It may seem like a routine moment: A surgeon picks up a surgical stapler to use in a procedure. But the availability of that particular device, at that exact moment, for that specific operation, is actually the result of a complex and interconnected process that started months ago, all the way back in an R&D facility hundreds (perhaps even thousands) of miles from the hospital in which the operation is taking place.
Indeed, to deliver cutting-edge solutions and high-quality medical technology to patients and providers around the world, healthcare companies rely on a sophisticated, end-to-end product development ecosystem—from conception and development to supply chain and quality assurance to post-market customer experience—that can reliably manage and deliver an enormous volume of products. It adapts to disruptions1 and unforeseen challenges and evolves to keep pace with advances in technology.2
“Healthcare product lifecycle processes can be notoriously complex,” explains Guillermo Villa, Director, Digital Transformation at Johnson & Johnson MedTech. “They require a methodical approach to sourcing materials, managing inventory and inspecting and testing final products. And as technology evolves, there’s room to assess and enhance these processes to make them more efficient and resilient."
Room and reason to improve: “These processes can be quite cumbersome for quality, supply chain and even R&D team members,” says Nadine McFarren, Head of Strategy and Portfolio, Johnson & Johnson MedTech.
The proliferation of AI, machine learning and other tech advances are helping enable today’s healthcare systems to spur faster, more agile innovation, and increase speed to market. Unsurprisingly, research has shown that AI can help healthcare companies minimize waste, increase accuracy and improve cost efficiency. For example, AI can analyze anonymized patient data to forecast demand, monitor global events for potential disruptions and predict maintenance needs for supply chain equipment3—just to name a few use cases.
How is that coming to life at Johnson & Johnson MedTech? Read on to find out how the company is harnessing technology to enable innovation from the earliest stages.
Digital product lifestyle management programs are opening the door to faster, more efficient innovation
Product lifecycle management systems (PLMs) manage the entire lifecycle of a product, from initial concept and design through development, deployment, maintenance, and eventual retirement or replacement.
For many years in traditional healthcare product lifecycle ecosystems, there were still manual connections between isolated pockets of data that existed in—to use simple terms—"hard copy” format. As Villa puts it, it was a “rudimentary, archaic” way to be working at scale.
But large-scale digitization programs proliferated during the COVID-19 pandemic and spurred an appetite for more widespread adoption.4
At Johnson & Johnson MedTech, to evolve PLM capabilities and improve efficiency, collaboration, flexibility and cost savings, Villa’s team, in partnership with Johnson & Johnson MedTech’s R&D technology team, has been building a “digital thread” of data that connects processes, all the way from product conception to manufacturing and distribution through scalable, compliant and future-ready solutions. The goal is to transition from “a document-centric model to a data-centric model,” he says. Through this work, benefits are already being realized: Teams have been able to accelerate innovation, reduce time-to-market and deliver high-quality, digitally connected products that improve patient outcomes worldwide.
Think about it like the difference between navigating a road trip decades ago and today, he continues: Back then, you would have unfolded your atlas and spent 15 minutes or so interpreting the map. Today, you can plug in your destination to your GPS and instantly get directions.
“When data lives in documents, it’s very hard to extract, and engineers spend an enormous amount of time trying to search for legacy data,” Villa explains, “which would be much less cumbersome on a digital platform.”
These digital solutions also foster partnership among teams. “When you make data readily accessible to everybody, it enables effective collaboration,” Villa says. Whereas Johnson & Johnson MedTech previously operated using dozens of PLMs, operations will now be streamlined to one digital version across business units and operating companies. “It makes collaboration so much more seamless,” he adds.
Quality compliance processes get an efficiency boost
Before any product reaches customers or patients, its safety must be officially documented. At Johnson & Johnson MedTech, products go through batch release—a critical quality control step that confirms all required testing, reviews and documentation are complete and compliant. Historically, this process has been manual, paper-based and time-consuming.
According to Aileen Barreto-Rivera, VP Supply Chain Quality at Johnson & Johnson MedTech, like PLMs, supply chain quality processes are still largely run off paper documentation. Her team is working to solve this challenge through the Digital Transformation Excellence (DTx) program, a MedTech Quality & Compliance initiative focused on transforming complex supply chain quality processes.
DTx is “designed to simplify and digitize our supply chain quality processes across our internal manufacturing network ,” Barreto-Rivera says. The goal is to extract the data from paper forms and create an ecosystem to enable the use of digital workflows to drive efficiency and, ultimately, better outcomes for our patients.
