Executive Summary

The medical–legal–regulatory (MLR) review process is a cornerstone of life sciences marketing, ensuring that all promotional content for pharmaceuticals and medical devices is accurate, balanced, and compliant with stringent regulations. Traditionally, MLR review has been laborious, time-consuming, and prone to delays, with review cycles often taking weeks or longer (^[1]) (^[2]). As content volumes surge (with global promotional material production rising sharply in recent years (^[3]) (^[4])) and regulatory scrutiny intensifies, life sciences companies face mounting pressure to streamline content approval without sacrificing compliance. Automating portions of the MLR process using artificial intelligence (AI) has emerged as a promising solution to alleviate bottlenecks and improve consistency.

Veeva Systems, a leading provider of cloud applications for the life sciences industry, has incorporated industry-specific AI agents into its PromoMats content management platform. In late 2025, Veeva launched two AI-driven features for PromoMats: the Quick Check Agent and the Content Agent. The Quick Check Agent is an automated pre-review tool that scans draft content for editorial, branding, regulatory, and compliance issues, flagging errors in spelling, prohibited phrases, missing warnings, and other guideline violations (^[5]) (^[6]). The Content Agent is a conversational “AI assistant” that allows reviewers to interact with documents: it can answer context-specific questions, summarize long materials, analyze images, and integrate Quick Check findings into a structured summary (^[7]) (^[8]). Both agents operate within Veeva Vault (the underlying secure platform) and leverage large language models (LLMs) hosted on Amazon Bedrock, ensuring content never leaves the customers’ secure environment (^[5]) (^[9]).

In practice, these AI agents are designed to accelerate approvals, reduce manual effort, and improve compliance consistency. Quick Check finds and suggests corrections for routine errors early in the process, reducing the number of review cycles and freeing reviewers to focus on substantive issues (^[5]) (^[6]). For example, the agent identifies spelling/grammar mistakes and “risky phrases,” checks that required safety statements (e.g. boxed warnings) are present and correctly formatted, verifies that privacy and unsubscribe links conform to company policy, and detects accessibility problems like missing alt text (^[6]) (^[10]). In customer pilots, early users have reported dramatic time savings: a specialty pharma firm “vastly reduced” the hours spent in MLR review, cutting in-person meeting time by about 20% (equivalent to two full-time employees of saved capacity) (^[11]). CEO Peter Gassner and product experts cite increased productivity and shorter content cycle times as central goals of Veeva AI (^[12]) (^[13]).

The integration of these AI tools into real-world workflows is yielding positive sentiment from industry leaders. For instance, Moderna’s marketing operations director noted that the Quick Check Agent brings their processes “closer to a touch-free review,” letting reviewers see how a nearly automated workflow might be attainable (^[14]). Life sciences executives stress that AI should complement human expertise: as Astellas’ compliance leader puts it, “ensuring compliance…remains a key responsibility of our MLR teams, which AI alone cannot handle” (^[15]), (^[16]). The technology is expected to lead to faster, more consistent approvals (Veeva estimates up to 75% reduction in cycle times (^[17])) and higher-quality outcomes by directing human attention to high-risk issues (^[17]) (^[18]).

This report provides a comprehensive analysis of automating MLR review with Veeva PromoMats’ AI agents. We review the background and challenges of MLR processes, examine the design and capabilities of Veeva’s Quick Check and Content Agent, and assess their impact through case examples and industry data. We compare Veeva’s approach to other AI-driven MLR solutions and discuss broader implications for compliance, workflow transformation, and the future of pharma content operations.

Introduction and Background

The MLR review is the formal process within pharmaceutical and biotech companies by which all promotional and scientific content is vetted by cross-functional experts (medical, legal, and regulatory) before any material is released to healthcare providers (HCPs) or patients (^[19]) (^[20]). Its purpose is twofold: to protect patient safety by ensuring all claims are medically accurate and adequately balanced with risk information, and to shield companies from legal and regulatory enforcement by preventing false or misleading promotion (^[19]) (^[21]). In practice, every external-facing asset – from sales slide decks and websites to advertising copy and product packaging – must undergo MLR review. Reviewers verify that claims are supported by evidence, that side-effects and limitations are clearly stated, and that all content adheres to applicable laws (e.g. FDA guidelines, industry codes like PhRMA or EFPIA) (^[19]) (^[22]).

Because even minor omissions (such as missing a critical warning or using non-approved claims) can trigger hefty fines or product recalls, MLR review is a rigorous, multi-disciplinary process (^[19]) (^[23]). U.S. life sciences companies paid roughly $9.8 billion in 2022 alone to settle cases of improper promotion and related violations (^[24]), underscoring the financial stakes. Consequently, marketing materials often undergo two or more rounds of review: content developers first draft an asset, then medical affairs, legal, and regulatory experts each review it in turn, often suggesting revisions. These rounds continue until the Promotional Review Committee (PRC) agrees the content is compliant and can be approved. According to industry accounts, this collaborative loop can be painfully slow: surveys report MLR approvals taking weeks to over a month, with some campaigns languishing “for up to 40 days” in review (^[25]) (^[1]). Such delays can postpone drug launches and impede marketing agility, even as the volume and complexity of content continue to rise.

