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28 Nov 2025
Table of Contents
Executive Summary
As Medical, Legal, and Regulatory (MLR) functions take on a more strategic role in accelerating content delivery and maintaining compliance, the growing volume and complexity of materials has exposed the limitations of manual processes. Artificial Intelligence (AI) offers a powerful opportunity to augment, rather than replace, human reviewers, enabling greater speed, consistency, and scalability. This whitepaper outlines a pragmatic path forward through human-AI collaboration, highlighting a spectrum of maturity stages, governance frameworks, and practical use cases. Key recommendations include: (1) implementing hybrid workflows that preserve human oversight while leveraging AI efficiencies, (2) defining AI Collaboration Zones to guide task delegation between AI and reviewers, and (3) embedding cross-functional governance to ensure responsible and scalable AI adoption. Together, these strategies can future-proof MLR operations and transform them into drivers of scientific rigor, compliance integrity, and commercial agility.
Introduction
Medical, Legal, and Regulatory (MLR) review has evolved to become a strategic differentiator in the life sciences industry. It is now a critical enabler of content velocity, market responsiveness, and scientific accuracy, particularly as organizations operate across an increasingly complex landscape of channels, geographies, and indications. This evolution has strained traditional MLR processes due to the increasing volume and complexity of content, exposing inefficiencies and delays that can blunt commercial momentum.
Forward-looking organizations that understand the nuanced compliance requirements of MLR review and approval processes are embracing artificial intelligence (AI) to augment human expertise as a force multiplier, using intelligent tools. Yet, the idea of fully automating MLR decisions remains aspirational. The nuanced judgments required, particularly around scientific claims, regulatory, and compliance risk, still demand expert human oversight, necessitating hybrid human-in-the-loop models that combine AI’s speed and scale with human discernment.
This whitepaper explores the current state of AI in MLR, identifies emerging human-AI collaboration frameworks, and outlines strategic steps for building a scalable, compliant, and future-ready review model. It answers questions such as what might successful human-AI collaboration in MLR really look like? And how can organizations move from isolated pilots to enterprise-wide impact?
Authored by MLR industry experts from Alexion Pharmaceuticals (a subsidiary of AstraZeneca), Takeda Pharmaceuticals, and Indegene, it brings together more than 35 years of combined experience across global medical affairs, regulatory operations, and content transformation. The insights shared here are grounded in practical industry experience and a deep understanding of how MLR processes are evolving.
The Evolving Role of MLR in Life Sciences
MLR has undergone a significant shift in recent years. In today’s omnichannel environment, the function plays a pivotal role in accelerating go-to-market strategies, maintaining scientific rigor, and ensuring brand and message consistency across content types. With the push toward personalized engagement and digital acceleration, MLR is no longer just a compliance checkpoint. It is a catalyst for scientific and commercial agility. The function must contend not only with increased complexity but also with sheer volume of content and the need to meet these demands at speed and scale, making timely MLR review more critical than ever.
Urgency for MLR Transformation
In today’s environment, MLR teams can no longer operate with legacy, linear workflows. Industry benchmarks show that a full review/approval cycle for promotional content often takes up to 40 days from draft to distribution. Companies that have optimized and streamlined their MLR review process have achieved up to a 28% reduction in lead time for approval and a 35% increase in right-first-time documents with selective automation.
This highlights the tangible impact of operational efficiency gains—and the growing role of AI in enabling them. As McKinsey notes1, pharmaceutical companies are beginning to realize 20 to 30 percent productivity gains in content-related functions by applying AI (generative AI) to tasks like drafting medical content and automating quality checks.
But the path must be governed: regulatory frameworks and academic literature2, emphasize that any AI applied in compliance critical workflows must include traceability, validation, auditability, and continuous monitoring.
Why Human-AI Collaboration Is Now a Strategic Imperative
AI in medical communications has reached an inflection point, not just in capability, but in necessity. Life sciences organizations are publishing more content, at higher speed, across more channels and more formats than ever before including emails and digital assets for HCPs and patients, omnichannel campaigns, medical slide decks, and real-time updates to labels and safety communications. In large enterprises, MLR teams may be asked to review thousands of content pieces per month. This volume simply cannot be handled with existing manual processes, especially when timelines are compressed, and market launches depend on rapid turnaround.
