic consequences follow. Documentation expands without a corresponding increase in risk insight. Quality review begins to lag development velocity, creating friction, not assurance. And in the most concerning cases, digital transformation initiatives proceed outside formal validation processes, not because quality is unimportant, but because it is perceived as unable to keep pace.

This outcome conflicts directly with the intent of modern life-cycle- and risk-based guidance, which were designed to enable flexibility and targeted approaches. The principles themselves haven’t failed, but they are being misapplied.

Overdocumentation Not Commensurate with Risk

The first failure mode emerges when we use static assumptions to validate systems with continuously changing operational statuses. Paperwork scales, but risk does not. So, instead of gaining greater control, this approach means we end up with more documentation and less efficiency.

Common examples of overdocumentation within digital validation tools include:

• Requiring full protocol execution and approval cycles for low-risk supplier patch releases already covered by supplier testing and release evidence
• Capturing screenshots to prove system states that are already time-stamped, audit-trailed, and retrievable within the application
• Creating separate traceability matrices in spreadsheets when traceability already exists natively within requirements and testing
• Redocumenting supplier specifications, release notes, or test evidence into local templates rather than leveraging supplier artifacts

Overdocumentation that is disproportionate to risk creates several unintended consequences:

• Validation efforts are disproportionate to risk drivers.
• Review time expands without improving risk visibility.
• Teams begin optimizing for “audit defensibility” rather than control effectiveness.
• Documentation becomes a proxy for assurance.

If every update requires exhaustive documentation rituals regardless of risk profile, validation effort scales linearly with change velocity. Eventually, effort shifts from thinking critically about risk to mechanically executing templates. When this happens, validation loses its analytical edge and becomes administrative.

As ISPE GAMP^® 5: A Risk-Based Approach to Compliant GxP Computerized Systems (Second Edition) explains in detail, risk-based guidance’s intent is proportionality.4 When documentation depth, rigor, and extent become detached from risk impact, proportionality is lost. Where digital systems change continuously, it’s understood that all the life-cycle documentation associated with the digital systems simply cannot iterate static versions at the same pace. This is why the step toward practicing real digital validation (not just adopting a digital validation platform) needs to happen now.

Bottleneck Due to Lagging Quality Review

The second failure mode emerges when validation review cycles can’t match digital delivery speed. Modern development pipelines can deploy weekly, with automated test results available instantly. However, formal validation review often remains sequential, manual, and batch-oriented, which creates a speed mismatch and friction. Quality review begins to lag deployment readiness. Release decisions wait on document compilation rather than control evidence. Review cycles expand not because risk increased, but because the review mechanism was not designed for continuous flow.

When quality becomes the slowest component in a digitally transformed operating model, several dynamics follow: Business stakeholders perceive validation as a bottleneck rather than an enabler. Pressure increases to classify systems as “non-GxP” to avoid delay. To quicken timelines, supplier documentation may be adopted wholesale without assessing gaps or evaluating contexts. Parallel review processes emerge outside formal governance. Quality teams face overload, leading to reactive rather than strategic oversight.

Digital transformation often increases objective control transparency. Automated testing provides deeper regression coverage than manual scripts ever did. DevOps pipelines enforce deployment gates consistently. Real-time monitoring surfaces deviations immediately. But if quality review mechanisms remain document-driven rather than data-driven, the benefit of those controls is muted. Validation doesn’t fail because the above controls are weak, but because the culture, review processes, and risk tolerances don’t match system behavior. Over time, this misalignment erodes trust, innovation teams view validation as friction, and quality teams feel overwhelmed and governance credibility begins to decline.

“Shadow IT” and Unapproved Workarounds

When validation frameworks can’t keep pace with digital delivery models, organizations don’t just stop innovating, they route around quality. “Shadow information technology (IT)” occurs when users operate with unapproved business systems for the sake of productivity or convenience. We can see an extension of this phenomenon applied to activities on the road to digital maturity occurring when:

• Business units use SaaS tools outside of controlled environments
• Data integrations are built without formal oversight
• Automation scripts are deployed informally
• AI-enabled analytics are used operationally before governance is defined
• Configurations change before risks are properly assessed

Companies may look favorably on using shadow tactics to adapt to friction. However, assurance and visibility must not be lost. When formal validation pathways are perceived as too slow, too rigid, or too documentation-heavy for low-risk digital changes, teams seek alternative routes. They justify it by classifying tools as “non-GxP,” “informational,” or incorrectly leveraging supplier content to replace or delegate their own responsibilities.

