Johnson & Johnson has shifted its generative AI strategy away from broad experimentation across the healthcare conglomerate to a more focused approach.
Chief Information Officer Jim Swanson said the move ensures that the company allocates resources only to the highest-value generative AI use cases, while it cuts projects that are redundant or simply not working, or where a technology other than GenAI works better.
“That was a pivot we made after about a year of learning,” Swanson said. “Now we’ve moved from the thousand flowers to a really prioritized focus on GenAI.”The “thousand flowers” approach involved a number of use case ideas germinating from across the company, which made their way through a centralized governance board. At one point, employees were pursuing nearly 900 individual use cases, many that were redundant or simply didn’t work, he said. And as the company tracked the broad value of AI, including generative AI, data science and intelligent automation, it found that only 10% to 15% of use cases were driving about 80% of the value, he added.
Now J&J is drilling down into high-value generative AI use cases around drug discovery and supply chains, as well as an internal chatbot to answer questions on company policy.
“We’re prioritizing, we’re scaling, we’re looking at the things that make the most sense,” he said. “That was part of the maturation process we went through.”
Since ChatGPT’s 2022 debut, companies have been grappling with where and how to start applying generative AI. Many, including J&J, encouraged experimentation, a strategy that set employees up to learn and test the technology and build applications that could see wider adoption across the enterprise.
Yet in some cases, they are finding that too much experimentation can be at odds with actually finding business value and as a result are looking to forge a new strategy
J&J began its pivot last year, removing a centralized governance board responsible for vetting employee GenAI ideas. It then distributed governance responsibilities to various corporate functions, including commercial, supply chain and research, that had a better handle on whether the use cases were actually driving value in their area. Those groups were able to shut down or consolidate redundant use cases and focus resources into the ones that were working.
One example that is working is a “Rep Copilot,” which helps coach sales representatives on how to engage with healthcare professionals about new treatments. The company is piloting this in its Innovative Medicine business segment, which develops new treatments for oncology and other areas, and is now working to expand that pilot to its MedTech segment, which sells robotics and hardware like hip replacements and lenses.
GenAI also is being used for an internal chatbot that ingests information about company policies and benefits to help reduce the some 10 million interactions employees have every year with the services team.
In drug discovery, the company is looking at whether GenAI can help researchers find the optimal moment to add a solvent to turn a liquid molecule into a solid. Swanson said J&J also is testing how AI can help identify and mitigate supply-chain risks, including the impact of a shortage of a given raw material.
The company is tracking progress in three buckets: first, the ability to successfully deploy and implement use cases; second, how widely they are adopted; and third, the extent to which they deliver on business outcomes.
Swanson said the broad experimentation phase was necessary to learn about the technology and what it was good at. “You had to take an iterative approach to say, ‘Where are these technologies useful and where are they not?’ And I’m still a believer that there’s a lot more hype than there is substance,” he said.
“We had the right plan three years ago, but we matured our plan based on three years of understanding,” he said. “This is the better way to run now.”
