The End of Digital Silos: A Founder’s Perspective

Premise: The Digitisation Fallacy, and the Human API

For the past decade, the Life Sciences industry has operated under a misconception regarding "digital transformation." The prevailing strategy has focused on digitizing documents, migrating paper-based Quality Management Systems (QMS) and batch records to cloud-based repositories. While this modernisation was necessary, it is becoming insufficient. Currently, half of mid-sized and the majority of start-ups and small companies heavily or entirely rely on manual systems for their GMP operations (1)(2).

We have created an ecosystem of high-value tools (e.g., Veeva, TrackWise, LIMS) that remain operationally siloed. The interoperability gap between these systems is currently bridged by manual data reconciliation performed by highly qualified Regulatory Affairs, Compliance, and Quality personnel. This "human API" (Application Programming Interface) is not merely an operational inefficiency; in the current regulatory landscape, it represents the most likely Data Integrity vulnerability.

As we approach 2026 and beyond, the competitive and compliance advantage will no longer belong to organisations that simply store digital files, but to those that can compute regulatory decisions across structured datasets.

1. The Hard Truth: Your Data is Not Ready for AI

Everyone wants to deploy a "regulatory LLM" to draft submissions, manage deviations, and conduct investigations. But no software vendor will admit that most organizations’ current and legacy data is too “unstructured” and “dirty”. We all know what happens when impure materials are used to manufacture products. Likewise, throwing data into the latest LLM does not replace a person’s 15 years’ experience in GMP. This has led the industry to question why any competent person would trust AI in GMP?!

Companies cannot build a Ferrari engine (AI) and put it in a horse cart. Most companies are sitting on 20+ years of "unstructured" legacy data, inconsistent naming conventions, scanned images, and narrative-heavy reports that machines cannot parse, as well as datasets that are far too large for teams to verify manually.

The Founder’s Take:

The immediate priority isn't purchasing AI tools; it's Data Remediation. We are consistently advising clients to adopt standards such as IDMP (Identification of Medicinal Products) and UDI (Unique Device Identification), not only because of authorities’ requirements and their movement towards harmonisation (3)(4), but additionally it's the only way to establish a coherent data backbone that makes future AI safe and compliant. Without structured data, AI will give confident lies (hallucinations).

2. Operational Speed vs. Regulatory Latency: The Field Alert Challenge

The main risk in legacy, disjointed systems is not the inability to capture data, but the delay in retrieving and integrating it within statutory reporting deadlines.

Consider the Field Alert Report (FAR) or Medical Device Report (MDR) requirements. When a potential reportable event is identified, the regulatory clock (often 3 days for FARs) begins immediately. In a disconnected architecture, the delay between event detection and submission readiness creates unmitigated compliance risk.

• The Current State: Deviation data resides in a local QMS; distribution data resides in ERP; complaint history resides in a separate safety database. Reconciling these sources to determine reporting is a manual, time-intensive process prone to manual errors.

• The Computational Standard: Speed must be a function of data structure, not personnel overtime. A robust compliance architecture enables instantaneous querying of structured data to assess reportability accuracy and speed, eliminating the "fire drill" dynamic of adverse event reporting.

3. The “Silent Audit” Threat: Data Analytics as Enforcement

Regulatory bodies (FDA, EMA, MHRA) are evolving their inspection methodologies. We are witnessing a shift from physical site inspections to Remote Regulatory Assessments (RRAs)(5) driven by advanced data analytics, combined with progress in Mutual Recognition Agreements (MRAs), allowing Regulatory bodies to accept reports from foreign inspectorates (6)(7).

Regulator initiatives such as Elsa and KASA (FDA) and Darwin EU (EMA) are increasingly capable of aggregating data from disparate sources (e.g., import alerts, adverse event databases, submission data) to identify discrepancies before an inspection is initiated (7)(8). If the regulator possesses superior analytics regarding a product's performance than the manufacturer, the inspection outcome is prejudiced from the start.

The "Traceability" Stress Test:

Consider the workflow required to assess a deviation on "Batch 123" for MDR reportability:

• The Legacy Workflow: Requires manual retrieval of the PDF Batch Record, independent cross-referencing with the complaint log, and email-based verification of distribution status. This fragmentation breaks the data lineage and delays critical decision-making.

