AI and LLM for pharmaceutical and regulatory quality: from reactive compliance to predictive quality

In 2025, Artificial Intelligence (AI) definitively enters the industry’s Quality Clouds, bringing a profound change in how pharmaceutical, biotech, and medical device companies manage quality and compliance. This is not just about automating manual activities but about introducing a new collaborative intelligence that connects data, processes, and people in real time. A concrete example comes from the Veeva Quality Cloud, which has integrated AI features capable of accelerating document review, deviation management, and compliance report generation. As Robert (Rob) Gaertner, VP of Quality Cloud at Veeva Systems, explained, this transition marks AI’s move into the mainstream adoption phase — where it becomes a true operational tool, no longer just an experimental concept.

In parallel, the regulatory framework has evolved to support this transformation:

• The FDA’s Computer Software Assurance (CSA), finalized in 2025, has solidified the shift toward a risk-based approach to software validation. The goal is to focus testing and documentation efforts where risk truly exists, reducing unnecessary administrative burden.
• Foundational regulations such as 21 CFR Part 11 and EU GMP Annex 11 remain essential, defining the principles for secure management of electronic signatures and computerized systems.
• The ICH Q9(R1) guideline reinforces the concept of Quality Risk Management, promoting decisions that are increasingly objective and data-driven.
• Finally, the EU AI Act, which came into force on August 1, 2024, introduces a unified regulatory framework for AI systems, with progressive obligations through 2027 to ensure transparency, safety, and accountability.

LLMs and Quality: What Makes Them Truly Different

Large Language Models (LLMs) represent a turning point because they transform the way information is managed. Unlike traditional deterministic systems that follow fixed rules, LLMs process natural language and understand the meaning behind text. This makes them ideal for automating highly cognitive tasks, such as analyzing SOPs, verifying technical documentation, or comparing international regulatory guidelines.

Imagine a system capable of reading hundreds of deviation reports, identifying recurring patterns, and suggesting improvements or corrective actions consistent with FDA or EMA guidelines. Or an AI assistant able to generate a draft of an Annual Product Quality Review (APQR) in just a few minutes using real process data. This is the true value of LLMs: making complex information accessible while enabling predictive and proactive quality.

However, their behavior is inherently non-deterministic. Therefore, robust human supervision and transparent governance are crucial. To maintain compliance with GxP principles, it is essential to:

• Ensure traceability of prompts and responses through a complete audit trail.
• Apply version control to models, datasets, and inference logs.
• Use validated datasets and document every update or retraining activity.

High-Value Use Cases

The application of LLMs to quality and regulatory processes is no longer a future vision. The most innovative organizations are already testing projects that combine operational efficiency and risk reduction. Here are some concrete examples:

1) Advanced Automation (Human-in-the-Loop)

• Deviation and CAPA triage: automatic classification and preliminary suggestions to accelerate closure times.
• Data extraction and normalization: retrieving information from audit reports, COAs, or change requests with automatic mapping into the QMS.
• Regulatory impact assessment: automatic identification of how new guidelines affect SOPs and internal documentation.

2) Insights from Unstructured Data

LLMs make it possible to interpret textual data such as audit notes or customer complaints, detecting patterns that signal emerging risk trends. This enables proactive Quality Risk Management, fully aligned with the philosophy of ICH Q9(R1).

3) Regulatory Content Generation

• Automated creation of APQR/PQR and batch disposition summaries, ensuring full traceability to original data sources.
• Regulatory intelligence: automatic synthesis and comparison of EMA, FDA, and ICH guidelines.

Compliance Map: Key References

Adopting AI in regulated environments requires a deep understanding of regulatory references to ensure compliance and trust in automated processes. Below are the main frameworks to consider:

• 21 CFR Part 11 (FDA): data integrity, electronic signatures, and system security.
• EU GMP Annex 11: requirements for validated computerized systems and risk management.
• GAMP 5 – Second Edition (2022): promotion of critical thinking and proportionate validation.
• ICH Q9(R1) (2023): harmonized framework for quality risk management.
• EMA Reflection Paper on AI (2024): human-centric principles and risk control.
• EU AI Act (2024–2027): progressive obligations for high-risk AI systems.

Within this landscape, Data Integrity remains the foundation. Applying the ALCOA+ principles — Attributable, Legible, Contemporaneous, Original, Accurate, Complete, Consistent, Enduring, Available — ensures that every piece of information is reliable and verifiable. As highlighted by the MHRA and PIC/S, data quality is the foundation of process quality.

Adoption Roadmap for GxP Environments (90–180 Days)

Integrating AI into a regulated environment is not about moving fast — it’s about building solid foundations. A well-planned adoption roadmap helps reduce risks, accelerate validation, and achieve tangible results.

• Phase 1 – Strategy and Governance: define use cases, roles, responsibilities, and oversight controls.
• Phase 2 – Data and Integrations: unify data sources (QMS, LIMS, ERP) and enforce Data Integrity policies.
• Phase 3 – Architecture and Security: design validated LLM+RAG systems with encryption and environment segregation.
• Phase 4 – Assurance and Validation: apply CSA principles to achieve proportionate, traceable evidence.
• Phase 5 – Change Management and Training: update SOPs and enable teams to adopt a risk-based mindset.
• Phase 6 – Scaling and Monitoring: measure KPIs such as time-to-closure and model drift to ensure continuous improvement.

Zero11 × Aiability: Partners for Predictive Quality

Zero11, in collaboration with Aiability, supports Life Sciences organizations in transforming quality and regulatory processes through AI solutions that comply with GxP requirements. The goal is to build predictive quality, leveraging data to prevent deviations and non-conformities before they occur.

• AI Readiness GxP: maturity assessment, data mapping, and definition of priority use cases.
• Blueprint LLM+RAG for QMS: validated architecture, prompt governance, and end-to-end validation.
• Proof of Concept (8–12 weeks): quick pilots on deviation triage, APQR generation, and regulatory intelligence.
• Assurance and Validation: applying CSA and GAMP 5 with reusable evidence.
• Change & Training: SOP updates, operational playbooks, and train-the-trainer sessions.

Let’s talk: tell us about your processes and current challenges. Together, we’ll create a tailored roadmap — with estimated ROI and clear milestones — to bring AI into your organization safely and effectively.