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.
Early adoption across the medtech value chain
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.
Faster, more productive R&D
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.3
Smoother commercial workflows
Gen AI is transforming key commercial workflows in medtech, including marketing, insight generation, and customer interactions: More than half of survey respondents report gen AI deployment across commercial use cases. These workflows have often been a bottleneck for medtech commercial organizations, given their complex portfolios, which have numerous SKUs to manage, and their need to deliver personalized content to healthcare providers from different specialties, procurement teams, and other clinical and nonclinical stakeholders. AI-powered content generation is enabling teams to create personalized marketing collateral at scale, which bolsters the digital marketing required to meet increasing demand in medtech for omnichannel engagement. Using gen AI for insight generation facilitates such tasks as prioritizing accounts, streamlining research, optimizing customer engagement strategies, and predicting key messages that will resonate with different buying personas.
Meanwhile, AI-assisted customer-facing content empowers sales teams by delivering tailored messaging, autogenerated emails, and easily accessible existing collateral. One-third of the medtech companies in our survey are using gen AI to accelerate marketing content creation and medical and legal reviews, while 40 percent are implementing or planning AI-powered customer service solutions to improve the speed and quality of responses from human agents.
Sales teams can benefit from an interactive gen AI tool that is integrated into their existing workflow and corresponding tools, such as a customer relationship management (CRM) or sales dashboard. This can help teams determine where to allocate their time, provide recommendations, and answer specific questions about how to engage stakeholders in their accounts.
The time savings can be reinvested into new business growth and allow sales teams to focus more on strengthening the customer relationship and less on administrative tasks. The most successful AI-assisted tools are tailored to specific roles and their workflows. For example, sales managers get tools for team coaching; key account managers receive insights about executive priorities in their accounts, based on a combination of public and interaction data across the account team; and sales reps receive alerts to changes in account buying patterns with recommendations for relevant marketing collateral. This enables users to make faster decisions, access key resources on the go, and deliver better customer experiences.
Streamlined operations
Medtech organizations are prioritizing AI solutions in three areas of operations: inventory management, contracting, and procurement negotiations. In inventory management, about 30 percent of survey respondents say they are utilizing AI tools for demand forecasting, disruption monitoring, and field inventory support. The potential for greater adoption and value generation is high, given the prevalence in medtech of inventory management models such as consignment, trunk stock, and loaner units that have led to increasingly complex inventory systems.
Procurement and contracting are being transformed by AI, which can analyze contract terms at scale, identify inconsistencies and flag risks, process data in real time during negotiations with suppliers, and monitor real spending patterns. Among the organizations surveyed, 20 percent are currently using or planning to use AI to scrutinize contract terms and conditions for cost-saving opportunities. Procurement use cases in contracting are also among the quickest to generate P&L impact: AI-powered invoice matching and contract reconciliation have resulted in cost reductions of 1 to 4 percent, while AI-enabled negotiation assistant tools have provided an additional 1 to 4 percent in savings.4
Challenges to gen AI implementation
The ambitious yet largely decentralized rollout of gen AI across medtech has limited some organizations’ ability to achieve scale and P&L impact. Although 65 percent of respondents to our survey say their companies are already implementing gen AI, many are pursuing a scattered approach, with over two-thirds reporting they are implementing across at least three functions. Further, 59 percent are taking a decentralized, or function-specific, approach (Exhibit 3).
This broad but diffused deployment strategy creates challenges that fall into three categories:
- Strategy. Most organizations lack a cohesive AI strategy. Among survey respondents who say their companies are already implementing gen AI, 31 percent say their organizations are “unsure how to prioritize use cases”—a challenge cited by 50 percent of those not yet implementing gen AI. Notably, medtech leaders are 40 percent more likely than their pharma counterparts to cite an ineffective (or nonexistent) strategic gen AI implementation road map.
- Data integration. Technical integration presents significant challenges. Over 50 percent of respondents cite system integration and data privacy as their primary concerns. At companies with a decentralized approach to AI implementation, IT teams often are not engaged early as strategic partners. This limits the ability to scale beyond pilots and deliver value. Additionally, insufficient data or business unit involvement may result in pilots failing to launch, which can lead to multiple units duplicating efforts and exacerbate problems with integration.