• The Computational Workflow: A unified query on "Batch 123" instantly visualizes the batch lineage, correlates it with the complaint signal, and algorithmically proposes a reportability classification based on historical precedents. This provides an auditable, data-driven basis for the regulatory decision.

4. The Shift to Assurance: Implementing CSA Principles

The industry's historical approach to validation, Computer System Validation (CSV), often prioritized documentation of functionality over the assurance of GxP functionality and patient safety. This resource-intensive model is unsustainable for modern, rapidly evolving software environments.

The FDA’s Final Guidance on Computer Software Assurance (CSA) creates a pathway to modernise this approach. It encourages a shift from "Test Script" volume to Critical Thinking and Risk Assessment (9).

Our Strategic Stance:

Validation resources should be reallocated from low-risk functional testing to high-risk data integrity flows. The objective is to validate the decision-making data path across systems, ensuring that the integration between LIMS, QMS, and Regulatory platforms preserves the integrity, traceability, and context of the data.

5. AI Governance: The Human-in-the-Loop Mandate

The integration of Artificial Intelligence into GxP workflows offers significant potential for efficiency but introduces new vectors for risk regarding "hallucinations" and model drift.

In alignment with the FDA’s Draft Guidance on AI Lifecycle Management, EMA’s Final Reflection Paper on AI, and ISO/IEC 42001, we advocate for a strict Governance Framework centered on the Human-in-the-Loop (HITL) principle.

• Role of AI: To aggregate data, identify patterns, and propose regulatory classifications based on data traceable to the source.

• Role of the Expert: To review the evidence, validate the proposal, and commit the decision to the permanent record.

The audit trail must unequivocally demonstrate that a qualified human expert retained final decision-making authority. This is essential to meet the regulators’ credibility expectations: “You must prove the model is fit for this specific context of use” (10).

6. Workforce Evolution: The "Computational Compliance Officer"

The complexity of modern data architectures necessitates an evolution in the skill set of Regulatory, Quality, and Compliance professionals. The traditional focus on technical writing and project management must be augmented with data stewardship competencies.

The Future Competency Model:

I foresee the emergence of the "Computational Compliance Officer", a professional capable of interpreting regulations through the lens of data lineage and automated workflows. This role requires the ability to audit data structures (e.g., Python scripts, SQL queries) with the same rigor applied to SOPs. Bridging this skills gap is essential for maintaining control in a data-driven regulatory environment.

The Verdict: From Submission to Access

We are transitioning from a "submission-based" model (pushing static documents to a portal) to an "access-based" model (regulators pulling structured data from partitioned environments).

• Organisations that continue to rely on manual data reconciliation will face increasing scrutiny and enforcement action due to data latency and integrity gaps.

• Organisations that prioritise Data Remediation, Automated Traceability, and CSA principles will establish a sustainable, defensible compliance posture.

Digital silos are breaking down, and the winners will be those who turn fragmented data into real-time regulatory intelligence. With the GxP AI Insights Platform, that shift from siloed digitisation to unified computation is already underway. Contact us to begin your transition today: https://www.gxpgroup.com.

1. Qualio, Life Science Quality Trends Report 2025.

2. Veeva MedTech, When to Implement a QMS Guidance For MedTech Startups.

3. FDA, Global IDMP Implementation - Getting Closer to the Goal, 2024.

4. MHRA, Statement of Policy Intent: International Recognition of Medical Devices, 2025.

5. FDA, Conducting Remote Regulatory Assessments Questions and Answers Guidance for Industry, 2025.

6. EMA, Questions & Answers on the Impact of Mutual Recognition Agreement (MRA) Between the European Union and the United States, 2025.

7. FDA, FDA Launches Agency-Wide AI Tool to Optimize Performance for the American People, 2025.

8. EMA, Data Analysis and Real World Interrogation Network (DARWIN EU), 2025.

9. FDA, Computer Software Assurance for Production and Quality System Software, Guidance for Industry and Food and Drug Administration Staff, 2025.

10. FDA, Considerations for the Use of Artificial Intelligence To Support Regulatory Decision-Making for Drug and Biological Products Draft Guidance for Industry and Other Interested Parties, 2025.