Artificial intelligence is rapidly reshaping the pharmaceutical landscape. What once seemed futuristic is now at our doorstep: clinical trial monitoring tools, digital QC assistants, generative models for documentation, predictive maintenance in manufacturing, and decision support in pharmacovigilance. Based on internal research of 174 GxP software vendors, almost half already advertise an AI offering. The flood is coming, and with it a fundamental challenge: how can we use models that may be inherently imperfect, probabilistic and opaque, yet still uphold the standards of quality and compliance that define our industry?
For many quality leaders, this feels like a contradiction. Regulators expect processes that are consistent, traceable, and well controlled. Machine learning models, by design, function through probabilistic reasoning based on data patterns. Probabilistic performance is not an anomaly but a feature of the system. In fact, if a model always produced the same outcome, it wouldn't be classified as machine learning at all. So how do we reconcile this contradiction?
In early conversations about GxP AI, it was common to hear simple slogans like “just trust the math” or “the model is smarter than us.” These sound reassuring but do little to help us validate models against real-world performance. Trust without verification has never been the basis of quality management. Trust is earned through demonstrated evidence, not granted blindly.
Instead, we need frameworks that allow us to measure what the model contributes, where it adds risk, and how to mitigate these risks in a controlled way. Fortunately, regulators are beginning to articulate such frameworks.
In its 2025 draft guidance Considerations for the Use of Artificial Intelligence To Support Regulatory Decision-Making for Drug and Biological Products, the FDA proposed a risk-based credibility assessment framework built on three pillars:
This comparative framing is key. A model does not need to be perfect. It needs to demonstrably improve the performance of the regulated process when paired with human oversight, while keeping risks acceptable and controlled.
The new ISPE GAMP® Artificial Intelligence Guide echoes this view. It stresses that AI-enabled computerized systems must still align with core GAMP 5 principles: product and process understanding, life cycle approach, scalable validation activities, science-based quality risk management, and supplier involvement.
Additional AI-specific expectations are layered on top: fit for purpose data, data and model governance, and knowledge management for AI literacy. Training data quality is critical, since biased or poorly curated data will directly shape model outputs. Governance provides standardized approaches for implementing, monitoring, and updating models so that decisions remain traceable and compliant. Finally, shared vocabularies and cross-functional knowledge management build AI literacy across Quality, IT, and Operations, reducing silos and enabling consistent oversight.
We can accept statistical imperfection as long as it is controlled, well-understood and explainable:
“Explainability is the degree to which a basis for a decision or action can be explained or how an output or result was reached, in a way that a person can understand”
Let’s ground this with an example. A vendor provides a model designed to assist in quality deviation categorization. The model is correct about 80% of the time. If viewed in isolation, that sounds like a failing grade. But compared to the human-only process, where deviation categorization consistency was closer to 65% across sites with heavy delays in turnaround, the human+AI process delivered better categorization, faster triage, and a measurable reduction in backlog.
The “imperfect” AI added value because it improved the system outcome without taking away human accountability. Importantly, the comparative evaluation gave management confidence that this wasn’t a gamble but a controlled, evidence-based improvement that is explainable.
So how should quality leaders think about this? A few principles stand out:
Quality in pharma has always been about evidence, not promises. The same applies to AI. The models may be probabilistic, but our approach to adopting them must remain structured, transparent, and documented. If we can prove and explain how that a human+AI process outperforms a human-only process, then imperfection is not seen as a deal-breaker, it is progress.
The focus for GxP AI in pharma is whether the human+AI process delivers better outcomes for patient safety, product quality, and data integrity while remaining compliant with GxP expectations. If it does, then even an imperfect model can be justified, documented, and implemented.
When it is recognized that our current baseline is already imperfect, with structured evaluation, AI can move that baseline forward.
A1: AI is increasingly embedded in pharmaceutical operations, including clinical trial monitoring, digital quality control assistants, generative documentation tools, predictive maintenance, and pharmacovigilance decision support. Internal research shows that nearly half of 174 GxP software vendors already advertise AI capabilities, signaling a rapid transformation.
A2: Yes, AI can be GxP-compliant if used within structured, risk-based frameworks. Regulatory bodies like the FDA emphasize that models do not need to be perfect. Instead, their performance should be evaluated in the context of use, with risks understood and mitigated. A human+AI process that improves outcomes while maintaining control, traceability, and compliance is acceptable; even if the model itself is not flawless.
A3: The FDA’s 2025 draft guidance outlines a three-part framework:
A4: The ISPE GAMP® Artificial Intelligence Guide reinforces that AI systems must align with GAMP 5 principles such as scalable validation, science-based risk management, and supplier oversight. It adds AI-specific guidance around:
A5: Explainability means being able to clearly describe how an AI-driven decision was made. This is essential for GxP compliance and trust. Imperfect models are acceptable if their impact is well-understood and beneficial. For example, a model improving deviation categorization from 65% to 80% accuracy, with faster turnaround, demonstrates explainable, measurable value that enhances rather than compromises quality oversight.
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