The 90-day pharma GEO mobilization plan.

CMI Media Group data shows AI Overviews now appear on 52% of pharma keywords, and when they do, click-through rates collapse from 25.8% to 7.4%. At the same time, IQVIA research finds 54% of HCPs use generative AI in clinical contexts. The window to shape your brand's AI representation is open. It will not stay open. The brands that complete this 90-day sequence will set the default answers in their category; the ones that wait will spend years correcting answers that have become entrenched.

This plan is structured in three phases of four weeks each: Diagnose, Repair, Scale. Each week has concrete deliverables, named owners, and measurable outputs. The KPI table at the end of each phase shows what you should expect to move.

Why 90 days is the right planning horizon

A 90-day horizon matches the pace at which AI citation graphs shift. Digital Bloom's 2025 analysis found 59.3% monthly volatility in the AI citation graph — meaning a brand's citation footprint can change materially in a single quarter. That same research found only 11% overlap between domains cited by ChatGPT and Perplexity, which means engine-specific work compounds quickly once started. Waiting for perfect governance alignment before acting means allowing that volatility to work against you.

Three additional realities make the 90-day frame urgent. First, while Codeless.io data confirms Google still receives roughly 373 times the search volume of ChatGPT — this is not today's traffic crisis, it is infrastructure positioning for the next three to five years as AI interfaces become primary. Second, the AI citation layer is already a safety surface: in metabolic therapy areas, IQVIA's March 2026 report documents FDA labels ranking as the top cited source for GLP-1 queries, meaning AI answers include boxed warnings and contraindications whether brands are ready or not. Third, the PharmaGEO public index (May 2026) shows engine-level gaps of 33 percentage points on a single brand across two engines — a gap that only closes through deliberate, structured intervention.

Phase 1 (Weeks 1–4): Diagnose

What this phase produces

A baselined, scored picture of where your brand stands across six axes, six engines, and however many language markets apply to your approval geography. Without this baseline, every repair effort in Phase 2 is directionally uncertain. With it, you know exactly which axes are dragging your composite score and which engines to prioritize first.

Week 1 — Build the prompt set and engine matrix

Owner: Digital / Medical Affairs

Construct an 80-prompt canonical set for each therapy area in scope. The set should cover condition-level queries ("What is the first-line treatment for moderate-to-severe atopic dermatitis?"), brand-level queries ("How does [brand] work?"), safety-sensitive queries ("What are the risks of [brand] in pregnancy?"), HCP-framed queries ("What does the AAD guideline recommend for biologic-refractory psoriasis?"), and competitor comparison queries. The 80-prompt count is not arbitrary: it is the minimum needed to capture variance across query intent, clinical framing, and demographic framing.

Simultaneously, configure your engine matrix. At minimum: OpenAI (GPT-4o), Perplexity, and Gemini. Ideally extend to Claude and Grok, and add Mistral for EU-language markets. The May 2026 PharmaGEO public index demonstrates why engine breadth matters: Adbry (lebrikizumab branded for the US market) holds an Answer Rate of 41.4% on OpenAI and only 8.2% on Perplexity — a 33-point gap on the same brand, same therapy area, same week. There is no single "AI visibility" score. There are at least six.

Week 2 — Run baseline measurement and build the language matrix

Owner: Digital (run), Medical Affairs (review)

Execute the 80-prompt set across the engine matrix. For each prompt-engine pairing, record: Answer Rate (was the brand mentioned at all?), Share of Voice (what fraction of brand-token surface area did your brand hold?), sentiment classification, source domains cited, and which competitor brands appeared.

