Table of Contents
Apr 13, 2026 | Jai Sadhwani

Traditional medical, legal, and regulatory (MLR) review methods can no longer keep pace as personalization in content scales. AI solutions introduce the opportunity to significantly speed up the process while maintaining quality and compliance.

Applying Veeva AI for PromoMats in MLR is a compelling business choice that structurally improves processes and frees highly skilled experts for strategic work where human input is critical — such as the difficult judgment calls needed in content approvals. Built into the Vault Platform, AI agents perform quality checks, provide document insights, and assist reviewers to deliver personalized and impactful material faster.

While the technology is largely turnkey, realizing its full value depends on adapting processes and investing in change management to ensure successful adoption. Veeva Business Consulting partnered with several large biopharmas to implement AI agents directly in users’ core processes, focusing on goals such as:

  • Improving the quality of materials submitted before MLR review
  • Enabling more harmonized and efficient review processes
  • Refocusing expert teams on high-value tasks rather than routine checks

These implementations surfaced key learnings for other biopharmas applying AI into MLR.

Learning #1: Successful adoption started with organizational readiness

Organizations most successful in adopting AI into MLR were those that aligned early on how AI would create value for all involved in the content lifecycle. Because every organization’s MLR process is unique, those that took time upfront to map opportunities to reduce friction with AI saw faster adoption and clearer impact.

Clear, role-based communication proved to be a differentiator. Teams that positioned AI as a way to improve their work, not disrupt it, experienced stronger engagement. We found that defining roles, responsibilities, and project champions early built alignment with stakeholders, including marketing teams, agencies, MLR reviewers, and operations leaders.

Define the value of AI for end users

Marketing value: Reduce delays and accelerate speed to market
Improve the quality of materials submitted for approval. Standardizing content quality and quality early in the workflow reduces delays and accelerates speed to market. With fewer editorial issues surfacing during MLR, spend less time on repetitive revisions and more time on high-value creative work. The result: reallocate resources toward strategic and impactful content development rather than routine content cleanup.

Agency value: Deliver stronger submissions to MLR and boost efficiency
Log fewer cycles of editorial revisions coming out of MLR reviews. Automated proofreading and content quality checks provide early support to identify and resolve issues before submission. This creates a safety net for content quality and reduces unnecessary rework to deliver stronger submissions to MLR and build more trusted relationships.

MLR team value: Gain time to ensure scientific accuracy and compliance
Focus expertise on evaluating scientific accuracy and ensuring regulatory compliance, rather than routine editorial tasks. With access to intelligent insights, quickly understand document content, surface relevant considerations, and make informed decisions.

Operations value: Address more high-value operational improvements
Automated checks on editorial, brand, market, channel, and compliance help validate submission quality before review begins. The result: a more efficient and harmonized MLR process with fewer review cycles and fewer live review meetings. With greater confidence in the quality of content submitted for review, focus on other valuable operational improvements.

Learning #2: Adoption accelerated when teams reframed mindset about AI

In practice, applying AI to MLR processes requires addressing common concerns and a shift in how teams view AI in content review. In a recent Veeva PromoMats survey of 101 content professionals across 10 biopharmas, more than half of respondents reported no concerns about using AI in promotional content review. However, 48% identified several areas that warrant attention, including:

  • Accuracy and reliability of AI outputs in regulated content
  • Compliance, auditability, and traceability
  • Data privacy and security
  • Ensuring human oversight

We observed that adoption improved significantly when teams understood not just what AI does, but how it behaves. Large language models (LLMs) are humanistic rather than deterministic, meaning they generate insights based on patterns rather than producing identical outputs. AI for MLR is a support tool that enhances and refocuses human expertise, freeing highly skilled experts for strategic work. Organizations that continually communicated everyday business value saw less resistance to adoption.

Learning #3: AI fatigue decreased when AI was embedded into core business processes

Teams today are inundated with AI across their professional and personal lives. In this environment, introducing yet another AI tool can create adoption friction and contribute to AI fatigue. Adoption of AI into MLR accelerated when AI was not positioned as an additional tool, but embedded directly into core MLR workflows.

Leading organizations integrated AI into the systems and processes teams leverage daily, making it a seamless part of how work gets done rather than a separate tool to log into, learn, or manage. This approach reduced the cognitive load on users. Instead of asking teams to seek out AI, AI met them within their existing workflows to support content creation, streamline review, and accelerate approvals. The result was not only higher adoption, but a more intuitive AI experience that reduced friction and made AI immediately useful.

Learning #4: Defining impacts to MLR process and operating model early reduced friction later

AI agents deeply embedded in a commercial content platform work in concert with other technology to drive efficiencies and produce relevant assets with faster approval times. However, one of the clearest lessons from implementation was that technology alone doesn’t drive impact, successful implementation requires focus on people and processes.

Organizations that clearly determined how AI capabilities fit into their existing operating model and review processes experienced smoother adoption and less friction down the line. Marketing and agency teams understood their role in the updated process, even as workflows shifted. Addressing these changes early supported adoption and minimized disruption.

Learning #5: Hands-on experience proved to be effective in driving adoption

When implementing AI into MLR, hands-on experience is one of the most effective ways to drive adoption. While process diagrams and training materials are helpful, we discovered that real confidence came when teams directly interacted with AI in realistic scenarios.

Leading organizations prioritized early process exercises, with users working in a sandbox environment by the second week of implementation. Using real promotional content helped organizations refine workflows and build trust in the system. This approach enabled hands-on exploration of AI agent behavior within familiar workflows, while maintaining compliance.

Ensure your teams are prepared to leverage the full impact of AI agents in MLR review with Veeva Business Consulting’s in-depth analysis.

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