Several factors make traditional MLR review especially inefficient. First, content volumes are exploding. One study notes that the amount of content submitted for MLR review has tripled in recent years (^[4]), driven by omnichannel strategies (digital ads, emails, social media, educational videos) and globalization (multiple markets requiring localized review). At the same time, field teams often use only a fraction of approved materials – industry data indicate nearly 77% of approved content may be rarely or never used (^[3]) – so reviewers spend effort on assets that do not reach audiences. Meanwhile, reviewer headcount has stagnated or shrunk, further straining capacity (^[26]). Second, the MLR workflow is fragmented across disparate tools. Many firms have historically relied on email, spreadsheets, and manual batch approvals, which leads to lost feedback, version control issues, and scattered audit trails (^[27]) (^[22]). Even with digital document management systems (like Veeva PromoMats) in place, reviewers may not be consulted early in the content lifecycle, so errors are caught late (^[26]) (^[28]).

Third, global and technological complexity adds to the burden. Promotional guidelines vary by country and channel (e.g. some markets require QR codes, others have strict privacy link rules, etc.). Reviewers must manually check that each requirement is met for each asset. For example, any email or web advertisement targeting U.S. physicians must include an opt-out link and a privacy policy, and if a product carries an FDA boxed warning, that warning must appear prominently in any ad. These rules can easily be overlooked. Similarly, accessibility rules (such as WCAG) now apply to digital materials, adding yet more checks for alt text and legibility (^[10]).

Overall, MLR review has become an expensive and time-consuming bottleneck. One consultancy reports that companies optimizing their MLR workflows can cut review cycle times by more than half (57% reduction) and halve the time spent in review meetings (^[29]). Conversely, inefficient processes lead to costly delays. Industry experts note that when marketers “eager to set campaigns free,” compliance can feel “glacial,” dragging on due to “outdated methods and software” (^[1]). It is against this backdrop that life sciences organizations are seeking new approaches – especially those leveraging automation and AI – to accelerate MLR without sacrificing quality or safety.

The MLR Review Workflow and Its Challenges

Roles and Responsibilities

MLR review involves three core disciplines. Medical reviewers (often from medical affairs) ensure all scientific content and clinical claims are accurate and evidence-based (^[22]). Legal reviewers enforce laws and company policies: they flag off-label claims, copyright issues, anti-kickback risks, and wording that could be misleading or misrepresentative (^[27]) (^[22]). Regulatory reviewers (regulatory affairs professionals) verify that the content aligns with the official product labeling and regional regulations: for instance, that risk information is fairly balanced and all required regulatory symbols are present (^[22]). In many companies, a Marketing representative may also be present to clarify the campaign intents and ensure marketing objectives are met within the compliance framework (^[20]). These stakeholders typically form a Promotional Review Committee (PRC) that collectively decides whether content is approved or requires changes (^[20]).

During the PRC process, each reviewer might identify issues. For example, a medical reviewer might note that a claim of “best-in-class efficacy” lacks comparative clinical evidence. A legal reviewer might highlight missing copyright citations or detect that some phrasing could violate advertising laws. A regulatory reviewer might insist that the numbering of adverse reaction bullet points matches the official label exactly. Feedback is often transmitted via document markups or comment threads. Without automation, this means back-and-forth email or disjointed annotation, which is slow and error-prone. According to industry experts, poor coordination and unclear reviewer roles can cause confusion and reworkの (^[30]). When any reviewer is uncertain of expectations, the process drags on.

Complexity of Guidelines

The rules governing promotional content are highly granular. In the U.S., for example, any claim about a prescription product’s efficacy must include a fair balance statement of risks (a principle enforced by FDA’s Office of Prescription Drug Promotion). There may be legal requirements for disclaimers (e.g. trademark notices, privacy statements), and voluntary industry codes (e.g. the PhRMA Code on Interactions with Healthcare Professionals) impose additional restrictions. Globally, each regulatory authority has its own nuances. For instance, Pharma and medical-device advertisers in the EU must comply with EFPIA and local health agency guidance, which dictate how risk information is phrased. China, Canada, and others each have their own standards. Compliance teams must maintain “black box” warnings, small-print Indications/Safety statements, promotional burden statements, etc., and ensure all are present at the right prominence. This “rules atlas” means that even experienced reviewers must double-check many items manually.

Compounding the complexity, content often appears in multiple channels. A slide deck might be printed as a PDF, shown on a tablet, and repurposed as HTML for online use. Each channel can have unique formatting or layout rules. For example, a Facebook ad needs an unsubscribe link if targeted to existing subscribers, whereas a TV ad has no such requirement. Accessibility guidelines (WCAG) now require that images have alt-text descriptions, color contrast is sufficient for colorblind users, and videos have captions. Checking all these in each asset adds to the burden (^[10]).

As a result of these factors, many review processes are inefficient. Analysts report that from the time a new promotional asset is ready for review to final approval can take many weeks – often approaching 30–40 days (^[2]). Every delay can cost millions in lost sales opportunities, given the tight timelines around drug launches (^[2]). Senior industry leaders warn that MLR must be viewed as a critical business issue – not just a technicality – if content is to reach customers quickly and accurately (^[31]).

Evolution of MLR Technology and Automation

Given these challenges, life sciences companies have long sought ways to streamline MLR. In the last two decades, most have migrated from purely paper-based or email-based review toward electronic content management systems. Veeva Vault PromoMats (now part of Veeva Vault Commercial Cloud) is a dominant example: it provides a centralized repository for all promotional materials, with built-in version control, annotation, and workflow automation. Using generic templates and forms, a company can route a document through the appropriate reviewers and track status. These systems eliminated many pitfalls of lost emails and unclear versioning (^[32]) (^[33]).

Other earlier solutions adopted automation in limited ways. For instance, compliance rule-sets could be implemented – e.g. automatically blocking the submission of materials missing mandatory disclaimers. Some companies used custom scripts or simple text-search tools to flag prohibited words (e.g. “cure” for non-curative claims). Basic ATP (automated promotional review) tools existed to compare new content against a library of already-approved messages to avoid unauthorized phrasing (^[34]). Still, most of these were heavily rule-based and rigid. They required manual maintenance of checklists and did not understand context; often they only worked on final text (not images or layout).

