Pharma regulatory and quality teams are being asked to figure out AI policy at the same time regulators are still writing theirs. That's not a comfortable position, but it's also not as open-ended as it feels — the emerging guidance is converging on a distinction that's more useful than most of the AI discourse: not "AI or no AI," but which kind of AI, doing what, with how much human oversight.
The regulatory picture moved quickly through 2025 and into 2026. FDA published a draft guidance in January 2025, "Considerations for the Use of Artificial Intelligence to Support Regulatory Decision Making for Drug and Biological Products," followed by jointly issued "Guiding Principles of Good AI Practice in Drug Development" with EMA in January 2026. The European Commission's draft Annex 22 to the EU GMP guidelines, published in mid-2025, addresses AI use in pharmaceutical manufacturing directly.
None of this is settled law yet — much of it remains draft guidance, and the frameworks will keep evolving. But a consistent theme runs through all of it: human oversight, auditability, and — most usefully for anyone trying to build an actual policy today — a distinction between different categories of AI model.
The EU's draft Annex 22 draws a specific line: static, deterministic models can be used in critical GMP processes, while dynamic, continuously learning models and generative AI are restricted to non-critical applications with documented human oversight. That distinction is worth sitting with, because it reframes the question pharma teams have been asking.
A deterministic system — one where the same input reliably produces the same output, every time, and where the logic behind that output can be inspected and explained — is a fundamentally different regulatory object than a generative system, where the same prompt can produce different outputs on different runs, and where explaining exactly why the model produced a specific result is often not fully possible. For a GxP process where a specific, reproducible, defensible outcome is the entire point — a batch release decision, a label verification, a data integrity check — that difference isn't a technical footnote. It's the difference between a tool that can sit inside a validated process and one that, under current regulatory thinking, largely can't, at least not without significant additional controls.
This matters because "AI" has become a single word doing a lot of different work. A machine learning model that decodes a barcode and grades it against ISO standards is doing something categorically different from a generative model drafting promotional copy or summarizing a clinical dataset. Both get called "AI." Only one of them behaves the way a GxP process requires.
The temptation for any software vendor right now is to describe everything as "AI-powered," because the term signals sophistication. For tools that support GxP decisions — including artwork and label verification — that instinct works against the exact distinction regulators are drawing. A comparison tool that decodes a barcode, extracts text, or flags a symbol mismatch using pattern recognition or trained classification is operating deterministically: the same document produces the same result, every time, and that result is traceable back to a specific rule or model behavior that can be documented and validated.
A tool that uses a generative model to produce or "improve" content — rewriting a label description, generating a summary of what changed, inferring intent behind a discrepancy — is doing something different, and under emerging guidance, that difference has real validation and oversight implications. Neither use case is inherently wrong. But conflating them, or marketing a compliance tool's generative capabilities without being precise about which parts of the system are deterministic and which aren't, creates exactly the kind of ambiguity a notified body or FDA inspector is now specifically trained to probe.
The organizations that will navigate this cleanly aren't the ones avoiding AI or the ones adopting it indiscriminately — they're the ones who can clearly state, for any AI-adjacent capability in their quality systems, which category it falls into, what oversight applies to it, and why. That's a documentation and governance exercise as much as a technology one, and it's achievable now, ahead of final guidance, by applying the same rigor pharma teams already apply to any other GxP-relevant system: know what the tool does, know how it produces its output, and be able to explain that to an inspector without hedging.
↗ Content Compare's comparison engine is deterministic by design — the same document produces the same verified result, every time, with a documented, traceable audit trail. Compare that against how other artwork verification tools describe their AI capabilities, or request a demo to see how it fits a validated GxP process.