The stark reality is that:
Content backlogs are slowing down crucial communications like post-approval messaging and label updates.
Skilled reviewers are bogged down by repetitive tasks like version checks and formatting reviews.
Compliance risks are rising as teams struggle to maintain consistency, increasing the chance of costly errors.
To address these challenges and gain a competitive edge, companies are increasingly integrating AI into their MLR workflows. The goal isn’t to replace human judgment, but to enhance it. AI tools that automate version comparisons, flag off-label risks, or validate claims against references can significantly reduce the time spent per asset. In addition, AI can help enforce brand and terminology consistency and identify incomplete submissions before they even reach MLR reviewers.
But here’s the hard truth: full automation of MLR is not only unrealistic, it’s also risky. AI cannot (and should not) be expected to make final decisions on complex scientific claims, contextual off-label discussions, or regulatory nuances that vary by geography. These decisions require medical, legal and regulatory expertise, deep contextual awareness, and ethical judgment.
That’s why human-AI collaboration and not AI replacement is the only sustainable path forward.
Current State of AI Adoption in MLR
MLR teams are actively integrating AI tools into their workflows. Early use cases include:
Automated document comparisons
Flagging of off-label claims
Terminology checks against standard lexicons
Pre-review checklists and metadata validations
Reference checks, including flagging missing or mismatched citations
Pharma, biotech, and medtech companies are experimenting with AI in both in-house and outsourced MLR models. Many of these efforts remain in pilot mode, but the direction is clear: AI is moving from optional experimentation to operational necessity.
Maturity Spectrum of AI in MLR: From Targeted Pilots to Broad Deployment
As AI adoption in MLR gains momentum, organizations are progressing along different levels of maturity. Each stage reflects a unique combination of priorities, challenges, and readiness factors. Knowing where an organization sits on this spectrum is essential for setting realistic goals, defining success criteria, and planning for responsible scaling of human-AI collaboration.
Nascent Stage: Exploring Tools and Conducting Isolated Pilots
AI is used in isolated, low-risk tasks like metadata tagging or terminology checks, often without cross-functional alignment. Projects are small-scale with manual output validation and limited governance.Human Intervention: High
Emerging Stage: Using AI to Support Specific Repeatable Tasks
Organizations are now systematically using AI to handle repeatable tasks, supported by defined KPIs, trained reviewers, and feedback loops. Scaling remains limited by data and workflow inconsistencies.Human Intervention: Medium to High
Advanced Stage: A Vision in Progress
Few organizations are here yet, but early movers are embedding AI into core MLR operations. Use cases include rationale generation, dynamic risk scoring, content reuse detection, and AI-driven triage. AI is no longer just a tool; it’s part of the operating model.Human Intervention: Medium
Some organizations have also established cross-functional governance committees or working groups, including stakeholders from Legal, Compliance, Medical, Regulatory and Technology, to evaluate and approve AI tools for specific tasks.
A few are piloting human-in-the-loop automation workflows where AI handles the first layer of triage or quality review, and humans provide the final decision. However, these efforts are often limited to specific brands, regions, or content types, and are not yet scaled enterprise-wide.
While true end-to-end human-AI collaboration remains rare, these early movers are laying the foundation for scalable, compliant AI deployment. They are building feedback loops, structured workflows, and oversight mechanisms crucial enablers for long-term success. For most organizations, this stage remains a work in progress, but it provides a clear direction for the future of MLR.
Conceptual Frameworks for Human-AI Collaboration
How do we move towards meaningful human-AI collaboration? The first is to bring clarity and structure to the way humans and AI work together. This begins by classifying decisions based on their level of risk and complexity:
Tasks that AI can handle autonomously, such as routine, low-risk, rules-based actions
Tasks that require mandatory human validation due to regulatory, ethical, or contextual sensitivity
Tasks best suited for shared, iterative workflows where AI provides assistance and humans finalize decisions
This includes defining escalation protocols and feedback loops to continuously improve AI tools based on reviewer input.