Over time, those informal systems become lodged in the operation. The reliance on shadow IT creates and compounds issues because of fragmented data governance frameworks, inconsistent audit trail management, unclear ownership of system controls, untracked configuration drift, and reduced enterprise-level risk transparency.

In effect, the organization increases operational risk while believing it is accelerating transformation. This failure mode is subtle but dangerous because it erodes trust among IT, quality, and business stakeholders. Quality becomes perceived as an obstacle rather than a business enabler, and innovation proceeds in parallel silos. When that happens, validation will have an opposite effect than what’s intended—no one will feel very assured.

Proposed Solution: Evolve Your Change Management

Instead of repeatedly trying to “freeze in a validated state,” ISPE Good Practice Guide: Validation 4.0 suggests that we need to constantly monitor for continuous validation.5 Dashboards, notification tools, and review-by-exception features make this monitoring easier. Digital transformation has fundamentally altered how systems behave. Change is no longer episodic; it’s continuous. Releases are no longer rare; they are part of operational cadence. Validation must therefore shift from proving control at a single moment to demonstrating control over time.

Validation should be reframed as an embedded, continuing control strategy rather than a discrete release activity. It aligns with life-cycle principles and risk-based thinking but adapts them to digital operating models. The starting point remains unchanged: clarity of intent. Validation must begin with a disciplined definition of intended use, understanding the critical aspects of the business process, and data integrity risks. In digital ecosystems, ambiguity about purpose leads to over-testing low-risk functionality or under-controlling high-impact changes. From there, the focus shifts from documenting what was tested to designing how risk is controlled.

In modern systems, the primary control environment is technical, not procedural. Access controls, audit trails, segregation of duties, deployment gates, automated regression frameworks, and real-time monitoring dashboards provide enforceable, repeatable assurance mechanisms. Documentation should describe the control strategy, but the controls themselves must be built into the system.

Digital ecosystems continuously generate evidence that operators and auditors can see within applications and easily understand. This is thanks to user experience iterations aimed at optimal usability. Automated test results, configuration baselines, and monitoring metrics already exist as machine-readable artifacts via the use of entities, exportable .csv files, and application programming interface endpoints. This allows content to be more easily interpreted by and embedded into digital platforms that may make up regulated organizations’ enterprise solutions stack. This contributes to the overall goal of integrated data in an interconnected digital landscape, which aligns with approaches outlined in ISPE Good Practice Guide: Digital Validation.2

As change quickens, governance must also quicken. Not every change warrants the same level of scrutiny. Start with an intended-use impact assessment, followed by risk classification, automated testing where feasible, and targeted manual verification only when necessary. This allows quality operations to accommodate the speed of change and scale with innovation.

Finally, validation must extend beyond go-live. In a streaming digital world, control is demonstrated through performance trends, deviation signals, and continuous oversight. Ongoing verification through monitoring, data review, and risk evaluation provides stronger assurance than reliance on manual addendum triggers.

Taking a continuous approach to validation to improve your change management shouldn’t add burden. If done correctly, it should redistribute burden. Instead of concentrating effort around release events, it embeds control into pipelines, dashboards, and governance models. Instead of proving compliance once, it demonstrates stability over time. Ongoing assurance is gained during routine operations so systems retain a state of control. Digital transformation requires digital assurance. Anything less creates friction at best and governance awareness gaps at worst.

Choose Trusted Suppliers

An important contributing factor to a regulated organization’s resistance to change is that organizations retain all the regulatory responsibility over the systems they select from suppliers but also have little or no control over the change scope or timing of automatic updates, which are released based on the supplier’s cadence.

Understandably, total accountability without total control creates friction; it means the regulated organization must be prepared to react to changes as soon as they are announced. This leads to fear that the organization might not be able to continue to control the system if the evidence and documentation are not available or don’t match the current state. The reflexive response is predictable: reexecute full validation for every update. That response feels conservative, defensible, and safe, but unfortunately, it’s neither scalable nor proportionate.

Supplier management is a critical component of the regulated organization’s ability to provide quality assurance for the systems they use. Qualifying your supplier ensures that you have everything you need to explain all the evidence and documentation during inspections, whether it originated in-house or is supplier-leveraged.

ISPE GAMP® 5 (Second Edition) encourages the risk-based leveraging of supplier activities and documentation to reduce duplicate and unnecessary effort.4 Where appropriate, supplier-generated test evidence and verification artifacts may be relied on, particularly for functionality not specific to the regulated organization’s intended use, configuration, or operating environment, if the organization is confident about supplier quality practices.

