How Baptist Health’s Marketing Team Became the Go-To Resource for AI

Baptist Health’s marketing team was still figuring out AI when other departments started asking for help.

For Christine Kotler, chief marketing and communications officer at Baptist Health South Florida, that became an opportunity to show employees how far they had come. The work had been hard. People had learned unfamiliar tools, confronted doubts about their expertise, and tried ideas that sometimes went nowhere. Now, colleagues elsewhere in the healthcare organization wanted to learn from them.

That’s because the department had developed something worth sharing: a way to help people learn AI and apply it to their own work.

In Episode 242 of The Artificial Intelligence Show, part of the AI Transformations series presented by Google Cloud, Kotler describes how that capability developed. It began with a structured curriculum, grew through peer support and real workflow problems, and eventually gave marketing a broader role in helping the organization adapt.

But first, Kotler had to work through her own surprise.

A Roomful of Experienced Marketers Went Quiet

In late 2024, Kotler attended a presentation by Paul Roetzer, founder and CEO of SmarterX, with a small group of seasoned leaders at Baptist. He described how AI could change the work they knew, including research, creative development, decisions, and team structures.

When he finished, the room fell silent.

Kotler had been comfortable with marketing technology. This felt different. She began to understand how much of the work around that technology could change, and how quickly.

“This was something that was going to truly, fundamentally change how we worked, which was both exhilarating and entirely intimidating at the same time,” Kotler says.

She brought that realization back to her work and her team. Marketing and communications, she believed, had a useful role to play because its work already required understanding human behavior, explaining change, and helping people take action.

That belief shaped the first decision: build a deliberate AI learning program people could follow.

Learning Got a Place in the Workday

To start, the team created a year-long AI curriculum, organized like a course syllabus. It combined outside learning modules with internal lunch-and-learns, webinars, and resources from IT and human resources.

Employees also received dedicated time during the workday to take classes and practice. They could listen to podcasts, explore tools, or watch major presentations together.

The time mattered. A learning program that employees must squeeze around everything else gives them a reason to postpone it. Baptist Health made room for the learning it expected.

Those expectations were real. Kotler linked AI development to performance conversations and evaluations. Participation was part of the job, even as leaders learned how much support different employees needed.

As the curriculum progressed, the team added ways to get help that included learning labs focused on particular tools or applications, office hours, and an internal resource hub. A department AI council connected to the enterprise council, while marketing-specific usage guidelines gave employees direction on responsible use.

The curriculum provided a route into AI. But it was really the support around it that gave employees somewhere to go when a lesson became a problem in their actual work.

The AI Sherpas Helped Teams Build for Themselves

Some employees were more comfortable experimenting than others. Baptist Health identified a small group of these early adopters as “AI Sherpas”: colleagues who could test tools, answer questions, and help teammates get unstuck.

At first, much of their work involved tutoring and exploration. Then their role became more defined as teams began bringing them business problems.

A team might need to redesign a workflow but not know how to start. The Sherpas would work through the problem with them and suggest an approach. The team would build a little, return for more help, and keep developing the solution.

“So it’s much more iterative and supportive rather than, ‘Hey, go do this for me,’” Kotler says.

That distinction made the coaching model useful beyond the first solution. The people who knew the work stayed involved in changing it. Each round gave them more experience applying AI themselves.

The learning hub Chrstine and her team had developed also helped capture that progress. It held outside resources alongside the team’s own use cases, successes, failures, and explanations of what had happened. Employees could share what they learned and bring forward new ideas.

That also addressed a problem created by early enthusiasm. The team had built plenty of things, some useful and some discarded. Kotler wanted to preserve the appetite to experiment while giving people more guidance about which problems deserved attention and whom a solution would help.

Resistance Sometimes Meant Pride

The support system ended up having to accommodate very different reactions to AI.

Some employees were eager to work with AI. Others were cautious or skeptical. The team began with surveys and conversations to understand that starting point, then continued surveying quarterly.

Kotler also reconsidered what she was seeing in some of her most experienced colleagues who resisted AI. They were resisting it not out of fear or skepticism, but pride.

“It’s really pride of ownership. It’s pride of expertise,” she says.

People had spent years developing their expertise. Caring about their craft was one of the qualities Kotler valued in them. Asking those same people to let AI perform pieces of that work could unsettle them.

So, leaders responded with more visible support and empathy, while keeping the expectation that employees would learn. Peer coaching gave people help close to the work. Conversations gave leaders a chance to understand what was causing hesitation.

Kotler also describes a change in creative services that illustrates how expertise can carry into a redesigned workflow. Designers used Adobe Express to create templates that account teams could use themselves. Which meant designers supplied the brand judgment up front, and a human still approved the work at the end.

Kotler reports that the self-service model reduced the burden on the internal design team and its reliance on external freelancers. It gave employees a concrete example of expertise shaping a new process while repetitive production moved elsewhere.

The Team’s Learning Became Useful to Others

As marketing shared its successes and failures, other departments began asking for help. Baptist Health also gave other functions access to the marketing and communications learning hub.

Kotler used that interest to reinforce her team’s progress. Employees who had struggled through unfamiliar work could now recognize that they knew enough to help someone else.

The broader transformation is still developing. Connecting tools has introduced new challenges, and Kotler is working through how roles and workflows should change. The team has not reached a point where every question has an answer.

Keeping that learning going also carries a responsibility for leaders. Kotler’s advice is direct:

“Leaders model that behavior and they cannot outsource their own AI literacy,” she says.

For another department hoping to play a similar role, the first task is close to home. Give employees enough support to solve problems in their own work, then make those solutions and lessons available to colleagues.

Baptist Health’s marketing team became a resource for others while it was still learning. The ability to help someone take the next step was already valuable.

Did you enjoy this transformation story? Go deeper on how AI is reshaping work and business with The Artificial Intelligence Show. Each week, we break down what matters in AI and what leaders should do about it.