Johnson & Johnson’s Big Bet on Smart Automation

Johnson & Johnson's Big Bet on Smart Automation

Three years ago, Johnson & Johnson (J&J) set out to advance Intelligent Automation (AI) for every aspect of its business. With the start of the global COVID-19 pandemic, the company, one of the largest suppliers of pharmaceuticals, medical devices and consumer packaged goods in the world, needed to reduce costs, speed up tasks and improve the accuracy of its core business processes.

Robotic Process Automation (RPA) It was already gaining momentum as organizations sought to implement “bots” software to automate rules-based business processes. But organizations like J&J wanted to take automation even further. By combining RPA, machine learning (ML), and artificial intelligence (AI), they have sought to automate the most complex tasks. This opportunity prompted J&J’s Ajay Anand and Stephen Sorenson to place a very big bet in 2021.

“The only way to get attention at Johnson & Johnson from your senior leaders is by how much impact you can make,” says Anand, vice president of pharma for global services strategy and transformation. “In general, J&J prefers everything with the billions.”

Anand and Sorenson, the company’s senior vice president of technology services, supply chain, data integration, and reliability engineering, have proposed creating an enterprise-wide intelligent automation council that they chair. They said they would generate half a billion dollars in impact over the next three years. The team has already come this far. Anand notes that in a recent review, an Executive Committee member asked them to double that number based on the current pace.

Early intelligent automation barriers

Thanks to the work of the Intelligent Automation Council, J&J is now applying artificial intelligence to everything from basic business processes, to chatbots that can help employees and customers, to algorithms that can monitor a company’s supply chain and help it adapt to changing conditions — such as doubling demand. on Tylenol in the early days of the epidemic.

Stephen Sorenson, Senior Vice President of Technology Services, Supply Chain, Data Integration, and Reliability Engineering, Johnson & Johnson

Johnson & Johnson

But when Anand and Sorenson helped J&J take its first steps on its automation journey, they quickly ran into roadblocks.

“We were going offshore and using low-cost labor and trying to streamline our operations, but it was very difficult to scale up and turnover was high,” Sorenson says. “We had this scenario where we were constantly retraining people and exceptions were killing us.”

Sorenson explains that it’s hard to imagine how many exceptions a process has until you actually implement it or train people to do it. Exceptions can spoil even seemingly simple tasks, such as submitting confirmation forms. Typos, Sorenson says, a new job title — any little thing can send those forms straight to the bug queue.

“We’ve tried to automate it, and we’ve realized that people don’t know their business processes as well as they thought,” he explains. “They knew their jobs and could get work from point A to point Z, but if you tried to automate that, very few automation had an easy path to the end.”

It didn’t take long to realize that the traditional approach to business process mapping — sitting down with employees, understanding how they do their work, and capturing it — wouldn’t give the automation team what they needed. For a complete view of the business operations, J&J has brought in the task mining tool.

“We picked a few employees who were willing to engage with us in the early stages and tried and trained all of their privacy concerns, then put this tool on their desktop to record actual activity,” Anand explains. “When they started a certain process, they would record, and then we would capture it on that instrument. We ended up creating the swim lane and all the documentation associated with it.”

Instead of interviewing employees about the process in advance, the team took the recordings and reviewed them with the employees, asking them if there were any differences that were not captured that they wanted to share.

Adopt a digital mindset first

J&J has started using RPA technology for simple business process tasks such as document transfer, filling spreadsheets, sending key messages, email integration, and the like. It grew from there.

Ajay Anand, Vice President of Global Services Strategy and Transformation, Johnson & Johnson

Ajay Anand, Vice President of Global Services Strategy and Transformation, Johnson & Johnson

Johnson & Johnson

Says Anand, pointing to the bill-to-cash conversion as a prime example of the company’s new perspective. Like any company, when implementing this process, J&J sometimes had errors or disputes with customers.

“By reimagining these processes with a digital mindset first, we were able to look at things end-to-end and look for places where we could not only automate, but also incorporate some intelligence,” he says. “Can we anticipate which clients we might have some disagreements with, and can we start taking some steps proactively?”

By applying intelligent automation to billing into cash, J&J has been able to increase cash collection, reduce error rate, and reduce the number of hours worked and dollars spent to achieve the same results.

Anand explains that the core of J&J’s first digital mindset about intelligent automation is 3E: Expertise, Effectiveness, and Efficiency. Is automation changing the experience for employees, customers, and suppliers? Do they make operations more effective and efficient?

Success comes from small gains

Sorenson says the team learned that the key to successful automation, as with many IT projects, was starting small, realizing gains, and educating people about the possibilities.

“We had a saying, ‘Don’t try to run home.’ Just get on the base, put the guys on the base, and we’ll move them, start getting some hits. And then we’ll start doing some running,” Sorenson says. That really helped people believe they didn’t have to worry about everything, they just needed to make those few steps automated and then we could see where we could go from there.”

Sorenson notes that the small wins were able to help the automation team gain confidence, but they also produced the data that allowed them to show that a digital mindset first, machine first led to more accurate results.

“If you think about it differently, you can actually automate the steps so they are more granular and discovery-based so you can find issues where things have historically failed, or even compromise steps that have allowed us to confirm that things are fully working along,” he says. Sorenson.

Soon, as trust grew, the conversations were no longer about convincing stakeholders of the value of automation; They were about what the team could do as well.

Anand notes that managing concerns by showing examples of colleagues and partners was key.

“When people saw those examples, it really inspired them,” Anand says. “There was always this little fear that automation meant people would lose their jobs. And they were able to see that it really moved employees to higher-ranking jobs and freed them up to do more innovation.”


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