Natalie Harb, Vice President, Offering Development, Global Commercial Solutions, IQVIA
Twenty years ago, pharmaceutical product launches were dominated by primary care drugs. These launches were focused on achieving the largest share of voice and were led primarily by sales representatives. Since then, the market has advanced to the specialty era, which includes a more complex patient journey and numerous physician specialties—sometimes as many as seven. Engagement has become more multichannel, and payers are demanding more integrated evidence with increased requirements. These increasingly complex product launches can benefit from the many advantages of generative artificial intelligence (AI), and yet the debate about its usefulness and ethics continues.
To further compound these issues, we see the trajectories of post-pandemic launches underperform and decline. For example, median cumulative sales of post-pandemic drug launches were 28% lower than they were pre-pandemic. This was seen in an analysis of innovative launches during the first six months and across the top eight markets. As we know, the first six months is a pivotal time when determining a drug’s long-term success, as only 20% or fewer struggling launches are able to significantly change their trajectory.1
WHY GENERATIVE AI SHOULD BE YOUR LAUNCH ASSISTANT
Getting the right information and insights quickly is imperative for a drug launch. For those launches that are more complex, which involve updating and generating hundreds of documents and have multiple teams searching for information, this is especially true. Using large language models to perform a multitude of language processing tasks allows generative AI to assist marketing teams by optimizing the search and retrieval of information. For example, a generative AI assistant that is trained using online data and documents can understand the semantic context of each query and provide results that are specific to what the user is seeking. This enables users to have an easier time finding what they need quickly and accurately without having to sort through irrelevant content.
A generative AI assistant can also compile a response that connects information from a variety of sources. As a result, responses can be tailored to different personas and be based on preferences that determine the information that is necessary. These assistants can also be used to generate cognitive responses, much like the way individuals process, interpret, and respond to information. Additionally, the assistant can provide the logic behind its produced answers, which can help instill the user’s confidence in the results.
Medical, legal, and regulatory (MLR) reviews of marketing content are another area in which generative AI can help and even accelerate the process. Sample promotional messages that received MLR approval can be used to train a generative AI model. In this way, the model can emulate language flow and produce messages that have a high probability of getting through the review process. Market access teams can also use this model to adapt to global values.
Need insights on the drivers of launch success? Generative AI models can help here, too, and account for variations and the impact of numerous factors unique to specific countries and markets. These models can evaluate timelines and ascertain physician perceptions six months post-launch versus one year. They can also be used to inform tactics by comparing the channel mixes of successful launches versus those that were not effective and determine the optimal channel mix.
Success from using generative AI assistants has been evident. For example, one customer completed a targeted literature search in just two weeks. This was significantly faster than the previous six-week norm. Others using generative AI saw a 50% gain in productivity in creating value dossiers. Value dossiers are the information that manufacturers submit to payers to determine whether the effectiveness of a product justifies its proposed price. Additionally, using a conversational AI model on top of anonymous real-world data enabled customers to conduct the analysis 70% faster. The team didn’t need to know programming, the right international classification of diseases, or other codes to quantify the segment size. This helped expand the use of real-world data.
ENHANCE CUSTOMER ENGAGEMENT WITH GENERATIVE AI
Generative AI can enhance the understanding of various healthcare professional (HCP) segments and allow for more tailored engagements. As AI assistants are used among payers, HCPs, and physicians to help inform prescribing and treatment decisions, pharma may need to start viewing these assistants as another type of stakeholder segment.
As we see other industries using AI-powered chatbots for salesforce training, it may also be advantageous for pharma to create a field force assistant for a launch. A generative AI field force assistant can be invaluable when launching in a new therapeutic area or a new indication, especially when the understanding of key stakeholders isn’t clear. The use of “digital twins” personas can mimic how HCPs communicate and increase the overall familiarity with different personas before sales reps go into the field. This can enhance conversations on specific topics and facilitate responding to difficult questions with new data and insights, as well as promote which answers best resonate with a particular persona.
INNOVATIONS ARE NEEDED TO ENSURE LAUNCH SUCCESS
Launch planning and execution can benefit greatly from generative AI and the game-changing opportunities it provides. The ability to analyze and learn from terabytes of structured and unstructured data is a key advantage of using generative AI. Its ability to generate new content via text, images, or code, and learn to become more intuitive and conversational, are other advantages that can help overcome the hurdle of adoption. Generative AI can also increase productivity in launch planning and execution by providing real-time, personalized, and smarter insights to generate content. Predicting uptake curves and the outcomes of health technology assessments are other benefits, and they can be used to create capability models to predict the future events in the product lifecycle.
DRIVING LAUNCH PRODUCTIVITY AND VALUE FORWARD
By assisting, augmenting, and automating insights, generative AI models can provide enhanced clarity and focus for evidence-based decision-making. Better access to insights encourages and empowers cross-functional collaboration. These insights can facilitate operational agility and allow teams to respond more quickly and with certainty to changing internal and external environments.
The future of pharma is now, which is why incorporating generative AI into product launches should be a serious launch team consideration. Generative AI offers numerous advantages in addition to enhancing launch speed and efficiency. These assistants can also improve the effectiveness of different work streams by enabling quicker processing of reams of data and insights for faster decision-making. Their ability to personalize customer engagement messages and get these messages through the MLR-approval process more quickly provides further support.
Generative AI can mitigate launch challenges and drive productivity, enhance decision-making, and elevate customer engagement to achieve launch excellence. Those who incorporate generative AI into their launch plans are sure to see its incredible value realized. Will your launch team be among them?
Natalie Harb is Vice President, Offering Development, Global Commercial Solutions, IQVIA