To establish this confidence, regulated companies should evaluate the supplier’s quality management system (QMS), software development life cycle (SDLC), testing methodology, change management, release processes, recordkeeping practices, and ability to provide ongoing transparency and support throughout the system life cycle.

Trusting a supplier means trusting that the supplier’s quality system is robust, its software development processes are well-defined and consistently followed, and its testing is traceable and meets requirements. In practice, this means you, the regulated company, need to review, understand, and agree with the supplier’s policies, procedures, and fit-for-purpose evidence. It also means that you’ve ensured the supplier will continue to provide the required information and support throughout the system life cycle.

Supplier qualification under a robust supplier management process officially establishes that trust. And trustworthy suppliers will ensure that you can access and understand all the resources available so that the suppliers can be easily qualified for use.

Challenge: Applying to SaaS

SaaS platforms are often where digital transformation collides most visibly with outdated validation approaches. Updates are frequent. Release cycles are supplier-controlled. Infrastructure is abstracted. Configuration replaces custom code. Traditional notions of “version lock” no longer apply. This creates understandable anxiety for regulated companies.

Updates must be classified intelligently. Not every release warrants the same response. Distinguishing between infrastructure changes, security patches, minor enhancements, and functional changes that affect intended use lets organizations align effort with potential risk. A security patch addressing a vulnerability may require confirmation of deployment and compatibility, not fully functional regression. A user-interface enhancement may be informational rather than operationally significant. A functional change affecting critical workflows may warrant deeper evaluation.

Equally important is leveraging existing evidence. Effort should scale with risk impact, not with update frequency. SaaS suppliers often provide release notes, testing summaries, validation packages, and preassessments. Internal automated regression suites may already exist. Configuration management systems track deltas precisely. The objective is not to duplicate evidence, but to assess its relevance and sufficiency.

Under delivery pressure, however, two unhealthy patterns often emerge. Either organizations default to exhaustive retesting, regardless of risk, or they accept supplier documentation wholesale, without checking for gaps between intended use and enterprise controls. Neither approach reflects mature governance.

What the Right Evidence Looks Like

As described in ISPE GAMP^® 5 (Second Edition), assurance should establish and maintain objective evidence demonstrating that the system remains fit for intended use and in a state of control.4 This evidence may include requirements and intended-use definitions; risk assessments; supplier assessments; specifications and configurations; verification and test results; traceability among requirements and testing; change records; deviations; and documented decisions supporting release and continuing operation.

Knowing which artifact applies to which situation is harder to discern. This will rely heavily on how the artifact would affect the workflows and frameworks quality representatives established for that particular business process. Based on the established quality systems in place, validation professionals should then critically ask:

• What changed?
• Does it affect intended use?
• Does it alter critical data flows?
• Does it introduce new interfaces?
• Are existing controls sufficient?
• Is additional verification required?
• What evidence will be needed to prove the effectiveness of the existing or new controls?

The answers to these questions should yield decision-tree-style logic to determine what evidence is required for each situation or change. This evaluation should be targeted, proportionate, and repeatable, where the workflow built to demonstrate the state of control is sensible and defensible.

We must stop pretending SaaS behaves like static, on-premises software frozen between upgrades. SaaS operates as a constantly evolving service. Validation must evolve with it, shifting to structured, risk-based oversight of continuing change. Better control of change leverages both live statuses and quality rationales for risk-based approaches, making control sustainable in SaaS environments.

Where the Evidence Should Live

In a mature digital transformation model, validation evidence needs to not reside in a single document or platform. Consistent with emerging ISPE digital validation guidance, evidence may originate across interconnected systems including SDLC, testing, deployment, change management, and operational platforms; DVTs provide traceability, governance, and visibility into the validated state.2 Rather than re-creating evidence in static documents, organizations should focus on establishing trusted, inspection-ready data flows across the digital ecosystem.

Evidence may originate across interconnected systems in the software delivery and operational ecosystem, requiring experts to build integration points, interfaces, and connectors to ensure data flows consistently and as expected. Some modern SaaS suppliers increasingly provide customer-accessible specifications, testing evidence, release documentation, and risk-relevant change information within the platform ecosystem. This lets regulated customers more efficiently leverage supplier activities rather than needing to rebrand, wrap up, or manually link to assurance evidence.

Why Culture Is the Hardest to Transform

The most difficult shift triggered by digital transformation isn’t technical, but professional, dealing with learning curves, role traditions, company culture, and resistance to change. The regulated organization retains ultimate accountability for intended use, risk management, and data integrity. Suppliers can provide the most helpful, comprehensive validation package possible. But only the regulated customer understands how that system is configured, integrated, and used within its specific GxP context.

