Executive Summary
Legal teams at Pharmaceuticals firms in international jurisdictions rely on the 2025 OECD Merger Review Recommendation as a baseline reference when scoping remedy strategy, briefing management, and aligning cross-border filings ahead of major deals. Pharmaceutical M&A regularly triggers multi-jurisdictional merger notifications, and the Recommendation's remedy hierarchy, particularly its internal priority ordering within structural remedies, is a live input to deal structuring, negotiation with competition authorities, and board-level risk assessment. Across the question set tested, AI assistants produced a hallucination on the single most operationally consequential point: the priority ordering within structural remedies under Section IV.3.
The failure pattern was a misattribution, the AI confidently cited the Recommendation's text as support for a three-tier sub-hierarchy (fix-it-first, upfront-buyer, crown-jewel) that does not appear anywhere in the Recommendation and is in fact drawn from EU merger-control practice. For a Legal team drafting internal remedy playbooks or briefing deal teams against this text, that substitution produces a wrong deliverable with direct downstream exposure at the negotiating table.
How AI gets this regulation wrong
The dominant failure mode on this Recommendation is misattribution: AI assistants cited the Recommendation as authority for a detailed sub-hierarchy that is not in the text, and that is drawn from a different jurisdiction's enforcement practice entirely. The AI's answer was internally coherent and referenced real concepts, which is precisely what makes it dangerous, the error is not detectable without consulting the primary source directly.
| AI's Failure Mode | Count | Affected findings |
|---|---|---|
| Misattributed | 1 | Finding#1 |
What that means for your team
For Legal teams at Pharmaceuticals firms, the material risk from this failure lands squarely on deliverable quality: internal remedy strategy memos, authority briefings, and deal-team guidance built on the AI's misattributed hierarchy encode an EU-derived framework as if it were OECD text. The table below maps that risk to the specific outputs and processes where it surfaces.
| Risk Impact | Count | Affected findings |
|---|---|---|
| Wrong deliverable | 1 | Finding#1 |
When this affects your department
Pharmaceutical M&A generates merger filings across multiple jurisdictions simultaneously, and the OECD Recommendation functions as a common reference point when Legal is mapping remedy posture, particularly in deals that span OECD member states without a dominant single regulator. When a deal team is assessing whether a proposed divestiture package will satisfy review standards in a portfolio of jurisdictions, Legal typically produces internal strategy memos, board papers, and negotiating frameworks that reference the Recommendation's remedy hierarchy directly. AI assistants are often pulled into that workflow to accelerate first-draft summaries of the operative text, especially under timeline pressure in deal execution.
The specific failure documented here, importing the EU's fix-it-first / upfront-buyer / crown-jewel sub-hierarchy as if it were OECD text, is consequential in that context. If a junior lawyer uses the AI output to draft internal guidance on the priority ordering within structural remedies, the resulting document presents EU merger-control preferences as a multi-lateral standard. That error can propagate into board papers, external counsel briefings, and negotiating positions without surfacing until the firm is already engaged with a competition authority that operates under a different framework.
The Recommendation's actual text on this point is precise and narrow: structural remedies are preferred over behavioural ones, and within structural remedies, standalone-business divestitures are prioritised. There is no timing-based sub-tier. A firm that has briefed its deal team on a three-tier hierarchy derived from EU practice may find itself defending a remedy package structured around the wrong analytical framework, or misreading an authority's response because the team's internal model of "standard" structural remedy ordering does not match the authority's own benchmark.
The findings at a glance
The table below summarises each finding tested against this Recommendation for Legal teams at Pharmaceuticals firms, including the AI failure mode and the risk impact category.
| # | Finding title | Type | Citation ID |
|---|---|---|---|
| 1 | Section IV.3 structural remedy priority order misattributed to EU practice | Hallucination | RLB-F-INT-OECD-OECD-MERGER-REVIEW-RECOMMENDATION-2025-Q002 |
Aggregate impact
The single finding in this cell targets the structural remedy priority question under Section IV.3, one of the most operationally loaded provisions in the Recommendation for any firm anticipating a remedy discussion with competition authorities. The failure is not a gap or an omission; it is a confident substitution of a different jurisdiction's enforcement practice into the body of OECD text, dressed as a citation. That pattern is harder to catch than a straightforward factual error because the AI's answer is internally consistent, references real merger-control concepts, and uses authoritative-sounding language throughout.