To ensure this framework is effective, it must be reinforced with clear escalation protocols, periodic review checkpoints, and continuous feedback loops, enabling AI models to learn and evolve based on real-world reviewer input.
Defining AI Collaboration Zones to Balance AI Autonomy and Human Oversight
Establishing clearly defined AI collaboration zones helps organizations determine where AI can operate with minimal oversight. Few examples are outlined in the table below:
| AI Collaboration Zone | AI Autonomy (Low-Risk Tasks) | Human Validation (High-Risk/Complex Tasks) |
|---|---|---|
| Content Structuring | Auto-generating standard section headers in MLR review documents such as promotional materials, HCP emails, or slide decks | Reordering sections to align with therapeutic messaging hierarchy and narrative flow for launch campaigns |
| Terminology Checks | Flagging non-preferred terms like "patient-friendly" vs "patient-centric" | Justifying exceptions to standard lexicons when scientific nuance (e.g., mechanism of action specificity) requires deviation |
| Reference Linking | Auto-linking referenced publications to source documents and claims they support | Auto-linking references to the claims they support, with AI-enabled validation to check if cited studies truly substantiate those claims (e.g., statistical significance of efficacy). |
| Tone Consistency | Detecting use of informal or promotional tone like "breakthrough" | Deciding if the overall message, when viewed in therapeutic context, could be perceived as misleading or non-compliant |
| Translation Review | Identifying untranslated strings or inconsistent localization terms | Reviewing nuanced translations of safety language or boxed warnings to ensure alignment with regulatory intent |
| Version Control | Flagging missing safety disclaimers between document versions | Assessing whether a subtle content change (eg., rephrased claim) alters the scientific integrity or introduces new regulatory risk |
| Scientific Claims and Pre-Approved Content | Suggesting pre-approved digital SOPs with scientific statements or claim libraries for preliminary checks including auto-linking associated references | Confirming that scientific claims are still valid in the promotional context and that references remain current and compliant |
This type of calibrated approach allows teams to progressively expand AI’s role by starting with trusted, repetitive tasks and gradually layering in more human assistance, ultimately creating a scalable, governed collaboration model.
Real-World Spotlight: Early Signals from Augmentation Pilots
Across the life sciences industry, select teams have begun piloting AI-assisted tools in controlled MLR environments. In one such case, a limited-scale initiative deployed AI to support pre-review processes, such as version comparisons, reference validation, and terminology checks on promotional materials within a narrow therapeutic scope. These efforts revealed early signs of improved reviewer throughput and greater submission consistency, while maintaining strict human oversight and compliance protocols. Success depended not only on the technology itself, but on structured onboarding, clear governance guardrails, and trust-building between reviewers/stakeholders and AI tools.
These signals suggest that trust-building is a prerequisite for scale, and successful adoption hinges as much on change management as on technology readiness. In future pilots, organizations can further accelerate pre-review by integrating AI with digital scientific communication platforms (SCPs) and structured claims libraries. These assets enable automated checks for reference validity, scientific accuracy, terminology consistency, and alignment with approved lexicons, laying the groundwork for more intelligent and scalable automation.
Risk, Governance, and Compliance Considerations
To move forward with human-AI collaboration, organizations need to embed risk governance and compliance mechanisms directly into their AI operating models and not as an afterthought but as foundational elements.
This begins with defining clear principles of responsible AI use:
Explainability: AI outputs must be transparent, with clear rationale for each suggestion or flag. This is particularly important in mitigating risks like hallucinations, where the AI generates plausible but inaccurate content, which could lead to misinformation if unchecked.
Auditability: Every AI-assisted decision should leave behind a traceable review trail, enabling regulatory audits and internal quality checks.
Accountability: The final decision must always be made or endorsed by a qualified human reviewer, with defined roles for AI assistance vs. human judgment. This ensures that errors such as hallucinations are caught and corrected before content is approved.
