At Vodori, we are obsessed with MLR. For more than seven years, our sole focus has been optimizing the MLR process for life sciences companies. We believe every claim, every piece of content, and every communication deserves rigorous review to safeguard patients, protect brand trust, and ensure regulatory accountability. This singular focus has taught us that while MLR is indispensable, it is often seen as a bottleneck. Mid- and large-sized pharmaceutical companies report that MLR review cycles can often stretch 50–60 days for each content piece under current workflows (Indegene). At the same time, regulators worldwide are intensifying enforcement. In the U.S., the FDA has issued thousands of warning letters since 2020, with concentrated enforcement waves in 2025 targeting pharma, biologics, and devices (biopharmaapac.com). In Europe, the EU MDR and proposed Pharma Package reforms demand that all promotional claims align with intended purpose, be fully substantiated, and avoid misleading content, with penalties for violations expected to increase (podymos.com; lexology.com).
Artificial intelligence (AI) offers a timely opportunity to address these pressures. Early pilots show that AI can automate repetitive pre-checks, identify claims, link them to approved references, and flag gaps before content ever reaches a reviewer. According to McKinsey, some pharmaceutical companies have reduced regulatory submission timelines by 50–65% through AI-enabled automation and workflow redesign. These outcomes highlight how AI can meaningfully accelerate compliance-heavy processes like MLR while preserving auditability and quality. The future is clear: AI will not replace MLR, but it will transform it into a faster, safer, and more strategic safeguard.
Why Now
Several forces are driving urgency. Digital content is exploding, with each new launch generating dozens of assets across channels that traditional MLR processes cannot scale to support. Regulatory scrutiny has intensified, with greater financial and reputational consequences for noncompliance. At the same time, enterprises are rethinking their technology stacks, and AI-ready platforms are quickly becoming a top priority.
The broader technology ecosystem is also evolving rapidly. Across industries, software vendors are racing to introduce new AI features for content and compliance. While these developments signal momentum, many solutions are generic and not built for the unique demands of life sciences. The real opportunity lies in explainable, GxP-validated & auditable, and MLR-native, domain-specific AI.
Applications Today
Practical AI applications are already making an impact. Pre-check automation ensures safety language, disclaimers, and references are included before a reviewer sees content. Claims identification and smart reference linking automatically match statements to approved sources and flag potential risks. Risk-based triage helps teams prioritize, ensuring high-risk materials get deeper scrutiny while routine updates move quickly. Together, these applications free reviewers from tedious work and allow them to focus on higher-value analysis and decision-making.
The Road Ahead
Looking forward, we anticipate four transformative directions:
- Agentic AI workflows that compile review packages, highlight risks, and provide auditable rationales for reviewer validation.
- Generative, compliant content creation that produces drafts from approved claims and brand guidelines, ensuring content is “born compliant.”
- Real-time guardrails and pro-active compliance that enables safe, compliant dialogue in digital channels at the speed of conversation.
- Connected compliance ecosystems that integrate CRM, content, and analytics, embedding compliance seamlessly into the entire commercial process.
Risks and Challenges
Alongside its promise, AI carries risks that must be carefully managed. The foremost challenge is compliance and auditability. Regulators require an auditable trail of every promotional decision, which demands deterministic algorithms that consistently produce the same output for the same input. Deterministic models enable reviewers to validate results, understand the logic, and confirm compliance. Non-deterministic or opaque systems, by contrast, may generate different outcomes under the same conditions and cannot explain their reasoning. In a regulated environment where reproducibility and traceability are non-negotiable, this unpredictability is unacceptable.
Beyond compliance, adoption depends on trust and change management. MLR professionals are cautious, and with good reason—non-deterministic models can hallucinate unsubstantiated claims, introduce off-label risks, or compromise data privacy. Over-reliance is another danger if humans become too dependent on automation. The safest path forward is a human-in-the-loop model where AI removes repetitive tasks while reviewers retain accountability and final judgment. This balance ensures compliance, builds confidence, and creates a foundation for responsible adoption, even as regulatory frameworks continue to evolve.
Vodori’s Perspective
Vodori’s approach is optimistic but measured. We are guided by our Core Values. We are Customer-first, designing AI features that alleviate the tedium of review while enhancing compliance. We are Bold in tackling the intersection of one of the most complex regulated processes with AI, but never reckless. We rely on Teamwork, collaborating across functions and with customers to ensure trust and adoption. We are Swift, recognizing the urgency of our customers’ work and the need to support them at the speed of business. We pursue Excellence in building solutions that are validated, reliable, and auditable, never experimental. And we value Simplicity, eliminating unnecessary complexity so reviewers can focus on what matters most.
Our roadmap reflects these values. We began with low-risk, high-value features such as pre-checks and smart reference linking. We are moving toward deeper automation, compliant generative content, and real-time review capabilities. At every step, our focus is on building AI that is transparent, auditable, purpose-built and complementary to the human experts, all within the unique needs of MLR. By embedding these principles into our platform, we ensure that customers gain speed and efficiency without compromising compliance or trust.
Conclusion
The shift to AI-augmented MLR is inevitable. The question is not if but how it will be adopted. Companies that invest now in AI-ready infrastructure, with trusted partners who understand compliance, will accelerate launches, reduce risk, and lead in customer engagement. Regulators, too, must provide clarity to ensure AI strengthens compliance rather than weakens it.
Vodori is building the foundation today for compliant, transparent, and auditable AI in MLR. We will remain MLR obsessed, where every decision we make, every feature we build, and every partnership we pursue is focused on optimizing MLR performance. Guided by our customer-first philosophy, bold innovation, and relentless pursuit of excellence, we are ready to lead this transformation.
The future of MLR is faster, smarter, and safer. The only question is who will be ready.
