Author: John Lerch, Director, Commercial Content Strategy, MedTech
AI has the potential to significantly improve how medtech companies create, review, and distribute commercial content, but the transformation has yet to fully take shape. While many organizations are actively exploring AI, few have achieved measurable impact. McKinsey recently reported that nearly eight in ten companies have deployed generative AI in some form, but roughly the same percentage report no material impact on earnings. 1
Despite this, commercial and regulatory executives at the 2025 Veeva MedTech Summit, were optimistic about AI’s potential impact on the industry’s ability to deliver compliant content more efficiently and effectively.
Reimagining medtech content workflows with AIAI took center stage at this year’s Summit, coinciding with the introduction of Veeva AI, a new initiative to embed generative AI into core Veeva applications. Last year commercial leaders discussed how AI could be leveraged for content creation, but this year, following a sneak peek of MLR Bot—Veeva’s first planned AI Agent designed to streamline content validation, reduce errors, and accelerate approvals—the conversation shifted to use cases beyond content authoring.
Below are five key areas where medtech regulatory and commercial leaders see the potential for AI to deliver value across the content supply chain.
Many delays in the medical, legal, and regulatory (MLR) review process stem from easily preventable issues such as poor grammar, inconsistent formatting, or missing substantiation for claims. These errors often surface late in the content development cycle, slowing down approvals.
AI tools can act as a pre-screening mechanism, offering suggestions to fix common quality issues before content ever reaches MLR. This allows reviewers to focus on substance rather than syntax and helps teams reduce the number of review cycles.
The use of large language models (LLMs) can open new frontiers in claims management. AI can help identify promotional claims in existing content and detect new claims embedded in clinical research.
This enables teams to proactively track, verify, and align promotional language with approved claims libraries, reducing risk and improving oversight across markets and formats.
While a human-in-the-loop will likely remain essential in MLR reviews given the criticality of compliance in medtech, AI can serve as a powerful assistant. For example, LLMs can provide contextual assistance by referencing relevant regulations, internal guidelines, and previous decisions.
AI can also enhance consistency across reviewers, especially in scenarios where subjectivity plays a role. The goal isn’t to replace MLR reviewers but to elevate their ability to make faster, more informed decisions.
Many medtech companies already employ tier-based MLR review processes, streamlining approval for lower-risk content like minor copy updates, while reserving in-depth review for high-stakes materials like clinical white papers.
AI can take this model even further by automating tier classification and routing, based on pre-set business rules. This reduces reliance on manual triage and accelerates the path to approval.
As companies expand into new markets, the demand for high-quality, localized content continues to grow. AI-enabled translation tools offer near real-time adaptation of content into multiple languages, helping teams launch campaigns faster and more cost-effectively.
However, acceptance of AI translations remains a hurdle. A recent Forbes article2 notes that AI translation is likely to be held to a higher standard than human translators, and will require human review for the foreseeable future, much like the MLR process itself.
While medtech excitement about AI is high, whether it can produce measurable results remains elusive. The aforementioned June 2025 report from McKinsey3 introduces what it calls the “gen AI paradox”: noting that “nearly 80% of companies have deployed generative AI in some form, but roughly the same percentage report no material impact on earnings.”
The report points to a key insight: Most efforts to date have focused on broad, horizontal tools (e.g., chatbots, copilots), which can scale quickly but deliver diffuse results. In contrast, domain-specific use cases, those that reimagine function-specific workflows, are where transformative gains lie. Yet, 90% of these use cases remain stuck in pilot mode.
The implication for medtech? True value from AI will only come when companies move from experimenting with general-purpose tools to embedding specialized AI agents into the software that runs their most critical workflows.
This was consistent with what we heard from commercial and regulatory executives at the recent Veeva MedTech Summit: while results aren’t there yet, optimism for future impact is strong as companies like Veeva begin to engineer AI directly into the software that is running processes with domain-specificity.
Read how leaders like Zeiss are using AI for content creation and localization in medtech.