AI validation in GMP manufacturing should be risk-based and matched to the system’s intended use, quality impact, and role in decision-making.
Data governance is foundational to AI governance because model reliability depends on controlled, contextualized, and well-documented source data.
AI-enabled computerized systems require life cycle control, including performance monitoring, version management, change control, and defined procedures for model updates or re-training.
Sponsors and CDMOs should align early on AI system ownership, validation responsibilities, data access, model updates, exception handling, and quality-record documentation.
Trustworthy AI in pharmaceutical manufacturing depends on connecting digital capability with GMP disciplines, such as documentation, accountability, human review, and product-quality oversight.
From Computerized-System Validation to AI Life Cycle Control
Computerized-system validation remains a critical foundation for artificial intelligence (AI)-enabled manufacturing, but validation can no longer be treated only as a single qualification event completed before routine use. The AI practice principles established by the U.S. Food and Drug Administration (FDA) and the European Medicines Agency (EMA) emphasize a risk-based approach, clear context of use, data governance and documentation, model design and development practices, risk-based performance assessment, and life cycle management.1,2 Those concepts suggest a more dynamic validation posture, where the question is not only whether a system worked as tested but whether it remains appropriate for the decision it supports over time.
That distinction matters because AI-enabled systems can differ from conventional automation in how they are developed, maintained, and evaluated. A rule-based system may execute a defined instruction in a predictable way, while an AI-enabled system may depend more heavily on training data, model architecture, performance thresholds, and ongoing monitoring. For a manufacturing use case, the validation strategy must therefore begin with the role the model will play. A system used to support process monitoring, deviation triage, or control recommendations should be assessed according to the quality impact of that role, the data on which it depends, and the level of human review required before any action is taken.
The International Society for Pharmaceutical Engineering (ISPE) good automated manufacturing process (GAMP) AI Guide addresses the development and use of AI-enabled computerized systems in GxP areas while safeguarding patient safety, product quality, and data integrity.3 For regulated manufacturers, this places AI within the same life cycle discipline already applied to GxP computerized systems but with additional attention to model behavior over time. Manufacturers need to understand whether a model can drift from its original performance, when re-training is permitted or required, how new model versions are assessed before release, and what evidence shows that a model remains fit for use in the regulated workflow.
The initial validation effort establishes confidence that the system is suitable for its defined role. Life cycle control preserves that confidence as data, processes, software, models, and operating conditions change.
Data Governance Is the Foundation of AI Governance
AI governance in manufacturing should begin with the data supply chain that makes the model possible. The FDA/EMA good AI practice principles include data governance and documentation among the 10 principles, reinforcing that the reliability of AI-supported evidence depends on how data are generated, managed, documented, and controlled.1,2 For manufacturing applications, the quality of an AI output cannot be separated from the quality and context of the data used to produce it.
Source systems, metadata, access permissions, change control, retention practices, audit trails, and data transformations all shape whether an AI-enabled system can be trusted for its defined role. A model trained or operated on poorly contextualized data may produce outputs that appear precise but are difficult to defend. A model connected to well-governed data gives the organization a stronger basis for explaining what information was used, where it came from, how it was controlled, and why it was suitable for the manufacturing decision at hand.
ISPE’s AI governance discussion frames governance around policies and procedures, including data ownership, consent, and access.4 In a manufacturing setting, those issues become practical quality questions. Who owns the process data used to develop or operate the model? Who can access the data and the system? What procedures govern data extraction, transfer, transformation and use? How are changes to source systems or data pipelines evaluated for impact on model performance?
The link between machine learning, data integrity by design, and life cycle approaches also points to a more proactive posture.5 Rather than treating data governance as a documentation clean-up exercise after a system is built, manufacturers should define data controls early in the AI life cycle. The rationale for using particular data sets, the controls around those data, and the procedures for maintaining them should be established before the model becomes part of a regulated workflow.
For AI-assisted manufacturing, the model may attract the most attention, but the data environment will often determine whether the system can be trusted.
Risk-Based Validation Must Be Matched to the Use Case
Not every AI application in manufacturing carries the same level of risk, and validation should not treat all use cases as equivalent. The FDA/EMA good AI practice principles include both a risk-based approach and risk-based performance assessment, which supports a validation strategy scaled to potential impact.1,2 That distinction is especially important in manufacturing, where AI may be used for activities ranging from operational analytics to quality-relevant decision support.
A model used for non-GxP scheduling analytics does not require the same level of control as a model that supports batch disposition, process control, deviation triage, or quality investigations. If an AI-enabled system has limited impact on product quality, patient safety, or data integrity, the control strategy may focus on basic system performance, appropriate documentation, and clear boundaries around use. If the system influences decisions with direct or indirect quality consequences, the evidence needed to justify reliance on that system becomes more substantial.
The FDA’s AI regulatory decision-making guidance is specific to AI models used to support regulatory decision-making for drugs and biological products, but its emphasis on risk-based credibility assessment applies to AI-supported quality decisions more broadly.6 The relevant question is whether the model has been evaluated with enough rigor for the decision it is meant to support. That evaluation should consider the context in which the model operates, the consequences of an incorrect or unsupported output and the controls that prevent inappropriate reliance on the system.
