EU GMP Annex 22 and FDA AI guidance for pharma AI 2026

Updated February 2026 – Leading Minds Network, a leading global peer-to-peer community of pharma, life science, healthcare and logistics industry professionals.

Key Facts

• AI is transforming pharmaceutical and life science manufacturing, offering GxP-compliant opportunities from process optimization to predictive maintenance. Regulators in the EU and U.S. are carefully defining how AI can be used in GMP (Good Manufacturing Practice) environments to ensure product quality, data integrity, and patient safety.

• The EU’s draft Annex 22 provides detailed guidance on acceptable AI applications, emphasizing static, deterministic models and human oversight for non-critical uses.

• In the U.S., the FDA’s discussion paper explores AI integration within existing cGMP frameworks, suggesting a risk-based credibility assessment for model validation.

It appears frameworks aim to balance innovation with compliance, particularly in temperature-controlled and highly regulated manufacturing settings. 

What is EU GMP Annex 22 and the FDA’s AI Guidance? 

Overview of Draft Annex 22 for AI in Pharma Manufacturing 

The European Commission’s draft Annex 22, published in mid-2025, supplements existing GMP rules for computerized systems and documentation. It specifically addresses AI use in pharmaceutical manufacturing, highlighting that static, deterministic models can be used in critical processes, while dynamic, continuously learning models and generative AI are restricted to non-critical applications with documented human oversight. Regulators stress robust documentation, validation, and risk management to ensure consistent outputs and compliance. 

FDA Discussion Paper: How AI Fits Within cGMP Regulations

The FDA’s discussion paper on AI in drug manufacturing outlines how AI might fit within existing cGMP regulations. While not formal guidance, it highlights opportunities for AI in process optimization, predictive maintenance, and quality trend monitoring. The FDA emphasizes a risk-based approach to evaluate AI models, particularly regarding their impact on product quality and regulatory compliance. Early engagement with regulators is encouraged to align expectations.

What are Regulatory Expectations for AI in Pharmaceutical Manufacturing? 

EU Draft Annex 22: Validation, Documentation, and Human Oversight

EU regulators expect manufacturers to clearly define the AI model’s intended use, scope, and limitations. Validation using independent test data, documented acceptance criteria, and human-in-the-loop controls for non-critical tasks are essential. Risk management should be applied to ensure patient safety, product quality, and data integrity. 

“Beyond being static and deterministic, AI models must be explainable and manageable in complexity to ensure compliance and operational clarity.”

 

 Philipp Triet,

Chief Technology Officer at ELPRO-BUCHS AG 

FDA Risk-Based Approach and Lifecycle Monitoring

The FDA encourages defining the context of use and applying a risk-based credibility assessment. Models must be monitored throughout their lifecycle to detect performance drift, and processes must follow change control and quality system principles similar to traditional GMP requirements.

What’s Working, Risky, and Under Debate with AI in Pharma? 

Successful AI Applications in Process Optimization and Quality Control

AI has proven useful in quality control analytics, process optimization, and predictive maintenance. Regulators see benefits in using AI to enhance monitoring and early detection of deviations, improving overall manufacturing reliability. 

Risks of Dynamic and Generative AI in GMP Environments

Dynamic AI models and generative AI carry significant risks because outputs can vary unpredictably, challenging GMP compliance and validation. Data integrity and traceability in cloud or multi-vendor systems also remain concerns.

Regulatory Debates on Adaptive AI in Critical Applications

In the EU, some industry groups advocate controlled use of advanced AI models in critical applications if supported by strong validation and monitoring. In the U.S., the FDA continues to consider how adaptive AI should be regulated under cGMP rules.

What Are Some Practical Guidelines for Manufacturers Using AI?

Simple Checklist for AI in Pharma Manufacturing

1. Document purpose, scope, inputs, and outputs.
2. Use static, deterministic models for critical processes.
3. Validate with independent test data and define acceptance criteria.
4. Monitor models post-deployment for drift and maintain change control.
5. Ensure human oversight where AI influences product quality or safety.

Early Use Cases for AI in Temperature-Controlled and Critical Processes

• Predictive maintenance for equipment and sensors.
• Trend detection for deviations or CAPA effectiveness.

Quality control analytics, such as defect classification using computer vision. 

Planning for Risks: Model Drift, Data Integrity, and Regulatory Uncertainty

Manufacturers should prepare for model drift, potential data integrity gaps, and regulatory uncertainty if AI outputs change without clear records.

What Are the AI Implications for Temperature-Controlled Facilities and Equipment?

AI in Environmental Monitoring and Cold Chain Management

Highly regulated environments, such as biologics and cold chain operations, must integrate AI carefully:

• AI supporting environmental monitoring should be deterministic and validated with clear criteria.
• Temperature control models require defined performance limits and re-validation plans.
• Data from sensors and systems must be traceable and integrated into quality management systems.

Human oversight remains crucial to maintain compliance and patient safety. 

 

“The future of cold chain monitoring is intelligent, data-driven automation. AI combined with continuous temperature monitoring can enable process automation and supply chain optimization that directly reduces total cost of ownership. At the same time, a strong and uncompromising focus on data integrity and regulatory compliance is irreplaceable to guarantee patient safety.”

 

 Patrik Senn,

 Head of Product Development, ELPRO-BUCHS AG 

What are the Common Questions About AI Compliance in Pharma Manufacturing? 

Is Annex 22 legally binding?

As of this writing, no. It is still a draft under consultation. Regulators are actively gathering feedback from the pharmaceutical industry, technology providers, and other stakeholders.

Does the FDA have formal AI regulations yet?

Not yet — only a discussion paper and draft guidance exist. The FDA has acknowledged the rapid evolution of artificial intelligence technologies in drug manufacturing, but has not issued formal, enforceable regulations specific to AI at this stage. Instead, it encourages early engagement and dialogue between manufacturers and regulators to clarify expectations and address emerging risks.

Can generative AI be used in GMP environments?

Generative AI can be integrated only into non-critical functions, provided there is clear, documented human oversight at each decision point to ensure regulatory compliance and data integrity.

What AI models are acceptable for critical applications?

Static, deterministic models that consistently produce the same output.

How should post-deployment monitoring be handled?

Implement monitoring and change control processes to detect performance drift and re-validate models as needed.

What Are Key AI Takeaways for Pharma Leaders?

• EU Annex 22 focuses on safe AI use in pharma manufacturing, allowing static models in critical processes and human oversight for non-critical applications.
• The FDA promotes a risk-based approach under existing cGMP, emphasizing lifecycle monitoring and model credibility.
• Both regulators prioritize safety, quality, and data integrity over rapid adoption of AI.
• Temperature-controlled facilities must integrate AI validation, monitoring, and documentation into GMP quality systems.

NOTE: The content of this article should not be used or construed as legal or regulatory guidance or advice in any manner. Consult with your legal and regulatory authorities in any and all matters relating to the use of AI. 

Sources (Integrated)

• European Commission Draft GMP Annex 22 (2025)
• FDA Discussion Paper: Artificial Intelligence in Drug Manufacturing (2023)
• FDA Draft Guidance: Considerations for the Use of AI in Regulatory Decision Making (2025)
• Industry analysis and commentary: PharmaGMP, GMP Compliance, Mondaq, PharmTech