Extend the matrix across languages relevant to your market geography. The Adtralza (tralokinumab, EU brand) pattern from the May 2026 PharmaGEO public index is the clearest evidence for why this step cannot be skipped. In English on OpenAI, Adtralza holds a 13.8% Answer Rate. In French, that figure rises to 48.8%. In Spanish, 51.9%. The mechanism: tralokinumab was approved and marketed earlier in the EU, so the non-English internet carries more European clinical content, and LLMs reflect that asymmetry precisely. A brand spending its entire GEO budget on English content is invisible to a structurally different competitive landscape in its own approved markets. Global brands with FDA plus EMA approvals — Dupixent, Rinvoq — show within-2.3-percentage-point language stability. Mid-tier brands with regional approval profiles show swings of 35 to 38 points. That asymmetry is your language matrix.

Week 3 — Apply the 6-axis scoring model

Owner: Digital (score), Medical Affairs + Regulatory (review)

Score every prompt-engine-language combination across all six axes:

• Visibility — Answer Rate and Share of Voice, disaggregated.
• Accuracy — Are the indication, mechanism, dosing, and approved population stated correctly? Flag hallucinations and omissions.
• Sentiment — Is the framing neutral, positive, or cautionary toward your brand versus competitors?
• Source quality — What tier of sources is the engine citing? Society guidelines, regulatory primaries, peer-reviewed literature, or lower-authority domains?
• Citation diversity — How many distinct authoritative sources is the engine drawing from? Concentration on a single domain is a fragility signal.
• Competitive share — What fraction of the answer surface does your brand hold versus named competitors?

The source quality axis is particularly revealing. In the May 2026 PharmaGEO public index, Perplexity's lung cancer answers drew 218 of roughly 258 total citation uses from nccn.org — a near-monopoly by a single guideline body. Atopic dermatitis answers on the same engine drew from a three-way mix: aad.org (168 uses), pmc.ncbi.nlm.nih.gov (94 uses), aafp.org (92 uses). There is no universal source playbook; the playbook is TA-specific. Scoring source quality reveals which authoritative domains you are competing against and which you are absent from.

Week 4 — Map repair priorities and brief Phase 2 owners

Owner: Brand Lead, Medical Affairs lead

Rank the six axes by gap severity. Produce a written brief for Phase 2 that specifies: the three highest-priority repair actions, the content types required, the governance path through MLR/PRC for each asset type, and the owner per workstream. Confirm Phase 2 resources are allocated before exiting Week 4.

Phase 2 (Weeks 5–8): Repair

What this phase produces

Three simultaneous repair tracks, each running in parallel. The sequencing within Phase 2 is not week-by-week serial — all three tracks open in Week 5 and run concurrently to the end of Week 8. The reason for parallel rather than serial: MLR review timelines (typically two to four weeks for new content) mean the first assets must enter the governance queue before later assets are even drafted.

Track 1 — Front-load owned content (Weeks 5–8)

Owner: Medical Affairs (draft), Brand (brief), Digital (publish)

Kevin Indig's analysis of 1.2 million ChatGPT responses found that 44.2% of citations come from the first 30% of a source document. This is not a UX rule — it is a retrieval rule. Generative engines chunk content from the top. If your mechanism of action explanation is buried in paragraph nine of a brand page, the engine extracts a competitor's top-loaded paragraph instead.

Every owned content asset requiring repair should be restructured so that the direct, citable answer — indication statement, mechanism, key trial result, safety profile — appears in the first substantive paragraph. This applies to: the brand HCP page, the prescribing information summary page, the disease-state resource page, the patient FAQ, and any structured Q&A content deployed specifically for GEO. All must enter the MLR queue in Week 5 for publication by Week 7 at the latest, leaving Week 8 for any iteration. Simultaneously, deploy FAQPage and Drug JSON-LD schema on the top 20 owned pages — schema-marked answers are parsed more cleanly by every major engine.

