Medtech companies are at the forefront of healthcare innovation, developing life-changing devices and solutions to help clinicians diagnose diseases earlier, perform interventions more precisely, and monitor patient health more effectively. Innovations such as minimally invasive surgical robots, AI-powered diagnostic imaging, and connected diabetes management systems have transformed patient care.

As the industry emerges from a period of margin compression and dampened multiples, AI, enhanced by gen AI, can play a critical role in advancing medtech value creation priorities to boost productivity and, ultimately, profitability. McKinsey estimates that medtech companies could capture $14 billion to $55 billion per year in value from productivity gains and add $50 billion or more in annual revenue from product and service innovations.1 Gen AI’s potential is particularly compelling for the medtech sector, given the ubiquity of data-enabled products and numerous critical but repetitive workflows ripe for digital enablement, including regulatory documentation, contract compliance tracking, and customer support.

A fall 2024 survey of 40 medtech executives responsible for gen AI strategy and budgets shows encouraging early adoption trends.2 Roughly two-thirds of respondents say their companies are already implementing gen AI (Exhibit 1). Although about half are still in the pilot phase, nearly 20 percent are scaling their solutions with successful early results. All respondents reported positive qualitative or quantitative improvements from gen AI, with nearly half seeing measurable quantitative and qualitative productivity benefits. Most notably, 15 percent of those implementing gen AI have reported a positive impact on their P&L.

This article explores prominent gen AI use cases where medtech organizations are seeing results and shares a framework that companies can take to successfully scale and generate value from this transformative technology.

According to the results of our survey, gen AI is deployed across several domains, including R&D, commercial, and operations (Exhibit 2). The most promising use cases emerging from these efforts include innovation acceleration (particularly in R&D and product development), process automation (such as content generation for marketing and regulatory compliance), and decision support (including sales enablement and supply chain optimization). Companies seeing the greatest impact are prioritizing use cases that drive meaningful business value through specialized knowledge agents and industry-specific process improvements.

R&D is the most frequently identified medtech domain for gen AI adoption; this group is typically tech-savvy and more willing to explore new tools. Individuals in these groups often use off-the-shelf gen AI tools to search for relevant articles or synthesize research papers.

Tools more customized to the medtech R&D workflows could provide additional impact and help R&D teams streamline processes and get products to market faster. In medtech, R&D teams face numerous documentation requirements throughout the product development life cycle—from clinical study design to regulatory submissions, technical specifications, and product labeling. Without AI, the process of compiling and reviewing trial data takes weeks. Compressing that timeline through AI-assisted drafting of key materials, such as product development documentation or labeling—with a human in the loop to review final submissions—can enhance quality and free up time for higher-value research and innovation and ultimately accelerate the pace to market for critical healthcare products. For example, we have observed medtech organizations that have used AI to help improve their labeling productivity by 20 to 30 percent.

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