Data Privacy: AI systems must be designed and deployed with stringent data privacy safeguards to ensure compliance with evolving global regulations (e.g., GDPR, HIPAA). This includes controlling access to sensitive data, anonymizing inputs where appropriate, and ensuring that AI models do not inadvertently retain or leak proprietary or personal information.
But true governance goes beyond principles; it must be operationalized:
Risk-tiering AI Tasks: Each AI use case should be classified based on the level of risk it carries (e.g., low for automated disclaimer checks; high for claim substantiation). The higher the risk, the stricter the human controls and oversight required. Risk assessments must also consider potential biases in individual reviewer tolerance (e.g., conservative vs. flexible interpretation of risk), which can influence how AI outputs are evaluated.
Human-in-the-loop automation protocols: Define when human validation is mandatory, when AI can operate independently, and when collaboration is iterative. These protocols must be codified into workflows—not left to individual discretion.
Dynamic compliance thresholds: Establish feedback loops where performance of AI models is continuously monitored. If accuracy dips below acceptable thresholds, automatic escalation to human reviewers should be triggered.
Model lifecycle governance: Track versioning of AI models, training data changes, and usage context to ensure continued regulatory alignment. Just like with human processes, AI models require documentation and ongoing validation.
Data and model safeguards: AI models must be designed and deployed with stringent controls to prevent exposure of proprietary or sensitive data. Data privacy, security, and confidentiality must be ensured, especially when handling unpublished clinical information or internal regulatory strategy. Safeguards against data leakage, model inversion attacks, and unauthorized access must be built into operating environment.
Building Enterprise Readiness for Scalable Human-AI Collaboration
Scalable human-AI collaboration requires more than just individual reviewer preparedness. It demands enterprise-wide alignment across people, processes, and platforms.
Key elements of enterprise readiness include:
Cross-functional AI literacy : MLR reviewers, medical writers, regulatory teams, and IT stakeholders must have a shared understanding of how AI models work, their limitations, and the decision types they are best suited for.
Integrated AI tools in core workflows : AI capabilities should be embedded into existing platforms such as MLR review systems, content management tools, or regulatory submission portals to minimize friction and ensure adoption.
Robust change management : Transitioning to AI-assisted processes requires structured onboarding, internal champions, and regular upskilling. Teams can overcome resistance through targeted training and onboarding.
Building Reviewer Trust
Start with transparency—clearly define what AI tools do (and don't do) to dispel myths.
Use "shadow pilots" where reviewers can compare AI-suggested outputs with their own for validation.
Engage reviewers early in the pilot phase to build shared ownership and confidence.
Celebrate reviewer wins (e.g., time saved, reduced rework) to demonstrate value.
Leveraging Internal Champions
Designate respected reviewers or functional leads to serve as AI champions across Medical, Legal, and Regulatory teams.
Host internal forums for early adopters to share learnings and showcase use cases.
Ensure leadership alignment across functions to prevent fragmentation of adoption strategies.
Capturing Lessons from Cross-Functional Pilots
Conduct retrospectives after each pilot to capture what worked and what didn't.
Create structured feedback loops that translate reviewer insights into AI model or workflow improvements.
Codify pilot learnings into updated SOPs or operating manual, reviewer training materials, and onboarding programs.
Infrastructure for monitoring and feedback: Enterprise readiness also means having the ability to track model performance, flag inaccuracies, and incorporate reviewer feedback to improve models continuously. This helps close the quality and compliance gap.
Governance frameworks that scale: AI governance must evolve from pilot-level oversight to enterprise-level standards. These standards should cover explainability, traceability, accountability, and role-based permissions.