Track 2 — Fix off-label leakage on safety-sensitive prompts (Weeks 5–8)

Owner: Regulatory Affairs (lead), Medical Affairs (content), Brand (awareness)

The May 2026 PharmaGEO public index shows a systemic AI behavior that every metabolic brand team should be tracking: Ozempic and Mounjaro — both T2D-indicated — appear with non-zero Answer Rates on obesity-specific prompts. On Perplexity, Ozempic holds approximately 6% Share of Voice in the obesity therapy area and Mounjaro approximately 3%. This is not a promotional claim; it is a documented engine behavior. AI systems blur indication boundaries between same-molecule branded products, and each unprompted off-label mention is a potential MLR exposure for competing brands whose indication boundaries the AI is misrepresenting.

The repair strategy is not to suppress a competitor — engines cannot be directly instructed to do that. The repair is to provide such clear, authoritative, correctly-indicated content that the engine has a better answer to draw from. This means: precision on indication language in every owned asset (approved population, approved indication, not broader therapeutic class), clear distinction between brand and INN in all published text, and explicit annotation of what the brand is not indicated for where regulatory guidance permits that framing. Run your 80-prompt set specifically on safety-sensitive queries — pregnancy, pediatric use, contraindications, off-label uses — and document the engine behavior. This documentation is your evidence base for ongoing MLR monitoring.

Track 3 — Align society guideline, EPAR, and PI text retrievability (Weeks 5–8)

Owner: Medical Affairs (lead), Market Access (regulatory liaison)

The May 2026 PharmaGEO public index shows EMA EPARs appearing in English-language answers, including for brands primarily marketed in the US. The English-language EPAR document on ema.europa.eu is a free citation channel that pharma routinely underuses outside EU markets. Similarly, FDA label text on accessdata.fda.gov ranks as the top two citation sources for obesity therapy area queries on Perplexity — engines are already surfacing regulatory primary documents. If your PI text is not cleanly indexed and retrievable, the engine falls back to lower-authority summaries or, worse, hallucinated paraphrase.

Concrete actions for Track 3: confirm your DailyMed Structured Product Label is current and reflects the most recent labeling revision; confirm your EPAR is current on ema.europa.eu; audit whether society guidelines that include your brand (AAD, NCCN, NICE, ESMO, or equivalent for your TA) are in formats that allow clean text extraction; identify gaps between your guideline inclusion status and that of named competitors. Where gaps exist, begin the multi-year engagement process with the relevant society — guideline inclusion is a GEO lever that cannot be bought or rushed, which is exactly why the engagement must start now.

Phase 3 (Weeks 9–12): Scale

What this phase produces

The earned and shared content layer that drives long-term AI citation share — and the governance handoff that ensures the program outlives the 90-day sprint.

Week 9–10 — Earned-content syndication and third-party mention placements

Owner: Communications / PR (lead), Medical Affairs (review)

The Ahrefs December 2025 analysis of 75,000 brands found web mention count correlates with AI Overview citation rate at r = 0.664, and YouTube mentions at r = 0.737 — the single strongest factor measured. These correlations do not mean "run a press release and hope." They mean the AI citation graph rewards genuine third-party named-entity mentions, and YouTube is uniquely powerful because it is a platform LLMs have been trained on heavily and that Perplexity cites regularly.

By Weeks 9 and 10, Phase 2 content should be clearing MLR. That cleared content becomes the source material for earned syndication. Priority targets: trade media with high-authority domain ratings (STAT News, Fierce Pharma, Endpoints News for brand mentions without promotional framing), conference proceedings and KOL-authored commentaries that name the brand in clinical context, and medical education platforms where the brand is named as part of the treatment landscape. Every third-party mention should contain the brand name explicitly — not just the INN — since the May 2026 PharmaGEO index shows the same molecule under different brand names (Adbry in the US, Ebglyss in the EU) is treated by AI engines as distinct entities with distinct visibility profiles.

For YouTube specifically: medical education content, MOA animations, and conference presentation recordings that name the brand are highest value. These do not need to be promotional; they need to be indexed, discoverable, and consistently use the brand name in titles, descriptions, and spoken audio.