Not to mention, Barreto-Rivera sees DTx as a stepping stone and enabler to future AI projects, because “you need data to be able to feed your AI models, get product-quality insights and drive customer-outcome improvements.”
The company has deployed tools to automate the batch release process, increasing automation by 65%. This system provides teams with real-time visibility into whether a batch meets release criteria, potential delays or bottlenecks for release, and documentation for every step in the product development lifecycle, reducing release cycle time by 50%. “We are simplifying the way we work,” Barreto-Rivera says.
In traditional medtech product lifecycle ecosystems, processes and roles are often siloed: One person or department is responsible for a single element of a larger goal. But that structure can limit collaboration and innovation. The solution? AI integration across these systems and processes makes collaboration among teams more fluid and efficient.
Digitized inventory management systems get critical medical devices to the right patients—at the right time
A disorganized inventory can result in delayed access to critical equipment or accidental use of expired or recalled products. One example is surgery, in which an intricate interconnection among technology, medical devices and inventory management enables doctors to be able to efficiently do multiple procedures in a single day.
Take Johnson & Johnson MedTech’s electrophysiology Control Tower, a digital capability designed to strengthen supply chain decision‑making by integrating, curating and transforming data from enterprise systems through a common data layer. In 2025, the company began deploying GenAI inside this Control Tower so teams throughout supply chain, including in planning, procurement, manufacturing, and logistics, can ask plain‑language questions and get instant answers from trusted demand, inventory, and backorder data.
The AI assistant helps company associates move faster by highlighting where shortages are forming, when recovery is expected and the best actions to take. By turning data into decisions in seconds, users can clear backorders sooner, optimize stock across the network and accelerate product availability. The result? Speeding deliveries to hospitals so clinicians have what they need when they need it and patients receive care without interruption.
Elevating accuracy, efficiency and collaboration by digitizing quality management systems
In traditional medtech product lifecycle ecosystems, processes and roles are often siloed: One person or department is responsible for a single element of a larger goal. But that structure can limit collaboration and innovation, says John Leamy, VP, MedTech Quality Systems and Digital Solutions for Johnson & Johnson MedTech.
The solution? AI integration across these systems and processes makes collaboration among teams more fluid and efficient.
To that end, J&J MedTech has created “a digital platform that's transforming our quality management system (QMS) through the utilization of data, technology and generative AI.”
“We've got a multitude of workflows within our quality management systems that have, to date, been quite manually intensive and highly administrative,” Leamy says. “We’re still following the same workflows, thus ensuring compliance, but now we’re digitizing these workflows end-to-end with the primary objective of eliminating nonvalue-add activities.”
Johnson & Johnson is “rich in data,” he adds, and by digitizing the QMS, the company can leverage generative AI to analyze all that data to deliver insights and analysis in seconds, allowing them to lean into proactive risk management over the next year.
The platform launched in November 2024 and expanded earlier this month to thousands of Johnson & Johnson MedTech users, Leamy says, allowing team members to efficiently make and track product changes, easily track and contain issues, and quickly resolve customer complaints with the first-ever global MedTech Product Complaint Center intake portal. “It is truly set to become a digital platform that transforms the way we work and engage end-to-end with our quality system.”
The goal is to simplify and optimize Johnson & Johnson MedTech's Quality System workflows, drive compliance, improve the customer experience and support employees when developing corrective and preventive actions and audits. It will eliminate human and transactional errors, improve technical writing and accelerate consistency and efficiency in investigations and monitoring, Leamy explains.
At Johnson & Johnson MedTech and in the larger medtech and healthcare community, there’s been a “dawning of the fact that generative AI is here to stay, so we need to embrace responsible AI and put it to work for us,” Leamy says.
[1] https://www.deloitte.com/us/en/insights/industry/health-care/healthcare-supply-chain.html
[2] https://www.scl.gatech.edu/news/predicting-future-supply-chains-learning-past-navigate-uncertainty#:~:text=Continual%20Learning%20and%20Adaptation:%20Finally,Conclusion
[3] https://www.ey.com/en_us/insights/health/how-generative-ai-can-optimize-health-care-supply-chains
[4] https://www.deloitte.com/us/en/insights/industry/health-care/end-to-end-digitalization-of-biopharma-supply-chain.html