The ISPE GAMP AI Guide is explicitly focused on AI-enabled computerized systems in GxP areas and on protecting patient safety, product quality, and data integrity.3 In practice, this means validation should account for both the technical behavior of the model and the regulated workflow around it. Human review, procedural controls, escalation pathways, and change control can all affect the level of risk associated with a given AI use case.
A risk-based approach also helps avoid two unhelpful extremes: treating low-impact AI tools as if they were batch-release systems or treating high-impact AI outputs as ordinary automation. The goal is proportionality. Manufacturers should define the intended use, assess potential impact, establish the evidence needed to support that use, and maintain controls that remain appropriate as the model, data, and process environment evolve.
What CDMOs and Sponsors Should Align on Early
AI-assisted manufacturing creates alignment needs that should be addressed before a system is implemented in an outsourced environment. For contract development and manufacturing organizations (CDMOs) and drug developers, the issue is not only whether an AI-enabled tool performs as expected but who owns, controls, validates, monitors, and documents that performance across the manufacturing relationship. The FDA Draft Chapter 4 places accountability for the integrity of documents, records, and raw data produced or processed with AI or other automatic means on the regulated user, while the ISPE GAMP AI Guide frames AI-enabled GxP computerized systems around the need to safeguard patient safety, product quality, and data integrity.3,7
That accountability becomes more complex when the data, system, model, and quality decision do not all sit within a single organization. A CDMO may operate the manufacturing process, maintain the system, manage the data environment, or use AI-enabled tools as part of process monitoring or deviation management. A drug developer may own the product, hold regulatory responsibility, require access to batch or quality records, or expect visibility into how AI-supported outputs influence decisions. Without early alignment, the parties may discover too late that they have different assumptions about data access, model ownership, review authority, or the documentation needed to support a quality event.
The FDA/EMA good AI practice principles emphasize clear context of use, documentation, data governance and life cycle management.1,2 In an outsourced manufacturing setting, those principles should translate into practical questions: What is the AI-enabled system intended to do? Who validates the system for that use? Who approves changes, re-training or new model versions? Who can access raw data, derived data, metadata and model outputs? How are exceptions reviewed, escalated and documented?
Quality agreements and project governance structures may need to reflect those answers, even when no source specifically dictates CDMO–client contract language for AI. If model outputs influence batch records, deviations, process adjustments, or investigations, the parties should define how those outputs are captured, reviewed, and retained. If a system is updated, they should agree on how the change is assessed and how the sponsor is notified when appropriate. If an output is overridden or accepted, the record should make clear who made that decision and on what basis.
Early alignment also helps prevent AI from becoming a hidden layer in the manufacturing process. Drug developers do not need to own every system used by a CDMO, and CDMOs do not need to disclose proprietary details beyond what is necessary for quality and regulatory assurance. Both parties, however, need enough shared understanding to show that AI-enabled tools are fit for their intended use, appropriately governed, and supported by records that can withstand review.
Building AI Control into the Manufacturing Life Cycle
For AI-enabled manufacturing systems, governance should not be added after implementation. It should be built into the life cycle from selection and development through validation, routine operation, monitoring, change control, and retirement. That lifecycle framing follows the same logic running through the FDA/EMA good AI practice principles, the ISPE GAMP AI Guide, and the emerging GMP-specific focus reflected in draft Annex 22: AI systems must be evaluated in context, controlled according to risk and supported by evidence throughout use.1–3,8
In practical terms, this means manufacturers should define the system’s role before deciding how to validate or govern it. A model used to flag an unusual trend may require different controls than one used to recommend a process adjustment or support a deviation assessment. The validation plan should reflect that difference, including the data required, performance expectations, review process, escalation triggers, and change-control requirements. The operating procedures should then make clear how outputs are used, who reviews them, what happens when outputs conflict with human judgment, and how exceptions are documented.
The same life cycle logic applies when the system changes. New data sources, software updates, model re-training, configuration changes, or process changes can all affect system performance. Manufacturers should define how such changes are assessed before the system is returned to use. They should also establish how ongoing performance will be monitored and what thresholds or signals will trigger review.
For CDMOs and their pharma clients, life cycle control also depends on communication. If an AI-enabled system supports a regulated manufacturing activity, both parties should understand how changes are governed and when they require notification, review, or approval. That does not require turning every technical adjustment into a sponsor-level decision, but it does require a shared understanding of which changes could affect product quality, data integrity, or the reliability of quality records.
From Model Performance to GMP Confidence
The promise of AI-assisted manufacturing lies in its ability to help organizations interpret complex data, detect emerging patterns, and support more responsive operations. In a GMP environment, however, model performance alone is not enough. The system must be governed in a way that supports patient safety, product quality, and data integrity, with controls proportionate to the risk and purpose of the application.
That means validation cannot stop at initial testing. Data governance, system documentation, performance monitoring, version control, change control, human oversight, and CDMO–client alignment all become part of the confidence-building framework around AI-enabled systems. The more directly a model influences manufacturing or quality decisions, the more important it becomes to show that the system remains fit for use and that its outputs can be reviewed, challenged, and documented.
AI governance in manufacturing is therefore less about adopting a single new technology framework than about extending familiar GMP disciplines to a more complex class of systems. The manufacturers and CDMOs best positioned to use AI will be those that can connect digital capability with quality accountability: defining the role of the system, controlling the data that feed it, validating performance for the decision at hand, and maintaining confidence throughout the life cycle.