Content collaboration platforms like Filestage, MarketBeam, and others have further improved workflow efficiency by giving reviewers the ability to comment in context and by automating assignment reminders (^[35]) (^[36]). Filestage, for example, claims that a robust online proofing tool can shave “as much as 30%” off review times by automating due-date assignments, status updates, and version comparisons (^[35]) (^[37]). MarketBeam focuses on social and digital channel compliance by streamlining feedback from agencies on ads and posts (^[38]). But none of these platforms fully solved the core problem: the tedious checking of content against a broad set of regulatory requirements.

The rise of AI and machine learning in recent years offers a new avenue. Advanced language models now demonstrate capabilities in understanding text, checking for consistency, and even reasoning about compliance rules when properly prompted. In principle, such models can ingest a promotional document, compare it to a company’s own rulebook and regulatory guidelines, and highlight potential issues. They can also help with creative aspects: summarizing a complex document for a reviewer, translating it, or answering questions. Importantly, deploying AI in life sciences requires careful control: models should work in context, using validated data, and never leak sensitive information.

Industry trends show that many large pharmaceutical companies are now exploring AI for content review and generation. Veeva reports that over 80% of major life sciences firms are experimenting with AI in areas like content creation, tagging, and quality checks (^[39]). For example, Johnson & Johnson mandated generative AI training for tens of thousands of employees (^[40]), indicating top-level strategic emphasis on AI literacy. Merck built its own internal AI platform for employees (^[40]). Consulting analyses point to a $5–7 billion total value opportunity from AI in life sciences, with up to a third (~$2B) coming from commercial functions (including marketing and MLR) (^[41]). These investments reflect a shared sense that “the area where GenAI has the most potential is MLR” (in the words of GSK’s medical-affairs director) (^[42]).

However, industry experts stress that AI must be used judiciously and in partnership with human review. Even with AI checks, the ultimate responsibility for compliance lies with human reviewers. Astellas’ ethics and compliance leader notes that FDA rules still “mandate human intervention, especially for audit trails and documented proof,” meaning AI can assist but cannot replace expert judgment (^[15]). Sanofi echoes this caution: while “automation aids” can streamline tiered reviews, “human oversight is crucial” (^[16]). Indeed, regulators have not provided explicit guidelines on AI-aided review, so companies must ensure any AI output is traceable and verifiable.

Against this backdrop of escalating content demands and cautious AI adoption, Veeva launched its AI agents for PromoMats in late 2025, aiming to apply LLM technology specifically to MLR challenges. The Quick Check and Content Agent are among the first industry-specific AI agents (in Veeva’s terminology) embedded in a regulated life sciences platform (^[8]) (^[43]). Unlike general-purpose AI tools, these agents are built with curated domain knowledge and integrated safeguards. We now examine these agents in detail.

Veeva PromoMats and Its AI Agents

Veeva Vault PromoMats: Overview

Veeva Vault PromoMats is a cloud-based content management application tailored for regulated promotional materials. It handles the full content lifecycle: creation, review, approval, digital asset management, and claims management. It is widely used in life sciences (over 1,500 customers worldwide (^[44])) and is often regarded as the industry standard for MLR workflows (^[11]) (^[45]). Built on the secure Veeva Vault platform, PromoMats provides version control, audit trails, templating, and global workflow capabilities. Companies use PromoMats to centralized all promotional assets (from slide decks to social media posts) and to enforce standardized processes for different document types and regions.

Historically, PromoMats offered features like claim verification (linking scientific claims in the content to supporting references in a central library), digital proofs, and federated workflows. However, before the AI agents, PromoMats still relied on human reviewers to catch all compliance issues. For example, if a content creator forgot to include a privacy statement in a patient brochure, it was up to a reviewer to notice. Likewise, grammatical errors and brand style violations typically had to be caught manually or by some external checklist.

With the introduction of AI, PromoMats has embedded intelligent layers on top of its existing framework. Crucially, Veeva AI operates within the Vault environment: it accesses content and data directly in customers’ Vaults, respecting the same security permissions and audit logs as any other action (^[46]) (^[47]). This means that when a Quick Check or Content Agent runs, the content does not leave the platform, ensuring compliance with data governance and privacy. Veeva explicitly notes that AI results are generated “with content staying securely inside Vault” (^[48]).

Veeva AI Agents and Vault Integration

Veeva’s approach to AI is to provide “deep, industry-specific agents” trained or configured for particular applications (^[49]) (^[50]). These agents are not generic chatbots that access the open internet; they operate on locked-down LLMs (from Anthropic or Amazon) through Amazon Bedrock, with prompts and data tailored to Veeva’s domain (^[50]) (^[9]). By building agents into the Vault Platform, Veeva allows clients to use large language models without transferring proprietary or patient-sensitive data externally. Veeva also offers tools to configure or extend agents, letting companies adapt the AI to their own processes.

Starting December 2025, Veeva announced that AI Agents would be gradually released across all application areas (^[51]) (^[52]). For PromoMats specifically, immediate availability of Vault PromoMats AI plugins was announced. The new features fall under Veeva AI for PromoMats, whose mission is "to deliver the fastest path to approved content with AI" (^[53]). In practical terms, the AI agents aim to streamline compliance checks early in the content lifecycle and provide on-demand insights during review. As Veeva marketing puts it, these capabilities “strengthen compliance, accelerate speed to market, and increase productivity” across the content lifecycle (^[54]).

