Embracing Generative AI: Why Its Disruption is Positive for Pharma

Ask any pharmaceutical marketer or strategist about ChatGPT or any of the more than 2,000 new artificial intelligence (AI) platforms introduced in the last three months, and you’ll get answers that range from, “This is the most amazing thing” to “I’m not sure how to use it effectively.” No matter how you feel about this shift in technology, it’s here, and it’s disrupting everything we know about marketing and engagement.

For the digital natives, there is wild optimism about where AI and machine learning can take us. It’s the science in “science fiction” that drives much of the enthusiasm for the possibilities. But everyone should approach AI with an eye on the potential dangers and share advice on how to avoid them as we navigate forward.

While there have been jumps in technology in the past few years, it’s undeniable that what is taking place now is a giant leap forward. Shifts like this usually affect a few areas of commerce at a given time. But this disruption is similar to the introduction of smartphones—when technology becomes natural and intuitive to use, almost every industry must change to embrace it.

Traditional AI has focused on detecting patterns, making decisions, honing analytics, classifying data, and detecting fraud. Generative AI produces new content, chat responses, designs, synthetic data, or deep fakes. Programs like Google’s Bard or OpenAI’s ChatGPT have the potential to revolutionize pharma marketing by changing the way companies market their products and allowing them to create content that is specifically tailored to the needs and preferences of individual patients based on patterns that previously could not be detected.

Some benefits of using generative AI in pharmaceutical marketing are easy to see. It increases content generation that pulls your marketing strategies in whole new directions based on the consumption of vast libraries of information and data. Generative AI can also improve compliance, which can be daunting when you consider the multitude of guidelines and regulations that exist in the pharma industry. The technology can serve as both a brainstorming bonanza at the outset of strategic development as well as the first pass to flag sensitive data or information to confirm that the content complies with defined legal requirements at the end.

As we unravel the threads of generative AI, there are unique benefits that may not be readily seen as well as unique challenges that need to be considered.

A new way of working

While it would be melodramatic to characterize traditional marketing work as disappearing, there is a fundamental shift in how marketing and sales teams will approach planning and strategy in the future. Ultimately, there will always be a need for a human in the driver’s seat, but generative AI is a valuable copilot.

Specific benefits that marketers will see from utilizing generative AI in their strategy meetings and tactical implementations will be in areas that often are the most time-consuming or require a significant amount of data crunching.

• Automating tasks. Face it, there are some tasks that are crucial but are long, arduous, and tedious. The possibility to automate many of the laborious tasks that are currently done by humans, such as creating content, sending emails, and tracking leads, can free up time for marketers and salespeople to focus on more strategic tasks, such as developing customer relationships and creating new marketing campaigns.
• Personalizing content. One-to-one marketing is the key to creating a successful engagement and conversion, but it takes time to analyze the data and connect the touchpoints. In a fraction of that time, generative AI can help personalize content for each individual user by helping to find relationships in the data and making it more relevant and engaging, which leads to improved customer satisfaction and loyalty.
• Creating new products and services. Tailoring products and services to the needs of individual users is a fundamental step to creating deep engagement that can lead to conversion. And, yes, you guessed it—it’s a long process. Iterations of products move at the pace of humans, but machine learning makes running hundreds of scenarios quickly within reach, greatly cutting down on the time to market without sacrificing crucial steps.
• Prediction. Generative AI will not quite predict the future. But by utilizing data modeling, it can predict customer behavior, such as which products they are likely to buy and when they are likely to buy them. While this concept exists today, having the sheer vastness of data and the ability to run thousands of tests over a weekend can be used to create more targeted 1:1 marketing campaigns as well as to improve the customer experience in hours versus days.

Time: the most invaluable currency

Generative AI has the potential to transform the way we work. But more importantly, what generative AI buys us is time—time to create 50 more campaign strategies, run more A/B subject-line tests, sift through mountains of data to find potential clinical trial participants, and personalize engagement directly to the healthcare professional (HCP) or patient.

In the past eight months, we have often heralded ChatGPT, Bard, and other AI technologies as being partially a search engine and partially a personal assistant. But ultimately, time is the greatest benefit they give us—time to be more creative and more human.

