An Insider’s View of Humana’s AI Program

How do you bring AI into an organization that has been doing well without it for decades? That was the challenge Humana Slawek Kierner’s Head of Data and Analytics faced when he joined healthcare insurance company Fortune 42 in December 2018.

Kerner, who served as director of data and analytics for Microsoft PowerBI, Dynamics, Cloud, and AI before joining Humana, recounted his experience bringing AI to Humana during the AI ​​Summit in New York this month.

In a session at the event, Kerner introduced the process for introducing artificial intelligence to the organization, and Adam Winchell, co-founder and CEO of machine learning monitoring startup Arthur, introduced Kerner and facilitated an after-question-and-answer session with his own questions. In addition to questions from the audience.

Winchell noted how quickly AI is developing in the enterprise. Referring to Wall Street Journal headlines, he noted that in 2016, people were still trying to figure out what it really was. In 2017, the headlines were about how artificial intelligence is taking over everyone’s jobs. Then the headlines focused on the fact that more qualified professionals are needed to facilitate AI. Winchell said the headlines in the past few years have focused on why AI projects have failed.

“In 2022 we are starting to see this coming together,” he said. “We see the era of the original enterprise of artificial intelligence.” But this is mainly for large companies, not medium and small companies.

Big Fortune 500 companies have started deploying AI models. One of these is Humana.

What does AI look like in Humana

Humana operates conventional Medicare and Medicaid, provides medical insurance to members of the US military, and has also expanded to providing home health care, sending 50,000 nurses to care for people in their homes and provide care, according to Humana Kerner. It also operates a value-based sponsorship model.

“This generates a lot of data,” Kerner said. “The important factor that drew me to Humana from Microsoft is that there is a lot of data. This creates a lot of opportunities for someone who is interested in analytics and artificial intelligence.”

But the new role was not without its challenges. For example, most computing was in the workplace, he said, without access to some of the more modern technologies.

Early in the transformation, Kerner said it started with people working on projects and areas that needed updating.

“We knew we needed to migrate from premises to get all the potential that the cloud could provide,” he said. This was one of the ways Humana has followed the typical enterprise digital transformation journey over the past few years. The operation also drew lessons from the tactics of successful IT projects in the past years.

For example, one of Kerner’s best approaches to AI transformation was to choose important transformational use cases as initial projects so that he could demonstrate their value to stakeholders.

He explained that the organization is a hub and spoke model, with data governance, data analytics, and data science groups building horizontal platforms. But there were also a number of vertical units, he said, with integrators and product managers. Amid this team structure, some of the core team members were those professionals who understood the business process and could translate customer needs to data scientists and engineers.

Kerner noted that it was important to measure the successes of projects and report their value, but it was not a simple task. I’ve taken it in several different stages.

It first measured the number of users migrating to the new platforms. Next, the team measured the number of times the member touched an AI-powered “hot point”.

Finally, there was a point where the finance and strategy teams took an interest in the projects, analyzing the dollar value. For example, they will consider the number of hospital admissions avoided due to better operations being followed.

But don’t expect to get to that point overnight, if your organization is pursuing a new AI program. Asked by the audience, Kerner said organizations should expect results to take at least a year.

“It depends on the use case,” he said. “In our case, small use cases should make a financial impact in that time frame.”

Finally, for those still worried that AI will take their jobs, Kerner said his organization has worked to provide a lot of training for senior executives and from one to two levels below.

“We’re still coming back with another wave,” he said. “We are still finding areas of friction. But we have found that the more we use AI, the more job opportunities we have.”

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