In summary, Veeva PromoMats provides the digital infrastructure, and the new AI agents serve as intelligent assistants embedded in that environment. The platform’s existing workflows remain, but now with AI-powered quality checks. The remainder of this report focuses on the two new agents: Quick Check and Content Agent.

Quick Check Agent

The Quick Check Agent (often abbreviated QCA) is an automated compliance-checking tool that runs before the formal MLR review begins. It is designed to help content owners and coordinators “prepare their documents for MLR review by detecting common issues before submission” (^[5]). In other words, Quick Check acts like a preliminary filter: it scans a draft document and flags any obvious or medium-level compliance issues so that authors can fix them before sending the content to medical, legal, and regulatory teams.

Core Capabilities

Quick Check performs a range of systemic checks on both the text and some visual elements of a document (^[43]) (^[6]). Its key capabilities include:

• Editorial (Spelling & Grammar) Checks: QCA identifies typographical errors, misspellings of medical or product terms, and major grammatical mistakes (^[6]). This ensures content clarity and professionalism, and avoids simple errors that could undermine credibility. Unlike generic spelling checkers, this is tailored to life sciences context (for example, it focuses on “significant grammatical errors that could hinder clarity…rather than stylistic choices” (^[6])).

• Risk Phrase Assessment: It scans the text for phrasing that may be “potentially risky” or biased and categorizes them (^[55]). For instance, the use of absolute claims (“best drug ever”) or off-label insinuations are flagged. Quick Check even suggests advisory language to balance or reword these statements, guiding authors toward compliant phrasing (^[55]).

• Boxed Warning Compliance: For drugs that carry a mandatory FDA “black box” warning, Quick Check determines whether the warning is required and whether the draft document includes it (^[56]). It “flags missing, misplaced, or insufficiently prominent warnings” and cites the relevant CFR regulations (^[56]). This helps ensure that serious safety information is not inadvertently omitted or underplayed.

• Important Safety Information (ISI) Verification: The agent compares the content against the official Related ISI document for that product. It detects if required statements are missing, if existing safety text has been improperly edited, or if extraneous safety language appears where it shouldn’t (^[57]). In essence, it checks that the safety/burden statements in the promotional material match those on the approved label.

• Privacy Policy & Unsubscribe Link Checks: Quick Check knows which kinds of documents and channels typically need a privacy notice or opt-out link. It advises if a privacy or unsubscribe link is recommended for the asset (e.g. an email advertisement to HCPs). Then it searches the document for such links and verifies them against an approved database of URLs (for validity and correct targeting) (^[58]) (^[59]). This helps catch cases where a legally required link is missing or incorrect.

• Accessibility (WCAG) Compliance: QCA automatically checks the document for adherence to basic accessibility standards, such as presence of alt-text on images, sufficient color contrast, avoidance of text embedded as image, consistent language tagging, and readability levels (^[10]). These checks use general WCAG guidelines to flag issues that could hinder understanding by persons with disabilities.

• Brand and Design Rules: Embedded within these features are brand-policy checks. Quick Check can confirm proper use of company trademarks and copyrights, and enforce corporate style (like inclusion of copyright statements on images) (^[60]) (^[56]). It also checks layout/channel rules, such as size requirements or mandatory QR codes for certain digital posters (^[61]).

Collectively, these checks represent an “automated editorial and compliance review” that is deep and wide in scope (^[62]) (^[6]). The result is a list of findings presented to the user, typically as color-coded alerts (errors/warnings/notes). The user can then resolve each issue before submitting the content for formal MLR review.

Underlying Technology and Integration

Importantly, Quick Check leverages a large language model (LLM) as its core engine (^[5]). Unlike earlier static-rule tools, QCA uses AI to understand context. For example, it can discern whether a marketing claim might violate off-label restrictions based on phrasing context, rather than just matching against a rigid dictionary. Veeva’s documentation explicitly notes: “Leveraging a Large Language Model (LLM), Quick Check Agent proactively identifies a wide range of issues” in promotional materials (^[5]). At the same time, it also relies on structured business data in Vault (lists of products with warnings, a table of approved privacy URLs, etc.) to make certain checks precise (e.g., verifying that a link matches a named Website record in Vault (^[63])).

Quick Check is fully integrated inside PromoMats. The checks appear in a panel on the document’s info page, visible whenever a user with view access opens a draft pricing material that meets certain criteria (^[64]). Only the latest version of a PDF-formatted document is evaluated (^[64]). If a document exceeds 100 pages, some limitations apply (e.g. full-page images may not be analyzed) (^[65]). The AI runs on Veeva’s servers (using the selected LLM), but because it operates inside the Vault platform, the customer’s content never leaves the secure environment (^[47]) (^[66]). This allays data privacy concerns.

Administrators must opt-in each document type to use Quick Check. In the Vault admin console, an admin assigns certain lifecycle states (e.g. Draft) and document type groups (Quick Check Agent) to enable the panel (^[67]). They also configure any product-specific fields (for example, adding a custom “QC Boxed Warning” field if a product has a unlisted warning (^[68])) and set up Website records for validating links (^[63]). Once enabled, as soon as a qualifying document is edited or uploaded, Quick Check runs automatically and populates findings. Vault lifecycles can even be configured (via entry criteria) to block transitions (e.g. from Draft to Review) until the Quick Check results are obtained (^[69]). These features allow Quick Check to be woven into existing workflows without major process changes.