As systems become more configurable, interconnected, and data-driven, the risk profile is determined less by supplier code and more by enterprise architecture decisions and frameworks designed through collaboration between quality and IT for business needs. These decisions drive configuration choices, access models, integration pathways, data flows, retraining triggers, and monitoring thresholds.

Tomorrow’s successful validation professionals will understand how SaaS platforms interact with upstream and downstream systems. They will be able to map data lineage across application programming interfaces, evaluate whether a configuration change alters intended use, and assess whether an AI retraining strategy introduces drift risk.

Most importantly, these validators will be able to translate regulatory principles into digital control strategies. Company culture is the hardest to transform because it requires new competencies—digital fluency, systems thinking, and architectural awareness. It requires moving from “Was the protocol executed?” to “Is the control design sufficient?”

Without this evolution, validation remains reactive and documents get reviewed only after decisions are made. With it, validation becomes strategic by shaping how digital systems are governed from the outset. By increasing the importance of ensuring fitness for intended use, the digital transformation increases the role of validation. But validation professionals can thrive only by transforming alongside the systems they govern.

Figure 2. Ranked validation challenges reported by regulated organizations (2022–2026)

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Why Regulators Aren’t the Bottleneck

Most validation overhead today is self-inflicted. Some regulated organizations will reject automation features that could save time and improve quality simply because of superficial formatting preferences and styles. The top validation challenges for the past two years were audit readiness and compliance burden, as shown in Figure 2.1

We need to challenge the assumption that regulators are the bottleneck, when the culprit really is audit-readiness behavior within the regulated organization that leads to excessive compliance burden.

Regulatory oversight aims to demonstrate control, regardless of document volume. Regulators are simply looking for objective evidence, and whatever is sufficient to explain what the risks are, how they are effectively controlled, and how your decisions were made. If the focus were switched to risk-prioritized content rather than volume-prioritized content, the overall burden could be reduced when a robust quality framework has already been applied and is backed by continued assurance checks.

Multiple guidance documents from regulators and industry bodies support this. From ISPE GAMP^® 5 (Second Edition) to the US FDA’s Computer Software Assurance Guidance, risk-based validation is in, and document-heavy process for process’s sake is out.4^, 6 The issue is cultural inertia: We confuse visibility with safety, signatures with control, and volume with rigor. Digital transformation requires replacing artifact accumulation with evidence design.

A Pragmatic Call to Action

Digital transformation will not slow down to accommodate legacy validation models. If the culture around validation does not modernize, one of three things will happen:

• Digital initiatives will bypass quality
• Innovation velocity will create governance gaps
• Trust in validation will erode

But none of those outcomes improve compliance. Modernization, however, changes the dynamic entirely. When validation evolves into continuous, risk-based assurance, quality becomes an innovation accelerator rather than a release checkpoint. Reactive change burden gives way to continued visibility. Transparency increases because evidence is generated continuously rather than assembled retrospectively.

Organizations can take key, intentional steps now. Audit validation processes and frameworks to determine whether historical artifacts are relevant to the organization’s risk tolerances. Map validation activities to actual control objectives. Collaborate with suppliers and partners for contribution of complimentary evidence and expertise to reduce isolated burden. If proposed internal deliverables offer no added value over supplier deliverables, leverage those instead to avoid duplication. Introduce automated testing frameworks where feasible to reduce manual efforts. Redesign change control to include risk-tiered pathways. Pilot continuous verification dashboards. Train validation professionals in digital systems literacy. Stop asking, “Did we execute the protocol?” Start asking, “Did we demonstrate control of critical risks?”

Conclusion

Digital transformation is already happening. The question is not whether systems will continue to advance, it’s whether validation can keep up. Validation’s efficacy is not determined by how many documents are produced, but by how effectively risk is governed. It will depend on whether organizations embrace proportionate, evidence-driven oversight rather than ritualized re-testing. As digital ecosystems grow more complex, interconnected, and data-driven, the regulated organization’s responsibility increases. Suppliers provide tools. Regulators define expectations. But it is the enterprise that must design, enforce, and monitor control.

Validation professionals and quality representatives are uniquely positioned to lead this shift. We must do this not as document custodians or as post-development gatekeepers, but as risk governance architects in a digital world. To be sustainable, we must choose innovation over tradition, but we can do that without compromising compliance. Digital transformation demands continuing assurance to ensure modern computerized systems remain consistently fit for intended use. We will need evolved leadership to operationalize this.