Strategic Recommendations for the Path Forward
Organizations should focus first on augmenting human reviewers with AI-driven efficiencies while laying the foundation for more advanced, selective automation. To operationalize this approach, organizations can adopt a roadmap that gradually increases MLR review automation maturity in alignment with governance readiness and team confidence:
| Build Readiness [Do Now] | Operationalize at Scale [Do Next] | Embed Intelligence - Future Vision (3 to 5 Years Out) |
|---|---|---|
| Quick wins to improve MLR efficiency with minimal disruption: | Lay the groundwork for scaled deployment: | Designing a truly adaptive, scalable, and intelligent MLR model: |
| Identify low-risk, repeatable tasks (e.g., terminology checks, version control) suitable for AI augmentation. Example action: Run a 90-day pilot focused on automated version checks in one brand team. | Integrate AI tools directly into submission workflows and content management platforms. Example action: Connect AI-enabled risk triage module to your content management system. | MLR teams orchestrate end-to-end content flow using AI-assisted triage and intelligent routing. Example action: Implement AI that auto-routes content based on risk level, content type, and past reviewer feedback. |
| Establish cross-functional AI governance working groups (Medical, Legal, Regulatory, IT). Example action: Set up bi-weekly governance syncs with representatives from each function. | Standardize risk-tiering models and escalation protocols across therapeutic areas. Example action: Build a risk scoring matrix aligned with global content types and therapeutic areas. | Real-time compliance checks run in parallel to modular content development, enabling faster approvals. Example action: Deploy AI agents that check compliance language during real-time content authoring. |
| Pilot AI tools in narrow therapeutic areas or content types, with strict human-in-the-loop oversight. Example action: Test AI-based claim annotation in one content type (e.g., email templates). | Expand human-AI collaboration to support pre-review, risk triage, and metadata enrichment at scale. Example action: Deploy AI to auto-tag content with metadata for faster intake and triage. | Enterprise-level governance frameworks support continuous learning, feedback loops, and adaptive thresholds. Example action: Create a cross-functional AI feedback council to fine-tune model parameters quarterly. |
| Develop clear "AI Collaboration Zone" that define when and how AI can be applied within existing MLR workflows. Example action: Map a swimlane chart marking approved AI intervention points within the MLR process. | Use performance dashboards to monitor reviewer AI interactions and track compliance signals. Example action: Launch a dashboard tracking AI usage, override frequency, and flagged issues. | AI models are trained on historical decision data, product nuance, and risk profiles for context-aware decision support. Example action: Fine-tune AI models using archived review comments and approval outcomes from the past 3 years. |
| Begin AI literacy and change management training for reviewers to foster early adoption. Example action: Host an AI literacy workshop series for MLR reviewers and brand teams. | Update SOPs and documentation to reflect AI-enhanced workflows and governance thresholds. Example action: Create a revised SOP annex that defines roles, auditability, and compliance checks for AI-assisted reviews. | Medical Affairs, Legal, and Regulatory operate within shared digital ecosystems that embed AI guidance at each step of the review process. Example action: Enable modular content reviews where AI validates scientific claims against an approved library in real time. |
Conclusion
As MLR review becomes more central to commercial agility and compliance integrity, the role of AI will only grow. But this growth must be guided by a clear-eyed understanding of what AI can and cannot do.
Organizations that view AI as a partner rather than a replacement will build stronger, more adaptive MLR functions. By focusing on augmentation today and laying the foundation for responsible automation tomorrow, they can turn MLR into a source of both operational efficiency and strategic advantage.
In the age of information overload and regulatory complexity, the future of MLR isn’t human or AI. It’s human plus AI, working together, better.
Industry leaders have the opportunity to shape this transformation now, by embracing pilots, investing in reviewer readiness, and codifying governance that can scale. The organizations that act early won’t just keep pace; they’ll set the standards for the next era of compliant, agile MLR.
Sources:
1. Generative AI in the pharmaceutical industry: Moving from hype to reality, McKinsey & Company
2. AI governance: a systematic literature review, Batool, A., Zowghi, D. & Bano, M. AI Ethics 5, 3265–3279 (2025)
Disclaimer
The views and opinions expressed in this white paper are those of the authors and do not represent or reflect the views of their employers. No proprietary, product, or confidential information from any employer is included.
About the Authors
Director, Head of US Promotional Review and Operations (PRO)
Alexion, AstraZeneca Rare Disease
Director, Group Lead Center of Excellence, Scientific Communications
Takeda Pharmaceuticals
Associate Vice President - Material Review, Operations and Compliance
Indegene
Associate Vice President - Client Services
Indegene
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Associate Marketing Manager
Indegene
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