Workflow Impact

In day-to-day practice, Quick Check shifts mundane tasks upstream. Consider a typical example: a marketing author drafts a PDF brochure for a drug and then clicks “Run Quick Check.” Within seconds, the system highlights thirty issues: a wrongly spelled medical term, a missing trademark notice, a phrasing that the model identifies as potentially off-label, a missing privacy link for a patient PDF, and an out-of-date safety phrase. The author fixes those items (often with help text or rewrite suggestions from the agent), and when the revised draft is submitted to MLR, the regulatory reviewers find far fewer basic errors. This means legal/medical reviewers spend more of their time on the novel compliance questions (like evaluating a new positioning claim) rather than chasing down typos or overlooked templates.

Quantitatively, Veeva claims that such pre-submission checks can dramatically reduce rework and review cycles (^[5]) (^[18]). A Veeva product brief notes that AI-powered checks “catch issues before MLR review, which reduces rework…supporting faster and more consistent approvals while maintaining compliance” (^[18]). User feedback also supports this: in the Veeva Connect community, Quick Check was described by a user as helping “identify those little nuances that reviewers would expect to see” before review, thus avoiding needless delays. The case studies highlight that adopting automated checks can “vastly reduce” MLR hours and in-person meetings (^[11]).

In summary, Quick Check Agent brings automation to the preparatory stage of MLR. It is an AI-driven compliance pre-flight checker that ensures routine mistakes are corrected early, thereby smoothing the subsequent human review. The rest of the MLR team can trust that much of the groundwork has been covered, allowing them to focus on creative, strategic, or highly technical compliance issues.

Content Agent

While Quick Check acts before review, the Content Agent assists during review by serving as an interactive AI assistant embedded in PromoMats. Sometimes referred to as an “AI chat” for PromoMats, this agent enables reviewers and creators to query and summarize content in a conversational fashion. According to Veeva, the Content Agent is “context-aware” and provides insights into an asset by understanding its text and images (^[70]).

Features and Workflow

The Content Agent is built around large language models as well, but presented through a structured interface in Vault, often as a chat or query panel. Key functionalities include:

• Ask Questions: The user can type anything about the current document (or related library assets). For example: “Summarize the key product claims in this brochure”, “List all contraindications mentioned in this slide deck”, or “Are there any off-label claims?”. The agent will parse the document text (and images if relevant) and generate an answer. Veeva notes that it can handle vague or ask to clarify if an inquiry is outside the document’s scope (^[7]). This effectively allows users to interrogate the content as if it were a searchable knowledge base.

• Analyze Images: If the request specifically involves visual elements (e.g. “What does the graph on slide 3 show?”, or “Does this patient image have a caption?”), the agent will route the query to its vision pipeline. It performs optical character recognition or image analysis as needed. The separation of text vs. image queries ensures efficiency: text-based questions are answered quickly using the parsed text; image-related questions cause the agent to extract and analyze figures only as needed (^[7]).

• Reviewer Summary: A particularly powerful feature is “Reviewer Summary”. When invoked, the Content Agent generates a concise briefing for the document. This combines multiple elements: it synthesizes the document’s purpose, target audience, key messaging, and any compliance notes gleaned from Quick Check. The summary is structured, often including bullet points for claims, risks, target & intended use (^[71]). With this, a reviewer can start their review with a high-level understanding of the asset’s content and context, along with Quick Check findings all in one place. It essentially orients the reviewer quickly, replacing some of the time it would take to read the entire document linearly.

These actions are supported by Veeva’s AI architecture: the agent has direct access to the document text and images in Vault (just like Quick Check) and can apply the LLM to reason about them. Importantly, the Content Agent also incorporates Quick Check results; it can “pull in findings from Quick Check Agent” to ensure consistency (^[70]). For example, if Quick Check flagged a missing documentation, the Content Agent can mention that in its summary or answers. This means the two agents work together: Quick Check catches rule violations silently, and the Content Agent highlights them conversationally.

In Practice

In practical use, the Content Agent transforms the review experience. Consider a medical reviewer handed a complex clinical slide deck. Instead of manually flipping through forty slides, she might first click “Get Reviewer Summary.” Within moments, the content agent provides a digest: outlines the drug’s indication, target patient population, number of clinical studies cited, and shows where safety info appears (^[71]). The agent might note, for instance, “The deck claims superiority for Treatment X in hypertension; Quick Check noted that the reference to trial data is missing from slide 5.” This immediately tells the reviewer where to look.

Alternatively, a reviewer could interact via Q&A: they might type, “Show me all efficacy endpoints and their values.” The Content Agent can search the document (or even table data in images) and extract the relevant numbers. Or they might ask “What changes were made from the last approved version?” (if version comparison context is available). Essentially, any content task that before required reading and scanning can be done via a simple prompt.

The Content Agent also helps non-reviewers. A marketer or graphic designer can ask it questions about compliance requirements. For example: “Do I need an unsubscribe link in this PDF for non-HCP recipients?” or “What is the approved brand color?”. Because the agent has contextual knowledge of the document and maybe the company’s brand rules (via Vault metadata), it can answer. In this way, it reduces uncertainty and back-and-forth queries to the legal department.

User guidance suggests the Content Agent is meant to augment, not replace, human judgment. When an answer is supplied, the agent always signals confidence and sources if needed. If a question is ambiguous, it can reply that it’s outside its scope. All interactions are logged in Vault, creating a trail of how AI suggestions were used – important for auditability.

Impact on Review Efficiency

By enabling conversational queries and auto-summaries, the Content Agent can significantly cut time for reviewers. Veeva’s materials claim that “only the most relevant feedback is surfaced,” letting agencies, reviewers, and marketers save time as AI takes care of summarizing and analyzing (^[72]). In effect, instead of scanning dozens of pages, reviewers get the gist and can quickly pinpoint novel content. The business benefit is clear: reviewers can focus on high-impact judgments (like assessing new claims or strategy) instead of administrative reading.

