Several years ago, when I first wrote about potential uses of artificial intelligence by pharma and healthcare organizations, I was impressed by the enthusiasm but not the expertise. Companies’ designated futurists would rhapsodize about the glories of an AI-enhanced future, but sputter when pressed for specifics. They spouted buzzwords with fire-hose intensity and hoped that their audiences were even more clueless than they were.

Thus it was easy to differentiate between the charlatans and the pros. I remember talking with Joseph Kim, then the senior advisor of patient experience and design innovation at Eli Lilly, about the extent to which AI would eventually play a role in clinical trials. He more or less laughed in my face: “That’s almost like asking, ‘Where should electricity fit in the mix?’” he responded, before sharing a meticulously detailed (and, in retrospect, quite prescient) vision for the decade ahead.

Seven years later, pharma has narrowed its knowledge gap, with often striking results. AI-infused chatbots have driven administrative efficiencies, while natural language processing has helped researchers mine nuggets of insight from terabytes upon terabytes of patient data. The immediate future is bright. Disease may soon be detected earlier; finding appropriate clinical trial participants for therapies that treat rare conditions may soon become less of a needle-in-a-haystack exercise.

But even amid those existing and imminent innovations, there’s a sense that AI literacy in pharma and healthcare – broadly defined by the nonprofit Bipartisan Policy Center as “the ability to recognize, use and evaluate AI technologies… knowing what AI can and can’t do, how it works and its risks and benefits” – is far lower than it should be, especially for an industry hellbent on maximizing AI’s transformational potential.

“You hear a lot of talk about [AI] from the technical standpoint of using it to make medicine more precise,” says Google head of health intelligent strategic operations systems Begench Amangeldiev, who previously held analytics and compliance roles at AbbVie and Walgreens Boots Alliance. “But I don’t think industry is there yet in terms of daily enterprise-level understanding or knowledge or accessibility. It’s still pretty limited.”

Sanjay Tripathi, who worked on digital and AI strategy and innovation at Bristol Myers Squibb before leaving for the financial sector in 2023, agrees. “There’s not as much curiosity [about AI] in some of these companies.”

Given the direction in which nearly every healthcare entity’s business is trending, that represents a clear industry-wide blind spot. It might be an individual failing as well as an organizational one: In making the case for AI literacy, companies might position it as an important component of career development.

The argument goes something like this: Marketers who are unwilling or unable to use AI aren’t necessarily going to be replaced by AI. But they might find themselves pushed aside in favor of somebody who appreciates everything AI has to offer. In other words, keep your skills current or prepare to be replaced by somebody with current skills.

“People coming into pharma or other businesses now – they are open to AI in a way that maybe people who have been there awhile aren’t,” Tripathi says.

Research suggests AI literacy deficits aren’t confined to pharma. Bain & Company’s most recent quarterly survey of executives, published in December, found across-the-board bullishness about potential uses of generative AI. It also revealed a troubling trend: Asked why their generative AI-driven programs haven’t always met expectations, 43% of respondents pointed to a “lack of skill with tools.” More tellingly, that sum was up from 29% in late 2023. 

While not knowing how to use these tools doesn’t automatically correlate with a lack of AI literacy, the self-professed lack of facility with them speaks to a comfort gap that will only widen as AI is further embedded in all aspects of the business. It’s not a stretch to suggest that widespread AI skill and literacy deficiencies could chip away at enthusiasm and/or support for AI-backed programs. Without true understanding, every bump in the road might feel like a pothole.

That’s why a healthy respect for the sanctity of healthcare data is at the core of AI literacy. The ultimate value of AI models, after all, largely hinges upon the quality of the data they learned from. Healthcare data and AI are complicated issues independent of one another; in concert, data and AI illiteracy could wreak havoc. Without a more fundamentally sound data and literacy underpinning, it’s hard to feel confident about the prospects of organizational AI success.

Then there are the ethical issues that come with greater utilization of AI in and around pharma. Individuals working on a program with a significant AI component need to be able to articulate how it steers clear of basing decisions on biased data and how the privacy of individual data isn’t compromised at any point in the process.

To that end, the most forward-minded pharma and healthcare organizations have more than a passing idea about what they must do to drive AI literacy throughout their ranks. Company-wide education is an obvious first step, but typical training programs are at odds with the belief that AI literacy is best taught by experience.

Thus any potential education component needs to be both dynamic and minimalist, Amangeldiev says. “The AI course should only be two hours long, and then people should just go play with it,” he explains. “You need to enter that world for yourself, because the hardest part is getting started.”

Companies hoping to foster an appreciation of the importance of AI literacy also need to prioritize transparency with internal and external audiences. It’s already challenging to distinguish between AI-generated and non-AI-generated content; as AI models grow more sophisticated, it will become even more difficult. The burden will fall on companies to transparently and ethically distinguish between the two.

Ultimately, pharma will become AI literate because it won’t have any other choice. “You’re seeing enough money spent on AI that it’s not going to fail,” Amangeldiev says succinctly.

Beyond the financial aspect, however, lies a deeper and more important motivation. The quicker pharma and healthcare companies drive AI literacy among their ranks, the more effectively they’ll truly integrate AI – and the more successfully they’ll drive better patient outcomes.

“AI is coming, like it or not,” Amangeldiev adds. “If your mission is to help patients, AI is the fastest way to get there.”

What action has your organization taken to ensure high levels of AI literacy among your marketing and brand people? Drop us a note at [email protected], join the conversation on X (@KinaraBio) and subscribe on the website to receive Kinara content.