Week 11 — Governance handoff to Medical Affairs and Brand

Owner: Digital (outgoing), Medical Affairs + Brand (incoming)

A 90-day sprint that ends without a governance structure produces a single data point, not a program. Week 11 is the formal handoff: establishing the cross-functional cadence that will maintain the measurement and content function beyond the sprint. The PharmaGEO playbook governance model includes Medical Affairs owning the content accuracy monitoring function (reviewing sampled AI answers monthly for accuracy, flagging adverse event mentions for pharmacovigilance triage), Brand owning the Share of Voice and competitive benchmarking function (quarterly re-runs of the 80-prompt set), and Communications owning the earned syndication pipeline (monthly review of which third-party domains are appearing in AI citation lists for your brand queries).

MLR/PRC workflow updates are part of this handoff. Every GEO asset — FAQ pages, JSON-LD schema markup, llms.txt summaries, contributed Wikipedia content, sponsored CME — is a promotional asset subject to regulatory certification under FDA, EMA, ABPI, and equivalents. The 2026 FDA warning letter on AI overreliance makes clear that reliance on AI generation is not a defense against regulatory violation. The governance structure must include a log of AI-assisted content generation and a certification step for every GEO-specific asset type.

Week 12 — Re-measure, document delta, build the year-2 investment case

Owner: Digital (measurement), Brand Lead (investment case)

Re-run the full 80-prompt set across the engine and language matrix established in Phase 1. Score against the same 6-axis framework. Document the delta from baseline to Week 12. Most brands completing this sequence move 8 to 15 composite score points in the first quarter — the source of that movement being predominantly the Visibility and Source Quality axes, which respond fastest to owned-content and citation-graph improvements. Accuracy and competitive share move more slowly and are the primary focus of year-2 programs.

The Week 12 re-measurement is also the foundation for the HCP AI layer engagement plan. IQVIA's March 2026 research documents 54% of HCPs using generative AI clinically, with OpenEvidence reaching 18 million monthly queries from verified physicians. OpenEvidence and DoxGPT run closed-corpus RAG over peer-reviewed literature and guideline content — pharma-owned websites are largely invisible to them. The only levers that move HCP clinical AI are guideline inclusion, peer-reviewed publication, and structured drug-monograph data. The engagement plans launched in Phase 2, Track 3 now have a 90-day evidence baseline to inform investment prioritization for year 2.

Week-by-week deliverables and owners

KPI targets at weeks 4, 8, and 12

The table below shows the metrics you should be tracking at each phase gate, what directional movement indicates the program is working, and the primary driver of that movement. These are directional benchmarks based on PharmaGEO cross-TA patterns, not contractual guarantees — engine volatility, competitive activity, and the starting position all affect the rate of change.

The compounding case for moving now

The urgency framing in this plan is not speculative. CMI Media Group's analysis puts 52% of pharma keywords under AI Overview coverage, with the click-through rate penalty already at 25.8% to 7.4%. IQVIA's March 2026 report documents 54% HCP AI adoption in clinical contexts. OpenEvidence recorded one million clinical consultations in a single day in March 2026. These are not projections — they are the current state.

At the same time, Codeless.io's research confirms Google still receives approximately 373 times the search volume of ChatGPT. GEO is not a replacement for SEO; it is the infrastructure layer being built now, while AI interfaces are growing, so that when the balance shifts further — and it will — the brand's AI representation is already accurate, authoritative, and well-cited rather than reconstructed under pressure.

The Ahrefs correlation data makes the compounding dynamic concrete: branded web mentions (r = 0.664) and YouTube mentions (r = 0.737) are the strongest predictors of AI citation rate measured in a 75,000-brand study. Every month of earned syndication, every cleared KOL commentary, every YouTube video that names the brand in its title and transcript builds the citation graph that AI engines draw from. The brands that start this month will have a nine-month head start by year end. The 90-day plan above is how you start.

Want a real audit on your brand? Request a sample report or get the full PharmaGEO Playbook.