How Eli Lilly and Allergan marketers are navigating AI in healthcare advertising

Healthcare marketers are increasingly experimenting with AI, but many say the technology still requires strong guardrails around data, measurement and decision-making.

Rather than replacing marketers, brands and agencies say AI is proving most useful in helping teams process massive amounts of campaign data, accelerate insights and automate time-consuming operational work behind media campaigns.

Those themes surfaced repeatedly at Healthcare in Focus, hosted by The Trade Desk in New York City last Thursday, where brand, agency and technology leaders discussed how AI is beginning to influence healthcare advertising.

Brands are moving carefully on AI

Healthcare organizations are adopting AI cautiously, in part because the industry depends on clean data and strict compliance standards.

During one panel, Zachary Schroll, associate vice president of global consumer and HCP media analytics at Eli Lilly and Company, said the effectiveness of AI depends heavily on the quality of the data feeding it.

“How clean is your data?” he said. “Have you built the semantic layers? Do you have decision trees?”

Without that groundwork, Schroll said, AI systems can produce misleading insights. “I’ve seen a lot of really terrible answers on optimizations and what’s working and what’s not because it’s not trained the way we think of it.”

For that reason, many healthcare companies are moving deliberately as they experiment with AI tools. “We’re very particular in moving slowly in this AI space,” he said.

Measurement is one of the first areas AI is changing

AI is already influencing how healthcare marketers measure performance across campaigns.

Pharmaceutical brands have historically relied on marketing mix modeling (MMM) to evaluate marketing impact — but those models often lag behind fast-moving campaigns.

“We look at MMM as the ultimate arbiter of our truth,” said Nate Ng, director of marketing innovation and measurement at Allergan Aesthetics. “But we know that MMM is imprecise. It takes forever to run, and it misses a lot of nuance.”

To address that gap, Ng said teams increasingly combine modeling with incrementality testing and engagement signals. “We actually run monthly incrementality tests at a geo level so that we can rein in MMM,” he said.

AI is also helping marketers analyze those signals faster, connecting engagement metrics like site visits or video views to outcomes like prescription lift. “We’ve been looping in more data scientists to help us understand: Does website activity matter?” Ng said.

Agencies are expanding what marketing data can reveal

For agencies, AI is also enabling deeper insights about patient populations and campaign reach, combining campaign data with external datasets — including census and prescription data — to understand how marketing aligns with patient populations.

“What we’re able to see is how our campaign is performing against how our patient population is comprised in that state, that geo, that zip code,” said Faryn Brown, vice president of addressable strategy and activation at Kinesso.

Those insights can reveal where campaigns are missing audiences or where strategies should shift. “There might be a market where we’re over-delivering or under-delivering, and there’s a patient population that we’re not able to reach,” Brown said.

The ad tech industry is building AI around those concerns

As marketers grapple with data complexity and campaign fragmentation, ad tech companies are developing AI tools designed to help navigate those challenges. According to Aravind Chandrasekharan, senior vice president of engineering at The Trade Desk, the company is approaching AI through what it calls “agentic” systems — tools designed to collaborate with marketers rather than replace them. “We don’t think about this as a black box AI that’s going to take over everyone’s jobs and run all the media for you,” Chandrasekharan said.

Instead, agentic AI analyzes enormous volumes of data and surfaces insights that help marketers make decisions faster. “You’re going to get a lot more insights and data that’s going to help agents and you make combined decisions together,” he said.

The goal is to reduce manual work, such as digging through campaign reports or troubleshooting performance issues while leaving strategic decisions for marketers. Even as AI becomes more embedded in advertising workflows, speakers across the event said human judgment will remain central to healthcare marketing.

“It’s still going to be humans making decisions,” said Caitlin Malone, executive vice president of data and analytics at Publicis Alchemy. “Hopefully with better data and better tools.”

As healthcare marketers experiment with AI, speakers said the organizations that succeed won’t simply adopt the technology fastest; they’ll adopt it most thoughtfully.

The Current is owned and operated by The Trade Desk Inc.