For Legal teams at Pharmaceuticals firms operating across OECD jurisdictions, the systemic risk is that the Recommendation gets treated as a floor standard across multi-jurisdictional filings, meaning errors in how its text is characterised get replicated across the full filing portfolio. A remedy strategy memo that misrepresents Section IV.3's internal ordering as a three-tier EU-style hierarchy does not affect one filing; it shapes the firm's negotiating posture in every jurisdiction where that memo serves as the internal reference.
The exposure is not regulatory penalty directly, but rather a mispriced deal risk and a remediation cost when the firm has to re-scope its divestiture package mid-negotiation.
There is also an internal credibility risk. Legal teams at Pharmaceuticals firms often brief competition counsel, investment banking advisers, and board-level deal committees using the same internal summary materials. If an AI-assisted summary mischaracterises the applicable standard, and that characterisation survives into external engagements, the error becomes visible to counterparties and authorities who do have the primary text, at the worst possible moment in a transaction timeline.
What your team should do
The default position for any AI-assisted work on this Recommendation should be primary-source verification before any output leaves Legal. The structural remedy hierarchy question demonstrates exactly why: the AI produced a plausible, well-structured answer that imported EU practice into OECD text without flagging the substitution. That failure mode, confident misattribution rather than acknowledged uncertainty, means the usual signal of hedged language is absent. Teams cannot rely on the AI's tone to identify when it has gone wrong here.
For the Section IV.3 priority question specifically, the verification step is straightforward: the Recommendation's text is short and publicly accessible. Any summary or guidance document that characterises the internal ordering within structural remedies should be checked against the operative paragraph before it is circulated. That check should be a standing requirement for junior output on remedy strategy, not an optional quality step.
Where AI tools are used to accelerate first-draft summaries of the Recommendation, those drafts should be scoped explicitly to paraphrase only, not to characterise sub-hierarchies or internal priority rules that require the reader to have already read the provision correctly.
AI tools are useful on this Recommendation for lower-stakes tasks: formatting comparison tables across jurisdictions, summarising procedural timelines, or drafting index structures for internal guidance documents. The risk is concentrated in any question that asks the AI to characterise priority rules, internal orderings, or the specific text of operative provisions, where the misattribution pattern is most likely to produce a wrong deliverable that reads as correct.
How RLB Can Help
RegLeg's published Hallucination Research gives the Legal team at a Pharmaceuticals firm a practical pre-flight check before placing reliance on AI-assisted regulatory analysis. Because the research is organised by regulator and regulatory instrument, Legal counsel can look up the specific rules their team works with most, pharmacovigilance reporting obligations, marketing authorisation conditions, GMP requirements, cross-border distribution frameworks, and see precisely where AI tools have been shown to misstate statutory text, confuse version histories, or fabricate citation details.
Using that record as a standing reference costs nothing and takes minutes; it converts a vague concern about AI reliability into a concrete checklist against known failure modes.
Beyond the published research, RegLeg works with Pharmaceuticals Legal teams on bespoke regulator deep-dives that map which AI-supported workflows in the function carry the highest hallucination exposure. Regulatory affairs correspondence, internal compliance opinions, licence variation submissions, and pharmacovigilance signal assessments each carry different risk profiles, and the relevant regulators, EMA, FDA, MHRA, TGA and others, are not equally represented in AI training data. A structured deep-dive surfaces where the gap between an AI tool's apparent confidence and its actual accuracy is widest, allowing the team to concentrate human review where it matters most.
For firms that have already adopted AI use policies, RegLeg offers a confidential review of those policies against its failure-mode catalogue, returning a prioritised remediation plan that is practical to implement rather than aspirational in scope. Where the Legal team needs to build internal capability, whether for onboarding new staff, satisfying CPD requirements, or briefing business partners on appropriate use of AI tools, RegLeg can supply training material and CPD-aligned content tailored to the Pharmaceuticals regulatory environment and the specific audience within the Legal function.