In 2015, I founded a healthcare technology start-up that used AI and machine learning to help health systems select the exact right medication, for the right patient, at the right time in treatment, all at the right price. Our value, while ambitious and evidence based, were relatively primitive, and extremely constrained by data availability. Needless to say, those were the “early days” of traditional AI, and solutions such as ours, focused primarily on pre-defined questions, and quantitative responses, to solve a common problem. Now, along comes Generative AI applications such as ChatGPT, and traditional AI seems like child’s play, by comparison.

Generative AI breaks through the natural language processing barriers that existed in healthcare for decades, and now allows us to pursue true, next level, customer experiences. And this will allow us to go far beyond what most large biopharmaceutical companies originally conceived when implementing their most recent digital transformation initiatives. In order to get a feel for the power, range and accuracy of generative AI, I have made a deliberate attempt to incorporate ChatGPT and other generative AI tools into the day-to-day workflows of my change consulting practice over the past 90 days. I have used generative AI to conduct research, draft marketing materials, brainstorm problem resolution, scan literature, construct work plans, and much, much more. In summary, generative AI is a game changer. Not as a replacement for human intervention and know-how, but more as a “personal assistant” who gives you a pretty good first draft, most of the time.

As a former life science executive, my experience with generative AI left me thinking about its future role in the ongoing quest to transform the physician and patient customer experiences. First, let me state upfront a strong caveat to what I’m about to suggest regarding the role of generative AI in the biopharma customer experience – NEVER accept output of a generative AI algorithm on face value – human insight, input and oversight is still required! With that said, Generative AI can play a significant role in enhancing the digitally enhanced customer experience delivered by commercial organizations in today’s top biopharmaceutical companies.

Here are my top three most promising early use cases (within the next 0-3 years) of generative AI in the context of a better biopharma customer experience:

  1. Next Level, Personalized Customer Engagement: Generative AI can analyze customer and internal biopharma data, including demographics, previous historical interactions, and preferences, to create true, micro-personalized interactions and recommendations. In today’s vernacular, we are calling this omnichannel marketing and salesforce “next best action”. And before generative AI came along, these new capabilities, while effective, were certainly much more complex and time consuming to do well. Generative AI will enable infinitely more precise and refined marketing messages, and in the near future, there will be built-in promotional regulatory review to ensure materials remain legally and ethically compliant. As mentioned above, this won’t become a capability devoid of human intervention, BUT it will remove significant cycle times to produce better than imagined, personalized content that has been greatly de-risked via a first pass generative AI compliance review.
  2. Life-Like, Digital Assistants and Chatbots: Generative AI-powered virtual assistants and chatbots will provide instant and personalized support to biopharma physicians and their patients. Doesn’t this happen already today? Sure it does, but recent capabilities without generative AI, had an extremely limited range of customer understanding, highly constrained and repetitive response sets, and no ability to “think on their feet” and demonstrate human processing qualities. The new generative AI systems will answer queries, provide more specific information about products and services, offer guidance on medication usage, assist with scheduling appointments, and check on available clinical trials, to name a few. By leveraging natural language processing and machine learning, virtual assistants will continuously improve their responses, adapt to customer needs, speak with human qualities, and in the future, be used in powerful combination with the deep, personalized customer engagement described above.
  3. Evidence Backed, Drug Information, Education & Monitoring: Generative AI can generate interactive and engaging content to educate physician and patient customers about medications, diseases, and treatment options. This can include animated videos, interactive visualizations, and augmented/virtual reality experiences that help customers understand complex medical concepts and make informed decisions about their healthcare. Generative AI can also analyze social media platforms, online forums, and other digital sources to monitor and identify potential adverse events related to medications. By detecting early signals and patterns, biopharmaceutical companies can proactively address safety concerns, improve drug safety profiles, and communicate updates to healthcare professionals and patients. Today’s drug information, education and monitoring capabilities are very difficult to maintain as new, real world, drug experiences can shift dramatically, causing materials to be quickly out of date. Generative AI, in the future, will become adept at catching new information and automatically making revisions to massive datasets.

Despite the potential benefits (and they will be immense), there are a few watchouts that organizations should consider, to avoid tripping up their early efforts with generative AI tools:

First, biopharmaceutical companies must ensure robust data privacy and security measures when leveraging generative AI for customer experience. They should comply with applicable regulations, safeguard customer information, and implement appropriate data handling practices to maintain trust and protect sensitive data. Biases and unfair practices can inadvertently be introduced by generative AI algorithms. Organizations should be vigilant in addressing biases and ensuring fairness in the customer experience. Regular monitoring, ethical guidelines, and diverse training data can help mitigate these risks. And finally, the use of generative AI in the customer experience should align with legal and regulatory requirements, including those related to advertising, data protection, and healthcare regulations. Compliance with guidelines from regulatory authorities, such as the FDA, is essential to avoid legal pitfalls.

Finally, it is worth repeating the need for continuous human intervention and oversight of generative AI systems. While generative AI, could be viewed by some, as a means to reduce the number of people required to execute a commercial capability, this seems extremely short- sighted. Generative AI algorithms must offer clear transparency to ensure they remain accurate, up-to-date, unbiased, ethical, and legal. Customers should be aware when they are interacting with AI systems, and there should be mechanisms for customers to escalate issues to human representatives when needed. If the goal is to deliver a transformative, personalized customer experience, human intellect, experience, and oversight are table stakes.

By addressing these watchouts, and leveraging generative AI thoughtfully, biopharmaceutical companies can enhance the digital customer experience, improve engagement, and deliver personalized and valuable services to their customers. Who’s ready to change their game?