Challenges to using generative AI

At this point, singing the praises of generative AI has been easy. It's all the things we love rolled into one simple interface—efficient, effective, and intuitive. However, as we move past the euphoria of potential, we must also understand that the use of generative AI in marketing for pharma products raises a number of ethical concerns, including accuracy, bias, the perpetuation of inequality, and patient autonomy.

It is important for companies and regulators to address these concerns and ensure that the use of generative AI in pharma marketing is ethical and responsible. Here are some areas that marketing teams should be looking out for when embedding AI into their processes:

• Inaccuracy and inconsistency. Generative AI models are trained on large data sources. However, generative AI programs don’t necessarily prioritize data or vet the data based on whether it’s a reputable source, which means that these datasets can be biased or inaccurate. This can lead to generative AI models that produce erroneous or inconsistent content. For example, a generative AI model could create marketing materials that make false or misleading claims about a pharma product based on all the sources (real and imaginary). Creators of AI platforms call this a “hallucination.” Marketers must take the additional step to verify AI-generated content.
• Marketing and promotional regulations. Pharma companies are subject to an incredibly complex variety of regulations. But the world of marketing and advertising also has a set of regulations that govern the content and delivery of marketing materials, such as the use of claims and the disclosure of risks. Generative AI models must be calibrated to take legislation like CAN SPAM and FTC rules into consideration when providing strategic output for teams.
• Fraud or misinformation. Fake content runs rampant on the internet and is lightly policed at best. Generative AI models could be used to create fake news articles that claim a certain product is more effective than it is. This could lead consumers to make uninformed decisions about their health as well as create confusion around actual treatments.
• Privacy concerns. Data is king. Marketers must find the “Goldilocks scenario” when using consumer data in strategic planning and campaigns (i.e., what’s not too much or too little but just right). What makes a consumer feel seen but not feel uncomfortable with the level of personalization? While generative AI models are extremely good at collecting and analyzing large amounts of data about consumers, concerns are starting to rise around the security of this data, the permission to use it, and the ownership of it.
• Discrimination and bias. Generative AI models can be used to make decisions about who sees certain marketing messages, which is significantly useful in creating personalized cohort groups to focus on or finding target segments. For example, a generative AI model could be used to decide which consumers see ads for a certain pharmaceutical product based on data that it reviews from its sources. If the data used to train generative AI is biased or reflects existing inequalities, the resulting advertising may reinforce these biases and perpetuate inequality. A generative AI model could also be used to decide that certain consumers will be more interested in a certain pharma product, even if this is not the case. Lastly, it could also be as dangerous as perpetuating existing social and economic inequalities. For example, if the data used to train a generative AI model on a particular drug only includes information from a certain demographic group, the resulting advertising may not be effective or appropriate for other demographic groups.

These are just some of the challenges that pharma marketers face when using generative AI. It is important to be aware of these challenges and to take steps to mitigate them. By doing so, pharma commercial teams can maximize the benefits of generative AI while minimizing the risks.

The future of generative AI in pharma

While we’ve focused on the marketing and sales aspects of generative AI, we cannot ignore the amazing impact this technology will have on the treatment side of the pharma industry. In the future, it may be possible to have generative AI create personalized treatment plans that are tailored to the specific needs of individual patients, leading to more effective treatments and improved patient outcomes.

We are already seeing generative AI make an impact on drug discovery. Due to its power in analyzing large sets of data and simulating drug interactions, it can identify potential drug candidates that may have been overlooked by traditional drug discovery methods. This could lead to the development of new treatments for diseases that previously had no effective therapies.

These are positive disruptors in our industry—forcing change and speeding up the processes in all aspects of the chain. As pharma marketers, we are always running in parallel with development teams, keeping up with the changes and making fast adjustments to engagement plans. Once again, we’re talking about how generative AI gives us time. This includes time to think of strategy and build campaigns that bring awareness to diseases as well as time for HCPs to spend with their patients after learning about late-breaking treatment options from a sales representative.

Generative AI is disrupting marketing in ways we never imagined, and that’s a good thing.