Johnson & Johnson is one of the world’s largest healthcare companies, generating more than $85 billion in annual revenue and employing roughly 130,000 people across more than 60 countries.
As a focused healthcare player, it operates through two main businesses: Innovative Medicine and MedTech.
Its Innovative Medicine arm discovers, develops, and commercializes prescription drugs in complex disease areas like oncology, immunology, etc. The MedTech division designs and manufactures medical devices and technologies that enable smarter, more personalized treatments.
Together, these businesses position J&J at scale across the full continuum of healthcare.
In today’s AI at the top, we will learn about J&J’s AI approach, winning use cases, and lessons from the enterprise.
How J&J’s approach to AI changed over the years
The enterprise’s initial AI strategy was intentionally open-ended.
J&J’s CIO, Jim Swanson, mentioned it as the “let a thousand flowers bloom” philosophy. This meant employees across different business domains could suggest and build AI use cases. A centralised governance board evaluates and greenlits projects, eventually overseeing nearly 900 experiments.
Between 2021 and 2024, this experimental approach helped teams familiarize themselves with AI and test the technology’s limits. It served its purpose.
After tracking outcomes for three years, the data revealed a Pareto distribution. A small fraction of initiatives drove the majority of business value.
With this insight, J&J evolved by removing the centralised governance board and distributing responsibility to business units like supply chain, research teams, etc. This gave ownership of the AI agendas to the units closest to the problems.
This also meant the company would focus only on the 10-15% of initiatives that drive massive ROI.
The company now evaluates AI projects often highlighted in three broad themes:
Focus on productivity across all broad business domains. These are foundational, internal applications.
Focus on end-to-end processes. Here, the company identifies complex workflows and optimises for efficiency and decision-making.
Focus on core products and services. AI is a fundamental feature used to improve the outcomes for patients and healthcare providers.
After the experimentation and narrowing down to the most valuable use cases, here are some AI use cases at J&J driving impact
1/ Surgical training gets smarter with video analysis
The Polyphonic digital ecosystem (a tool that analyses thousands of hours of raw, surgical footage and identifies key moments) uses AI to analyse videos and create highlight reels in minutes. Instead of watching hours of footage, surgeons can review only the critical moments from their procedures.
“Surgeons are a lot like high-performance athletes. New and learning surgeons want to see how they performed and learn from their performances and how others performed. But it’s a lot of work to sit and watch hours of footage from the full procedure and cut it down to clips.”
2/ NVIDIA partnership transforms surgical robotics
J&J partnered with NVIDIA to create virtual operating rooms. This helps surgeons practice robotic procedures before treating real patients by building digital replicas of actual operating room environments and simulating realistic scenarios.
Clinical staff can test techniques, refine skills, and understand how equipment behaves under different circumstances; all without any risk to the patients. This reduces the learning curve for complex robotic surgeries.
3/ AI-powered mapping in cardiac procedures
During heart procedures to treat irregular heartbeats, doctors need to navigate inside the heart with extreme precision.
The CARTO 3 System uses AI to generate detailed 3D maps of a patient’s heart anatomy in real time. The system guides doctors on where to place catheters and which tissue to treat, making procedures safer and more effective for patients with atrial fibrillation.
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4/ AI speeds up drug discovery
Finding new medicines traditionally takes years of trial and error. J&J uses AI to analyze large medical datasets with patient records, lab results, and genetic information. This helps the company identify which biological targets drive specific diseases. Once researchers identify promising targets, AI helps design molecules to treat them.
“By applying AI, we can advance the most promising drug candidates into clinical development, with the goal of improving the probability of successfully bringing a drug to market.”
5/ Clinical trials reach more diverse patients
Most clinical trials happen at major academic medical centers, but not all patients can access these locations. J&J uses AI to analyze where different patient populations receive care, then brings trials to those communities instead of waiting for patients to find them.
“Our goal is to leverage the power of AI to bring trials to more patients, rather than waiting for patients to come to us.”
This makes trials more accessible and ensures medicines are tested on diverse populations.
6/ Personalised cancer treatment through AI biomarkers
Some bladder cancer patients have tumors with specific genetic alterations that respond to targeted treatments. But testing for these alterations is limited, so many patients never receive the right therapy.
J&J is developing an AI system that analyzes digitized biopsy images to detect whether a patient's tumor likely has these genetic markers. This helps doctors identify which patients could benefit from personalized treatments without requiring additional specialized tests.
7/ AI to coach the sales team
Rep Copilot coaches sales representatives on how to discuss new treatments with healthcare professionals. The tool provides guidance on medical information, communication strategies, and how to address specific questions from doctors.
Initially piloted for pharmaceutical products, it's now expanding to medical devices like hip replacements and surgical tools. This ensures sales teams deliver accurate, relevant information to doctors.
YOU DECIDE
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What can we learn from J&J’s AI strategy?
Two things.
One, experimentation leads to optimisation. J&J’s experiment with 900 use cases was not a wasted effort. The company needed to test AI across functions to understand where it delivered value.
Two, decentralise ownership once you understand tech. Moving AI ownership from a central board to business units allows teams closest to the work to make better decisions.
AI strategy isn’t the same at all stages. Models evolve quickly, workflows change, and the best teams usually evolve on the fly.
Sources:
Case studies from Emerj, Chief AI officer, LinkedIn articles, Wall Street Journal, CBInsights, and Klover 1, 2.
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