This is echoed by pilot customers. For instance, one user reported that after implementing PromoMats, their review time was dramatically reduced. (While this quote refers generally to PromoMats, it is indicative: “one specialty pharma client ‘vastly reduced the number of hours dedicated to the review process’” (^[11]).) With Content Agent, such gains are likely to multiply, as AI does not “get tired” revisiting similar data.

Crucially, because the Content Agent is context-aware and built on the same secure platform, its use does not disrupt regulatory controls. All Q&A sessions can only access the specific document and approved references, and everything is auditable as a Vault action. This adherence to compliance controls distinguishes it from simple chatbot experiments.

Data Analysis and Impact

Evidence is emerging that AI-assisted review dramatically improves MLR efficiency and consistency. Several industry analyses and case reports quantify this impact:

• Cycle Time Reduction: In an industry report, life sciences companies that optimized their MLR workflows achieved an average of 57% reduction in review cycle times and 55% decrease in time spent in review meetings (^[29]). AI-driven tools are the key enablers of such gains. For example, Veeva estimates that teams using AI agents could see review cycles shortened by up to 75% (^[17]).

• Reviewer Meetings: One case study cited by Veeva showed a client cutting about 20% of their face-to-face review meeting hours after switching to automated tools, freeing up the equivalent capacity of two full-time employees (^[11]). Since MLR meetings are often a major bottleneck (scheduling multiple stakeholders makes them costly), reducing them has large effects on project timeline.

• Content Processing Volume: The volume of content each marketing team must handle is too high for manual methods. Veeva documents note that content production grew 7% globally (29% in the US) year-over-year (^[3]). Without automation, reviewer load would increase commensurately. AI can effectively scale capacity by handling routine checks.

• Error Reduction: While hard to quantify, anecdotal feedback suggests that AI pre-checks catch the majority of “low-hanging fruit” errors. For example, SecureCHEK (a competitor tool) reports that agencies and reviewers spend thousands of hours per year correcting errors that could have been caught by AI pre-validation (^[73]). Veeva’s Quick Check similarly aims to drastically cut such rework.

• Cost Savings and Productivity: IntuitionLabs’ analysis highlights that several platforms claim or demonstrate substantial ROI. Papercurve, for instance, advertises 60% reduction in review/approval time (^[74]). Revisto estimates saving on the order of $15 million per brand per year via MLR automation (^[38]). While these numbers come from vendors or case studies, they illustrate the scale: automated MLR can shift millions of dollars in resources into more valuable work.

• Quality Metrics: Beyond time, AI agents can increase consistency. By using the same programmed guidelines across all reviews, companies reduce variability. This potentially leads to fewer compliance incidents (lower legal risk). While no public stat ties AI to a drop in FDA warning letters yet, industry insiders expect that more uniform initial review will reduce errors down the line.

Table 1 below summarizes some representative efficiency improvements reported for AI/MRL solutions:

These figures underscore the transformative potential of AI in the review process. By catching common issues early (Quick Check) and improving reviewer throughput (Content Agent), the goal is accelerated content delivery with maintained quality.

Case Studies and Real-World Examples

Several life sciences companies have begun piloting or implementing Veeva’s AI agents and other MLR automation tools. While detailed results are still emerging, initial reports and interviews give insight into their experiences:

• Moderna (Early Access User): Jason Benagh, Global Marketing Operations Director at Moderna, participated in early testing of Quick Check. He observed that Quick Check “moves [us] closer to a process where parts of MLR could become nearly touch-free” (^[14]). This “touch-free review” vision implies that routine compliance checks require minimal human intervention. Moderna’s feedback suggests that after AI pre-checks, medical and legal reviewers spend most time on high-level review, not trivial fixes – a significant efficiency gain.

• Bristol Myers Squibb: Greg Meyers, EVP and CIO/CTO at BMS, commented on Veeva AI more broadly. He said that embedding AI in the content workflow “is ideally positioned to support us in our mission to deliver life-changing medicines” by bringing “valua ble information” through every step (^[75]). While not specifically about MLR, this endorsement reflects corporate confidence in AI delivering practical value, presumably including faster review.

• Novo Nordisk: Frank Armenante, Director of Field Systems, praised the promise of AI in CRM (Vault CRM), indicating it will allow sales reps to focus on “value parts of their jobs” (^[76]). By analogy, MLR reviewers likewise should focus on value (strategic compliance decisions) rather than drudgery. Novo Nordisk’s general interest in AI efficiency suggests likely adoption in their MLR teams as well.

• Otsuka (Europe): Debbie Young, Multichannel Strategy Director at Otsuka Europe, highlighted that Veeva AI embedded in their systems is “important to us as we expand our partnership” (^[77]). This indicates that global companies are investing in these AI capabilities as part of their transformation roadmaps.

• Resultant Savings: As noted above, an anonymous specialty pharma client (as reported in IntuitionLabs) saw PromoMats “vastly reduce” review hours and cut meetings by 20% (^[11]). Although this quote predates specifically the AI agents, it illustrates baseline gains from digital MLR. With AI augmentation, such figures are expected to improve further.

• Other AI Solutions: In parallel, other firms have launched AI MLR tools. SecureCHEK AI (acquired by IQVIA) touts its GenAI-powered prechecks: it builds a “master library” of approved brand phrases and compares drafts against it to flag changes (^[34]). Authority reviews using SecureCHEK have reported thousands of hours saved per year by avoiding repeat corrections. Similarly, companies like Papercurve use AI to auto-annotate claims against references, claiming order-of-magnitude speedups. These cases highlight a broader industry trend: life science companies testing different AI vendors to accelerate MLR. Where Veeva’s advantage lies is in its integration: the AI is built directly into the already-trusted PromoMats workflows, rather than a bolt-on tool.

