Most pharmaceutical brands cannot answer a deceptively simple question: what, exactly, has medical, legal, and regulatory review approved us to say, and where is each of those statements being used right now? The approved content exists. It is just scattered across PowerPoint decks, agency servers, email threads, slide libraries, and a promotional content vault that nobody treats as a queryable source of truth. That scattering was tolerable when content lived inside controlled channels. In the AI era, where ChatGPT, Gemini, Perplexity, Google AI Overviews, and Claude assemble answers about your product from whatever they can find, scattering is a liability you can measure.
A pharma claims library fixes this. Shaped like the proven PromoMats model, it treats claims, variations, references, and a where-used ledger as a structured system of record rather than a pile of finished files. This article explains why scattered approved content fails when machines do the reading, and how a claims library, paired with reuse scoring and semantic where-used, becomes the foundation of both faster review and durable AI visibility.
Why scattered approved content fails in the AI era
The classic content problem in pharma was never a shortage of approval. It was a shortage of structure. Approval was granted at the level of the finished asset, the detail aid or the email, so the approved knowledge inside that asset, the actual claim and its substantiation, was locked into a PDF and effectively invisible to everything except a human reading that one file.
That model breaks twice in the AI era.
First, content volume has grown. What was once a single campaign asset is now dozens of tailored versions across segments, channels, and markets, driven by omnichannel engagement and personalization expectations from healthcare professionals (Veeva). Reviewing every variation from scratch does not scale, and biopharmas already take roughly three weeks on average to deliver new content to market (Veeva). When the unit of approval is the whole asset, every new variant restarts the clock.
Second, and more consequential, the readers changed. HCPs increasingly query AI tools directly. The American Medical Association's annual Physician Survey on Augmented Intelligence found that in 2026 more than four in five physicians (81 percent) use AI in their practices, more than double the 2023 rate of 38 percent (American Medical Association). When a clinician asks an answer engine about a therapy, the model does not open your approved detail aid. It performs retrieval, searching for and pulling snippets of relevant information, then synthesizes an answer grounded in whatever it retrieved (IBM Research). If your approved claims are buried inside flat PDFs and unstructured slideware, they are poor retrieval targets. The model fills the gap with third-party summaries, forum posts, and stale press coverage. Your most carefully substantiated language loses to whatever was easiest to parse.
Scattered approved content, in other words, is content that exists but cannot be found, governed, or projected. It fails review on speed and it fails AI on visibility.
The claims library as system of record
A claims library inverts the unit of approval. Instead of approving finished assets, you approve the smallest reusable truths, the core claims, and treat finished assets as assemblies of those truths. In the Veeva model that the industry has standardized around, modular content is content pre-approved with its own material number, governed by business rules describing how it can be used, and core claims are pre-approved statements about the product substantiated by references (Veeva). The library is the registry of those approved units.
Four structures make a claims library a system of record rather than a folder.
Claims. The atomic, approved statement. Each one is a governed record with status, owner, and approval lineage, not a sentence trapped in a deck.
Variations. The same approved meaning expressed in multiple ways. Veeva calls these match text variations, alternative wordings that carry the same approved sentiment or meaning so teams keep flexibility without re-opening review (Veeva). This matters enormously for AI, because answer engines paraphrase. A library that knows its own approved paraphrase space can recognize when a model is echoing you faithfully versus drifting.
References. The substantiation. Every claim links to the evidence that supports it, which is not a nicety but a regulatory baseline: FDA guidance directs firms to develop promotional communications so that they are accurate, truthful, and non-misleading (FDA). A claims library makes the claim-to-reference link a first-class, auditable relationship. Modern systems automatically link claims to references so reviewers can trust that linked language has already cleared substantiation (Veeva).
The where-used ledger. The record of every place a claim currently appears. This is the structure most organizations lack entirely, and it is the one that turns a library from a content catalog into a governance instrument. Without it, a label change or a safety update means a frantic manual hunt across every asset. With it, you query.
The payoff is documented. Organizations adopting this modular, claims-led approach have reported content reuse increases of roughly 40 percent and approval-time reductions near 30 percent, with time-to-market improvements of more than 50 percent, and one company reaching 75 percent of approvals in a single review cycle (Veeva, via PR Newswire). Those gains come from one shift: approving truths once and reusing them, instead of re-approving assets endlessly.
Reuse scoring and semantic where-used
A registry alone is passive. Two capabilities make a claims library active and connect it to AI visibility.
