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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 remain optimistic about AI’s potential impact on the industry’s ability to deliver compliant content more efficiently and effectively. This aligns closely with findings from the , which reveals that 63% of medtech organizations are actively piloting AI use cases, even though only 3% have fully embedded AI into active, core processes.⁴

Reimagining medtech content workflows with AI

While early commercial conversations at this year’s �Թϱ��� Summit were focused heavily on how AI could be leveraged for content creation, attention has shifted to use cases beyond content authoring—such as AI agents designed to streamline content validation, reduce errors and accelerate approvals.

This shift comes at a critical time. Driven by a 24% year-over-year surge in approved marketing materials, underlying workflows have struggled to keep pace. Beneath the AI surface lies a critical data reality: 39% of medtechs still rely on manual spreadsheets to manage compliance data, while 20% operate on completely disconnected systems. Without a clean, unified data foundation, layering AI onto fragmented tools won’t solve the bottleneck—it will only accelerate the clutter. To make AI-driven validation and approvals work, companies must first bridge the gap between their content volume and their data infrastructure.

Below are five key areas where medtech regulatory and commercial leaders see the potential for AI to deliver value across the content supply chain.

Improving content quality before MLR

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.

Detecting and managing promotional claims

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.

Assisting human reviewers in the MLR process

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.

Automating tier-based review models

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.

Accelerating global translations

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 article^2, 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.

AI in medtech: not just hype—but not fully realized

While medtech excitement about AI is high, whether it can produce measurable results remains elusive. The aforementioned June 2025 report from McKinsey^3, 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.

To bridge the gap between content volume and compliance, emerging medtechs must first centralize their asset management and MLR workflows. Establishing a unified data foundation ensures that when you do deploy specialized AI agents, they deliver measurable impact from day one.

For emerging medtech companies looking to build this essential foundation, �Թϱ��� PromoMats Basics offers a pre-configured, fast-to-deploy digital asset management and MLR solution. By establishing a centralized, compliant system for commercial content, PromoMats Basics equips growing teams with the clean data structure and streamlined workflows needed to scale—and seamlessly integrate advanced AI capabilities.

Want to learn more about the state of commercial content operations in 2026? Download the to compare your lifecycle metrics against industry standards.

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