Anecdotal report from IQVIA (SecureCHEK’s developer) mentions an unspecified life sciences firm that implemented SecureCHEK: they halved the review cycle time of promotional content within a few months of use. Dr. Samin Saeed (formerly of GSK, now at Moderna) has stated that using intelligent automation in content pipelines is “critical to focus our resources” efficiently while still supporting all initiatives (^[78]). These qualitative findings reinforce that AI adoption is moving from “pilot” to real cost savings stage.

Discussion: Implications and Future Directions

The advent of agentic AI in MLR review heralds a fundamental shift in content operations. Here we discuss the broader implications, potential benefits, and the considerations that must accompany this transformation.

Transforming Content Workflow

Reduced Bottlenecks. With Quick Check and Content Agent, many of the tedious manual checks in the MLR chain can happen without human input. Early removal of errors means marketing teams get faster feedback on drafts. In practice, workflows will evolve so that content-triaging becomes an automated first step. This could reduce manpower needed for initial compliance review, or allow those reviewers to be reassigned to higher-level tasks (like strategic oversight, global coordination, or field support).

Tiered Review Models. AI enables more sophisticated tiering of content review. For example, low-risk assets that pass automated checks with no red flags could skip intensive MLR review altogether or go through a lightweight sign-off. High-risk content (e.g. major claims, new indications) would still get full human review. This risk-based review approach matches reviewer effort to risk level, a strategy many compliance experts advocate. Veeva’s content strategy already talks about tiered rules advancing “a large percentage of content through streamlined tiers” (^[79]). AI tools could operationalize that by automatically classifying content as low/medium/high risk based on its checks.

Enhanced Collaboration. By summarizing and indexing content, AI agents can enable more effective discussions with marketing and agencies. Instead of presenting a draft from scratch, companies might start reviews with Quick Check reports and AI summaries to highlight topics to discuss. Cross-functional PRC meetings could be more data-driven: participants come prepared with the AI-generated analysis in hand.

Scalability Across Geographies. Global companies produce local language versions of content. AI can assist with translation checks (e.g. ensuring the same warnings appear in each language) and maintain consistency across regions. Veeva’s secure, cloud-based approach means multinational teams everywhere can access the same AI tools and datasets.

Compliance and Risk Management

Consistency and Auditability. Embedding AI within a regulated platform (Veeva Vault) means all checks and interactions are logged. Any suggestion made by the agent is associated with the document’s audit trail. This is critical for compliance: if regulators were to audit the review process, companies can demonstrate exactly what automated checks were applied and what human decisions were made. This tight integration preserves the auditability that regulators require.

Human-in-the-Loop Safeguards. Veeva emphasizes that reviewers retain final authority. The AI agents aid decision-making but do not override human judgment. This aligns with best practices in pharmaceutical AI: using AI as “augmented intelligence” rather than replacing expert oversight. Reviewers can accept or reject each AI finding. Over time, the system could even learn from reviewer corrections (for example, if users repeatedly override a certain kind of alert, that could refine the models’ behavior).

Quality Control. Any AI model can make mistakes or oversights. In the MLR context, even a single missed nuance could be critical. Therefore, companies will likely implement quality checks: for example, requiring a second human review for any content marked as “critical” by the AI, or periodically auditing random samples of AI-cleared content. Over time, vendor-driven fine-tuning of the LLMs (based on customer feedback) should improve accuracy. Veeva’s platform allows periodic model updates and configurations, which can incorporate real MLR outcomes to train the system.

Regulatory Acceptance. It remains to be seen how regulators feel about AI involvement. The FDA and other agencies have not explicitly banned or encouraged AI use in promotional review. Because the AI is not replacing human analysis, regulators will likely focus on whether the final materials are compliant, regardless of how many automated checks were used. However, companies should note internal policies: some may require documentation of the AI use just for transparency (not for compliance necessity, but for corporate governance).

Broader Industry Trends

The broader integration of AI agents like these suggests several trends:

• End-to-End Content Operations: Beyond pre-review, we can anticipate AI tools at other content lifecycle stages. For example, use of AI in content creation is already being piloted (drafting initial copy, generating images, etc.). Veeva notes that future PromoMats AI applications will empower marketers in ideation and creation, not just in review (^[80]). Similarly, after publication, AI could help analyze field usage of materials (identifying which content resonates with HCPs).

• Data-Driven Compliance: With AI agents embedded, companies accumulate new data on review patterns and error types. This could feed analytics: for example, identifying which types of assets most often fail compliance checks, or which departments find the most Quick Check issues. Over time, this can inform training and process improvements.

• Focus on Critical Content: If AI allows low-hanging compliance tasks to be automated, human reviewers might shift focus toward strategic, high-stakes content. This could enable teams to take on more sophisticated marketing (e.g. real-world evidence content, personalized medicine messaging) because they are not bogged down by routine checks.

• Global Harmonization Efforts: The use of AI in compliance might also push regulators toward more harmonized guidelines. As companies deploy AI across regions, it will highlight differences in rules; industry pressure might mount for simplifying global promotional codes.

• Custom AI Agents: Beyond Veeva’s built-in agents, the platform allows building custom AI agents. A company could, for example, create an agent trained on their proprietary content style or on specific therapeutic guidelines. This extensibility means Veeva AI can evolve with customer needs. It also suggests a future market of third-party “apps” for Vault, analogous to smartphone apps, focused on niche tasks in life sciences.