- Training and change management. Over 70 percent of respondents rank skill gaps and resistance to change among their top three concerns for implementing gen AI in their organizations. Technology deployment is often hindered by the absence of the right talent. Even after securing the needed talent, organizations may fail to capture additional value from gen AI tools if they don’t implement meaningful change management, including efforts to help end users change their behavior patterns or modify their workflows.
Keys to effective gen AI implementation
Leading gen AI adopters exhibit the success factors articulated in our rewired framework for AI transformation. Medtech organizations can apply the following factors to accelerate impact and create the conditions for a successful gen AI deployment.
A strategic, focused road map
Develop a strategic road map that focuses transformation efforts on one or two domains. Organizations that build solutions within a focused area—whether a specific function, such as regulatory or marketing, or a product suite—are more likely to realize value at scale. With this approach, a single accountable business owner collaborates with the chief information officer and chief technology officer to deliver value and remove roadblocks, allowing the business to implement interconnected use cases and manage change across common teams. For instance, a procurement team might start with invoice reconciliation before advancing to category management tools, or an R&D team could begin automating a few submissions and then expand to automate most other documentation tasks.
Business owners should also focus on use cases that can deliver quick wins, particularly in areas such as procurement, marketing, customer contracting, or regulatory compliance. Domain transformations can then be sequenced to create a transformation road map.
Centralized talent management
Medtech businesses and functions are often highly autonomous, which sets the stage for fragmented efforts that fail to build scalable platforms. Medtech leaders launching gen AI initiatives should balance creating solutions for an individual business with developing technical foundations that can be leveraged across businesses and use cases. Leaders can establish a gen AI center of excellence, staffed with centralized AI engineering talent, to build solutions on a common data foundation using shared data products and services. This team can support business units, ensuring alignment and reducing silos while preserving necessary functional expertise.
A central digital or IT team can also manage tech partnerships and develop enterprise-level AI capabilities. For example, one organization assigned product-focused use cases to R&D leadership while placing enterprise-level capabilities—such as enterprise-level tool selection and model creation for content generation or analytics—within the center of excellence.
An agile operating model with IT and business participation
Build with an agile operating model that includes IT and business partnership from day one. Too often, organizations either develop AI solutions without IT input or let IT drive deployment without sufficient business engagement. Early partnership of these functions throughout the journey improves feasibility assessments, accelerates scaling beyond pilots, and addresses critical integration and data privacy challenges. This collaboration also helps identify adjacent domains where solutions can be readily deployed in subsequent transformations.
For most medtech organizations, successful IT and business partnerships will depend on strengthening certain talent pools, including technical skills such as data engineering and data science, machine learning, and prompt engineering. Success also requires a strong product owner in each domain to drive integration. The product owner can coordinate cross-functionally to ensure appropriate risk management (such as ensuring appropriate separation between medical and commercial), safety, and adverse-event reporting.
An expansive IT–business partnership
Expand the IT-and-business partnership to technical and data needs. Too often, data sources in medtech reside within locally maintained spreadsheets or other one-off or manual documentation. Advancing gen AI use cases across domains will require more reusable data products. However, leaders should not let perfection stop early progress; instead, they can do an initial cleanup or mapping effort for the first data domain while establishing a broader data strategy and operating model for the future. Similarly, the first deployments may make some more pragmatic architecture choices while setting the stage for longer-term decisions around tech stack, cloud-based platforms, and software delivery automation, ultimately reducing the total cost of ownership.
Prioritized adoption
Prioritize adoption as much as technical implementation. The success of gen AI deployment depends on how well employees integrate AI into their daily workflows. Embedding AI into existing processes, rather than treating it as an add-on, increases productivity and maximizes return on investment.
To achieve these outcomes, gen AI tools can be built in collaboration with end users and guided by a clear understanding of relevant workflows and a knowledge of the needed support. When possible, the tools should be customized to fit into the group’s existing processes. We have observed that organizations using generic copilot tools that aren’t fit-for-purpose typically struggle with low adoption rates. Leading organizations also prepare for launch by investing in structured change management, integrating training into field events, offering incentives, and ensuring transparency on usage and target tracking for business impact.
The moment for medtech companies to act on gen AI is now. Early adopters are already seeing measurable results, because the technology demonstrates clear potential to address long-standing industry challenges that have hampered innovation and operational efficiency. Companies that move decisively but strategically will be best positioned to capture the technology’s full potential. Those that delay or act without planning risk falling behind as gen AI becomes an increasingly critical driver of competitive advantage in the industry.
