How Johnson & Johnson Discovered the AI Use Cases That Really Move the Needle

In healthcare and pharmaceuticals, companies often invest in artificial intelligence initiatives hoping to unlock breakthroughs and improve operations. But not all AI projects deliver equal value, and finding the right focus can be challenging. Johnson & Johnson recently revealed an interesting insight: only about 10 to 15 percent of its AI use cases account for roughly 80 percent of its value. This raises an important question for innovators everywhere: how can organizations identify and prioritize the AI efforts that truly make a difference?

A closer look at Johnson & Johnson’s AI journey shows their solution lies in targeted applications across key areas aligned with patient care and operational impact. For example, they use AI in drug discovery to analyze genetic and medical data, speeding up the identification of promising molecules and tailoring treatments. In personalized medicine, AI helps match therapies to individual genetic profiles, improving outcomes and enhancing clinical trial effectiveness.

Beyond R&D, Johnson & Johnson leverages AI to enhance supply chain resilience, predicting and responding to demand fluctuations and disruptions efficiently. They also apply AI to understand disease progression better, enabling more proactive patient care. On the consumer side, AI tools analyze behavior and preferences to refine marketing and customer support, ultimately generating better product matches and higher satisfaction.

The key takeaway is that focusing on fewer, high-impact AI use cases enables Johnson & Johnson to concentrate resources where they deliver the greatest returns for patients and the business. Rather than spreading efforts thin across many projects, a strategic approach amplifies their innovation power and speeds measurable benefits.

What AI applications in your organization have proven most valuable? How do you decide which projects deserve focused investment to maximize impact? Sharing your experiences could provide useful perspectives on converting AI potential into meaningful outcomes.