Reuse scoring measures how much of your approved language is actually being put to work. It answers questions a content catalog cannot: which approved claims are reused across many assets and channels, and which were approved once and then abandoned. High-reuse claims are your real message, the language your organization has effectively standardized on. Low-reuse claims are either redundant, off-strategy, or signals that approved language is not reaching the teams who build assets. Scoring reuse turns the library into a feedback loop on messaging discipline, and it is the prerequisite for asking the AI-era question that follows: of the claims we reuse most, how many are the models actually echoing back?
Semantic where-used upgrades the ledger from exact-match tracking to meaning-based tracking. Exact-match where-used finds the literal sentence. Semantic where-used finds every place the meaning appears, including paraphrases, partial restatements, and channel-adapted variants, using the kind of similarity matching over vector representations that retrieval systems rely on (IBM Research). This is what lets you govern variations honestly. When a claim's reference is updated or a statement is retired, semantic where-used surfaces not just the exact copies but every near-restatement that now needs attention. It is also the mechanism that connects the inside, your approved library, to the outside: the answers HCPs receive from answer engines.
That outside connection is where AI visibility becomes engineerable rather than accidental. Controlled research into how generative engines choose what to surface, the Princeton-led GEO study, built a large-scale benchmark of queries drawn from nine different sources and found that targeted, well-designed textual enhancements can boost a source's visibility in AI answers by up to 40 percent, with adding citations to credible sources lifting visibility for a lower-ranked source by as much as roughly 115 percent (Aggarwal et al., arXiv). A claims library already holds exactly these ingredients in structured form: substantiated claims, named references, governed numbers. The library is the cleanest possible substrate to project into retrievable, AI-readable approved content. Unstructured slideware is the worst.
A worked example: Varigel
Consider Varigel, a fictional specialty therapy. Its launch team approved a strong efficacy message eighteen months ago. Over time, fourteen agencies and internal teams built assets referencing it, each phrasing the message slightly differently to fit a webinar, an email, a rep-triggered follow-up, and a conference microsite.
Then the Varigel label is updated, narrowing the population for which the efficacy statement holds. In the scattered world, the medical reviewer knows the original approved sentence but has no reliable inventory of where it lives or how it was reworded. The hunt takes weeks, some variants are missed, and one stale phrasing keeps circulating. Meanwhile, a clinician asks Perplexity about Varigel's efficacy and receives an answer grounded in a third-party congress recap that quotes the old, broader claim, because that recap was the most parseable source the model could retrieve.
With a claims library, the same event plays out differently. The efficacy claim is a governed record. Reuse scoring already flagged it as one of Varigel's top-three most-reused claims, so the team knows it matters. Semantic where-used returns every asset that states or paraphrases it, including the loose webinar rewording that never matched on exact text. The reviewer retires the affected variations, links the narrowed claim to its updated reference, and pushes the corrected, substantiated language into the brand's structured, AI-readable content. The next time an answer engine retrieves on Varigel efficacy, the freshest, best-substantiated, properly scoped statement is the strongest retrieval target available. The off-label-leaning paraphrase loses, by design.
Nothing here is exotic. It is the difference between owning a system of record and owning a pile of approved files.
Where this leaves you
The AI era did not create the scattered-content problem. It made the cost of scattering visible and external. Approved content that cannot be queried fails review on speed and fails answer engines on visibility, and the two failures share one root cause: approving assets instead of approving structured, reusable truths.
A pharma claims library, claims, variations, references, and a where-used ledger, is the fix, and it is the foundation everything else in AI-era content sits on. This is precisely what Content Intelligence is built to be: the approved-content system of record, with reuse scoring that shows which approved language is doing real work and semantic where-used that tracks meaning, not just matching text, across every asset. Because the library is structured and substantiated, it is also the cleanest substrate to project into AI-readable content, the join that lets Answer Monitor measure how much of your approved language the models actually echo back, and lets Pre-Check verify new assets against the same governed claims before they ship. Start by making your approved truths queryable. Everything downstream, from MLR throughput to AI visibility, depends on it.
Sources
- Veeva, Getting Started with Modular Content
- Veeva, Lundbeck Modernizes Claims Management
- Veeva, Modular Content Powers Omnichannel Engagement at Speed and Scale (PR Newswire)
- IBM Research, What is Retrieval-Augmented Generation (RAG)?
- Aggarwal et al., GEO: Generative Engine Optimization (arXiv)
- FDA, Promotional Labeling and Advertising Considerations for Prescription Biological Reference Products, Biosimilar Products, and Interchangeable Biosimilar Products: Questions and Answers (guidance PDF)
- American Medical Association, AI usage among doctors doubles as confidence in technology grows