AI can produce a patient leaflet draft in under a minute. It can suggest claims, adapt tone for different audiences, translate across markets, and summarise reference papers. Some of this output is genuinely useful as a starting point. None of it is approved.
That distinction is becoming the most expensive misunderstanding in pharma marketing. A recent industry survey found that 65% of U.S. pharmaceutical marketing and promotional review professionals do not trust AI for creating regulatory compliance submissions, with hallucinations, lack of traceability, and lack of transparency cited as the top three concerns. The discomfort is not technophobia. It is a accurate read of where accountability sits in a regulated workflow.
Approval is not a writing task. It is a regulated process with named accountable persons, documented audit trails, and source verification against the SmPC and country-specific licences. The question is not whether AI belongs in pharma content production. It clearly does. The question is where the compliance lines sit, and who signs at the end.
AI works well as an accelerator inside a controlled workflow. It works badly as a shortcut around one. The difference is whether every output stays traceable to a named human reviewer.
Useful applications in a compliant setup include:
Positioning AI as a pre-submission quality checker, not a content originator, is the cleanest way to capture speed gains without shifting accountability. The Promedia view on the MLR review cycle is that most cycles fail because of what happens before submission, not during it. Pre-marking sources, co-creating with medical and regulatory at the brief stage, and validating claims against the SmPC are all activities AI can support but cannot complete.
An LLM cannot be a signatory. It cannot certify that a claim is on-label. It cannot be held accountable when a regulator asks who approved a piece of promotional content for a specific market.
Look at what real MLR review looks like in practice. Comments on a live pharma asset typically include named reviewers flagging on-label accuracy ("the actual clinical data do not really support this statement"), country-specific regulatory constraints ("in France, documents can only be used for a maximum period of 24 months"), and jurisdiction-specific approval status ("licenses may vary by country, please always refer to the Product Information in your country before prescribing"). Each comment is attributed to a named human. Each represents a judgment AI cannot make, because the judgment is not about language. It is about regulatory standing.
Regulators reinforce this directly. Companies are held accountable for AI-generated content associated with their products, even when that content was produced by third-party AI systems. The brand carries the compliance liability, not the model vendor. Add to that the financial exposure: a 2025 cross-industry survey reported that 44% of organisations experienced negative consequences from generative AI use, with average losses of $4.4 million per incident. In pharma, where a hallucinated claim can propagate into a regulated material, that risk profile is unacceptable without human-in-the-loop oversight.
A workable AI workflow in regulated pharma communications has four anchor points.
First, scope AI use deliberately. Define which content types AI may touch (internal drafts, brand-compliance checks, reference summaries) and which it may not (final claims, safety information, promotional copy without human rewriting). Promotional and non-promotional materials follow different rules and different review paths, and the AI scope should reflect that.
Second, log every input and output. If AI generated a first draft, the prompt, the source documents fed into it, and the raw output should sit in the document management system alongside the human-edited version. Auditability is not optional.
Third, keep named accountability intact at every stage. Medical writers, regulatory affairs professionals, and MLR reviewers retain full responsibility for validating AI output against original clinical and scientific evidence. The workflow exists to make that validation faster, not to remove it.
Fourth, separate the agency capability question from the tool question. A generalist team uses AI to move faster and assumes MLR will catch the rest. A pharma-specialist team uses AI inside a workflow where every output is traceable, every source is logged, and every claim is validated by a reviewer with a name and a signature. The tool is the same. The discipline is not.
The concrete risks fall into three categories.
Hallucinated claims are the most visible. An LLM can generate a confident sentence that misstates a clinical endpoint, overstates efficacy, or invents a citation. Forty percent of pharma marketing professionals cite hallucinations as their top AI concern, and the reason is straightforward: a fluent error is harder to catch than an obvious one.
Lost traceability is the second. If AI suggested a phrasing and the writer accepted it without logging the source, the MLR reviewer has nothing to verify against. Twenty percent of surveyed professionals named lack of audit trail as their leading concern.
Regulatory liability is the third, and the most consequential. The FDA and EMA released joint guiding principles on AI in drug development in early 2026, and the FDA has been explicit about AI limitations including hallucinations and data drift. Regulators expect human accountability. They will not accept "the AI generated it" as a defence when a non-compliant claim reaches market.
No. Approval is a regulated process that requires a named accountable person with the authority to sign off on medical, legal, and regulatory aspects of a piece of content. An AI system cannot be a signatory, cannot be audited as an individual, and cannot be held legally accountable. AI can support the drafting and pre-submission checking phases, but the approval decision belongs to a human reviewer.
The marketing authorisation holder is accountable, regardless of who or what produced the draft. Regulators have made clear that companies bear liability for AI-generated content associated with their products, even when third-party AI systems created it. Internally, the named MLR reviewers who sign off carry direct accountability for the content as published.
AI is best positioned as a pre-submission accelerator, not a participant in the review itself. It can check brand and design system compliance, flag missing references, and surface inconsistencies before the asset reaches MLR. This reduces the volume of mechanical corrections reviewers have to make, freeing their attention for the judgment calls that matter: on-label accuracy, source validity, and jurisdiction-specific constraints.
By validating every claim against the approved SmPC and product information for each market, through a human reviewer with the regulatory expertise to make that judgment. AI-generated text should be treated as a draft to be checked, never as a source of compliant phrasing. Pre-marking sources inside the document before MLR submission, a practice that predates AI, becomes even more important when AI is involved in the drafting.
AI will change how pharma content is produced. It will not change who is accountable for it. The agencies and in-house teams that get this right are the ones building AI into workflows where speed and traceability coexist, not the ones treating AI as a way to bypass the parts of MLR they find slow.
If you are designing an AI-assisted content workflow and want a second view on where the compliance lines should sit, the Promedia team works with pharma brands on exactly this question.
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