Potential Risks and Mitigations

While promising, automating MLR review with AI also has potential downsides:

• Overreliance on AI: There is a risk that users might over-trust the agents, assuming “if Quick Check didn’t catch it, it must be fine.” To mitigate this, companies should train reviewers to view AI cues as advisory. Standard operating procedures may emphasize double-checking critical sections manually, especially early in adoption.

• False Positives/Negatives: AI models can sometimes produce irrelevant alerts (false positives) or miss subtle violations (false negatives). Veeva’s Quick Check and Content Agent, being LLM-based, have these limitations. Continuous monitoring is needed. Initially, teams might find that the agent flags minor style issues that distract from bigger problems. Administrators can adjust sensitivity levels or skip certain checks if they prove unhelpful.

• Security and IP Concerns: It is vital that the AI environment is secure. Veeva’s design keeps content on-premise (in Vault) to avoid IP leak. Customers should ensure appropriate firewall and compliance with corporate policies (e.g., not integrating Vault with external web search). The Vault-based integration addresses many concerns by design, but companies may have to pay attention to data entitlements and how models are updated.

• Regulatory Scrutiny: If an AI system were to recommend a promotional claim that turned out to be non-compliant, liability (in terms of internal review) is unclear. Companies will have to maintain a clear record that the ultimate sign-off was done by qualified personnel.

Overall, experts emphasize that process changes (clear roles, training, update of SOPs) must accompany technology introduction (^[81]) (^[82]). AI is an enabler, but operational governance remains critical.

Future Directions

Looking ahead, the principles underlying Veeva’s agents will likely extend further:

• Enhanced Multimodal Analysis: Content Agent already handles text and static images. It could evolve to handle rich media: analyzing video scripts, embedded interactive elements, or even voiceover content. Future agents may incorporate real-time speech-to-text checks for webinar content or chat analysis from tele-sales calls.

• Smarter Global Support: Future iterations may automatically apply country-specific rules based on localization metadata. For instance, if a French-language version of a brochure is being prepared, the agent would apply French health authority guidelines (e.g., particular wording required by ANSM) without separate configuration. Veeva already has “market guidelines” in Quick Check (^[83]), but this could become fully automated per country.

• Continuous Learning: As more customers use the agents, Veeva could harness aggregated learning (with privacy safeguards). Patterns in corrections might be used to refine the model. For example, if most users ignore a flagged “minor” issue, the model can deprioritize it for all customers.

• Integration with Other Systems: Veeva PromoMats does not work in isolation. Future AI extensions could span CRM (integrating with customer interactions) or regulatory submissions (flagging content for filing in specific formats). There are hints Veeva wants Agents across all applications (^[84]). A future scenario: an agent helps generate an FDA-compliant submission document by pulling content and warning of missing sections.

• Standardization of AI in Pharma: As AI tools become commonplace, industry groups or regulators may issue guidance. For instance, there could be a formal principle of “AI-assisted review” requiring firms to document AI usage and validation. Proactive adopters like Veeva will have an advantage, having already integrated into their quality systems.

In the long run, “Agentic AI” like Veeva’s could blur the lines between content ideation, production, and compliance. Teams might ideate a campaign using generative models, have those models output draft content, and automatically flow it through agents like Quick Check to ensure compliance before a human even touches it. This vision of a semi-automated content pipeline is compelling but will need careful change management.

Conclusion

Automating the MLR review process with AI holds transformative promise for life sciences marketing. Veeva PromoMats’ new Quick Check and Content Agents directly address the historic pain points of MLR review: the tedium of checking against a vast body of rules and the slow, batch-mode nature of committee review. By using AI to perform editorial and regulatory pre-checks (Quick Check) and to assist human reviewers with summarization and Q&A (Content Agent), PromoMats aims to accelerate the fastest path to approved content (^[53]).

The evidence gathered so far – from vendor analysis, early adopter testimonials, and industry research – suggests substantial benefits. Companies are already reporting major reductions in review hours and meetings (^[11]), and ample case studies (like Papercurve’s and others’) highlight that intelligent review tools can cut approval times by 50–60% (^[29]) (^[74]). Veeva’s estimates (up to 75% faster reviews (^[17])) indicate that when AI is properly aligned with domain knowledge, the MLR bottleneck can be dramatically relieved.

However, success will depend on rigorous implementation and ongoing oversight. Organizations must treat AI as a tool that augments expertise, not as an infallible oracle. Guardrails, clear governance, and performance tracking will be essential to ensure that compliance outcomes are as good as (or better than) the old system. When done correctly, though, the shift is likely to yield a step-change in how content flows from idea to market. Reviewers will spend their time on the most critical strategic issues, rather than routine checks. Creative teams will get faster feedback, enabling truly agile marketing. Ultimately, this means faster patient access to information about new therapies – a core goal of both industry and regulators.

In closing, Veeva’s integration of AI into MLR workflows exemplifies a broader digital transformation in healthcare marketing. It stands at the intersection of advanced LLM technology and the heavily regulated world of drug promotion. The Quick Check and Content Agents are pioneering tools, and early signs are that they deliver on the promise of AI in life sciences: increasing productivity and compliance simultaneously (^[18]) (^[14]). As these tools mature and more companies adopt them, we can expect to see MLR review evolve from a lengthy gatekeeper to a mostly seamless, high-value layer of the content lifecycle. Regulators and industry alike will need to adapt as well, but those that harness this technology wisely will gain a competitive edge – bringing quality information to patients and healthcare providers more quickly and safely than ever before.

All claims in this report are supported by industry sources and product documentation (^[1]) (^[85]) (^[52]) (^[6]) (^[11]).