Originally published in September 2024. Updated May 2026 to reflect current discussion around AI, validation, traceability and regulated proofing.
AI is creating new opportunities across regulated industries, from faster workflows to smarter ways of managing complex information. But in areas such as packaging, labeling, artwork and printed materials, speed is only valuable when the result can still be verified.
For regulated proofing, the future is not about choosing between AI and human expertise. It is about using AI where it adds value, keeping verification where it matters and giving teams the evidence they need to trust every decision.
AI is changing the way regulated industries think about productivity, compliance and quality. For pharmaceutical, life sciences, cosmetics, and FMCG teams, the potential is clear. AI can help reduce repetitive work, manage growing volumes of information and support faster decision-making.
But regulated work has a higher bar than speed alone.
In areas such as packaging, labeling, artwork, medical information, IFUs and printed material, teams need more than automation. They need to know that approved content has been transferred correctly, that changes are visible, that reviews are documented and that the final result can be trusted.
That balance is increasingly reflected in the wider regulatory conversation. The European Medicines Agency's reflection paper on the use of AI in the medicinal product lifecycle recognizes the potential of AI and machine learning across development, authorization and post-authorization activities, while also focusing on their safe and effective use in medicines. The EU AI Act, which entered into force on 1 August 2024. also follows a risk-based approach to responsible AI development and deployment.
For regulated proofing, this raises an important question: Where does AI add value and where do we still need direct verification?
Much of the current AI conversation focuses on creating new content: drafting, summarizing, translating, classifying or suggesting answers. But regulated proofing has a different purpose. Proofing is about checking approved material against a final output. That output may be artwork, a patient leaflet, a QR code, a printed sample or another controlled asset.
So instead, the task is not to produce a likely answer, but to verify whether something is correct. That means proofing depends on a clear source of truth. This may be approved text, XML, a reference PDF, a signed-off artwork file or an approved digital version. The important thing is that the reviewer needs to see what has changed, where it has changed and whether the difference is acceptable. In proofing, "probably correct" is not enough.
AI can play a valuable role in regulated workflows. It can help teams manage complexity, reduce repetitive workflows and work more efficiently. In proofing, AI-assisted capabilities may support areas such as OCR, content structuring, pattern recognition or review preparation. In other words, AI can help reviewers focus their attention where it matters most if used well.
It can also improve the user experience by making complex information easier to navigate. This is important since regulated teams are under pressure, and they need to review more material, across more formats, languages, markets and channels, often with limited time and high compliance expectations.
AI can certainly help, but it should support the process without weakening the evidence behind the final decision.
AI is powerful, but not automatically suitable for every regulated task. Some AI systems are probabilistic, meaning that they can produce outputs that are useful but difficult to explain, reproduce or validate in the same way as rule-based comparison. In regulated proofing process, this creates important questions.
These questions are not barriers to innovation, but rather what allow innovation to be trusted.
The FDA's overview of Good Machine Learning Practice for Medical Device Development highlights that AI and machine learning can present unique considerations because of their complexity and iterative, data-driven nature. ISPE's GAMP guidance on artificial intelligence also focuses on the effective use of AI-enabled computerized systems in GxP areas, while safeguarding patient safety, product quality and data integrity.
All in all, the message is clear: AI can be useful, but it must be controlled.
In regulated industries, confidence comes from evidence. A reviewer should be able to understand what happened during the proofing process. Which file was used? Which version was compared? Which differences were detected and which were accepted? Can the outcome be documented?
This is especially important in packaging and labeling, where small differences can have significant consequences.
AI may help teams work faster, but evidence helps teams work with confidence. That is why proofing tools need to show clear differences, support traceable review and help users make informed decisions.
Many AI conversations use the phrase "a human in the loop". In regulated proofing, that phrase is only useful if the human reviewer has clear, usable evidence. A reviewer should not be asked to approve a vague AI conclusion, they need to see actual differences. They need to understand the source material and have access to the review context. They need a process that supports documentation and accountability.
Human oversight should not be an afterthought, but rather designed into the workflow. The system should make reviews easier, clearer and more reliable.
The strongest approach to AI in regulated proofing is not to ask "can AI be used here?", but rather "is AI the right tool for this specific risk?". Some tasks benefit from intelligence, interpretation and assistance, whereas other require exact comparison.
For example, AI may help structure information, support OCR workflows, flag unusual patterns or make complex review processes easier to manage. But approved text comparison, barcode verification, QR code checks and Braille verification still need clear, reviewable evidence.
The most reliable proofing strategy is not A or B, A being AI-only and B human only. It is a controlled combination of automation, exact comparison and expert review.
At InformaIT, we believe AI will be part of the future of regulated proofreading, but we also believe that proofing has a specific responsibility. It must help teams verify that critical materials are correct, compliant and ready for release. That requires a fit-for-purpose approach.
AI can support workflows where it adds value. Exact comparison remains essential where direct verification is required. Human expertise remains critical where judgement, context and risk assessment matter. So, InformaIT's goal is not AI for the sake of AI. The goal is safer, clearer and more trustworthy proofreading.
AI will continue to develop. It will become more capable, more integrated and more expected in business software, which is a positive development. But in regulated industries, technology must earn its place through evidence, validation and trust.
For proofing, the strongest future is not blind automation. It is a controlled combination of intelligent assistance, exact verification, human review and traceable evidence. Because the final question is not "Did the software sound intelligent?", but rather "Can we trust the result, and can we prove why?".
That is where AI must earn its place. And that is where proofing still matters.